Candidacy II Slides

Understanding young stellar flare activity with TESS

Transcript

1. Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of TESS Data Adina Feinstein NSF Graduate Research Fellow Ben Montet (UNSW), Megan Ansdell (NASA HQ), Brian Nord (UChicago/KICP/Fermi), Jacob Bean (UChicago), Max Günther (MIT), Michael Gully-Santiago (UT Austin), Josh Schlieder (NASA GSFC) arXiv:2005.07710 ! 1
2. Know thy star, know thy planet. !2
3. Flares can lead to increased photoevaporation of inner disks. (Benz & Güdel, 2010) !3
4. Flares can lead to increased photoevaporation of inner disks. (Benz & Güdel, 2010) !3
5. Flares can lead to increased photoevaporation of inner disks. (Benz & Güdel, 2010) !3
6. Flares have been shown to increase atmospheric erosion, especially when they are still forming & contracting. (Lammer et al. 2007; Owen & Wu 2017) !4
7. Flares have been shown to increase atmospheric erosion, especially when they are still forming & contracting. (Lammer et al. 2007; Owen & Wu 2017) !4
8. Flares have been shown to increase atmospheric erosion, especially when they are still forming & contracting. (Lammer et al. 2007; Owen & Wu 2017) !4
9. Flares have been seen to have long term effects on atmospheric chemical compositions. (Venot et al. 2016) !5 Metals & molecules
10. Flares have been seen to have long term effects on atmospheric chemical compositions. (Venot et al. 2016) !5 Metals & molecules Different metals & molecules
11. What is the relationship between stellar flare energy and spectral type? Age? Spot phase? !6
12. What is the relationship between stellar flare energy and spectral type? Age? Spot phase? !7
13. 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 !8
14. 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 !8
15. 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 !8
16. 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 !8
17. What is the relationship between stellar flare energy and spectral type? Age? Spot phase? !9
18. Analysis of the Pleiades (0.125 Gyr) and Praesepe (0.63 Gyr) showed a decrease in flare rate with age. (Ilin et al. 2019) 3000-3249 K 3250-3499 K 3500-3749 K 3750-4000 K Pleiades Praesepe !10
19. What is the relationship between stellar flare energy and spectral type? Age? Spot phase? !11
20. In the same year, two papers presented conflicting results on where flares occur with relation to spots. !12
21. In the same year, two papers presented conflicting results on where flares occur with relation to spots. (Doyle et al. 2018) !12
22. In the same year, two papers presented conflicting results on where flares occur with relation to spots. (Doyle et al. 2018) !12
23. 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 !12
24. 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 !12
25. 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 !12
26. What is the relationship between stellar flare energy and spectral type? Age? Spot phase? !13
27. The Transiting Exoplanet Survey Satellite (TESS) is a four+ year, nearly all-sky survey monitoring millions of stars. 27 days 54 days 81 days 108 days 189 days 351 days !14
28. There is no one best way to determine the age of a star or group of stars. !15
29. There is no one best way to determine the age of a star or group of stars. (Curtis et al. 2019) Gyrochronology !15
30. There is no one best way to determine the age of a star or group of stars. (Curtis et al. 2019) Gyrochronology !15 Isochrone Fitting (Murphy et al. 2020)
31. There is no one best way to determine the age of a star or group of stars. (Curtis et al. 2019) Gyrochronology !15 Isochrone Fitting (Murphy et al. 2020) (Schneider et al. 2019) Kinematics
32. We have completed a literature search for young moving group members with ages 1-750 Myr. !16
33. Young stars are great and active. Octans Columba AB Doradus !17
34. Young stars are great and active. Octans Columba AB Doradus !17
35. However, the outlier metrics used will bias studies towards identifying only the strongest flares. (Chang, Byun, & Hartman, 2015) !18
36. However, the outlier metrics used will bias studies towards identifying only the strongest flares. (Chang, Byun, & Hartman, 2015) !18
37. However, the outlier metrics used will bias studies towards identifying only the strongest flares. (Chang, Byun, & Hartman, 2015) :( !18
38. Machine learning can be used when searching for signals with a characteristic shape. (Pearson et al. 2017) !19
