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

arXiv: https://arxiv.org/abs/2105.07530 · HTML: https://mlsanity.com/paper/2105.07530/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 93 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs](https://mlsanity.com/paper/1802.10026/) (2018)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [Essentially No Barriers in Neural Network Energy Landscape](https://mlsanity.com/paper/1803.00885/) (2018)
- [QCD-Aware Recursive Neural Networks for Jet Physics](https://mlsanity.com/paper/1702.00748/) (2017)
- [Deep learning in color: towards automated quark/gluon jet discrimination](https://mlsanity.com/paper/1612.01551/) (2016)
- [INFERNO: Inference-Aware Neural Optimisation](https://mlsanity.com/paper/1806.04743/) (2018)
- [Jet Flavour Classification Using DeepJet](https://mlsanity.com/paper/2008.10519/) (2020)
- [Black-Box Optimization with Local Generative Surrogates](https://mlsanity.com/paper/2002.04632/) (2020)
- [The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating](https://mlsanity.com/paper/1611.08256/) (2016)
- [Using Machine Learning to Speed Up and Improve Calorimeter R&D](https://mlsanity.com/paper/2003.12440/) (2020)

## Built on by (most cited first, 1 of 1)

- [Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey](https://mlsanity.com/paper/2403.11934/) (2024)

## 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.