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danhey/for_sukanya.ipynb

Created September 12, 2022 22:28
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for_sukanya.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "1634722a-1918-444b-98af-0e47f71294ee",
"metadata": {},
"source": [
"First we need to convert our GAIA IDs to TIC IDs. Let's do this for two random GAIA IDs. To do this, we can query the Tess Input Catalog:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7d6bda2b-625e-4722-b585-da74d910bfcf",
"metadata": {},
"outputs": [],
"source": [
"from astroquery.mast import Observations, Catalogs\n",
"import numpy as np\n",
"import lightkurve as lk"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6d90e61b-2a37-44bc-ae39-bee91a9589cf",
"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>ID</th>\n",
" <th>version</th>\n",
" <th>HIP</th>\n",
" <th>TYC</th>\n",
" <th>UCAC</th>\n",
" <th>TWOMASS</th>\n",
" <th>SDSS</th>\n",
" <th>ALLWISE</th>\n",
" <th>GAIA</th>\n",
" <th>APASS</th>\n",
" <th>...</th>\n",
" <th>splists</th>\n",
" <th>e_RA</th>\n",
" <th>e_Dec</th>\n",
" <th>RA_orig</th>\n",
" <th>Dec_orig</th>\n",
" <th>e_RA_orig</th>\n",
" <th>e_Dec_orig</th>\n",
" <th>raddflag</th>\n",
" <th>wdflag</th>\n",
" <th>objID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>63294105</td>\n",
" <td>20190415</td>\n",
" <td>&lt;NA&gt;</td>\n",
" <td>3556-00768-1</td>\n",
" <td>682-068499</td>\n",
" <td>19302561+4622521</td>\n",
" <td>NaN</td>\n",
" <td>J193025.62+462252.3</td>\n",
" <td>2128262333622315008</td>\n",
" <td>53715040</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>1.397138</td>\n",
" <td>1.167974</td>\n",
" <td>292.606775</td>\n",
" <td>46.381199</td>\n",
" <td>0.040115</td>\n",
" <td>0.043861</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>294717361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2760710</td>\n",
" <td>20190415</td>\n",
" <td>&lt;NA&gt;</td>\n",
" <td>NaN</td>\n",
" <td>367-183508</td>\n",
" <td>23460664-1641284</td>\n",
" <td>NaN</td>\n",
" <td>J234606.59-164129.0</td>\n",
" <td>2394480109022713856</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>cooldwarfs_v8</td>\n",
" <td>1.674030</td>\n",
" <td>1.551799</td>\n",
" <td>356.527357</td>\n",
" <td>-16.691464</td>\n",
" <td>0.056203</td>\n",
" <td>0.054633</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1725445499</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 125 columns</p>\n",
"</div>"
],
"text/plain": [
" ID version HIP TYC UCAC TWOMASS SDSS \\\n",
"0 63294105 20190415 <NA> 3556-00768-1 682-068499 19302561+4622521 NaN \n",
"1 2760710 20190415 <NA> NaN 367-183508 23460664-1641284 NaN \n",
"\n",
" ALLWISE GAIA APASS ... splists \\\n",
"0 J193025.62+462252.3 2128262333622315008 53715040 ... NaN \n",
"1 J234606.59-164129.0 2394480109022713856 NaN ... cooldwarfs_v8 \n",
"\n",
" e_RA e_Dec RA_orig Dec_orig e_RA_orig e_Dec_orig raddflag \\\n",
"0 1.397138 1.167974 292.606775 46.381199 0.040115 0.043861 1 \n",
"1 1.674030 1.551799 356.527357 -16.691464 0.056203 0.054633 1 \n",
"\n",
" wdflag objID \n",
"0 0 294717361 \n",
"1 0 1725445499 \n",
"\n",
"[2 rows x 125 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaia_ids = np.array(['2394480109022713856', '2128262333622315008'])\n",
"result = Catalogs.query_criteria(catalog=\"Tic\", GAIA=gaia_ids).to_pandas()\n",
"result"
]
},
{
"cell_type": "markdown",
"id": "a9269fa4-e230-48f2-a266-26ce3040e5e9",
"metadata": {},
"source": [
"The TIC ID is stored as ID in the result dataframe."
]
},
{
"cell_type": "markdown",
"id": "ed354067-0a24-4f73-985d-659adcff2d2b",
"metadata": {},
"source": [
"Now we can find the corresponding light curves. Let's do this for the first star."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c5052e2d-e7f3-48d8-ad12-98c8fa7e556c",
"metadata": {},
"outputs": [],
"source": [
"row = result.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5912dcf4-09f1-4628-8586-6ffcb3f7b8f0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"SearchResult containing 11 data products.\n",
"\n",
"<table id=\"table4396499200\">\n",
"<thead><tr><th>#</th><th>mission</th><th>year</th><th>author</th><th>exptime</th><th>target_name</th><th>distance</th></tr></thead>\n",
"<thead><tr><th></th><th></th><th></th><th></th><th>s</th><th></th><th>arcsec</th></tr></thead>\n",
"<tr><td>0</td><td>TESS Sector 14</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>1</td><td>TESS Sector 14</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/qlp'>QLP</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>2</td><td>TESS Sector 14</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/cdips'>CDIPS</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>3</td><td>TESS Sector 15</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>4</td><td>TESS Sector 15</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/qlp'>QLP</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>5</td><td>TESS Sector 15</td><td>2019</td><td><a href='https://archive.stsci.edu/hlsp/cdips'>CDIPS</a></td><td>1800</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>6</td><td>TESS Sector 40</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>7</td><td>TESS Sector 40</td><td>2021</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>600</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>8</td><td>TESS Sector 41</td><td>2021</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>9</td><td>TESS Sector 41</td><td>2021</td><td><a href='https://archive.stsci.edu/hlsp/tess-spoc'>TESS-SPOC</a></td><td>600</td><td>63294105</td><td>0.0</td></tr>\n",
"<tr><td>10</td><td>TESS Sector 54</td><td>2022</td><td><a href='https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html'>SPOC</a></td><td>120</td><td>63294105</td><td>0.0</td></tr>\n",
"</table>"
],
"text/plain": [
"SearchResult containing 11 data products.\n",
"\n",
" # mission year author exptime target_name distance\n",
" s arcsec \n",
"--- -------------- ---- --------- ------- ----------- --------\n",
" 0 TESS Sector 14 2019 TESS-SPOC 1800 63294105 0.0\n",
" 1 TESS Sector 14 2019 QLP 1800 63294105 0.0\n",
" 2 TESS Sector 14 2019 CDIPS 1800 63294105 0.0\n",
" 3 TESS Sector 15 2019 TESS-SPOC 1800 63294105 0.0\n",
" 4 TESS Sector 15 2019 QLP 1800 63294105 0.0\n",
" 5 TESS Sector 15 2019 CDIPS 1800 63294105 0.0\n",
" 6 TESS Sector 40 2021 SPOC 120 63294105 0.0\n",
" 7 TESS Sector 40 2021 TESS-SPOC 600 63294105 0.0\n",
" 8 TESS Sector 41 2021 SPOC 120 63294105 0.0\n",
" 9 TESS Sector 41 2021 TESS-SPOC 600 63294105 0.0\n",
" 10 TESS Sector 54 2022 SPOC 120 63294105 0.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lk.search_lightcurve(f'TIC {row.ID}', mission='TESS')"
]
},
{
"cell_type": "markdown",
"id": "69dafb8c-c437-4fd5-9b58-be79f6c2bedd",
"metadata": {},
"source": [
"The output says there's a lot of different TESS light curves available for this star, with the corresponding exposure times and sectors listed. Let's just download the 120 s cadence data (which is the best!)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "bfaf96ed-16c9-444f-be86-f8d69e7205e1",
"metadata": {},
"outputs": [],
"source": [
"lc = lk.search_lightcurve(f'TIC {row.ID}', mission='TESS', exptime=120, author='SPOC').download_all().stitch().remove_nans()"
]
},
{
"cell_type": "markdown",
"id": "6719133c-cc4e-4cb2-b703-91029056c5f7",
"metadata": {},
"source": [
"Now we can easily plot the light curve:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2a4fbcac-1dd5-4ab1-98b6-9bf567edc81d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Time - 2457000 [BTJD days]', ylabel='Normalized Flux'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 848.5x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lc.plot()"
]
},
{
"cell_type": "markdown",
"id": "f5459911-3c09-4e20-a748-b4a0c595a960",
"metadata": {},
"source": [
"And it's amplitude spectrum using the LS periodogram:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "402d223b-c0ea-42f9-86f1-3e2314cbe8c5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='Frequency [$\\\\mathrm{\\\\frac{1}{d}}$]', ylabel='Power'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 848.5x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lc.to_periodogram().plot()"
]
},
{
"cell_type": "markdown",
"id": "ff5e056b-6c9c-4716-87a1-a0d39f808f47",
"metadata": {},
"source": [
"Cool! This star is clearly pulsating .. !"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24f2b298-f974-485d-8929-a7f6b467e0ed",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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