diff --git a/lsst/solar_system/0_lsst_sso_statistics.ipynb b/lsst/solar_system/0_lsst_sso_statistics.ipynb
new file mode 100644
index 0000000..f82b0aa
--- /dev/null
+++ b/lsst/solar_system/0_lsst_sso_statistics.ipynb
@@ -0,0 +1,577 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0b67c87e-cbd5-433d-99f0-787742a8eb5b",
+   "metadata": {},
+   "source": [
+    "# Fink Solar System statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b656061-392b-4b39-9802-046b46b10cd7",
+   "metadata": {},
+   "source": [
+    "This tutorial shows how to get statistics about the LSST alert stream using Fink API. \n",
+    "\n",
+    "````{note} API & documentation\n",
+    "Full documentation is at https://doc.lsst.fink-broker.org/services/api/statistics/, and endpoint can be found at https://api.lsst.fink-portal.org\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e42c2866-6d11-40d6-94ed-d2fdc58e15c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "import io\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 15})\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "\n",
+    "def make_date_for_plot(indate):\n",
+    "    \"\"\"Change int(YYYYMMDD) to datetime object\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    indate: int, or str\n",
+    "        Integer or string of the form YYYYDDMM\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    out: datetime\n",
+    "    \"\"\"\n",
+    "    if not isinstance(indate, str):\n",
+    "        indate = str(indate)\n",
+    "    return datetime.date.fromisoformat(\n",
+    "        indate[0:4] + \"-\" + indate[4:6] + \"-\" + indate[6:]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1e09b34c-876f-4003-b3a5-f5c5cfefd6a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get all statistics for 2026\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/statistics\",\n",
+    "    json={\"date\": \"2026\", \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "pdf = pd.read_json(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "94d1dac1-f0a4-47d6-ab20-4a5d98f514d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>f:alerts</th>\n",
+       "      <th>f:alerts_g</th>\n",
+       "      <th>f:alerts_i</th>\n",
+       "      <th>f:alerts_r</th>\n",
+       "      <th>f:alerts_u</th>\n",
+       "      <th>f:alerts_y</th>\n",
+       "      <th>f:alerts_z</th>\n",
+       "      <th>f:fink_broker_version</th>\n",
+       "      <th>f:fink_science_version</th>\n",
+       "      <th>f:glint_trail</th>\n",
+       "      <th>...</th>\n",
+       "      <th>f:is_cataloged</th>\n",
+       "      <th>f:is_first</th>\n",
+       "      <th>f:is_sso</th>\n",
+       "      <th>f:lsst_schema_version</th>\n",
+       "      <th>f:objects</th>\n",
+       "      <th>f:pixelFlags_cr</th>\n",
+       "      <th>f:pixelFlags_saturated</th>\n",
+       "      <th>f:pixelFlags_streak</th>\n",
+       "      <th>f:visits</th>\n",
+       "      <th>f:night</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12276</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12276</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>8.39.0</td>\n",
+       "      <td>12276</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>10246</td>\n",
+       "      <td>12276</td>\n",
+       "      <td>12276</td>\n",
+       "      <td>12276</td>\n",
+       "      <td>16</td>\n",
+       "      <td>20260101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12642</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12642</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>8.39.0</td>\n",
+       "      <td>12642</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9909</td>\n",
+       "      <td>0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>11462</td>\n",
+       "      <td>12642</td>\n",
+       "      <td>12642</td>\n",
+       "      <td>12642</td>\n",
+       "      <td>18</td>\n",
+       "      <td>20260102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8822</td>\n",
+       "      <td>2413</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2905</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>8.39.0</td>\n",
+       "      <td>8822</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7015</td>\n",
+       "      <td>0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>7997</td>\n",
+       "      <td>8822</td>\n",
+       "      <td>8822</td>\n",
+       "      <td>8822</td>\n",
+       "      <td>14</td>\n",
+       "      <td>20260103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8416</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4392</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>8.39.0</td>\n",
+       "      <td>8416</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6571</td>\n",
+       "      <td>0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>8013</td>\n",
+       "      <td>8416</td>\n",
+       "      <td>8416</td>\n",
+       "      <td>8416</td>\n",
+       "      <td>12</td>\n",
+       "      <td>20260104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9411</td>\n",
+       "      <td>3586</td>\n",
+       "      <td>5825</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>8.39.0</td>\n",
+       "      <td>9411</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6786</td>\n",
+       "      <td>0</td>\n",
+       "      <td>lsst.v10_0</td>\n",
+       "      <td>8768</td>\n",
+       "      <td>9411</td>\n",
+       "      <td>9411</td>\n",
+       "      <td>9411</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20260105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>227916</td>\n",
+       "      <td>1228</td>\n",
+       "      <td>154477</td>\n",
+       "      <td>21525</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50686</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.52.0</td>\n",
+       "      <td>227916</td>\n",
+       "      <td>...</td>\n",
+       "      <td>122154</td>\n",
+       "      <td>119514</td>\n",
+       "      <td>22859</td>\n",
+       "      <td>lsst.v11_1</td>\n",
+       "      <td>185240</td>\n",
+       "      <td>227916</td>\n",
+       "      <td>227916</td>\n",
+       "      <td>227916</td>\n",
+       "      <td>89</td>\n",
+       "      <td>20260710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>125442</td>\n",
+       "      <td>2210</td>\n",
+       "      <td>70512</td>\n",
+       "      <td>52720</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.52.0</td>\n",
+       "      <td>125442</td>\n",
+       "      <td>...</td>\n",
+       "      <td>48071</td>\n",
+       "      <td>53591</td>\n",
+       "      <td>29197</td>\n",
+       "      <td>lsst.v11_1</td>\n",
+       "      <td>83024</td>\n",
+       "      <td>125442</td>\n",
+       "      <td>125442</td>\n",
+       "      <td>125442</td>\n",
+       "      <td>87</td>\n",
+       "      <td>20260711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>71683</td>\n",
+       "      <td>4255</td>\n",
+       "      <td>43264</td>\n",
+       "      <td>24164</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.52.0</td>\n",
+       "      <td>71683</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20165</td>\n",
+       "      <td>34259</td>\n",
+       "      <td>11262</td>\n",
+       "      <td>lsst.v11_1</td>\n",
+       "      <td>54893</td>\n",
+       "      <td>71683</td>\n",
+       "      <td>71683</td>\n",
+       "      <td>71683</td>\n",
+       "      <td>38</td>\n",
+       "      <td>20260712</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>744559</td>\n",
+       "      <td>34501</td>\n",
+       "      <td>264297</td>\n",
+       "      <td>187532</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>258229</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.52.0</td>\n",
+       "      <td>744559</td>\n",
+       "      <td>...</td>\n",
+       "      <td>339250</td>\n",
+       "      <td>487906</td>\n",
+       "      <td>12544</td>\n",
+       "      <td>lsst.v11_1</td>\n",
+       "      <td>677429</td>\n",
+       "      <td>744559</td>\n",
+       "      <td>744559</td>\n",
+       "      <td>744559</td>\n",
+       "      <td>82</td>\n",
+       "      <td>20260713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>473344</td>\n",
+       "      <td>5431</td>\n",
+       "      <td>266446</td>\n",
+       "      <td>129170</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72297</td>\n",
+       "      <td>5.0rc0</td>\n",
+       "      <td>8.52.0</td>\n",
+       "      <td>473344</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>233499</td>\n",
+       "      <td>32641</td>\n",
+       "      <td>lsst.v11_1</td>\n",
+       "      <td>403821</td>\n",
+       "      <td>473344</td>\n",
+       "      <td>473344</td>\n",
+       "      <td>473344</td>\n",
+       "      <td>129</td>\n",
+       "      <td>20260714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>95 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    f:alerts  f:alerts_g  f:alerts_i  f:alerts_r  f:alerts_u  f:alerts_y  \\\n",
+       "0      12276           0       12276           0           0           0   \n",
+       "1      12642           0       12642           0           0           0   \n",
