mlsanityData to 7 Oct 2026

Goodness of fit by Neyman-Pearson testing

arXiv 2305.14137 ↗First posted 23 May 2023hep-ph stat.MLcited by 10

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Abstract (arXiv)

The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations. The New Physics Learning Machine (NPLM) methodology has been developed as a concrete implementation of this idea, to target the detection of new physical effects in the context of high energy physics collider experiments. In this paper we conduct a comparison of this approach to goodness of fit with others, in particular with classifier-based strategies that share strong similarities with NPLM. From our comparison, NPLM emerges as the more sensitive test to small departures of the data from the expected distribution and not biased towards detecting specific types of anomalies. These features make it suited for agnostic searches for new physics at collider experiments. Its deployment in other scientific and industrial scenarios should be investigated.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 26 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

6 of 26 references.

References · 6

  1. 2016Revisiting Classifier Two-Sample Testscited by 258
  2. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  3. 2022Evaluating generative models in high energy physicscited by 31
  4. 2022Learning new physics efficiently with nonparametric methodscited by 20
  5. 2023Fast kernel methods for Data Quality Monitoring as a goodness-of-fit testcited by 7
  6. 2021A goodness-of-fit test based on a recursive product of spacingscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.

  1. 2024Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciencescited by 8
  2. 2024Multiple testing for signal-agnostic searches of new physics with machine learningcited by 7
  3. 2025Comparing Generative Models with the New Physics Learning Machinecited by 4
  4. 2025Learning to Validate Generative Models: a Goodness-of-Fit Approachcited by 4
  5. 2025Forecasting Generative Amplificationcited by 3
  6. 2026Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practicecited by 3
  7. 2025AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testingcited by 2
  8. 2026Regularized f-Divergence Kernel Testscited by 2
  9. 2026Generative Models and Statistical Validationcited by 2
  10. 2025Sparse, self-organizing ensembles of local kernels detect rare statistical anomaliescited by 1