$30 off During Our Annual Pro Sale. View Details »

Candidacy I slides

Adina
September 26, 2019

Candidacy I slides

Adina

September 26, 2019
Tweet

More Decks by Adina

Other Decks in Science

Transcript

  1. Exploiting TESS data to Understand Young Stars Adina Feinstein NSF

    Graduate Research Fellow Advisors: Jacob Bean & Benjamin Montet !1 Candidacy I September 26, 2019
  2. The Transiting Exoplanet Survey Satellite (TESS) is a four+ year,

    nearly all-sky survey monitoring millions of stars. !2
  3. 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
  4. !3 The entire 96° x 24° sector is observed in

    the TESS Full-Frame Images (FFIs).
  5. Predictions indicate the detection of thousands of exoplanets which will

    not be covered in the 2-minute data. !4 (Barclay et al. 2018)
  6. !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
  7. !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
  8. !7 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
  9. 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
  10. 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
  11. !9 All FFIs for a given sector are about 1TB

    of data, which is not user friendly.
  12. !9 All FFIs for a given sector are about 1TB

    of data, which is not user friendly.
  13. !10

  14. !10

  15. !11 From the “postcards” we create Target Pixel Files (TPFs)

    which we perform aperture photometry on. (Feinstein et al. 2019)
  16. !12 We provide four different types of light curves for

    the community to use. (Feinstein et al. 2019)
  17. !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
  18. !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
  19. !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
  20. !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
  21. !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
  22. !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
  23. !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
  24. !16 The eleanor data products are being transferred to Mikulski

    Archive for Space Telescopes (MAST), but the software is available now.
  25. !18 Future plans including using eleanor to find young planets

    for Rossiter-McLaughlin studies with MAROON-X.
  26. !19 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
  27. !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
  28. !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
  29. !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
  30. !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
  31. !26 Previous flare studies have relied on sigma clipping methods

    to identify flares. (Chang, Byun, & Hartman, 2015)
  32. !26 Previous flare studies have relied on sigma clipping methods

    to identify flares. (Chang, Byun, & Hartman, 2015) :(
  33. !28 Machine learning can be used when searching for signals

    with a characteristic shape. (Pearson et al. 2017)
  34. !28 Machine learning can be used when searching for signals

    with a characteristic shape. (Pearson et al. 2017)
  35. !31 Every light curve receives a label that the neural

    network learns over several epochs.
  36. !32 Feeding in a test training set assigns a probability

    that the object is or is not a flare.
  37. !34 The first results of applying the neural network to

    a TESS two-minute target are promising.
  38. !36 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
  39. !37 In the same year, two papers presented conflicting results

    on where flares occur with relation to spots.
  40. !37 In the same year, two papers presented conflicting results

    on where flares occur with relation to spots. (Doyle et al. 2018)
  41. !37 In the same year, two papers presented conflicting results

    on where flares occur with relation to spots. (Doyle et al. 2018)
  42. !37 In the same year, two papers presented conflicting results

    on where flares occur with relation to spots. (Doyle et al. 2018) (Roettenbacher et al. 2018) 1-5% increase > 5% increase
  43. !37 In the same year, two papers presented conflicting results

    on where flares occur with relation to spots. (Doyle et al. 2018) (Roettenbacher et al. 2018) 1-5% increase > 5% increase
  44. !37 In the same year, two papers presented conflicting results

    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
  45. !38 The take-away figure will show the location of flares

    with respect to the phase of spot modulation.
  46. !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.