+       "2       8822        2413        3504           0           0        2905   \n",
+       "3       8416           0           0        4024           0           0   \n",
+       "4       9411        3586        5825           0           0           0   \n",
+       "..       ...         ...         ...         ...         ...         ...   \n",
+       "90    227916        1228      154477       21525           0           0   \n",
+       "91    125442        2210       70512       52720           0           0   \n",
+       "92     71683        4255       43264       24164           0           0   \n",
+       "93    744559       34501      264297      187532           0           0   \n",
+       "94    473344        5431      266446      129170           0           0   \n",
+       "\n",
+       "    f:alerts_z f:fink_broker_version f:fink_science_version  f:glint_trail  \\\n",
+       "0            0                   4.1                 8.39.0          12276   \n",
+       "1            0                   4.1                 8.39.0          12642   \n",
+       "2            0                   4.1                 8.39.0           8822   \n",
+       "3         4392                   4.1                 8.39.0           8416   \n",
+       "4            0                   4.1                 8.39.0           9411   \n",
+       "..         ...                   ...                    ...            ...   \n",
+       "90       50686                5.0rc0                 8.52.0         227916   \n",
+       "91           0                5.0rc0                 8.52.0         125442   \n",
+       "92           0                5.0rc0                 8.52.0          71683   \n",
+       "93      258229                5.0rc0                 8.52.0         744559   \n",
+       "94       72297                5.0rc0                 8.52.0         473344   \n",
+       "\n",
+       "    ...  f:is_cataloged  f:is_first  f:is_sso  f:lsst_schema_version  \\\n",
+       "0   ...               0        9200         0             lsst.v10_0   \n",
+       "1   ...               0        9909         0             lsst.v10_0   \n",
+       "2   ...               0        7015         0             lsst.v10_0   \n",
+       "3   ...               0        6571         0             lsst.v10_0   \n",
+       "4   ...               0        6786         0             lsst.v10_0   \n",
+       "..  ...             ...         ...       ...                    ...   \n",
+       "90  ...          122154      119514     22859             lsst.v11_1   \n",
+       "91  ...           48071       53591     29197             lsst.v11_1   \n",
+       "92  ...           20165       34259     11262             lsst.v11_1   \n",
+       "93  ...          339250      487906     12544             lsst.v11_1   \n",
+       "94  ...               0      233499     32641             lsst.v11_1   \n",
+       "\n",
+       "    f:objects f:pixelFlags_cr  f:pixelFlags_saturated  f:pixelFlags_streak  \\\n",
+       "0       10246           12276                   12276                12276   \n",
+       "1       11462           12642                   12642                12642   \n",
+       "2        7997            8822                    8822                 8822   \n",
+       "3        8013            8416                    8416                 8416   \n",
+       "4        8768            9411                    9411                 9411   \n",
+       "..        ...             ...                     ...                  ...   \n",
+       "90     185240          227916                  227916               227916   \n",
+       "91      83024          125442                  125442               125442   \n",
+       "92      54893           71683                   71683                71683   \n",
+       "93     677429          744559                  744559               744559   \n",
+       "94     403821          473344                  473344               473344   \n",
+       "\n",
+       "    f:visits   f:night  \n",
+       "0         16  20260101  \n",
+       "1         18  20260102  \n",
+       "2         14  20260103  \n",
+       "3         12  20260104  \n",
+       "4          8  20260105  \n",
+       "..       ...       ...  \n",
+       "90        89  20260710  \n",
+       "91        87  20260711  \n",
+       "92        38  20260712  \n",
+       "93        82  20260713  \n",
+       "94       129  20260714  \n",
+       "\n",
+       "[95 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "83aa1b49-4729-40f8-99c5-1aebf387ef76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "plt.bar(x=pdf[\"f:night\"].apply(make_date_for_plot), height=pdf[\"f:is_sso\"])\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylabel(\"Number of SSO alerts per night\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9aa17b49-1f5f-4cab-b7b4-ceffb2734afc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "plt.bar(\n",
+    "    x=pdf[\"f:night\"].apply(make_date_for_plot),\n",
+    "    height=pdf[\"f:is_sso\"] / pdf[\"f:alerts\"] * 100,\n",
+    ")\n",
+    "plt.ylabel(\"Percentage of SSO alerts per night\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "86374d6c-ecc8-4dbb-9bc7-b2f633eb8c62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SSO alerts represent about 21.5% of the stream each night (median)\n"
+     ]
+    }
+   ],
+   "source": [
+    "fraction = pdf[\"f:is_sso\"] / pdf[\"f:alerts\"] * 100\n",
+    "mask = fraction > 0  # only nights with SSO reported\n",
+    "print(\n",
+    "    f\"SSO alerts represent about {(fraction[mask]).median():.1f}% of the stream each night (median)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d55264fe-ed2f-42ff-ac66-fda8fc4e8b31",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lsst/solar_system/1_ssobject.ipynb b/lsst/solar_system/1_ssobject.ipynb
new file mode 100644
index 0000000..586afc8
--- /dev/null
+++ b/lsst/solar_system/1_ssobject.ipynb
@@ -0,0 +1,656 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88d43a80-5db2-4265-9660-024abbf7a937",
+   "metadata": {},
+   "source": [
+    "# Fink REST API\n",
+    "\n",
+    "````{note} List of arguments\n",
+    "The list of arguments for running a search by Solar System name can be found at [https://api.lsst.fink-portal.org](https://api.lsst.fink-portal.org). The schema of the returned payload can be found on the [schema page](https://lsst.fink-porteal.org/schemas) and you can also retrieve it [programmatically](https://doc.lsst.fink-broker.org/services/api/definitions/).\n",
+    "\n",
+    "````\n",
+    "\n",
+    "Every time a new alert is emitted, a new `diaSource` is created. LSST makes an association (1'' matching radius) with a catalog of known Solar system objects (SSO) from the MPC prior to sending alerts. If the alert is matched to a known SSO, it is associated to an existing `ssObject`, and a `ssObjectId` is assigned to it. This notebook describes how to retrieve all `diaSources` information associated to the same `ssObject`. \n",
+    "\n",
+    "_Created on 2026/09/28. See https://doc.lsst.fink-broker.org/services/api/solar_system/ for up-to-date documentation._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a56baa-ce11-4205-9df8-515e2724d77d",
+   "metadata": {},
+   "source": [
+    "## Object data\n",
+    "\n",
+    "You can enter any name (e.g. Ukyounodaibu), number (e.g. 734394), or provisonal designation (e.g. 2015 BC557, K15Bt7C) of asteroids. Under the hood, we resolve the name using the [quaero](https://ssp.imcce.fr/webservices/ssodnet/api/quaero/) service from SsODNet. You can also search for comets, although I could not find any named comet in the Rubin alert stream."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e8d640d-20a2-410d-a147-eee156641623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# get all data for 2015 BC557\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\"n_or_d\": \"2015 BC557\", \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "pdf_obj = pd.read_json(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "11ecbe7d-9ca7-464d-a33b-6ffe97839331",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r:apFlux</th>\n",
+       "      <th>r:apFluxErr</th>\n",
+       "      <th>r:apFlux_flag</th>\n",
+       "      <th>r:apFlux_flag_apertureTruncated</th>\n",
+       "      <th>r:band</th>\n",
+       "      <th>r:bboxSize</th>\n",
+       "      <th>r:centroid_flag</th>\n",
+       "      <th>r:dec</th>\n",
+       "      <th>r:decErr</th>\n",
+       "      <th>r:designation</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r:trailRa</th>\n",
+       "      <th>r:trailRaErr</th>\n",
+       "      <th>r:trail_flag</th>\n",
+       "      <th>r:trail_flag_edge</th>\n",
+       "      <th>r:visit</th>\n",
+       "      <th>r:x</th>\n",
+       "      <th>r:xErr</th>\n",
+       "      <th>r:y</th>\n",
+       "      <th>r:yErr</th>\n",
+       "      <th>f:sso_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6712.976</td>\n",
+       "      <td>538.29175</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>r</td>\n",
+       "      <td>19</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8.367503</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>K15Bt7C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>185.584086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022600408</td>\n",
