# First Full-Event Reconstruction from Imaging Atmospheric Cherenkov Telescope Real Data with Deep Learning

arXiv: https://arxiv.org/abs/2105.14927 · HTML: https://mlsanity.com/paper/2105.14927/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 31 May 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2001.07645/) (2020)

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

- [Machine Learning in Gamma Astronomy](https://mlsanity.com/paper/2501.19064/) (2025)
- [Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope](https://mlsanity.com/paper/2608.09630/) (2026)

## Abstract (arXiv)

The Cherenkov Telescope Array is the future of ground-based gamma-ray astronomy. Its first prototype telescope built on-site, the Large Size Telescope 1, is currently under commissioning and taking its first scientific data. In this paper, we present for the first time the development of a full-event reconstruction based on deep convolutional neural networks and its application to real data. We show that it outperforms the standard analysis, both on simulated and on real data, thus validating the deep approach for the CTA data analysis. This work also illustrates the difficulty of moving from simulated data to actual data.