# Deep learning method for identifying mass composition of ultra-high-energy cosmic rays

arXiv: https://arxiv.org/abs/2112.02072 · HTML: https://mlsanity.com/paper/2112.02072/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 72 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)

## Built on by (most cited first, 2 of 2)

- [Rejecting noise in Baikal-GVD data with neural networks](https://mlsanity.com/paper/2210.04653/) (2022)
- [Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors](https://mlsanity.com/paper/2608.18190/) (2026)

## Abstract (arXiv)

We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. The first network predicts the type of a primary particle for individual events, while the second infers the mass composition of an ensemble of events. We apply this method to the Monte-Carlo data for the Telescope Array Surface Detectors readings, on which it yields an unprecedented low error of 7% for 4-component approximation. We also discuss the problems of applying the developed method to the experimental data, and the way they can be resolved.