+       "      <td>2456.39700</td>\n",
+       "      <td>0.098162</td>\n",
+       "      <td>1131.1658</td>\n",
+       "      <td>0.069399</td>\n",
+       "      <td>2015 BC557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6802.178</td>\n",
+       "      <td>543.02545</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>r</td>\n",
+       "      <td>23</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8.367444</td>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>K15Bt7C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>185.584170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022600407</td>\n",
+       "      <td>3198.65600</td>\n",
+       "      <td>0.112388</td>\n",
+       "      <td>1915.3632</td>\n",
+       "      <td>0.082791</td>\n",
+       "      <td>2015 BC557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7092.727</td>\n",
+       "      <td>558.86610</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>r</td>\n",
+       "      <td>20</td>\n",
+       "      <td>False</td>\n",
+       "      <td>8.367374</td>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>K15Bt7C</td>\n",
+       "      <td>...</td>\n",
+       "      <td>185.584247</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022600406</td>\n",
+       "      <td>270.15686</td>\n",
+       "      <td>0.093867</td>\n",
+       "      <td>2832.3271</td>\n",
+       "      <td>0.067676</td>\n",
+       "      <td>2015 BC557</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 144 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   r:apFlux  r:apFluxErr  r:apFlux_flag  r:apFlux_flag_apertureTruncated  \\\n",
+       "0  6712.976    538.29175          False                            False   \n",
+       "1  6802.178    543.02545          False                            False   \n",
+       "2  7092.727    558.86610          False                            False   \n",
+       "\n",
+       "  r:band  r:bboxSize  r:centroid_flag     r:dec  r:decErr r:designation  ...  \\\n",
+       "0      r          19            False  8.367503  0.000005       K15Bt7C  ...   \n",
+       "1      r          23            False  8.367444  0.000006       K15Bt7C  ...   \n",
+       "2      r          20            False  8.367374  0.000005       K15Bt7C  ...   \n",
+       "\n",
+       "    r:trailRa  r:trailRaErr  r:trail_flag  r:trail_flag_edge        r:visit  \\\n",
+       "0  185.584086           NaN           NaN              False  2026022600408   \n",
+       "1  185.584170           NaN           NaN              False  2026022600407   \n",
+       "2  185.584247           NaN           NaN              False  2026022600406   \n",
+       "\n",
+       "          r:x    r:xErr        r:y    r:yErr  f:sso_name  \n",
+       "0  2456.39700  0.098162  1131.1658  0.069399  2015 BC557  \n",
+       "1  3198.65600  0.112388  1915.3632  0.082791  2015 BC557  \n",
+       "2   270.15686  0.093867  2832.3271  0.067676  2015 BC557  \n",
+       "\n",
+       "[3 rows x 144 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf_obj.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5bcd9ecc-3db2-4d30-bc24-2591adee8be5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sso_utils import plot_lightcurve_allbands\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 15})\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
+    "plot_lightcurve_allbands(\n",
+    "    pdf_obj[\"r:midpointMjdTai\"],\n",
+    "    pdf_obj[\"r:psfFlux\"],\n",
+    "    pdf_obj[\"r:psfFluxErr\"],\n",
+    "    pdf_obj[\"r:band\"],\n",
+    "    ax=ax,\n",
+    "    label=\"Difference flux (r band)\",\n",
+    ")\n",
+    "\n",
+    "ax.grid(alpha=0.5)\n",
+    "ax.legend()\n",
+    "ax.set_ylabel(\"Flux in nanoJansky\")\n",
+    "ax.set_xlabel(\"Observation date\")\n",
+    "ax.set_title(\"Difference flux (psfFlux)\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "403c568c-bf4d-4df2-8f2b-f2b3ea164194",
+   "metadata": {},
+   "source": [
+    "The [schema](https://lsst.fink-portal.org/schemas) can be easily accessed using:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5af3542-6e07-41b6-bad9-232b692f9593",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/schema\",\n",
+    "    json={\"endpoint\": \"/api/v1/sso\", \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "schema = r.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3264c6c1-ca98-442a-a5de-aa5143ca3637",
+   "metadata": {},
+   "source": [
+    "````{tip} Faster queries\n",
+    "For faster queries, you can select only alert fields of interest by specifying the argument `columns` in your payload:\n",
+    "\n",
+    "```python\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\n",
+    "        \"n_or_d\": \"2015 BC557\",\n",
+    "        \"columns\": \"r:midpointMjdTai,r:psfFlux,r:psfFluxErr,r:ra,r:dec\",\n",
+    "        \"output-format\": \"json\",\n",
+    "    },\n",
+    ")\n",
+    "```\n",
+    "````\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f8ae9586-5d50-47df-bb26-61ea848d1fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def query_fink_sso(name, columns=None):\n",
+    "    payload = {\"n_or_d\": name, \"output-format\": \"json\"}\n",
+    "    if columns is not None:\n",
+    "        payload.update({\"columns\": columns})\n",
+    "    r = requests.post(\n",
+    "        \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "        json=payload,\n",
+    "    )\n",
+    "    return r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e88b992a-f01b-4e27-9cd9-db51c2c20784",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "761 ms ± 44 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit query_fink_sso(name=\"2015 BC557\", columns=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fe6c355a-2d44-4fa6-a452-d2c1744b957c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "521 ms ± 111 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit query_fink_sso(name=\"2015 BC557\", columns=\"r:midpointMjdTai,r:psfFlux,r:psfFluxErr,r:ra,r:dec\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10400fa0-5693-4577-b345-2bf0a3ce3af5",
+   "metadata": {},
+   "source": [
+    "You can also retrieve the data for several objects at once:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "08b972e2-9e02-462d-b3c2-9c1a203a26b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# ID as string\n",
+    "mylist = [\"734394\", \"K15Bt7C\", \"Schwarzschilda\", \"Ukyounodaibu\"]\n",
+    "\n",
+    "# get alert data for many objects\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\n",
+    "        \"n_or_d\": \",\".join(mylist),\n",
+    "        \"columns\": \"r:midpointMjdTai,r:psfFlux,r:psfFluxErr,r:ra,r:dec\",\n",
+    "        \"output-format\": \"json\",\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "pdf_multiple = pd.read_json(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d51e16b1-bba5-47b0-b62f-235d19f6fa7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1518, 3)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(\n",
+    "    len(pdf_multiple),\n",
+    "    len(pdf_multiple[\"r:packed_primary_provisional_designation\"].unique()),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f7957a4-4e40-4b82-b6b2-f41471b9735e",
+   "metadata": {},
+   "source": [
+    "## Adding ephemerides from Miriade\n",
+    "\n",
+    "````{warning} Slower queries\n",
+    "Beware it adds few seconds delay per API call.\n",
+    "````\n",
+    "You can also attach the ephemerides provided by the [Miriade ephemeride service :lucide-external-link:](https://ssp.imcce.fr/webservices/miriade/api/ephemcc/){target=\"blank_\"}:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6c0176be-0ba8-466a-8747-d246466f5c0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# get data for object 2015 BC557\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\"n_or_d\": \"2015 BC557\", \"withEphem\": True, \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "pdf_eph = pd.read_json(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04ce7d86-0833-4d6b-a77b-74292692df05",
+   "metadata": {},
+   "source": [
+    "Where columns not prefixed by `r:` or `f:` are fields returned from Miriade (again, check the schema above). Let's have a look at the difference between measured positions and ephemerides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "235aa7a7-08ef-4ef3-b032-5037dc493b5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "deltaRAcosDEC = (\n",
+    "    (pdf_eph[\"r:ra\"] - pdf_eph.RA) * np.cos(np.radians(pdf_eph[\"r:dec\"])) * 3600\n",
+    ")\n",
+    "deltaDEC = (pdf_eph[\"r:dec\"] - pdf_eph.DEC) * 3600"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9d81aada-4ebf-43a1-bd8e-f52759eed59e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\Delta$DEC (\")')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(deltaRAcosDEC, deltaDEC, alpha=0.5)\n",
+    "plt.axhline(0, ls=\"--\", color=\"grey\")\n",
+    "plt.axvline(0, ls=\"--\", color=\"grey\")\n",
+    "plt.xlabel(r'$\\Delta$RA cos(Dec) (\")')\n",
+    "plt.ylabel(r'$\\Delta$DEC (\")')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e69cb1c-e7c5-4985-a5fb-fb1f706be170",
