{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding Aperture Photometry & Making Custom Apertures. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Goals\n",
"\n",
"In our previous tutorials ([LightCurve Objects](LightCurve-objects.html) and [LightCurveFile Objects](LightCurveFile-Objects.html)) we learned about aperture photometry and the Simple Aperture Photometry (SAP) and Pre-search Data Conditioning SAP (PDCSAP) flux.\n",
"\n",
"We have not however discussed how these apertures are chosen by the pipeline or how to examine them. \n",
"\n",
"In this proposal we will cover the following, \n",
"- How apertures are defined.\n",
"- How can we examine a pre-defined aperture.\n",
"- How we can define and modify an aperture and recover the lightcurve."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SPOC Apertures\n",
"\n",
"In aperture photometry A set of pixels in the image are chosen and we sum those to produce a single flux value. \n",
"The [SPOC](https://heasarc.gsfc.nasa.gov/docs/tess/pipeline.html) produces an *optimal aperture*, which is used by default in *Lightkurve*, and determined by the [data processing pipeline](https://github.com/nasa/kepler-pipeline). This is the default aperture used by *Lightkurve* and is optimized to ensure that the stellar signal has a high signal to noise ratio, with minimal contamination from the background.\n",
"\n",
"We sum these same pre-selected pixels for every image at each cadence to produce a light curve.\n",
"\n",
"There are however, some cases where you might want to produce your own aperture. The field may be crowded, or you may wish to change the aperture size to change the relative contribution of the background. *Lightkurve* offers tools to select pixels programmatically.\n",
"\n",
"We will show you how to examine the pre-selected aperture and how to modify it if you wish."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports\n",
"This tutorial requires:\n",
"- **[Lightkurve](https://docs.lightkurve.org)** \n",
"- [**Matplotlib**](https://matplotlib.org/) \n",
"- [**numpy**](https://numpy.org)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Terms\n",
"\n",
"- Target Pixel File (TPF): A file containing the original CCD pixel observations from which light curves are extracted. \n",
"\n",
"- LightCurve Object: Obtained from a TPF and contains lightcurve information derived using simple aperture photometry.\n",
"\n",
"- LightCurveFile Object: Obtained from MAST and contains both SAP flux and PSDCSAP flux.\n",
"\n",
"- Cadence: The rate at which TESS photometric observations are stored. \n",
"\n",
"- Sector: One of TESS's 27 (to date) observing periods, approximately ~27 days in duration.\n",
"\n",
"- Simple Aperture Photometry (SAP): The act of summing all pixel values in a pre-defined aperture as a function of time.\n",
"\n",
"- Pre-search Data Conditioning SAP flux (PDCSAP) flux : SAP flux from which long term trends have been removed using so-called Co-trending Basis Vectors (CBVs). PDCSAP flux is usually cleaner data than the SAP flux and will have fewer systematic trends."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline \n",
"import numpy as np\n",
"import lightkurve as lk\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading the Data\n",
"For this tutorial lets use the [L 98-59 System](https://arxiv.org/pdf/1903.08017.pdf) again, focusing on planet c. First let's search for a TPF for this object."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"SearchResult containing 7 data products.\n",
"\n",
"
"
],
"text/plain": [
"SearchResult containing 7 data products.\n",
"\n",
" # observation author target_name productFilename distance\n",
"--- -------------- ------ ----------- ------------------------------------------------------- --------\n",
" 0 TESS Sector 2 SPOC 307210830 tess2018234235059-s0002-0000000307210830-0121-s_tp.fits 0.0\n",
