# A Two-Stage Deep Learning Detection Classifier for the ATLAS Asteroid Survey

arXiv: https://arxiv.org/abs/2101.08912 · HTML: https://mlsanity.com/paper/2101.08912/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit](https://mlsanity.com/paper/1702.00403/) (2017)

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

- [Towards Asteroid Detection in Microlensing Surveys with Deep Learning](https://mlsanity.com/paper/2211.02239/) (2022)
- [Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts](https://mlsanity.com/paper/2308.07538/) (2023)
- [Euclid: Identification of asteroid streaks in simulated images using deep learning](https://mlsanity.com/paper/2310.03845/) (2023)
- [Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning](https://mlsanity.com/paper/2409.15261/) (2024)
- [Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning](https://mlsanity.com/paper/2605.12391/) (2026)

## Abstract (arXiv)

In this paper we present a two-step neural network model to separate detections of solar system objects from optical and electronic artifacts in data obtained with the "Asteroid Terrestrial-impact Last Alert System" (ATLAS), a near-Earth asteroid sky survey system [arXiv:1802.00879]. A convolutional neural network [arXiv:1807.10912] is used to classify small "postage-stamp" images of candidate detections of astronomical sources into eight classes, followed by a multi-layered perceptron that provides a probability that a temporal sequence of four candidate detections represents a real astronomical source. The goal of this work is to reduce the time delay between Near-Earth Object (NEO) detections and submission to the Minor Planet Center. Due to the rare and hazardous nature of NEOs [Harris and D'Abramo, 2015], a low false negative rate is a priority for the model. We show that the model reaches 99.6% accuracy on real asteroids in ATLAS data with a 0.4% false negative rate. Deployment of this model on ATLAS has reduced the amount of NEO candidates that astronomers must screen by 90%, thereby bringing ATLAS one step closer to full autonomy.