3 papers
physics.data-an2026
Development of an LLM-Based System for Automatic Code Generation from HEP Publications
Masahiko Saito, Tomoe Kishimoto, Junichi Tanaka
Ensuring the reproducibility of physics results is one of the crucial challenges in high-energy physics (HEP). In this study, we develop a proof-of-concept system that uses large l…
physics.data-an2024
Signal model parameter scan using Normalizing Flow
Masahiko Saito, Masahiro Morinaga, Tomoe Kishimoto +1
This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probabili…
hep-ex2023
Pre-training strategy using real particle collision data for event classification in collider physics
Tomoe Kishimoto, Masahiro Morinaga, Masahiko Saito +1
This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider…