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Characterizing γ-ray maps of the Galactic Center with neural density estimation
Siddharth Mishra-Sharma · Kyle Cranmer

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in Machine Learning and the Physical Sciences »

Machine learning methods have enabled new ways of performing inference on high-dimensional datasets modeled using complex simulations. We leverage recent advancements in simulation-based inference in order to characterize the contribution of various modeled components to γ-ray data of the Galactic Center recorded by the Fermi satellite. A specific goal here is to differentiate "smooth" emission, as expected for a dark matter origin, from more "clumpy" emission expected for a population of relatively bright, unresolved astrophysical point sources. Compared to traditional techniques based on the statistical distribution of photon counts, our method based on density estimation using normalizing flows is able to utilize more of the information contained in a given model of the Galactic Center emission, and in particular can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the γ-ray map.

Author Information

Siddharth Mishra-Sharma (MIT)
Kyle Cranmer (New York University & Meta AI)

Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.

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