Three reasons why Kepler's discoveries will continue for a decade

Three reasons why Kepler's discoveries will continue for a decade

A talk presented by Geert Barentsen at the 2018 Wetton Workshop. Held at Christ Church College, Oxford, on June 19th, 2018.

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Geert Barentsen

June 19, 2018
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Transcript

  1. 3 Three reasons
 why Kepler’s discoveries
 will continue for a

    decade A talk presented by Geert Barentsen (Kepler GO Director)
 at the Wetton Workshop in Oxford on June 19th, 2018. 
 @GeertHub - geert.barentsen@nasa.gov - https://keplerscience.arc.nasa.gov
  2. Photo by Greg Rakozy on Unsplash Are we alone?

  3. Jupiter Earth

  4. Photo by Marina Khrapova on Unsplash

  5. 2,621 confirmed exoplanets discovered using Kepler & K2 data as

    of Jun 15th, 2018
  6. Small planets are ubiquitous Boxes show the average number of

    planets per star. 
 Kopparapu+ 2018
  7. None
  8. None
  9. Many of Kepler’s most fundamental discoveries are still emerging.

  10. The distribution of planet radii is bimodal Photoevaporation appears to

    herd small planets into either bare cores or mini-Neptunes.
 
 Fulton+ 2017 Also: Owen & Wu 2017
  11. Atmospheric erosion is a function of incident flux High-precision planet

    radii obtained via asteroseismology reveal a slope in the planet radius bimodality.
 Van Eylen+ 2017
  12. The number of planets discovered across open clusters is starting

    to constrain the timescales of inward planet migration. Rizzuto+ in prep Also: Mann+ 2017 1 10 100 1000 Age (Myr) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Relative Planet Occurence (P < 20 d) Migration? Kepler Upper Scorpius Pleiades Hyades & Praesepe Close-in planets appear to be less common around young stars
  13. Beehive cluster (600 Myr) before K2 Late M-dwarfs spin down


    in a radically different way
  14. Also: Rebull+ 2016a,b, 2017,
 Somers+ 2017,
 Barnes+ 2016,
 Stauffer+ 2016,


    Nardiello+ 2015,
 and others. Late M-dwarfs spin down
 in a radically different way
  15. Model prediction: Matt et al. (2015), 653 Myr Late M-dwarfs

    spin down
 in a radically different way Also: Rebull+ 2016a,b, 2017,
 Somers+ 2017,
 Barnes+ 2016,
 Stauffer+ 2016,
 Nardiello+ 2015,
 and others.
  16. Transients can have the shape of a Type Ia SN

    but show a rise time of just 2 days The median rise time of Type Ia SN is ~17 days?! 
 Rest+ 2018
  17. Oscillation frequencies in the brightness of active galaxies appear to

    be predictive of the central black hole mass Smith+ 2018
  18. Photo by Marina Khrapova on Unsplash

  19. These results were all published within the past year …

    nine years after Kepler’s launch!
  20. 2018 will be Kepler’s most productive year on record

  21. Why?

  22. Science takes time.

  23. But also: fully open data has grown our community.

  24. Will the discoveries continue?

  25. 1. The data analysis methods continue to improve. 2. Our

    computational capabilities are growing faster than the data set. 3. The field is increasingly valuing community software and tutorials.
  26. 1 The data analysis methods continue to improve

  27. Electronic “rolling band” noise limits Kepler’s sensitivity, but progress is

    being made towards modeling the varying background, e.g. using 2D Gaussian Processes. 
 Hedges+ in prep Our ability to model Kepler’s background systematics is improving
  28. The intra-pixel response function of Kepler’s CCDs is being measured

    to an unprecedented precision A new measurement apparatus was designed which uses small spots of light across a range of wavelengths. 
 Vorobiev+ in prep This opens the door towards high-precision PSF-fitting photometry with Kepler.
  29. Our understanding of data caveats, and the tools to identify

    them, are improving Video show the negative crosstalk signal produced by a bright asteroid on a different CCD channel. 
 Movie credit: Hugh Osborn
  30. Look at your data!

  31. Rackham+ 2018 But also … our understanding of the physics

    is improving
  32. Identification of 92 dipper stars using a Random Forest Classifier

    Careful feature engineering allows a classifier to provide a complete and unbiased census of stars showing occultations by accretion columns.
 Hedges+ 2018
  33. Transit shape validation of Kepler-80 g and 90 i using

    a Convolutional Neural Network Shallue+ 2018
  34. 2 Our computational capabilities are growing faster than the data

    set
  35. In optical astronomy, the “deluge of data" is often out-paced

    by the “deluge of computing power”.
  36. The number of pixels telemetered by NASA’s exoplanet surveys doubles

    every 28 months Also: MOST, CoRoT, Plato, Cheops, Ariel, and others. Assumptions detailed at https://github.com/barentsen/tech-progress-data
  37. Ground-based optical surveys show a pixel rate which doubles every

    44 months Caveat: radio astronomers are significantly worse off. Assumptions detailed at https://github.com/barentsen/tech-progress-data
  38. The number of transistors in CPUs has continued to double

    every 24 months (Moore’s original law)
  39. The speed of the world’s fastest supercomputer doubles every 13

    months The speed of research internet backbones doubles every 16 months Data sources detailed at https://github.com/barentsen/tech-progress-data
  40. The cost of storage halves every 28 months Storage bus

    speeds double every 35 months Data sources detailed at https://github.com/barentsen/tech-progress-data
  41. We are working with MAST to simplify bulk analyses of

    Kepler data in the cloud Smith+ 2018
  42. Key issue: the number of astronomers only doubles every ~13

    years Caveat: assumes the number of IAU members is a proxy for the number of astronomers.
  43. Your brain size doubles every 1.5 million years Caveat: brain

    size is a flawed proxy for intelligence.
  44. Our computational capability per unit (Kepler) data is increasing. How

    to leverage it?
  45. 3 The field is increasingly valuing community software and tutorials

  46. Credit: Arfon Smith Number of AstroPy contributors over time

  47. None
  48. Lintott+ 2018

  49. None
  50. None
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  52. None
  53. Software was key to enable Kepler’s resurrection: ‣ Field &

    target selection tool : github.com/KeplerGO/K2fov ‣ Asteroid selection tool: github.com/KeplerGO/K2ephem ‣ Motion systematics correction: github.com/KeplerGO/PyKE ‣ Raw data parser: github.com/KeplerGO/kadenza ‣ Quicklook tool: github.com/KeplerGO/K2flix ‣ Quality control: github.com/KeplerGO/k2-quality-control ‣ Mission website: github.com/KeplerGO/KeplerScienceWebsite ‣ Publication tracker: github.com/KeplerGO/kpub Also: community pipelines, e.g.: ‣ K2SC: github.com/OxES/k2sc ‣ EVEREST: github.com/rodluger/everest
  54. Ann Marie Cody Michael Gully-Santiago Christina Hedges Zé Vinícius

  55. lightkurve: a new Python package for Kepler, K2, and TESS

    analysis
  56. None
  57. Community software, tutorials, and data enable us to open ourselves

    to our peers and to the public.
  58. Video tutorial for Windows users

  59. Citizen science matters In 2013, volunteers of the Zooniverse brought

    the unusual dips in the light curve of “Tabby’s Star” to attention.
  60. We benefit from treating the public as our peers In

    2017, students from the Thacher School in California obtained 20k observations over 135 nights; finding that the dips are chromatic.
  61. Important ongoing conversations: - how to credit the authors of

    open software & data? - how to provide robust careers and funding opportunities to support scientists? - how to address the stress related to the fear of “getting scooped” in the era of openness.
  62. 3 I feel excited about the future of our field,

    because of: 1. new data analysis methods; 2. our computational capabilities; 3. community software and tutorials.
  63. Photo by Marina Khrapova on Unsplash

  64. Photo by Greg Rakozy on Unsplash Are we alone?