A Probabilistic Method of Background Removal for High Energy Astrophysics Data
Steven Ehlert, Chien-Ting Chen, Doug A. Swartz, Ryan C. Hickox,, Alexander A. Lutovinov, Andrey N. Semena, Roman Krivonos, Andrey E., Shtykovsky, Alexey Tkachenko

TL;DR
This paper introduces a new statistical background subtraction method for high energy astrophysics data that improves detection significance and robustness, especially in low count and high background scenarios.
Contribution
The paper presents a novel probabilistic background removal technique that accurately accounts for Poisson fluctuations and avoids unphysical negative counts, applicable to various high energy imaging data.
Findings
Enhanced detection of galaxy cluster emission in simulated data.
More robust source detection in real ART-XC observations.
Method converges to standard subtraction in high signal limit.
Abstract
We present a new statistical method for constructing background subtracted measurements from event list data gathered by X-ray and gamma ray observatories. This method was initially developed specifically to construct images that account for the high background fraction and low overall count rates observed in survey data from the Mikhail Pavlinsky ART-XC telescope aboard the Spektrum R\"{o}ntgen Gamma (SRG) mission, although the mathematical underpinnings are valid for data taken with other imaging missions and analysis applications. This method fully accounts for the expected Poisson fluctuations in both the sky photon and non X-ray background count rates in a manner that does not result in unphysical negative counts. We derive the formulae for arbitrary confidence intervals for the source counts and show that our new measurement converges exactly to the standard background subtraction…
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Taxonomy
TopicsParticle Detector Development and Performance · Particle physics theoretical and experimental studies · Gamma-ray bursts and supernovae