+   "metadata": {},
+   "source": [
+    "## Retrieving cutout stamps\n",
+    "\n",
+    "For each alert, you can easily retrieve the associated stamps using the `/api/v1/cutouts` endpoint. For this, you need first to retrieve the `diaSourceId`, and then query for the cutouts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "097ea6d0-86dd-4791-a6e0-aa69ca575d13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import requests\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "# Get all diaSourceId for object 2015 BC557\n",
+    "# Output is sorted from more recent to least recent\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\"n_or_d\": \"2015 BC557\", \"columns\": \"r:diaSourceId\", \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "# Get Science cutouts as FITS for the last 10 alerts\n",
+    "sids = r.json()[0:10]\n",
+    "for sid in sids:\n",
+    "    out = requests.post(\n",
+    "        \"https://api.lsst.fink-portal.org/api/v1/cutouts\",\n",
+    "        json={\n",
+    "            \"diaSourceId\": str(sid[\"r:diaSourceId\"]),\n",
+    "            \"kind\": \"Science\",\n",
+    "            \"output-format\": \"FITS\",\n",
+    "        },\n",
+    "    )\n",
+    "    data = fits.open(io.BytesIO(out.content), ignore_missing_simple=True)\n",
+    "    data.writeto(f\"{sid['r:diaSourceId']}_cutoutScience.fits\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "70354bad-1491-4997-a5e5-cc56638fbaf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open the last one\n",
+    "cutout = fits.open(f\"{sid['r:diaSourceId']}_cutoutScience.fits\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "087a563c-f14f-465c-a386-4f10231c5a47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x752989f2c4d0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(cutout[0].data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e975fd7f-2437-4ec9-8699-4dd899eb6ac6",
+   "metadata": {},
+   "source": [
+    "For more options when downloading images, see the [alert image data](https://doc.lsst.fink-broker.org/services/api/imagesearch/) page. Be careful, each image is about 30KB."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "75419cbb-220a-4588-87ac-45b028456a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clean\n",
+    "# !rm *fits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92ebef36-fb43-4636-aa38-11381f0f5795",
+   "metadata": {},
+   "source": [
+    "````{danger} Do not abuse!\n",
+    "Although the Fink API gives you access to millions of alerts anonymously, it is not designed to massively download data. If you have a lot of objects to query, you probably want to consider the `/api/v1/ssobulk` endpoint, or you can use the [Data Transfer service](https://doc.lsst.fink-broker.org/services/data_transfer/).\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "753656ab-c441-4415-8dc5-4abd3b79fdd0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lsst/solar_system/2_ssoft.ipynb b/lsst/solar_system/2_ssoft.ipynb
new file mode 100644
index 0000000..8ef8a08
--- /dev/null
+++ b/lsst/solar_system/2_ssoft.ipynb
@@ -0,0 +1,1058 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2bff1d29-2efe-4fd8-84f7-10dcb0c2d755",
+   "metadata": {},
+   "source": [
+    "# Solar System Object Fink Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4586451-87cf-4cb5-8527-00b65c372e90",
+   "metadata": {},
+   "source": [
+    "This service lets you query the table containing aggregated parameters for known Solar System objects in Fink. This table is updated once a month, with all data in Fink from 2026/04/04.\n",
+    "\n",
+    "```{note} Starting date: 2026/04/04\n",
+    "The SSOFT uses LSST alert data only from 2026/04/04. Prior to that date, the project was not providing correct units for the topocentric and heliocentric distance fields that are used to fit parameters for some models.\n",
+    "````\n",
+    "\n",
+    "_Created on 2026/09/28. See https://doc.lsst.fink-broker.org/services/api/solar_system/ for up-to-date documentation._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "037f59cc-1282-4ad5-a8ca-1493c811cc57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# Phase curve model, among: HG, HG1G2, SHG1G2\n",
+    "flavor = \"HG\"\n",
+    "\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/ssoft\",\n",
+    "    json={\"flavor\": flavor, \"output-format\": \"parquet\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "ssoft_lsst = pd.read_parquet(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b95364b5-dea8-48ea-8b0a-d49329a4a6ac",
+   "metadata": {},
+   "source": [
+    "This table contains basic statistics (e.g. coverage in time for each object, name, number, ...), fitted parameters (absolute magnitude, phase parameters, spin parameters, ...), quality statuses, and version numbers. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "13cf40ba-3bc5-470d-af25-2889ec2aa83b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>designation</th>\n",
+       "      <th>chi2red</th>\n",
+       "      <th>status</th>\n",
+       "      <th>fit</th>\n",
+       "      <th>rms</th>\n",
+       "      <th>rms_i</th>\n",
+       "      <th>rms_r</th>\n",
+       "      <th>rms_z</th>\n",
+       "      <th>median_error_phot</th>\n",
+       "      <th>median_error_phot_i</th>\n",
+       "      <th>...</th>\n",
+       "      <th>max_phase_y</th>\n",
+       "      <th>H_y</th>\n",
+       "      <th>err_H_y</th>\n",
+       "      <th>G_y</th>\n",
+       "      <th>err_G_y</th>\n",
+       "      <th>n_days_y</th>\n",
+       "      <th>sso_name</th>\n",
+       "      <th>sso_number</th>\n",
+       "      <th>version</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2013 CL29</td>\n",
+       "      <td>52.143506</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.062664</td>\n",
+       "      <td>0.085624</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.026386</td>\n",
+       "      <td>0.026739</td>\n",
+       "      <td>0.024591</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013 CL29</td>\n",
+       "      <td>451695</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015 PY171</td>\n",
+       "      <td>10.304822</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.111956</td>\n",
+       "      <td>0.066456</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.173901</td>\n",
+       "      <td>0.056465</td>\n",
+       "      <td>0.049532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2015 PY171</td>\n",
+       "      <td>822188</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 79 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  designation    chi2red  status  fit       rms     rms_i  rms_r     rms_z  \\\n",
+       "0   2013 CL29  52.143506       1    0  0.062664  0.085624    0.0  0.026386   \n",
+       "1  2015 PY171  10.304822       2    0  0.111956  0.066456    0.0  0.173901   \n",
+       "\n",
+       "   median_error_phot  median_error_phot_i  ...  max_phase_y  H_y  err_H_y  \\\n",
+       "0           0.026739             0.024591  ...          NaN  NaN      NaN   \n",
+       "1           0.056465             0.049532  ...          NaN  NaN      NaN   \n",
+       "\n",
+       "   G_y  err_G_y  n_days_y    sso_name  sso_number  version  flag  \n",
+       "0  NaN      NaN       NaN   2013 CL29      451695   202609     0  \n",
+       "1  NaN      NaN       NaN  2015 PY171      822188   202609     0  \n",
+       "\n",
+       "[2 rows x 79 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ssoft_lsst.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "55db6376-a363-4e77-bdce-63fdb7c93619",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "58163"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Number of objects passing the criteria to enter phase curve measurement\n",
+    "len(ssoft_lsst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f137a1f-23e5-48ab-9dcc-b4ebafdfa2f8",
+   "metadata": {},
+   "source": [
+    "````{warning} Table size\n",
+    "The table is several megabytes by default (and as the survey will progress, it will become several hundreds of megabytes), so you can also decide to transfer only a subset of fields:\n",
+    "```python\n",
+    "import requests\n",
+    "\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/ssoft\",\n",
+    "    json={\"columns\": \"sso_name,H_g,chi2red\", \"output-format\": \"parquet\"},\n",
+    ")\n",
+    "```\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c74de5f8-c73c-4ea0-8d3d-adc56a5e4645",
+   "metadata": {},
+   "source": [
+    "To know the parameters of interest, you can retrieve the schema of the table using using the schema argument:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1c805eb6-04fd-476a-a89d-bfcb302084e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/ssoft\",\n",
+    "    json={\"flavor\": flavor, \"schema\": True},\n",
+    ")\n",
+    "\n",
+    "schema = r.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07126744-6ae9-4655-ab54-343e305a65c9",
+   "metadata": {},
+   "source": [
+    "````{warning} Data quality\n",
+    "As of 2026/09, the survey is beginning. The lightcurve sampling for each object is not yet sufficient, and phase coverage is small. Do not expect ground breaking results at this stage!\n",
