# Enhanced Higgs to τ^+τ^- Searches with Deep Learning

arXiv: https://arxiv.org/abs/1410.3469 · HTML: https://mlsanity.com/paper/1410.3469/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2014.

## Built on by (most cited first, 5 of 5)

- [Activation Landscapes as a Topological Summary of Neural Network Performance](https://mlsanity.com/paper/2110.10136/) (2021)
- [FAIR Universe HiggsML Uncertainty Dataset and Competition](https://mlsanity.com/paper/2410.02867/) (2024)
- [Improving Deep Tabular Learning](https://mlsanity.com/paper/2509.16354/) (2025)
- [Can A Neural Network Hear the Shape of A Drum?](https://mlsanity.com/paper/2203.08073/) (2022)
- [Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions](https://mlsanity.com/paper/2308.05724/) (2023)

## Abstract (arXiv)

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight non-linear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated dataset of 25%.