# Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

arXiv: https://arxiv.org/abs/1701.00458 · HTML: https://mlsanity.com/paper/1701.00458/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2017.

## Built on by (most cited first, 12 of 12)

- [What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction](https://mlsanity.com/paper/2203.07390/) (2022)
- [Analysis of convolutional neural network image classifiers in a rotationally symmetric model](https://mlsanity.com/paper/2205.05500/) (2022)
- [PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework](https://mlsanity.com/paper/2106.14349/) (2021)
- [Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts](https://mlsanity.com/paper/2308.07538/) (2023)
- [MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning](https://mlsanity.com/paper/2104.13950/) (2021)
- [The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves](https://mlsanity.com/paper/2106.03736/) (2021)
- [GMR-Conv: An Efficient Rotation and Reflection Equivariant Convolution Kernel Using Gaussian Mixture Rings](https://mlsanity.com/paper/2504.02819/) (2025)
- [Identifying Transients in the Dark Energy Survey using Convolutional Neural Networks](https://mlsanity.com/paper/2203.09908/) (2022)
- [Benefits of mirror weight symmetry for 3D mesh segmentation in biomedical applications](https://mlsanity.com/paper/2309.17076/) (2023)
- [Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology](https://mlsanity.com/paper/2505.05689/) (2025)
- [Pre-training vision models for the classification of alerts from wide-field time-domain surveys](https://mlsanity.com/paper/2512.11957/) (2025)
- [Identifying Gems from Roman RAPIDly](https://mlsanity.com/paper/2606.05103/) (2026)

## Abstract (arXiv)

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RF). We show that our CNN significantly outperforms the RF model reducing the error by almost half. Furthermore, for a fixed number of approximately 2,000 allowed false transient candidates per night we are able to reduce the miss-classified real transients by approximately 1/5. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope (LSST). We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS.