Multiple testing for signal-agnostic searches for new physics with machine learning
Abstract In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. Fo...
Saved in:
Main Authors: | Gaia Grosso, Marco Letizia |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2025-01-01
|
Series: | European Physical Journal C: Particles and Fields |
Online Access: | https://doi.org/10.1140/epjc/s10052-024-13722-5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Distribution Agnostic Rank-Based Measure for Proximity Search
by: Mayur Garg, et al.
Published: (2025-01-01) -
Continual deep reinforcement learning with task-agnostic policy distillation
by: Muhammad Burhan Hafez, et al.
Published: (2024-12-01) -
A Lingual Agnostic Information Retrieval System
by: Umar Bn Abdullahi, et al.
Published: (2024-01-01) -
Hierarchical RIME algorithm with multiple search preferences for extreme learning machine training
by: Rui Zhong, et al.
Published: (2025-01-01) -
Correction: In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines.
by: PLOS ONE Staff
Published: (2025-01-01)