" 1 TESS Sector 5 SPOC 307210830 tess2018319095959-s0005-0000000307210830-0125-s_tp.fits 0.0\n",
" 2 TESS Sector 8 SPOC 307210830 tess2019032160000-s0008-0000000307210830-0136-s_tp.fits 0.0\n",
" 3 TESS Sector 9 SPOC 307210830 tess2019058134432-s0009-0000000307210830-0139-s_tp.fits 0.0\n",
" 4 TESS Sector 10 SPOC 307210830 tess2019085135100-s0010-0000000307210830-0140-s_tp.fits 0.0\n",
" 5 TESS Sector 11 SPOC 307210830 tess2019112060037-s0011-0000000307210830-0143-s_tp.fits 0.0\n",
" 6 TESS Sector 12 SPOC 307210830 tess2019140104343-s0012-0000000307210830-0144-s_tp.fits 0.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search_result = lk.search_targetpixelfile('TIC 307210830')\n",
"search_result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets pick and download the data for sector 2."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TessTargetPixelFile(TICID: 307210830)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpf_file = search_result[0].download()\n",
"tpf_file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have a TPF file for our object in sector 2. The optimal aperture is stored in the TPF as the `pipeline_mask` property. We can have a look at it by calling it here:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, True, True, True, False, False,\n",
" False, False],\n",
" [False, False, False, False, True, True, True, True, False,\n",
" False, False],\n",
" [False, False, False, False, True, True, True, True, False,\n",
" False, False],\n",
" [False, False, False, False, False, True, True, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False],\n",
" [False, False, False, False, False, False, False, False, False,\n",
" False, False]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpf_file.pipeline_mask"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, it is a boolean array detailing which pixels are included. We can plot this aperture over the top of our TPF using the `plot()` function, and passing in the mask to the `aperture_mask` keyword."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tpf_file.plot(aperture_mask=tpf_file.pipeline_mask);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now see the SPOC *optimal* aperture mask overlaid on top of our object of interest."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the provided optimal aperture in `pipeline_mask` and the TPF we can perform simple aperture photometry via the [`extract_aperture_photometry()`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.TessTargetPixelFile.html#lightkurve.targetpixelfile.TessTargetPixelFile.extract_aperture_photometry) function as shown below,"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"TessLightCurve targetid=307210830 length=18317\n",
"
\n",
"
time
flux
flux_err
centroid_col
centroid_row
cadenceno
quality
\n",
"
electron / s
electron / s
pix
pix
\n",
"
object
float32
float32
float64
float64
int32
int32
\n",
"
1354.1088231272427
21566.349609375
16.116119384765625
664.6090864691554
339.4764484490161
91191
0
\n",
"
1354.1102119888994
21563.88671875
16.118038177490234
664.6261723169015
339.46842003296774
91192
0
\n",
"
1354.112989712153
21475.162109375
16.089221954345703
664.606630403678
339.4604662968742
91194
0
\n",
"
1354.1143785738097
21583.30859375
16.12527084350586
664.6414481151693
339.4832617761526
91195
0
\n",
"
1354.1157674355243
21575.640625
16.121679306030273
664.6354584758038
339.4735678477034
91196
0
\n",
"
1354.1171562971804
21563.1015625
16.115528106689453
664.6334974032626
339.472138768046
91197
0
\n",
"
1354.1185451588947
21552.935546875
16.112627029418945
664.625177003332
339.46675685339096
91198
0
\n",
"
1354.1199340205515
21532.90234375
16.10567855834961
664.6301979867933
339.4699372207359
91199
0
\n",
"
1354.1213228822667
21533.828125
16.105731964111328
664.6262018316135
339.46553338843
91200
0
\n",
"
...
...
...
...
...
...
...
