ArticleThe European physical journal. C, Particles and fields2022
Learning new physics from an imperfect machine.
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 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 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
- Quantum anomaly detection for collider physics.Journal of high energy physics : JHEP · 2023Article
- Unsupervised Quark/Gluon Jet Tagging With Poissonian Mixture Models.Frontiers in artificial intelligence · 2022Article
- Learning new physics efficiently with nonparametric methods.The European physical journal. C, Particles and fields · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.
Identifiers
What OpenQuestion holds
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.