High-Energy Physics

Electromagnetics

Proposes two ML-based calibration methods to correct electromagnetic shower feature discrepancies between Monte Carlo simulations and real collision data in the CMS calorimeter.

ScientificML

Exploring vision-language models for jet analysis in high-energy physics, converting JetClass jets into physics-informed four-panel images for multi-task jet tagging classification.

HEP

This high-energy physics work develops a Parnassus conditional flow matching surrogate for the CLD detector, emulating full detector simulation and event reconstruction to address computationally expensive collider studies.

ScientificML

Proposes neural network-based pulse deconvolution for FCC-ee's high-granularity dual-readout calorimeter, leveraging timing separation of Cherenkov and scintillation signals to improve energy resolution.

ScientificML

Proposes hybrid neural density estimation combining neural ratio estimation with flow-based models to address density normalization and efficient sampling in high-dimensional unbinned simulation-based inference.

High-Energy Physics

A physics-informed self-supervised learning framework that jointly performs wire calibration and interaction position reconstruction in multi-wire parallel plate avalanche counters, enhancing adaptability to changing operating conditions and detector aging.