{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Understanding TargetPixelFile objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Goals\n",
"\n",
"In this tutorial we will learn the following, \n",
" - What a TESS Target Pixel File (TPF) is. \n",
" - How to obtain a TPF from the [MAST archive](https://archive.stsci.edu/tess/) via *Lightkurve*.\n",
" - How to plot the TPF image.\n",
" - How to access the metadata, and understand the file properties and units.\n",
"\n",
"We will also show the user where they can find more details about TESS Target Pixel Files."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is a Target Pixel File?\n",
"The *TESS* telescope observes stars for long periods of time, just under a month per sector. By doing so *TESS* observes how the brightnesses of a star change over time.\n",
"\n",
"Not all data for the stars in a given sector is recorded. Instead, pixels are selected around certain targeted stars. These cut-out images are called Target Pixel Files, or TPFs. By combining the amount of flux in the pixels where the star appears, you can make a measurement of the amount of light form a star in that observation.\n",
"\n",
"TPFs can be thought of as stacks of images, with one image for every telescopic time-stamp. Each time-stamp is referred to as a **cadence**. The TPF images are cut out 'postage stamps' of the full observations, making them easier to work with. TPFs also include information about the astronomical *background* to the image, which is removed from the raw flux.\n",
"\n",
"TPF files are stored in a [FITS file format](https://fits.gsfc.nasa.gov/fits_primer.html). The *Lightkurve* package allows us to work with FITS files without having to directly handle its detailed file structure.\n",
"\n",
"TPFs are typically the first port of call when studying a star with *TESS*. They allow us to see where our data is coming from, and identify potential sources of noise or systematic trends.\n",
"\n",
"In this tutorial we'll cover the basics of working with TPFs in *Lightkurve* using the [`TessTargetPixelFile`](https://docs.lightkurve.org/api/lightkurve.targetpixelfile.TessTargetPixelFile.html?highlight=tesstargetpixelfile) class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports\n",
"This tutorial requires:\n",
"- **[Lightkurve](https://docs.lightkurve.org)** to work with TPF files.\n",
"- [**Matplotlib**](https://matplotlib.org/) for plotting."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline \n",
"import lightkurve as lk\n",
"import matplotlib.pyplot as plt"
]
},
{
"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",
"- Cadence: The rate at which TESS photometric observations are stored. \n",
"\n",
"- Sector: One of TESS's 28 (to date) observing periods, approximately ~27 days in duration. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Downloading Data\n",
"The TPFs of stars observed by the *TESS* mission are stored on the [Mikulksi Archive for Space Telescopes](https://archive.stsci.edu/tess/) (MAST) archive, along with metadata about the observations, such as the CCD used.\n",
"\n",
"Besides raw data measured by the CCD, it will also carry information about the astronomical background, and the recommended aperture for extracting flux.\n",
"\n",
"You can search for a TPF using the [`search_targetpixelfile()`](https://docs.lightkurve.org/api/lightkurve.search.search_targetpixelfile.html#lightkurve.search.search_targetpixelfile) function. This will search for the right file in the MAST data archive.\n",
"\n",
"Note here that `search_targetpixelfile` can take several inputs as listed below, only the first is required,\n",
"\n",
"1. The ID number or name for the object of interest \n",
"\n",
"2. The mission that the object has been observed in.\n",
" \n",
"3. Which part of the survey you want to obtain the data from. The *TESS* the survey is divided up into sectors.\n",
" \n",
"4. The quality of the data you want to obtain. This is set using the keyword `quality_bitmask` and more information about this and can be found [here](https://docs.lightkurve.org/api/lightkurve.utils.KeplerQualityFlags.html#lightkurve.utils.KeplerQualityFlags.DEFAULT_BITMASK). \n",
"\n",
"In this case we want the Target Pixel File with TESS ID - TIC 307210830, which refers to [L 98-59](https://arxiv.org/pdf/1903.08017.pdf), a bright M dwarf star at a distance of 10.6 pc. This star is host to three terrestrial-sized planets. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"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": [
"The search function returns a [`SearchResult`](https://docs.lightkurve.org/api/lightkurve.search.SearchResult.html) object, displaying a list. \n",
"\n",
"In this list, each row represents a different observing period. We find that *TESS* recorded 7 sectors of data for this target across 1 year. The **observation** column lists the TESS sector. The **target_name** represents the TESS Input Catalogue (TIC) ID of the target, and the **productFilename** column is the name of the FITS files downloaded from MAST. The **distance** column shows the separation on the sky between the searched coordinates and the downloaded objects--- this is only relevant when searching for specific coordinates in the sky, and not when looking for individual objects.\n",
"\n",
"The `SearchResult` object also has several convenient operations, for example, we can select the second data product in the list as follows:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"SearchResult containing 1 data products.\n",
"\n",
"
"
],
"text/plain": [
"SearchResult containing 1 data products.\n",
"\n",
" # observation author target_name productFilename distance\n",
"--- ------------- ------ ----------- ------------------------------------------------------- --------\n",
" 0 TESS Sector 5 SPOC 307210830 tess2018319095959-s0005-0000000307210830-0125-s_tp.fits 0.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search_result[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows us look at the TPF from sector 5 only. Let's now download this data via the `download()` method. Note that we want to specify the quality of the data that we are obtaining, and as such use quality_bitmask='default'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TessTargetPixelFile(TICID: 307210830)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpf_file = search_result[1].download(quality_bitmask='default')\n",
"tpf_file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code has created a variable named `tpf_file` which is a Python object of type `TessTargetPixelFile`.\n",
"You would get the same result if you used the following code instead,\n",
"```\n",
"tpf_file = lk.search_targetpixelfile('TIC 307210830', mission=\"TESS\", sector=5).download(quality_bitmask='default')\n",
"```\n",
"This file object provides a convenient way to interact with the data file that has been returned by the archive, which contains both the TPF as well as metadata about the observations.\n",
"\n",
"Before diving into the properties of the `TessTargetPixelFile`, we can plot the data, also using *Lightkurve*."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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