# Loss function to optimise signal significance in particle physics

arXiv: https://arxiv.org/abs/2412.09500 · HTML: https://mlsanity.com/paper/2412.09500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 4 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 2 of 2)

- [The Lov'asz Hinge: A Novel Convex Surrogate for Submodular Losses](https://mlsanity.com/paper/1512.07797/) (2015)
- [The Lov'asz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks](https://mlsanity.com/paper/1705.08790/) (2017)

## Abstract (arXiv)

We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss have higher signal efficiency for similar values of estimated signal significance compared to ones trained with a cross-entropy loss, showing promise to improve sensitivity of particle physics searches at colliders.