Candidacy I slides

Exploiting TESS data to Understand Young Stars Adina Feinstein NSF
Graduate Research Fellow Advisors: Jacob Bean & Benjamin Montet !1 Candidacy I September 26, 2019

The Transiting Exoplanet Survey Satellite (TESS) is a four+ year,
nearly all-sky survey monitoring millions of stars. !2

The Transiting Exoplanet Survey Satellite (TESS) is a four+ year,
nearly all-sky survey monitoring millions of stars. !2 27 days 54 days 81 days 108 days 189 days 351 days

!3 The entire 96° x 24° sector is observed in
the TESS Full-Frame Images (FFIs).

Predictions indicate the detection of thousands of exoplanets which will
not be covered in the 2-minute data. !4 (Barclay et al. 2018)

!5 Although TESS’s primary objective is to find exoplanets, it
is a great data set for other fields. Exoplanet Solar System Extragalactic Stellar Astrophysics

!6 Outline 1. Developing eleanor, a tool for light curve
extraction from TESS 2. Robustly identifying and characterizing flares of young stars 3. Connecting flares and spots to understand stellar activity

Introducing eleanor, an open-source tool for light curve extraction from
the FFIs. !8 Feinstein, A. D., Montet, B. T., Foreman-Mackey, D., et al. 2019, PASP, 131, 094502 https://GitHub.com/afeinstein20/eleanor

!9 All FFIs for a given sector are about 1TB
of data, which is not user friendly.

!11 From the “postcards” we create Target Pixel Files (TPFs)
which we perform aperture photometry on. (Feinstein et al. 2019)

!12 We provide four different types of light curves for
the community to use. (Feinstein et al. 2019)

!13 We completed a quick search of Sectors 1 and
2 for new planet candidates. (Feinstein et al. 2019) Normalized Flux 0.8 1.0 1.2 0.99 1.00 1.01 0.99 1.00 1.01 0.98 1.00 0.98 1.00 1.02 Time [BJD-2457000] Time from Mid-Transit [Days] 1325 1330 1335 1340 1345 1350 -0.2 -0.1 0.0 0.1 0.2 TIC 350844139 TIC 394340349 TIC 139771134 TIC 159835004 TIC 38907808

!14 The eleanor package is a powerful tool for those
not interested in stars and exoplanets as well. (Feinstein et al. 2019: See also Fausnaugh et al. 2019; Vallely et al. 2019) Time [BJD-2457000] Flux SN2018fhw SN2018eph SN2018exc MOA 2018-LMC-002 MOA 2018-LMC-003 60 50 40 140 130 120 140 130 120 2900 2800 2700 300 200 100 1330 1340 1350 1360 1370 1380

!15 Combining the FFIs and the two-minute data can allow
for further confirmation of exciting systems. Planet Transit Stellar Eclipse (Kostov, Welsh, Feinstein et al. in prep) !15

!16 The eleanor data products are being transferred to Mikulski
Archive for Space Telescopes (MAST), but the software is available now.

!17 The eleanor software has been widely used by the
community.

!18 Future plans including using eleanor to find young planets
for Rossiter-McLaughlin studies with MAROON-X.

!20 What is the relationship between stellar flare energy and
age? Spectral type?

age? Spectral type?

!22 Similar studies with Kepler demonstrated a relationship between spectral
type, rotation period, and flare energy. (Davenport, 2016) 1.66 1.13 0.86 0.73 0.62 0.47 0.28 Mass [MSun ] Maximum Log(Flare Energy [ergs]) 32 33 34 35 36 37 38 39 40

!22 Similar studies with Kepler demonstrated a relationship between spectral
type, rotation period, and flare energy. (Yang & Liu, 2019) (Davenport, 2016) 1.66 1.13 0.86 0.73 0.62 0.47 0.28 Mass [MSun ] Maximum Log(Flare Energy [ergs]) 32 33 34 35 36 37 38 39 40

age? Spectral type?

!24 We have completed a literature search for young moving
group members with ages 1-750 Myr.

age? Spectral type?

!26 Previous flare studies have relied on sigma clipping methods
to identify flares.

to identify flares. (Chang, Byun, & Hartman, 2015)

to identify flares. (Chang, Byun, & Hartman, 2015) :(

!27 What is the most complete method for detecting flares
of all energies?

!28 Machine learning can be used when searching for signals
with a characteristic shape.

!28 Machine learning can be used when searching for signals
with a characteristic shape. (Pearson et al. 2017)

We can use machine learning techniques to automate finding flares
in TESS data. !29

!30 We simulate spot modulation and flares of different amplitudes
to pass in as the training set.

!31 Every light curve receives a label that the neural
network learns over several epochs.

!32 Feeding in a test training set assigns a probability
that the object is or is not a flare.

!33 We determine the probability that each data point is
a part of a potential flare.

!34 The first results of applying the neural network to
a TESS two-minute target are promising.

!35 https://GitHub.com/afeinstein20/stella Zooming in, we can see the confidence of
the neural network with identifying large flares.

!37 In the same year, two papers presented conflicting results
on where flares occur with relation to spots.

on where flares occur with relation to spots. (Doyle et al. 2018)

on where flares occur with relation to spots. (Doyle et al. 2018) (Roettenbacher et al. 2018) 1-5% increase > 5% increase

on where flares occur with relation to spots. (Doyle et al. 2018) (Roettenbacher et al. 2018) 1-5% increase > 5% increase Sample size: 32 Sample size: 119

!38 The take-away figure will show the location of flares
with respect to the phase of spot modulation.

!39 Summary • We have created an open-source software package
for extracting light curves from the TESS FFIs. • Light curve data products will be hosted on MAST for community use. • Currently, I am exploring flare properties of young (1-750 Myr) stars observed by TESS using a neural network, with the hopes of answering how ﬂares are related to starspots.

Candidacy I slides

Candidacy I slides

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