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Stingray PyAstro16

Stingray PyAstro16

"Stingray: Time series methods for asronomical X-ray data that aren't fishy at all!"
A presentation at the Python in Astronomy 2016 workshop in Seattle.

Dr. Abbie Stevens

March 21, 2016
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  1. X-ray variability —  X-ray binaries: can’t spatially resolve them — 

    Vary on timescales from tens of microseconds to months/years —  X-ray pulsations, zoology of quasi-periodic oscillations, broadband “peaked” noise —  Similar variability phenomena in gamma, optical, IR Figure: NASA
  2. How to Study X-ray Binaries Spectroscopy Timing Polarimetry?? Γ1 3.5

    3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 100 10 1 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 Fourier frequency f [Hz] 2.1–15 keV PSD ×f [rms2 × 102] IGR J17480 2446 GX 17+2 4U 1728 34 Frequency x (RMS/Mean) Hz 2 −1 Frequency x (RMS/Mean) Hz 2 −1 Frequency x (RMS/Mean) Hz 2 −1 L b L b L b L h L hHz Frequency (Hz) kHz QPOs kHz QPO kHz QPO HBO HBO Figures: Grinberg et al ‘14, Done et al ‘07, Altamirano et al ‘12
  3. Variability analysis 1016 1018 1020 1022 1024 5000 104 1.5×104

    Count/sec Time (s) Start Time 10168 18:16:52:570 Stop Time 10168 18:17:08:180 Bin time: 0.1562E−01 s Time domain Light curve Frequency domain Power spectrum FOURIER TRANSFORM
  4. X-ray Variability: Hard to see by eye 1016 1018 1020

    1022 1024 5000 104 1.5×104 Count/sec Time (s) Start Time 10168 18:16:52:570 Stop Time 10168 18:17:08:180 Bin time: 0.1562E−01 s 1700 1702 1704 1706 1708 1710 2000 4000 6000 8000 104 1.2×104 Count/sec Time (s) Start Time 12339 7:28:14:566 Stop Time 12339 7:29:32:683 Bin time: 0.7812E−02 s Light curves
  5. X-ray Variability: Hard to see by eye 1016 1018 1020

    1022 1024 5000 104 1.5×104 Count/sec Time (s) Start Time 10168 18:16:52:570 Stop Time 10168 18:17:08:180 Bin time: 0.1562E−01 s 1700 1702 1704 1706 1708 1710 2000 4000 6000 8000 104 1.2×104 Count/sec Time (s) Start Time 12339 7:28:14:566 Stop Time 12339 7:29:32:683 Bin time: 0.7812E−02 s Light curves Power spectra
  6. Which analysis methods? ALL of them! —  Power spectra (periodograms)

    —  Fitting profiles to power spectra —  Periodic and quasi-periodic signal detection —  Dynamical power spectra —  Cross-/co-spectra, cross-correlation functions —  Averaged and frequency-resolved energy spectra —  Energy- or frequency-dependent time lags —  Rms and covariance spectra, coherence —  Bispectra, bicoherence, deadtime compensation, simulation support…
  7. Why make Stingray? —  Relatively small sub-field of astronomy — 

    Almost everyone uses (variations on) the same analysis techniques —  Most code is private, not documented, not properly tested, not maintained --- “black box” codes —  Unnecessary duplication of efforts, high threshold for entering the sub-field, difficult to get new students started
  8. Why make Stingray? —  Easier implementation of Bayesian methods &

    machine learning specific to X-ray (spectral-)timing —  Many analysis methods are already used in finance, music analysis, health care, neuroscience, and general signal processing —  Make an interface for applying those techniques to X-ray timing data —  Goal: become an Astropy affiliate package!
  9. Support from the Community —  ESA support for GUI for

    exploratory data analysis —  Part of the Google Summer of Code! —  Likely support from HEASARC for developing data structures and I/O interface with existing & future missions —  Potential for interfacing with astropy.modeling and/ or Sherpa spectral fitting package, especially for cross-spectral models —  YOU CAN HELP! Extending to IR, optical fast variability (spectral-)timing?
  10. Stingray Development https://github.com/StingraySoftware/stingray Contributors: Anurag Hota Evandro Martinez Ribeiro Himanshu

    Mishra John Swinbank Akash Tandon Project coordinators: Matteo Bachetti Paul Balm Daniela Huppenkothen Simone Migliari Abigail Stevens Mailing list: https://groups.google.com/forum/#!forum/ spectraltiming-stingray