Skip To Main Content
Aishik Ghosh on Muck Rack

Aishik Ghosh

As seen in: Nature, arXiv

Get in touch with Aishik

Contact Aishik, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Articles

Towards AI-assisted neutrino flavor theory design

|
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.
Open in Who Shared Issue with article?

[2507.17831] On Focusing Statistical Power for Searches and Measurements in Particle Physics

|
By Aishik Ghosh, Rafael Izbicki, Ann Lee, Luca Masserano
| arXiv
Open in Who Shared Issue with article?

[2305.10399] End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

|
By Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi
| arXiv
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,...
Open in Who Shared View Duplicates (597) Issue with article?
See all 3 articles