Background subtraction and transient timing with Bayesian Blocks

Aims. We aim to incorporate background subtraction into the Bayesian Blocks algorithm so that transient events can be timed accurately and precisely even in the presence of a substantial, rapidly variable background. Methods. We developed several modifications to the algorithm and tested them on a simulated XMM-Newton observation of a bursting and eclipsing object. Results. We found that bursts can be found to good precision for almost all background-subtraction methods, but eclipse ingresses and egresses present problems for most methods. We found one method that recovered these events with precision comparable to the interval between individual photons, in which both source- and background-region photons are combined into a single list and weighted according to the exposure area. We also found that adjusting the Bayesian Blocks change points nearer to blocks with higher count rate removes a systematic bias towards blocks of low count rate.

[1]  Gunter Zech,et al.  Upper limits in experiments with background or measurement errors , 1989 .

[2]  R. S. Warwick,et al.  X-ray background measurements with XMM-Newton EPIC , 2002, astro-ph/0204147.

[3]  N. Grosso,et al.  Study of the X-ray activity of Sagittarius A* during the 2011 XMM-Newton campaign , 2014, 1409.6452.

[4]  Prasanth H. Nair,et al.  Astropy: A community Python package for astronomy , 2013, 1307.6212.

[5]  R. Wijnands,et al.  XMM-Newton light curves of the low-mass X-ray binary EXO 0748–676: Dips, eclipses, and bursts , 2003, astro-ph/0310271.

[6]  Jeffrey D. Scargle Studies in Astronomical Time Series Analysis: V. Bayesian Blocks, A New Method to Analyze Structure in , 1998 .

[7]  J. Chiang,et al.  STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS , 2012, 1207.5578.

[8]  Nicholas E. White,et al.  The Discovery of 3.8 Hour Periodic Intensity Dips and Eclipses from the Transient Low-Mass X-Ray Binary EXO 0748-676 , 1986 .

[9]  Herbert A. Sturges,et al.  The Choice of a Class Interval , 1926 .

[10]  D. W. Scott On optimal and data based histograms , 1979 .

[11]  J. Burgess,et al.  On Spectral Evolution and Temporal Binning in Gamma-Ray Bursts , 2014, 1408.3973.

[12]  A. Parmar,et al.  The bursting behavior of the transient X-ray burst source EXO 0748-676 - A dependence between the X-ray burst properties and the strength of the persistent emission , 1986 .

[13]  M. Audard,et al.  A statistical analysis of X-ray variability in pre-main sequence objects of the Taurus molecular cloud , 2006, astro-ph/0608651.

[14]  T. Loredo Promise of Bayesian Inference for Astrophysics , 1992 .

[15]  G. Belanger On Detecting Transient Phenomena , 2013 .

[16]  B. Ishak,et al.  Statistics, data mining, and machine learning in astronomy: a practical Python guide for the analysis of survey data, by Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas and Alexander Gray , 2017 .

[17]  D. Freedman,et al.  On the histogram as a density estimator:L2 theory , 1981 .

[18]  Giuliana Fiorillo,et al.  Nuclear Instruments and Methods in Physics Research , 2009 .