https://doi.org/10.1140/epjc/s10052-026-15314-x
Regular Article - Theoretical Physics
Mass-unspecific classifiers for mass-dependent searches
Instituto de Física Teórica IFT-UAM/CSIC, c/Nicolás Cabrera 13-15, 28049, Madrid, Spain
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
23
October
2025
Accepted:
11
January
2026
Published online:
4
February
2026
Abstract
Searches for new particles often span a wide range of mass scales, where the shape of potential signals and the SM background varies significantly. We make use of a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale, and is trained on a sample that is balanced across the entire mass range. The classifiers, either a neural network or a boosted decision tree, produce a continuous output across the full mass range and, at a given mass, achieve nearly the same performance as a classifier specifically trained for that mass. The performance of the classifiers is better than the one obtained with parameterised neural networks and similar methods.
© The Author(s) 2026
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Funded by SCOAP3.
