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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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2023 Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences »
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2022 : Invited talk: Catherine Nakalembe and Hannah Kerner »
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2022 : Contributed talk: Alexandre Adam, "Posterior samples of source galaxies in strong gravitational lenses with score-based priors" »
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2022 : Invited talk: Federico Felici, "Magnetic control of tokamak plasmas through Deep Reinforcement Learning" »
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2022 : Contributed talk: Aurélien Dersy, "Simplifying Polylogarithms with Machine Learning" »
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2022 : Invited talk: Vinicius Mikuni, "Collider Physics Innovations Powered by Machine Learning" »
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2022 : Invited talk: E. Doğuş Çubuk, "Scaling up material discovery via deep learning" »
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2022 : Contributed talk: Marco Aversa, "Physical Data Models in Machine Learning Imaging Pipelines" »
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2022 : Invited talk: Hiranya Peiris, "Prospects for understanding the physics of the Universe" »
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2022 : Contributed talk: Kieran Murphy, "Characterizing information loss in a chaotic double pendulum with the Information Bottleneck" »
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2022 : Invited talk: David Pfau, "Deep Learning and Ab-Initio Quantum Chemistry and Materials" »
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2022 Workshop: Machine Learning and the Physical Sciences »
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2021 : Kyle Cranmer »
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2021 Workshop: Machine Learning and the Physical Sciences »
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2020 Workshop: Machine Learning and the Physical Sciences »
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2020 Poster: Flows for simultaneous manifold learning and density estimation »
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2020 Poster: Discovering Symbolic Models from Deep Learning with Inductive Biases »
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2020 Poster: Set2Graph: Learning Graphs From Sets »
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2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
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2019 Poster: Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model »
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2017 : Panel session »
Iain Murray · Max Welling · Juan Carrasquilla · Anatole von Lilienfeld · Gilles Louppe · Kyle Cranmer -
2017 Workshop: Deep Learning for Physical Sciences »
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2017 Poster: Learning to Pivot with Adversarial Networks »
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2016 Invited Talk: Machine Learning and Likelihood-Free Inference in Particle Physics »
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2015 : An alternative to ABC for likelihood-free inference »
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