# Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning

arXiv: https://arxiv.org/abs/2108.03125 · HTML: https://mlsanity.com/paper/2108.03125/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 85 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies](https://mlsanity.com/paper/2007.06011/) (2020)

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

- [Interpretable machine learning in Physics](https://mlsanity.com/paper/2203.08021/) (2022)

## Abstract (arXiv)

We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models, XGBoost and a deep neural network, to exploit correlations between observables and compare this approach to the traditional cut-and-count method. We consider different methods to analyze the models' output, finding that a template fit generally performs better than a simple cut. By means of a Shapley decomposition, we gain additional insight into the relationship between event kinematics and the machine learning model output. We consider a supersymmetric scenario with a metastable sneutrino as a concrete example, but the methodology can be applied to a much wider class of models.