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| Modeling Populations Using Heterogeneous Data, with Application to GRBs |
| Category:
22. Astrostatistics (Siemiginowska and Kashyap) |
Thomas Loredo1, I. Wasserman1
1Cornell University. |
| Presentation Number: 03.03 |
| Facility Keywords:
CGRO, Swift, BeppoSAX |
| Astronomers often follow up survey observations with supplemental observations that provide additional information about a subset of surveyed sources. A motivating example is gamma-ray bursts (GRBs): survey missions such as CGRO and Swift provide basic information (e.g., direction, peak flux) for all bursts, but for a subset of bursts with counterparts at other wavelengths, other data are available, such as host galaxy redshifts or isotropic energy estimates. This heterogeneity significantly complicates global (population-level) analyses. Building on our earlier Bayesian framework for analyzing GRB and other survey data, we have developed a "data fusion" methodology that can optimally combine survey data from various sources. Using analysis of the GRB spatial and luminosity distribution as a concrete example, we describe the overall approach, present preliminary results for GRBs, and highlight benefits of Bayesian data fusion over more conventional approaches. This work is partially funded by the NASA Swift GI program. |
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