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Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study


Nov 15, 2022
David Ruhe, Kaze Wong, Miles Cranmer, Patrick Forré


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Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks


Nov 15, 2022
Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman, the LSST Dark Energy Science Collaboration

* 15 pages, 8 figures (+ 6 pages, 2 figures in Appendix). Submitted to ApJ. Code at https://github.com/jiwoncpark/node-to-joy 

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A Neural Network Subgrid Model of the Early Stages of Planet Formation


Nov 08, 2022
Thomas Pfeil, Miles Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr

* 6 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022 

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$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees


Oct 24, 2022
Christian Kragh Jespersen, Miles Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai

* 15 pages, 9 figures, 3 tables, 10 pages of Appendices. Accepted for publication in ApJ 

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The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback


Sep 05, 2022
Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles Cranmer, Daisuke Nagai, Daniel Anglés-Alcázar, Shirley Ho, Lars Hernquist

* 12+4 pages, 7+4 figures. The code and data associated with this paper are available at https://github.com/JayWadekar/ScalingRelations_ML 

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Automated discovery of interpretable gravitational-wave population models


Jul 25, 2022
Kaze W. K Wong, Miles Cranmer

* Published in ML4Astro Workshop at ICML 2022. 8 pages, 1 figure. Code at https://github.com/kazewong/SymbolicGWPopulation_paper 

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Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks


Jul 20, 2022
Pablo Lemos, Miles Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho

* 5 pages, 3 figures. Accepted at the ML4Astro Machine Learning for Astrophysics Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022) 

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Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks


Feb 28, 2022
Leander Thiele, Miles Cranmer, William Coulton, Shirley Ho, David N. Spergel

* 11 pages, 5 figures; condensed version accepted at the Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021) as "Equivariant and Modular DeepSets with Applications in Cluster Cosmology" 

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Rediscovering orbital mechanics with machine learning


Feb 04, 2022
Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia

* 12 pages, 6 figures, under review 

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