mlsanityData to 13 Oct 2026

Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

arXiv 2105.07530 ↗First posted 16 May 2021hep-ex cs.LG hep-phcited by 1

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Abstract (arXiv)

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 16 of its 93 references that are in the map. First posted on arXiv 16 May 2021.

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16 of 93 references.

References · 16

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
  2. 2016Densely Connected Convolutional Networkscited by 6,707
  3. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,021
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,601
  5. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,202
  6. 2017Self-Normalizing Neural Networkscited by 594
  7. 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 586
  8. 2017Snapshot Ensembles: Train 1, get M for freecited by 350
  9. 2018Essentially No Barriers in Neural Network Energy Landscapecited by 329
  10. 2017QCD-Aware Recursive Neural Networks for Jet Physicscited by 22
  11. 2016Deep learning in color: towards automated quark/gluon jet discriminationcited by 21
  12. 2018INFERNO: Inference-Aware Neural Optimisationcited by 16
4 more
  1. 2020Jet Flavour Classification Using DeepJetcited by 16
  2. 2020Black-Box Optimization with Local Generative Surrogatescited by 3
  3. 2016The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregatingcited by 2
  4. 2020Using Machine Learning to Speed Up and Improve Calorimeter R&Dcited by 2

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A surveycited by 1