Using `LIRA' To Quantify Diffuse Structure Around X-ray and Gamma-Ray Pulsars

Details Using `LIRA' To Quantify Diffuse Structure Around X-ray and Gamma-Ray Pulsars Using `LIRA' To Quantify Diffuse Structure Around X-ray and Gamma-Ray Pulsars

Sep 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009cfdd.confe..80c&link_type=abstract

Chandra's First Decade of Discovery, Proceedings of the conference held 22-25 September, 2009 in Boston, MA. Edited by Scott Wo

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Probability

Source Catalogs, Software

Scientific paper

In this poster, we exploit several capabilities of a Low-count Image Restoration and Analysis (LIRA) package, to quantify details of faint ``scruffy'' emission, consistent with PWN around X-ray and gamma-ray pulsars. Our preliminary results show evidence for irregular structure on scales of 1''-10'' or less (i.e. <500 pc), rather than larger smooth loops. Additionally, we can show this to be visible across several energy bands.LIRA grew out of work by the California-Boston Astro-Statistics Collaboration (CBASC) on analyzing high resolution, high energy Poisson images from X-ray and gamma-ray telescopes (see Stein et. al. these proceedings; also Esch et al 2004; and Connors and van Dyk in SCMAIV). LIRA fits: a ``Null'' or background model shape, times a scale factor; plus a flexible Multi-Scale (MS) model; folded though an instrument response (PSF, exposure). Embedding this in a fully Poisson probability structure allows us to map out uncertainties in our image analysis and reconstruction, via many MCMC samples. Specifically, for quantifying irregular nebular structure, we exploit the Multi-Scale model's smoothing parameters at each length-scale, as ``Summary Statistics'' (i.e low-dimensional summaries of the probability space). When distributions of these summary statistics, from analysis of simulated ``Null'' data sets, are compared with those from the actual Chandra data, we can set quantitative limits on structures at different length scales. Since one can do this for very low counts, one is able to analyze and compare structure in several energy slices. This work is supported by NSF and AISR funds.

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