39. Machine learning can be used when searching for signals with a characteristic shape. (Pearson et al. 2017) !19
40. We used the TESS flare catalog from Günther et al. (2020) as our training, validation, and test sets. !20
41. Every light curve example receives a label that the neural network learns over several hundred epochs. !21
42. The results of 10 stella models show good training demonstrated through the accuracy and loss metrics. (Feinstein et al. 2020) !22
43. We ensure the neural network is classifying our light curves correctly by looking at the test set. !23
44. The confusion matrix for the stella test set is used to visualize what the false cases look like. (Feinstein et al. 2020) !24
45. The CNN is used as a sliding-box detector, where the probability will increase as the flare nears the center of the box. !25
46. The CNN is used as a sliding-box detector, where the probability will increase as the flare nears the center of the box. !25
47. https://GitHub.com/afeinstein20/stella Looking at some example light curves, the cadences “light up” when believed to be a flare. (Feinstein et al. 2020) ! 26
48. What relationship did we find between stellar flare energy and spectral type? Age? Spot phase? !27
49. We find a clear difference in flare rate at Gaia Bp -Rp = 2 (Teff ~ 4000K). (Feinstein et al. 2020) !28
50. The coolest stars have stronger flares in relation to their luminosity. (Feinstein et al. 2020) !29
51. What relationship did we find between stellar flare energy and spectral type? Age? Spot phase? !30
52. Flares amplitudes and rates are higher the coolest stars across all ages, with noticeable evolution in other temperature bins. (Feinstein et al. 2020) !31
53. Subdividing by age, it’s easier to see the differences in flare rate between cool (< 4000K) stars and hot stars. (Feinstein et al. 2020) !32
54. What relationship did we find between stellar flare energy and spectral type? Age? Spot phase? !33
55. Across 1500 stars, there seems to be no preference for where flares happen. They happen everywhere! (Feinstein et al. 2020) !34
56. Across 1500 stars, there seems to be no preference for where flares happen. They happen everywhere! (Feinstein et al. 2020) !34
57. What do we think these stellar surfaces look like? What we think we’re seeing: !35
58. What do we think these stellar surfaces look like? What we think we’re seeing: !35
59. What do we think these stellar surfaces look like? What we think we’re seeing: What a flare-phase relationship would’ve looked like: !35
60. What do we think these stellar surfaces look like? What we think we’re seeing: What a flare-phase relationship would’ve looked like: The Sun: !35
61. Flares have been shown to alter atmospheric chemistry and could partially affect mass-loss rates during formation. (Feinstein et al. 2020) !36
62. Flares have been shown to alter atmospheric chemistry and could partially affect mass-loss rates during formation. (Feinstein et al. 2020) !36
63. Flares have been shown to alter atmospheric chemistry and could partially affect mass-loss rates during formation. (Feinstein et al. 2020) !36
64. What’s next? !37
65. Young planets are challenging to detect and occur on similar timescales to flares, so let’s “flip” stella! (David et al. 2019) !38
66. Young planets are challenging to detect and occur on similar timescales to flares, so let’s “flip” stella! (David et al. 2019) !38
67. There’s a huge (500 Myr!!) age range of missing planets. stella may be able to find them. (Exoplanet Archive, as of 6/25/2020) !39
68. There’s a huge (500 Myr!!) age range of missing planets. stella may be able to find them. (Exoplanet Archive, as of 6/25/2020) !39
69. My project at the tess.ninja workshop already demonstrates the power of stella when applied to transits. !40
70. We hope to search both the 2-minute and 30-minute TESS data for new young planet candidates. Photo credit: Ethan Kruse !41 (Feinstein et al. 2019)
71. Summary • This is the first application of CNNs to find stellar flares. It provides a new statistical analysis of these events. • We find no phase preference for flares, indicating the surfaces of young stars have an overall very high spot coverage. • Cool stars (Teff < 4000 K) have high and consistent flare rates at all ages between 1-800 Myr, while hot stars show decline over time. • Flares have negative consequences for photoevaporative mass loss of exoplanet atmospheres. • Next — let’s find more young planets! !42