Aperture Photometry

Learning Goals

In our previous tutorials (LightCurve Objects and LightCurveFile Objects) we learned about aperture photometry and the Simple Aperture Photometry (SAP) and Pre-search Data Conditioning SAP (PDCSAP) flux.

We have not however discussed how these apertures are chosen by the pipeline or how to examine them.

In this proposal we will cover the following, - How apertures are defined. - How can we examine a pre-defined aperture. - How we can define and modify an aperture and recover the lightcurve.

SPOC Apertures

In aperture photometry A set of pixels in the image are chosen and we sum those to produce a single flux value. The SPOC produces an optimal aperture, which is used by default in Lightkurve, and determined by the data processing 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.

We sum these same pre-selected pixels for every image at each cadence to produce a light curve.

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.

We will show you how to examine the pre-selected aperture and how to modify it if you wish.

Imports

This tutorial requires: - Lightkurve - Matplotlib - numpy

Defining Terms

• Target Pixel File (TPF): A file containing the original CCD pixel observations from which light curves are extracted.
• LightCurve Object: Obtained from a TPF and contains lightcurve information derived using simple aperture photometry.
• LightCurveFile Object: Obtained from MAST and contains both SAP flux and PSDCSAP flux.
• Cadence: The rate at which TESS photometric observations are stored.
• Sector: One of TESS’s 27 (to date) observing periods, approximately ~27 days in duration.
• Simple Aperture Photometry (SAP): The act of summing all pixel values in a pre-defined aperture as a function of time.
• 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.

%matplotlib inline
import numpy as np
import lightkurve as lk
import matplotlib.pyplot as plt

Downloading the Data

For this tutorial lets use the L 98-59 System again, focusing on planet c. First let’s search for a TPF for this object.

search_result = lk.search_targetpixelfile('TIC 307210830')
search_result

Lets pick and download the data for sector 2.

tpf_file = search_result[0].download()
tpf_file

TessTargetPixelFile(TICID: 307210830)

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:

tpf_file.pipeline_mask

array([[False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False,  True,  True,  True, False, False,
        False, False],
       [False, False, False, False,  True,  True,  True,  True, False,
        False, False],
       [False, False, False, False,  True,  True,  True,  True, False,
        False, False],
       [False, False, False, False, False,  True,  True, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False],
       [False, False, False, False, False, False, False, False, False,
        False, False]])

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.

tpf_file.plot(aperture_mask=tpf_file.pipeline_mask);

We now see the SPOC optimal aperture mask overlaid on top of our object of interest.

Using the provided optimal aperture in pipeline_mask and the TPF we can perform simple aperture photometry via the extract_aperture_photometry function as shown below,

lc = tpf_file.extract_aperture_photometry()
lc

The same result can also be obtained via,

lc = tpf_file.to_lightcurve(aperture_mask=tpf_file.pipeline_mask)

Creating your own masks

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 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.

target_mask = tpf_file.create_threshold_mask(threshold=10, reference_pixel='center')
n_target_pixels = target_mask.sum()
n_target_pixels

13

We have now created a target mask which covers 13 pixels. Lets plot this up and see what it looks like.

tpf_file.plot(aperture_mask=target_mask, mask_color='r');

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.

Specify an Aperture Array

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.

aper_new = np.zeros(tpf_file.shape[1:], dtype=bool)
aper_new[5:8, 4:7] = True
tpf_file.plot(aperture_mask=aper_new, mask_color='red')

<matplotlib.axes._subplots.AxesSubplot at 0x7fa1790b85c0>

OK great, it looks like we have covered our object,but not included too much background. Lets now make this into a lightcurve.

target_lc_new = tpf_file.to_lightcurve(aperture_mask=aper_new)
target_lc_new

TessLightCurve targetid=307210830 length=18317

time	flux	flux_err	centroid_col	centroid_row	cadenceno	quality
	electron / s	electron / s	pix	pix
object	float32	float32	float64	float64	int32	int32
1354.1088231272427	20049.26953125	15.38568115234375	664.5377949438838	339.37615072204693	91191	0
1354.1102119888994	20029.6015625	15.380707740783691	664.5539189658597	339.3674922369538	91192	0
1354.112989712153	19982.47265625	15.365057945251465	664.5351012281784	339.36145689194564	91194	0
1354.1143785738097	19994.115234375	15.368595123291016	664.5664578479	339.38061605675574	91195	0
1354.1157674355243	20003.728515625	15.371517181396484	664.5610361707227	339.37135598634495	91196	0
1354.1171562971804	19999.60546875	15.368359565734863	664.5594608485563	339.3702259541033	91197	0
1354.1185451588947	20019.8046875	15.375614166259766	664.5527183393958	339.3662359200094	91198	0
1354.1199340205515	19982.787109375	15.361979484558105	664.5567752197887	339.36859919546725	91199	0
1354.1213228822667	20002.78515625	15.369135856628418	664.5538246562905	339.3650181579328	91200	0
...	...	...	...	...	...	...
1381.5001032523294	19974.763671875	15.560335159301758	664.5105008707154	339.2613672501821	110913	0
1381.5014921207378	19988.509765625	15.56753158569336	664.5151411160459	339.25840629283164	110914	0
1381.5028809891458	19959.5	15.55550765991211	664.5136800320821	339.2605986305007	110915	0
1381.5042698574382	19942.931640625	15.547529220581055	664.5087002343405	339.2654600563855	110916	0
1381.5056587258466	19961.890625	15.553683280944824	664.514156119857	339.2574522478159	110917	0
1381.5070475942555	19938.53515625	15.542488098144531	664.5135819475233	339.25510331494837	110918	0
1381.508436462548	19942.68359375	15.544118881225586	664.513915654913	339.25242635657816	110919	0
1381.5098253309563	19965.841796875	15.549737930297852	664.5082952466375	339.2615657982662	110920	0
1381.5112141992488	19945.671875	15.542808532714844	664.5173951664624	339.25568362598784	110921	0
1381.5126030676577	19982.890625	15.55328369140625	664.5085713660441	339.2605124583506	110922	0

Compare Data & Lightcurves

We have now created lightcurve objects using both the SPOC pre-defined aperture and a mask of our own devising. Lets plot these lightcurves up and examine the difference.

lc.plot();

target_lc_new.plot();

As you can see the lightcurves from the two apertures look very different. It looks as if the aperture we have defined manually may have less background or contaminating flux from scattered than the SPOC aperture. We will learn more about contamination in our next tutorial.