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Aishik Ghosh

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Articles

Towards AI-assisted neutrino flavor theory design

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By Max Fieg, Vijay Ganesh, Aishik Ghosh, Jake Rudolph, Daniel Whiteson, Jason Benjamin Baretz, V. Knapp-Pérez
| Nature Verified
Abstract Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model’s construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data.
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[2507.17831] On Focusing Statistical Power for Searches and Measurements in Particle Physics

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By Aishik Ghosh, Rafael Izbicki, Ann Lee, Luca Masserano
| arxiv.org
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[2305.10399] End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

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By Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi
| arxiv.org
arXiv:2305.10399 (hep-ex) Download a PDF of the paper titled End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics, by Alexander Shmakov and 5 other authors Download PDF Submission history From: Michael Fenton [ view email] [v1] Wed, 17 May 2023 17:43:10 UTC (5,040 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code,...
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