+    "````\n",
+    "\n",
+    "Let's apply some quality cuts:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "eb2a3362-c1f9-46fd-a7e4-18e0631b3056",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c1 = ssoft_lsst[\"n_obs\"] >= 20  # at least 20 observations\n",
+    "c2 = ssoft_lsst[\"n_days\"] >= 14  # observed for at least 2 weeks\n",
+    "c3 = ssoft_lsst[\"chi2red\"] < 10  # avoid crazy reduced chi2\n",
+    "\n",
+    "ssoft_lsst_clean = ssoft_lsst[c1 & c2 & c3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "abdf83fb-4e8a-40ca-830e-8bafc3c750f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>designation</th>\n",
+       "      <th>chi2red</th>\n",
+       "      <th>status</th>\n",
+       "      <th>fit</th>\n",
+       "      <th>rms</th>\n",
+       "      <th>rms_i</th>\n",
+       "      <th>rms_r</th>\n",
+       "      <th>rms_z</th>\n",
+       "      <th>median_error_phot</th>\n",
+       "      <th>median_error_phot_i</th>\n",
+       "      <th>...</th>\n",
+       "      <th>max_phase_y</th>\n",
+       "      <th>H_y</th>\n",
+       "      <th>err_H_y</th>\n",
+       "      <th>G_y</th>\n",
+       "      <th>err_G_y</th>\n",
+       "      <th>n_days_y</th>\n",
+       "      <th>sso_name</th>\n",
+       "      <th>sso_number</th>\n",
+       "      <th>version</th>\n",
+       "      <th>flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>16512</th>\n",
+       "      <td>2003 BU55</td>\n",
+       "      <td>4.997225</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.049782</td>\n",
+       "      <td>0.015696</td>\n",
+       "      <td>0.020872</td>\n",
+       "      <td>0.024139</td>\n",
+       "      <td>0.009121</td>\n",
+       "      <td>0.006784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2003 BU55</td>\n",
+       "      <td>174505</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34717</th>\n",
+       "      <td>2002 VF21</td>\n",
+       "      <td>9.532202</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.066620</td>\n",
+       "      <td>0.040291</td>\n",
+       "      <td>0.026886</td>\n",
+       "      <td>0.041671</td>\n",
+       "      <td>0.014443</td>\n",
+       "      <td>0.011262</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002 VF21</td>\n",
+       "      <td>170032</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37770</th>\n",
+       "      <td>2017 SZ102</td>\n",
+       "      <td>4.599290</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.086021</td>\n",
+       "      <td>0.093004</td>\n",
+       "      <td>0.061906</td>\n",
+       "      <td>0.087236</td>\n",
+       "      <td>0.042538</td>\n",
+       "      <td>0.036399</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017 SZ102</td>\n",
+       "      <td>540525</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24711</th>\n",
+       "      <td>2006 OA12</td>\n",
+       "      <td>1.852437</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.062368</td>\n",
+       "      <td>0.050422</td>\n",
+       "      <td>0.056540</td>\n",
+       "      <td>0.092051</td>\n",
+       "      <td>0.043363</td>\n",
+       "      <td>0.040301</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2006 OA12</td>\n",
+       "      <td>319466</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8044</th>\n",
+       "      <td>1998 TT26</td>\n",
+       "      <td>3.561930</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.027610</td>\n",
+       "      <td>0.009423</td>\n",
+       "      <td>0.009891</td>\n",
+       "      <td>0.012465</td>\n",
+       "      <td>0.006253</td>\n",
+       "      <td>0.004921</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1998 TT26</td>\n",
+       "      <td>25223</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52799</th>\n",
+       "      <td>2021 ES60</td>\n",
+       "      <td>1.470383</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.078809</td>\n",
+       "      <td>0.075519</td>\n",
+       "      <td>0.083210</td>\n",
+       "      <td>0.134614</td>\n",
+       "      <td>0.085044</td>\n",
+       "      <td>0.085228</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2021 ES60</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14472</th>\n",
+       "      <td>2019 OB24</td>\n",
+       "      <td>2.862863</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.114405</td>\n",
+       "      <td>0.089921</td>\n",
+       "      <td>0.131394</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.100149</td>\n",
+       "      <td>0.111911</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019 OB24</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32929</th>\n",
+       "      <td>2018 PX75</td>\n",
+       "      <td>3.378440</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.135538</td>\n",
+       "      <td>0.080307</td>\n",
+       "      <td>0.192152</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.100563</td>\n",
+       "      <td>0.100862</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018 PX75</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8777</th>\n",
+       "      <td>2010 JU2</td>\n",
+       "      <td>2.086813</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.075390</td>\n",
+       "      <td>0.064764</td>\n",
+       "      <td>0.094691</td>\n",
+       "      <td>0.054870</td>\n",
+       "      <td>0.090526</td>\n",
+       "      <td>0.061328</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2010 JU2</td>\n",
+       "      <td>552655</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1432</th>\n",
+       "      <td>2002 RL295</td>\n",
+       "      <td>2.627295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.086884</td>\n",
+       "      <td>0.056303</td>\n",
+       "      <td>0.092790</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.076215</td>\n",
+       "      <td>0.078451</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2002 RL295</td>\n",
+       "      <td>620344</td>\n",
+       "      <td>202609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>348 rows × 79 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      designation   chi2red  status  fit       rms     rms_i     rms_r  \\\n",
+       "16512   2003 BU55  4.997225       2    0  0.049782  0.015696  0.020872   \n",
+       "34717   2002 VF21  9.532202       2    0  0.066620  0.040291  0.026886   \n",
+       "37770  2017 SZ102  4.599290       2    0  0.086021  0.093004  0.061906   \n",
+       "24711   2006 OA12  1.852437       4    0  0.062368  0.050422  0.056540   \n",
+       "8044    1998 TT26  3.561930       3    0  0.027610  0.009423  0.009891   \n",
+       "...           ...       ...     ...  ...       ...       ...       ...   \n",
+       "52799   2021 ES60  1.470383       1    0  0.078809  0.075519  0.083210   \n",
+       "14472   2019 OB24  2.862863       4    0  0.114405  0.089921  0.131394   \n",
+       "32929   2018 PX75  3.378440       1    0  0.135538  0.080307  0.192152   \n",
+       "8777     2010 JU2  2.086813       2    0  0.075390  0.064764  0.094691   \n",
+       "1432   2002 RL295  2.627295       1    0  0.086884  0.056303  0.092790   \n",
+       "\n",
+       "          rms_z  median_error_phot  median_error_phot_i  ...  max_phase_y  \\\n",
+       "16512  0.024139           0.009121             0.006784  ...          NaN   \n",
+       "34717  0.041671           0.014443             0.011262  ...          NaN   \n",
+       "37770  0.087236           0.042538             0.036399  ...          NaN   \n",
+       "24711  0.092051           0.043363             0.040301  ...          NaN   \n",
+       "8044   0.012465           0.006253             0.004921  ...          NaN   \n",
+       "...         ...                ...                  ...  ...          ...   \n",
+       "52799  0.134614           0.085044             0.085228  ...          NaN   \n",
+       "14472  0.000000           0.100149             0.111911  ...          NaN   \n",
+       "32929       NaN           0.100563             0.100862  ...          NaN   \n",
+       "8777   0.054870           0.090526             0.061328  ...          NaN   \n",
+       "1432   0.000000           0.076215             0.078451  ...          NaN   \n",
+       "\n",
+       "       H_y  err_H_y  G_y  err_G_y  n_days_y    sso_name  sso_number  version  \\\n",
+       "16512  NaN      NaN  NaN      NaN       NaN   2003 BU55      174505   202609   \n",
+       "34717  NaN      NaN  NaN      NaN       NaN   2002 VF21      170032   202609   \n",
+       "37770  NaN      NaN  NaN      NaN       NaN  2017 SZ102      540525   202609   \n",
+       "24711  NaN      NaN  NaN      NaN       NaN   2006 OA12      319466   202609   \n",
+       "8044   NaN      NaN  NaN      NaN       NaN   1998 TT26       25223   202609   \n",
+       "...    ...      ...  ...      ...       ...         ...         ...      ...   \n",
+       "52799  NaN      NaN  NaN      NaN       NaN   2021 ES60         nan   202609   \n",
+       "14472  NaN      NaN  NaN      NaN       NaN   2019 OB24         nan   202609   \n",
+       "32929  NaN      NaN  NaN      NaN       NaN   2018 PX75         nan   202609   \n",
+       "8777   NaN      NaN  NaN      NaN       NaN    2010 JU2      552655   202609   \n",
