ArticleThe European physical journal. C, Particles and fields2022
Learning new physics efficiently with nonparametric methods.
Article in The European physical journal. C, Particles and fields, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Multiple testing for signal-agnostic searches for new physics with machine learning.The European physical journal. C, Particles and fields · 2025Article
- Robust resonant anomaly detection with NPLM.The European physical journal. C, Particles and fields · 2025Article
- Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays.Nature communications · 2024Article
- CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.Frontiers in big data · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.