LSST Transients and Variable Stars Science Collaboration

2a1046385e6cf8e4d07d590f9821ece5?s=47 federica
April 02, 2017

LSST Transients and Variable Stars Science Collaboration

an intro to the Transients and Variable Stars Science Collaboration of the Large Synoptic Survey Telescope (LSST)

2a1046385e6cf8e4d07d590f9821ece5?s=128

federica

April 02, 2017
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  1. Transients and Variable LSST sky federica b. bianco, NYU The

    Transient and Variable Stars LSST Collaborations @fedhere fedhere
  2. federica bianco NYU Atacama Desert, Cerro Pachon

  3. federica bianco NYU effective aperture of 6.7 m FoV 9.6

    deg2 large etendue (collecting area x FoV) 2022-2032
  4. federica bianco NYU effective aperture of 6.7 m FoV 9.6

    deg2 large etendue (collecting area x FoV) Wide-Deep-Fast 2022-2032
  5. federica bianco NYU effective aperture of 6.7 m FoV 9.6

    deg2 large etendue (collecting area x FoV) 2022-2032 Wide-Deep-Fast cover large swaths of sky
  6. federica bianco NYU effective aperture of 6.7 m FoV 9.6

    deg2 large etendue (collecting area x FoV) 2022-2032 Wide-Deep-Fast cover large swaths of sky to faint magnitudes
  7. federica bianco NYU effective aperture of 6.7 m FoV 9.6

    deg2 large etendue (collecting area x FoV) 2022-2032 Wide-Deep-Fast cover large swaths of sky to faint magnitudes in a short amount of time
  8. federica bianco NYU Innovative Optical Design

  9. federica bianco NYU LSST 8.4m diameter Gemini South 8m diameter

    0.2 deg2 MIRROR: FIELD OF VIEW: 9.6 deg2
  10. federica bianco NYU

  11. federica bianco NYU 3.2 Gigapixels

  12. federica bianco NYU The LSST Data Stream

  13. federica bianco NYU each night is 30TB data ▪ 30

    Terabytes: 1,500,000 trees made into paper and printed; The LSST Data Stream
  14. federica bianco NYU each night is 30TB data ▪ 30

    Terabytes: 1,500,000 trees made into paper and printed; #OPENDATA #OPENSCIENCE The LSST Data Stream
  15. federica bianco NYU each night is 30TB data At 1Gbps,

    30TB would take 67 hours to download the LSST data
  16. federica bianco NYU time... in seconds

  17. None
  18. federica bianco NYU The LSST Science

  19. federica bianco NYU A stream of 1-10 million time-domain events

    per night, detected and transmitted within 60 seconds of observation. A catalog of orbits for 6 million bodies in the Solar System. A catalog of 37 billion objects: 20B galaxies, 17B stars characterized in shape, color, and variability. High resolution deep stacks that will allow measure weak lensing.
  20. federica bianco NYU Dark energy and dark matter (via measurements

    of strong and weak lensing, large-scale structure, clusters of galaxies, and supernovae)
 Exploring the transient and variable universe
 Studying the structure of the Milky Way galaxy and its neighbors via resolved stellar populations
 An inventory of the Solar System, including Near Earth Asteroids and Potential Hazardous Objects, Main Belt Asteroids, and Kuiper Belt Objects Science Drivers
  21. federica bianco NYU Dark energy and dark matter (via measurements

    of strong and weak lensing, large- scale structure, clusters of galaxies, and supernovae)
 Exploring the transient and variable universe
 Studying the structure of the Milky Way galaxy and its neighbors via resolved stellar populations
 An inventory of the Solar System, including Near Earth Asteroids and Potential Hazardous Objects, Main Belt Asteroids, and Kuiper Belt Objects moving objects Science Drivers all relevant to trasients + variable Universe!
  22. federica bianco NYU WFD: a pair of images per field,

    repeated twice/night. ~85% of the observing time DeepDrilling fields: a pair of images per field, repeated >twice/night >1 band 5-10 DD fields Galactic plane survey South Celestial Cap Northern Ecliptic Survey Strategy
  23. federica bianco NYU

  24. federica bianco NYU https://tvs.science.lsst.org/home Ashish Mahabal Federica Bianco Transients &