+       "1432   NaN      NaN  NaN      NaN       NaN  2002 RL295      620344   202609   \n",
+       "\n",
+       "       flag  \n",
+       "16512     0  \n",
+       "34717     0  \n",
+       "37770     0  \n",
+       "24711     0  \n",
+       "8044      0  \n",
+       "...     ...  \n",
+       "52799     0  \n",
+       "14472     0  \n",
+       "32929     0  \n",
+       "8777      0  \n",
+       "1432      0  \n",
+       "\n",
+       "[348 rows x 79 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ssoft_lsst_clean.sort_values(\"n_obs\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d705072-2b67-40aa-b8e5-ffef444b2b1a",
+   "metadata": {},
+   "source": [
+    "## Adding another survey: ZTF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cef31125-466f-47a4-9eb5-524fcde34864",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "# Phase curve model, among: HG, HG1G2, SHG1G2\n",
+    "flavor = \"HG\"\n",
+    "\n",
+    "r = requests.post(\n",
+    "    \"https://api.ztf.fink-portal.org/api/v1/ssoft\",\n",
+    "    json={\"flavor\": flavor, \"output-format\": \"parquet\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "ssoft_ztf = pd.read_parquet(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e2bcd938-1d78-4b47-b2b4-526be730c7c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_obs_lsst</th>\n",
+       "      <th>n_days_lsst</th>\n",
+       "      <th>n_obs_ztf</th>\n",
+       "      <th>n_days_ztf</th>\n",
+       "      <th>n_obs_tot</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sso_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2000 YZ49</th>\n",
+       "      <td>77.0</td>\n",
+       "      <td>37.945895</td>\n",
+       "      <td>203.0</td>\n",
+       "      <td>1647.662789</td>\n",
+       "      <td>280.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001 FA170</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>14.946738</td>\n",
+       "      <td>221.0</td>\n",
+       "      <td>2121.773993</td>\n",
+       "      <td>244.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003 BU55</th>\n",
+       "      <td>104.0</td>\n",
+       "      <td>54.033942</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>2333.704005</td>\n",
+       "      <td>232.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 WX174</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>19.993534</td>\n",
+       "      <td>187.0</td>\n",
+       "      <td>2073.773692</td>\n",
+       "      <td>219.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009 SV242</th>\n",
+       "      <td>88.0</td>\n",
+       "      <td>45.000101</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1046.871539</td>\n",
+       "      <td>188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 HN60</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>14.978665</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>1647.744803</td>\n",
+       "      <td>183.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 VF21</th>\n",
+       "      <td>103.0</td>\n",
+       "      <td>37.988796</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>2306.799005</td>\n",
+       "      <td>178.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 RA105</th>\n",
+       "      <td>65.0</td>\n",
+       "      <td>37.988796</td>\n",
+       "      <td>109.0</td>\n",
+       "      <td>1577.798623</td>\n",
+       "      <td>174.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 VK44</th>\n",
+       "      <td>80.0</td>\n",
+       "      <td>26.091979</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>2360.966204</td>\n",
+       "      <td>171.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 LR32</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>26.972501</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>2326.862326</td>\n",
+       "      <td>169.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003 UR205</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>29.931469</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>1495.822998</td>\n",
+       "      <td>167.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 VX7</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>31.925150</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>2378.703646</td>\n",
+       "      <td>162.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003 SM37</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>41.944446</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>2067.697766</td>\n",
+       "      <td>161.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012 XJ4</th>\n",
+       "      <td>68.0</td>\n",
+       "      <td>27.070407</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>1647.810995</td>\n",
+       "      <td>158.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 TU199</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>19.960790</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>2013.771192</td>\n",
+       "      <td>157.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004 GF79</th>\n",
+       "      <td>70.0</td>\n",
+       "      <td>17.083169</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>2202.805984</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001 SP324</th>\n",
+       "      <td>70.0</td>\n",
+       "      <td>31.915501</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>2271.833854</td>\n",
+       "      <td>139.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 SQ174</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>18.047602</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>965.925984</td>\n",
+       "      <td>137.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009 RN60</th>\n",
+       "      <td>68.0</td>\n",
+       "      <td>27.070407</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>1648.745891</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003 UX115</th>\n",
+       "      <td>71.0</td>\n",
+       "      <td>25.072996</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>2140.740602</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001 OP7</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>17.086456</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>973.934039</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003 AY39</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>18.977581</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>1997.676250</td>\n",
+       "      <td>113.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999 VT136</th>\n",
+       "      <td>59.0</td>\n",
+       "      <td>17.086456</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1606.860949</td>\n",
+       "      <td>111.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002 RC182</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>16.941802</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>2099.938009</td>\n",
+       "      <td>111.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 XD18</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>18.904399</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>2075.864965</td>\n",
+       "      <td>103.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009 SB140</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>17.999023</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>2347.933762</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000 JR9</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>15.989121</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>1845.919294</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005 QX168</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>43.001837</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1922.813438</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            n_obs_lsst  n_days_lsst  n_obs_ztf   n_days_ztf  n_obs_tot\n",
+       "sso_name                                                              \n",
+       "2000 YZ49         77.0    37.945895      203.0  1647.662789      280.0\n",
+       "2001 FA170        23.0    14.946738      221.0  2121.773993      244.0\n",
+       "2003 BU55        104.0    54.033942      128.0  2333.704005      232.0\n",
+       "2000 WX174        32.0    19.993534      187.0  2073.773692      219.0\n",
+       "2009 SV242        88.0    45.000101      100.0  1046.871539      188.0\n",
+       "2000 HN60         21.0    14.978665      162.0  1647.744803      183.0\n",
+       "2002 VF21        103.0    37.988796       75.0  2306.799005      178.0\n",
+       "2000 RA105        65.0    37.988796      109.0  1577.798623      174.0\n",
+       "2002 VK44         80.0    26.091979       91.0  2360.966204      171.0\n",
+       "2002 LR32         55.0    26.972501      114.0  2326.862326      169.0\n",
+       "2003 UR205        38.0    29.931469      129.0  1495.822998      167.0\n",
+       "2002 VX7          57.0    31.925150      105.0  2378.703646      162.0\n",
+       "2003 SM37         37.0    41.944446      124.0  2067.697766      161.0\n",
+       "2012 XJ4          68.0    27.070407       90.0  1647.810995      158.0\n",
+       "2002 TU199        27.0    19.960790      130.0  2013.771192      157.0\n",
+       "2004 GF79         70.0    17.083169       80.0  2202.805984      150.0\n",