\n",
"
1381.5001032523294
21262.494140625
16.291688919067383
664.5744500858646
339.3513278016392
110913
0
\n",
"
1381.5014921207378
21289.828125
16.302898406982422
664.5797804765874
339.3491398520347
110914
0
\n",
"
1381.5028809891458
21266.3515625
16.29288673400879
664.5790106545255
339.3513312907625
110915
0
\n",
"
1381.5042698574382
21234.845703125
16.279603958129883
664.5730941550626
339.3555631381705
110916
0
\n",
"
1381.5056587258466
21244.953125
16.281909942626953
664.5782007755507
339.3468316465567
110917
0
\n",
"
1381.5070475942555
21210.7578125
16.267162322998047
664.5770708377116
339.3442359060069
110918
0
\n",
"
1381.508436462548
21231.01171875
16.27315330505371
664.5786574675517
339.34217245510536
110919
0
\n",
"
1381.5098253309563
21250.466796875
16.277507781982422
664.5722297003167
339.3513272975753
110920
0
\n",
"
1381.5112141992488
21236.35546875
16.2720890045166
664.582152318805
339.3452178427711
110921
0
\n",
"
1381.5126030676577
21265.83984375
16.278945922851562
664.5729270180528
339.349710493043
110922
0
\n",
"
"
],
"text/plain": [
"\n",
" time flux flux_err ... cadenceno quality\n",
" electron / s electron / s ... \n",
" object float32 float32 ... int32 int32 \n",
"------------------ --------------- ------------------ ... --------- -------\n",
"1354.1088231272427 21566.349609375 16.116119384765625 ... 91191 0\n",
"1354.1102119888994 21563.88671875 16.118038177490234 ... 91192 0\n",
" 1354.112989712153 21475.162109375 16.089221954345703 ... 91194 0\n",
"1354.1143785738097 21583.30859375 16.12527084350586 ... 91195 0\n",
"1354.1157674355243 21575.640625 16.121679306030273 ... 91196 0\n",
"1354.1171562971804 21563.1015625 16.115528106689453 ... 91197 0\n",
"1354.1185451588947 21552.935546875 16.112627029418945 ... 91198 0\n",
"1354.1199340205515 21532.90234375 16.10567855834961 ... 91199 0\n",
"1354.1213228822667 21533.828125 16.105731964111328 ... 91200 0\n",
" ... ... ... ... ... ...\n",
"1381.5001032523294 21262.494140625 16.291688919067383 ... 110913 0\n",
"1381.5014921207378 21289.828125 16.302898406982422 ... 110914 0\n",
"1381.5028809891458 21266.3515625 16.29288673400879 ... 110915 0\n",
"1381.5042698574382 21234.845703125 16.279603958129883 ... 110916 0\n",
"1381.5056587258466 21244.953125 16.281909942626953 ... 110917 0\n",
"1381.5070475942555 21210.7578125 16.267162322998047 ... 110918 0\n",
" 1381.508436462548 21231.01171875 16.27315330505371 ... 110919 0\n",
"1381.5098253309563 21250.466796875 16.277507781982422 ... 110920 0\n",
"1381.5112141992488 21236.35546875 16.2720890045166 ... 110921 0\n",
"1381.5126030676577 21265.83984375 16.278945922851562 ... 110922 0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lc = tpf_file.extract_aperture_photometry()\n",
"lc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same result can also be obtained via, \n",
"```\n",
"lc = tpf_file.to_lightcurve(aperture_mask=tpf_file.pipeline_mask)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating your own masks\n",
"\n",
"You don't necessarily have to pass in the `pipeline_mask` to the `plot()` function, it can be any mask you choose yourself, provided it is the right shape. We will now explain how to adjust this mask using the [`create_threshold_mask`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.TessTargetPixelFile.html?highlight=create_threshold_mask#lightkurve.targetpixelfile.TessTargetPixelFile.create_threshold_mask) function. This method will identify the pixels in the TPF which show a median flux that is brighter than threshold times the standard deviation above the overall median. The standard deviation is estimated in a robust way by multiplying the Median Absolute Deviation (MAD) with 1.4826. In this example we will pick 10 as our threshold."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_mask = tpf_file.create_threshold_mask(threshold=10, reference_pixel='center')\n",
"n_target_pixels = target_mask.sum()\n",
"n_target_pixels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have now created a target mask which covers 13 pixels. Lets plot this up and see what it looks like."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tpf_file.plot(aperture_mask=target_mask, mask_color='r');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that this is slightly different to the *optimal* aperture previously defined. It also looks like there might be too many background pixels included. Lets see if we can adjust this. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specify an Aperture Array\n",
"We need to define a new aperture array such that our aperture will cover our object of interest only. We have seen that apertures are defined within boolean arrays, based on this lets make up a new array."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"aper_new = np.zeros(tpf_file.shape[1:], dtype=bool)\n",
"aper_new[5:8, 4:7] = True\n",
"tpf_file.plot(aperture_mask=aper_new, mask_color='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK great, it looks like we have covered our object,but not included too much background. Lets now make this into a lightcurve."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"TessLightCurve targetid=307210830 length=18317\n",
"