    Variable Stars collaboration co-chairs
  25. federica bianco NYU

  26. federica bianco NYU Nearly 160 members! Each member declares a

    primary affiliation and up to 3 secondary affiliations
  27. federica bianco NYU

  28. federica bianco NYU

  29. federica bianco NYU different variable and transient phenomena benefit from

    different observing strategies our group is working to reconcile the differences & understand the existing tensions & overlap AGNs supernovae LBVs Roadmapping LSST to success
  30. federica bianco NYU The Time is Now! we need a

    science based evalution of the baseline LSST observing strategy and its variants Observing Strategy White Paper Secion 1.2
  31. federica bianco NYU The success of TRANSIENTS & VARIABLES related

    science is tied to cadence choices
  32. federica bianco NYU TVS ROADMAPPING MEETING

  33. federica bianco NYU http://www.slac.stanford.edu/~digel/ObservingStrategy/whitepaper/LSST_Observing_Strategy_White_Paper.pdf https://github.com/LSSTScienceCollaborations/ObservingStrategy OBSERVING STRATEGY WHITE PAPER

  34. federica bianco NYU how to contribute we need a science

    based evalution of the baseline LSST observing strategy and its variants Observing Strategy White Paper Secion 1.2
  35. federica bianco NYU OpSim LSST developed operation simulations (A. Connoly)

    LSST simulates Observing Strategies
  36. federica bianco NYU OpSim LSST developed operation simulations (A. Connoly)

    LSST simulates Observing Strategies MAF API Metric Analysis Framework (Peter Yoachim, Lynne Jones) https://github.com/LSST-nonproject/
  37. federica bianco NYU OpSim LSST developed operation simulations (A. Connoly)

    MAF API Metric Analysis Framework (Peter Yoachim, Lynne Jones) SN Alert Fraction 0.6 0.0
  38. federica bianco NYU Median Intra-Night Gap in hours Any Filter

    Median Intra-Night Gap in hours Any Filter r band r band Median Inter-Night Gap in days Median Inter-Night Gap in days
  39. federica bianco NYU transients and variables from the Observing Strategy

    White Paper preliminary results
  40. federica bianco NYU E. Bellm

  41. federica bianco NYU Tensions: color or sampling? (SN/GW vs GRB)

    dense sampling or duration? (SN vs TDE) Rolling cadence? ToO? different variable and transient phenomena benefit from different observing strategies our group is working to reconcile the differences & understand the existing tensions & overlap
  42. federica bianco NYU

  43. federica bianco NYU

  44. federica bianco NYU

  45. federica bianco NYU

  46. federica bianco NYU Non-Time-Critical

  47. federica bianco NYU days to peak days to peak flux

    (units of peak flux) Olling+ 15 Marion+ 15 flux (units of peak flux) constraint RG progenitor systems to <20% (Bianco+ 2012, 3 year of SNLS data) LSST 3 month -> 1%
  48. federica bianco NYU days to peak days to peak flux

    (units of peak flux) Olling+ 15 Marion+ 15 constraint RG progenitor systems to <20% (Bianco+ 2012, 3 year of SNLS data) LSST 3 month -> 1% also: shock breakout, IIB double peaks flux (units of peak flux)
  49. federica bianco NYU

  50. federica bianco NYU

  51. federica bianco NYU Time-Critical: CLASSIFICATION: young/old FAST TRANSIENTS: GRB GW:

    counterpart discovery
  52. federica bianco NYU

  53. federica bianco NYU Days since explosion Gap between observations

  54. federica bianco NYU

  55. federica bianco NYU require 2 observations in 1 week after

    GW detection (Coperthwaite & Berger 2015) Median Intra-Night Gap in hours r band
  56. federica bianco NYU

  57. federica bianco NYU Deep Drilling Field

  58. federica bianco NYU Deep Drilling Field Wide Deep Fast

  59. federica bianco NYU Transients Classification challenge

  60. federica bianco NYU Transients Classification challenge in 2009 Kessler+ issued

    s SN classification challenge.
  61. federica bianco NYU things that happened since 2009 Ipad April

    2010 04/21/2016 instagram 2010
  62. federica bianco NYU we have learned a lot since 2010!

  63. federica bianco NYU Michelle Lochner+ 2016 Anais Moller+ 2016 Gautham

    Narayan, Tom Matheson working on ANTARES Kevian Stussen @Vanderbilt working on classifiers
  64. federica bianco NYU Transients Classification challenge SNLS, SDSSII CSP Time

    for a NEW TRANSIENT CHALLENGE! with more data and incorporating recent advances in ML and this is one of the TVS projects
  65. federica bianco NYU