+       "2001 SP324        70.0    31.915501       69.0  2271.833854      139.0\n",
+       "2000 SQ174        37.0    18.047602      100.0   965.925984      137.0\n",
+       "2009 RN60         68.0    27.070407       64.0  1648.745891      132.0\n",
+       "2003 UX115        71.0    25.072996       61.0  2140.740602      132.0\n",
+       "2001 OP7          57.0    17.086456       64.0   973.934039      121.0\n",
+       "2003 AY39         36.0    18.977581       77.0  1997.676250      113.0\n",
+       "1999 VT136        59.0    17.086456       52.0  1606.860949      111.0\n",
+       "2002 RC182        27.0    16.941802       84.0  2099.938009      111.0\n",
+       "2000 XD18         40.0    18.904399       63.0  2075.864965      103.0\n",
+       "2009 SB140        31.0    17.999023       58.0  2347.933762       89.0\n",
+       "2000 JR9          23.0    15.989121       58.0  1845.919294       81.0\n",
+       "2005 QX168        25.0    43.001837       55.0  1922.813438       80.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols = [\"sso_name\", \"n_obs\", \"n_days\"]\n",
+    "group = (\n",
+    "    ssoft_lsst_clean[cols]\n",
+    "    .set_index(\"sso_name\")\n",
+    "    .join(\n",
+    "        ssoft_ztf[cols].set_index(\"sso_name\"),\n",
+    "        on=\"sso_name\",\n",
+    "        how=\"inner\",\n",
+    "        rsuffix=\"_ztf\",\n",
+    "        lsuffix=\"_lsst\",\n",
+    "    )\n",
+    ")\n",
+    "group[\"n_obs_tot\"] = group[\"n_obs_ztf\"] + group[\"n_obs_lsst\"]\n",
+    "group.sort_values(\"n_obs_tot\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51edb54c-146e-4ec0-97b2-8ff4762171ca",
+   "metadata": {},
+   "source": [
+    "[2009 SV242](https://lsst.fink-portal.org/K09SO2V) is a good candidate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a37ac205-8e8d-4be0-b624-81a392ed9b5b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lsst/solar_system/3_fast_rotator.ipynb b/lsst/solar_system/3_fast_rotator.ipynb
new file mode 100644
index 0000000..e648416
--- /dev/null
+++ b/lsst/solar_system/3_fast_rotator.ipynb
@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88d43a80-5db2-4265-9660-024abbf7a937",
+   "metadata": {},
+   "source": [
+    "# Characterizing a Super-fast Rotating Asteroid in Rubin Observatory First Alerts with Fink "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01cc9152-0bc0-4831-b80f-ec9ef32deff7",
+   "metadata": {},
+   "source": [
+    "Let's re-do the analysis of `https://iopscience.iop.org/article/10.3847/2515-5172/ae67fe`\n",
+    "\n",
+    "_Created on 2026/09/28. See https://doc.lsst.fink-broker.org/services/api/solar_system/ for up-to-date documentation._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "75488931-e714-4e8f-9c5e-06fd690488da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: AstropyDeprecationWarning: The TestRunner class is deprecated and may be removed in a future version.\n",
+      "        Use pytest instead. [astropy.tests.runner]\n",
+      "WARNING: AstropyDeprecationWarning: The TestRunnerBase class is deprecated and may be removed in a future version.\n",
+      "        Use pytest instead. [astropy.utils.decorators]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import io\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 15})\n",
+    "\n",
+    "import nifty_ls  # noqa: F401\n",
+    "from astropy.time import Time\n",
+    "from astropy.timeseries import LombScargleMultiband\n",
+    "from phunk import PhaseCurve\n",
+    "\n",
+    "from sso_utils import plot_lightcurve_allbands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5a56baa-ce11-4205-9df8-515e2724d77d",
+   "metadata": {},
+   "source": [
+    "## Object data\n",
+    "\n",
+    "Observations of the object [2017 QJ216](https://lsst.fink-portal.org/K17QL6J) by Rubin were done in February 2026 -- so it is not available from the SSOFT or bulk download (these services expose data from 2026/04/04). You can however get the data from the Fink Data Transfer, or directly from the Fink API, that expose all Rubin alert data. Let's use the Fink API to retrieve data for the object [2017 QJ216](https://lsst.fink-portal.org/K17QL6J) with its ephemerides:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7e8d640d-20a2-410d-a147-eee156641623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.post(\n",
+    "    \"https://api.lsst.fink-portal.org/api/v1/sso\",\n",
+    "    json={\"n_or_d\": \"2017 QJ216\", \"withEphem\": True, \"output-format\": \"json\"},\n",
+    ")\n",
+    "\n",
+    "# Format output in a DataFrame\n",
+    "pdf_obj = pd.read_json(io.BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "11ecbe7d-9ca7-464d-a33b-6ffe97839331",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>IAU_code</th>\n",
+       "      <th>Phase</th>\n",
+       "      <th>Elong</th>\n",
+       "      <th>RA</th>\n",
+       "      <th>DEC</th>\n",
+       "      <th>Dobs</th>\n",
+       "      <th>RA_h</th>\n",
+       "      <th>DEC_h</th>\n",
+       "      <th>...</th>\n",
+       "      <th>r:trailRaErr</th>\n",
+       "      <th>r:trail_flag</th>\n",
+       "      <th>r:trail_flag_edge</th>\n",
+       "      <th>r:visit</th>\n",
+       "      <th>r:x</th>\n",
+       "      <th>r:xErr</th>\n",
+       "      <th>r:y</th>\n",
+       "      <th>r:yErr</th>\n",
+       "      <th>f:sso_name</th>\n",
+       "      <th>i:magpsf_red</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.461100e+06</td>\n",
+       "      <td>X05</td>\n",
+       "      <td>4.595807</td>\n",
+       "      <td>168.889631</td>\n",
+       "      <td>150.669233</td>\n",
+       "      <td>3.676251</td>\n",
+       "      <td>1.402627</td>\n",
+       "      <td>154.896635</td>\n",
+       "      <td>5.511119</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022700522</td>\n",
+       "      <td>3029.9412</td>\n",
+       "      <td>0.355364</td>\n",
+       "      <td>3268.7507</td>\n",
+       "      <td>0.252052</td>\n",
+       "      <td>2017 QJ216</td>\n",
+       "      <td>18.984646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2.461100e+06</td>\n",
+       "      <td>X05</td>\n",
+       "      <td>4.595631</td>\n",
+       "      <td>168.890063</td>\n",
+       "      <td>150.669351</td>\n",
+       "      <td>3.676216</td>\n",
+       "      <td>1.402625</td>\n",
+       "      <td>154.896523</td>\n",
+       "      <td>5.511170</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022700521</td>\n",
+       "      <td>1953.2726</td>\n",
+       "      <td>0.353900</td>\n",
+       "      <td>2801.7750</td>\n",
+       "      <td>0.241011</td>\n",
+       "      <td>2017 QJ216</td>\n",
+       "      <td>19.116253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.461100e+06</td>\n",
+       "      <td>X05</td>\n",
+       "      <td>4.594704</td>\n",
+       "      <td>168.892344</td>\n",
+       "      <td>150.669976</td>\n",
+       "      <td>3.676030</td>\n",
+       "      <td>1.402617</td>\n",
+       "      <td>154.895935</td>\n",
+       "      <td>5.511440</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2026022700519</td>\n",
+       "      <td>1567.2805</td>\n",
+       "      <td>0.298948</td>\n",
+       "      <td>1206.7303</td>\n",
+       "      <td>0.334995</td>\n",
+       "      <td>2017 QJ216</td>\n",
+       "      <td>19.726835</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 164 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index          Date IAU_code     Phase       Elong          RA       DEC  \\\n",
+       "0      0  2.461100e+06      X05  4.595807  168.889631  150.669233  3.676251   \n",
+       "1      1  2.461100e+06      X05  4.595631  168.890063  150.669351  3.676216   \n",
+       "2      2  2.461100e+06      X05  4.594704  168.892344  150.669976  3.676030   \n",
+       "\n",
+       "       Dobs        RA_h     DEC_h  ...  r:trailRaErr  r:trail_flag  \\\n",
+       "0  1.402627  154.896635  5.511119  ...           NaN           NaN   \n",
+       "1  1.402625  154.896523  5.511170  ...           NaN           NaN   \n",
+       "2  1.402617  154.895935  5.511440  ...           NaN           NaN   \n",
+       "\n",
+       "   r:trail_flag_edge        r:visit        r:x    r:xErr        r:y    r:yErr  \\\n",
+       "0              False  2026022700522  3029.9412  0.355364  3268.7507  0.252052   \n",
+       "1              False  2026022700521  1953.2726  0.353900  2801.7750  0.241011   \n",
+       "2              False  2026022700519  1567.2805  0.298948  1206.7303  0.334995   \n",
+       "\n",
+       "   f:sso_name  i:magpsf_red  \n",
+       "0  2017 QJ216     18.984646  \n",
+       "1  2017 QJ216     19.116253  \n",
+       "2  2017 QJ216     19.726835  \n",
+       "\n",
+       "[3 rows x 164 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pdf_obj.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5bcd9ecc-3db2-4d30-bc24-2591adee8be5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(12, 7))\n",
+    "plot_lightcurve_allbands(\n",
+    "    pdf_obj[\"r:midpointMjdTai\"],\n",
+    "    pdf_obj[\"r:psfFlux\"],\n",
+    "    pdf_obj[\"r:psfFluxErr\"],\n",
+    "    pdf_obj[\"r:band\"],\n",
+    "    ax=ax,\n",
+    "    label=\"Difference flux (r band)\",\n",
+    ")\n",
+    "\n",
+    "ax.grid(alpha=0.5)\n",
+    "ax.legend()\n",
+    "ax.set_ylabel(\"Flux in nanoJansky\")\n",
+    "ax.set_xlabel(\"Observation date\")\n",
+    "ax.set_title(\"Difference flux (psfFlux)\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "753656ab-c441-4415-8dc5-4abd3b79fdd0",
+   "metadata": {},
+   "source": [
+    "## Phase curve fitting\n",
+    "\n",
+    "These observations are not in the SSOFT, as they have been done in February 2026 (SSOFT data starts on April 2026). So let's fit for the parameters of the HG model using the [space-phunk](https://github.com/astrockers/phunk) package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4f1647cd-790b-488b-9bef-88fe56a95817",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def flux_to_mag(flux, flux_err):\n",
+    "    \"\"\"Convert flux to magnitude (and errors)\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    flux: array-like\n",
+    "        Flux in nJy\n",
+    "    flux_err: array-like\n",
+    "        Flux error in nJy\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    mag, mag_err: array-like\n",
+    "    \"\"\"\n",
+    "    mag = 31.4 - 2.5 * np.log10(flux)\n",
+    "    mag_err = 2.5 / np.log(10) * flux_err / flux\n",
+    "\n",
+    "    return mag, mag_err\n",
+    "\n",
+    "\n",
+    "# Compute reduced magnitude and error\n",
+    "mag, mag_err = flux_to_mag(pdf_obj[\"r:scienceFlux\"], pdf_obj[\"r:scienceFluxErr\"])\n",
+    "red_mag = mag - 5 * np.log10(pdf_obj[\"Dobs\"] * pdf_obj[\"Dhelio\"])\n",
+    "\n",
+    "pc = PhaseCurve(\n",
+    "    phase=pdf_obj[\"Phase\"],\n",
+    "    mag=red_mag,\n",
+    "    mag_err=mag_err,\n",
+    "    epoch=Time(pdf_obj[\"r:midpointMjdTai\"], format=\"mjd\", scale=\"tai\").utc.jd,\n",
+    "    band=pdf_obj[\"r:band\"],\n",
+    "    target=\"2017 QJ216\",\n",
+    ")\n",
+    "pc.fit([\"HG\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec1e603e-af92-45df-9323-e421d5ba31c7",
+   "metadata": {},
+   "source": [
+    "```{note} i-band analysis only\n",
+    "For simplicity, let's only focus on the i-band measurements for the rest of this demo.\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "89f5e34e-f7b9-4aef-b7d2-a7ceabf97428",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(18.881582076561752, 0.37385556431228795)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pc.HG.Hi, pc.HG.Hi_err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aa409d50-b0d1-4299-93de-f64379eac016",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask_band = pdf_obj[\"r:band\"] == \"i\"\n",
+    "pc_i = pc.HG.eval(pdf_obj[\"Phase\"][mask_band], H=pc.HG.Hi, G=pc.HG.Gi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2721ad-1604-4ed2-b1ce-f23bcf258176",
+   "metadata": {},
+   "source": [
+    "## Period fitting from the residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5541c0f4-c179-4f62-b73e-277c2627e0a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Period = 20.3 minutes\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LombScargleMultiband(\n",
+    "    pdf_obj[\"r:midpointMjdTai\"][mask_band],\n",
+    "    red_mag[mask_band] - pc_i,\n",
+    "    pdf_obj[\"r:band\"][mask_band],\n",
+    "    mag_err[mask_band],\n",
+    "    nterms_base=2,\n",
+    "    nterms_band=1,\n",
+    ")\n",
+    "\n",
+    "frequency, power = model.autopower(\n",
+    "    method=\"fast\",\n",
+    "    sb_method=\"fastnifty_chi2\",\n",
+    "    minimum_frequency=1 / 1.2,\n",
+    "    maximum_frequency=1 / 0.0001,\n",
+    ")\n",
+    "\n",
+    "freq_maxpower = frequency[np.argmax(power)]\n",
+    "\n",
+    "# one or two-peaked solution ??\n",
+    "period = 1 / freq_maxpower\n",
+    "\n",
+    "print(f\"Period = {period * 24 * 60:.1f} minutes\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8c664a-6e34-4e15-9739-3cedbb16a311",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f1bc3441-4b5c-4720-89cf-e19ba0fc114c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7c4ea039d050>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Time boundaries for plots\n",
+    "t0 = Time(\"2026-02-26 05:00:00\").tai.mjd\n",
+    "t1 = Time(\"2026-02-26 06:00:00\").tai.mjd\n",
+    "mask_time = (pdf_obj[\"r:midpointMjdTai\"] >= t0) & (pdf_obj[\"r:midpointMjdTai\"] <= t1)\n",
+    "mask = mask_band & mask_time\n",
+    "\n",
+    "tfit_unfolded = np.linspace(\n",
+    "    t0,\n",
+    "    t1,\n",
+    "    # Between 100 and 10000 points, to hopefully have 10 per period\n",
+    "    min(10000, max(1000, int(10 * (t1 - t0) / period))),\n",
+    ")\n",
+    "out_unfolded = model.model(\n",
+    "    tfit_unfolded, freq_maxpower, bands_fit=pdf_obj[\"r:band\"][mask].unique()\n",
+    ")\n",
+    "\n",
+    "\n",
+    "plt.errorbar(\n",
+    "    Time(pdf_obj[\"r:midpointMjdTai\"], format=\"mjd\", scale=\"tai\").utc.datetime[mask],\n",
+    "    pdf_obj[\"i:magpsf_red\"][mask] - pc_i[mask_time],\n",
+    "    yerr=mag_err[mask],\n",
+    "    ls=\"\",\n",
+    "    marker=\"o\",\n",
+    "    label=\"i-band residuals\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    Time(tfit_unfolded, format=\"mjd\", scale=\"tai\").utc.datetime,\n",
+    "    out_unfolded[0],\n",
+    "    ls=\"-\",\n",
+    "    marker=\"\",\n",
+    "    label=\"i-band residuals\",\n",
+    ")\n",
+    "\n",
+    "plt.axhline(0, ls=\"--\", color=\"grey\")\n",
+    "plt.ylim(-0.15, 0.15)\n",
+    "plt.xlabel(\"Observation date [UTC]\")\n",
+    "plt.ylabel(\"Residuals [mag]\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7dd8237-653f-4f69-bf7f-bd7afbf65e79",
+   "metadata": {},
+   "source": [
+    "````{note} SOCCA\n",
+    "In reality, with sparser cadence and longer baseline, you would model the lightcurve using e.g. [SOCCA](https://www.aanda.org/articles/aa/abs/2026/09/aa60190-26/aa60190-26.html) to derive the period alongside other phase curve parameters!\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12c255f1-8311-4854-9316-0542c77b7188",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lsst/solar_system/README.md b/lsst/solar_system/README.md
new file mode 100644
index 0000000..c15f41b
--- /dev/null
+++ b/lsst/solar_system/README.md
@@ -0,0 +1,21 @@
+# Fink Solar System tutorials
+
+These tutorials have been given during the [LSST@EUROPE 8](https://lsst-europe8.hu/index.php) conference on 2026/10/01. Slides can be found [online](https://docs.google.com/presentation/d/1ZXTh_gdrZ6j1hKZou-27jggY74BlEmugOSdWhifgzUk/edit?usp=sharing).
+
+- [0_lsst_sso_statistics.ipynb](0_lsst_sso_statistics.ipynb): play with stream statistics
+- [1_ssobject.ipynb](1_ssobject.ipynb): how to query object data
+- [2_ssoft.ipynb](2_ssoft.ipynb): how to query SSO Fink Table (SSOFT)
+- [3_fast_rotator.ipynb](3_fast_rotator.ipynb): Characterizing a Super-fast Rotating Asteroid in Rubin Observatory First Alerts with Fink tools.
+
+## Running the notebooks
+
+Python 3.10+ is recommended. We suggest to use `jupyter-lab` in a Python virtual environment:
+
+```bash
+# Assuming you are on Linux
+python3 -m venv venv
+source venv/bin/activate
+
+pip install -r requirements.txt
+jupyter-lab
+```
diff --git a/lsst/solar_system/lint.sh b/lsst/solar_system/lint.sh
new file mode 100755
index 0000000..59645c2
--- /dev/null
+++ b/lsst/solar_system/lint.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+ruff check --fix .
+ruff format .
diff --git a/lsst/solar_system/requirements.txt b/lsst/solar_system/requirements.txt
new file mode 100644
index 0000000..5313b99
--- /dev/null
+++ b/lsst/solar_system/requirements.txt
@@ -0,0 +1,21 @@
+# Numeric
+pandas<3.0.0
+numpy
+astropy
+
+# Visualisation
+matplotlib
+
+# Core
+ruff
+requests
+
+# Notebooks
+jupyter
+jupyterlab
+jupyterlab_myst
+ipympl
+
+# Phase curve & period estimation
+space-phunk
+nifty_ls
diff --git a/lsst/solar_system/sso_utils.py b/lsst/solar_system/sso_utils.py
new file mode 100644
index 0000000..53d452b
--- /dev/null
+++ b/lsst/solar_system/sso_utils.py
@@ -0,0 +1,56 @@
+from astropy.time import Time
+
+UNIQUE_BANDS = ["u", "g", "r", "i", "z", "y"]
+MARKERS = {
+    "u": "o",
+    "g": "<",
+    "r": ">",
+    "i": "s",
+    "z": "*",
+    "y": "p",
+}
+
+COLORS = {
+    "u": "#15284f",
+    "g": "#626d84",
+    "r": "#afb2b9",
+    "i": "#dbbeb2",
+    "z": "#e89070",
+    "y": "#f5622e",
+}
+
+
+def plot_lightcurve_allbands(time, flux, fluxErr, bands, ax, label=""):
+    """Wrapper to plot all filter bands"""
+    for band in UNIQUE_BANDS:
+        mask = bands == band
+        if mask.sum() == 0:
+            continue
+        ax.errorbar(
+            Time(time[mask], format="mjd", scale="tai").datetime,
+            flux[mask],
+            fluxErr[mask],
+            color=COLORS[band],
+            marker=MARKERS[band],
+            label=f"{band} band",
+            ls="",
+        )
+
+
+def plot_lightcurve_singleband(
+    time, flux, fluxErr, bands, ax, single_band, label="", color="C0", marker="o"
+):
+    """Wrapper to plot a single band"""
+    mask = bands == single_band
+    if mask.sum() == 0:
+        print(f"No data for band {single_band}")
+        return
+    ax.errorbar(
+        time[mask],
+        flux[mask],
+        fluxErr[mask],
+        color=color,
+        marker=marker,
+        label=label,
+        ls="",
+    )