# Effective Image Differencing with ConvNets for Real-time Transient Hunting

arXiv: https://arxiv.org/abs/1710.01422 · HTML: https://mlsanity.com/paper/1710.01422/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction](https://mlsanity.com/paper/2203.07390/) (2022)
- [Textual interpretation of transient image classifications from large language models](https://mlsanity.com/paper/2510.06931/) (2025)
- [A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques](https://mlsanity.com/paper/2301.04292/) (2023)
- [Pre-training vision models for the classification of alerts from wide-field time-domain surveys](https://mlsanity.com/paper/2512.11957/) (2025)

## Abstract (arXiv)

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as artifacts resulting from saturated stars, and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artifacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image subtraction pipeline -- image registration, background subtraction, noise removal, psf matching, and subtraction -- into a single real-time convolutional network. Once trained the method works lighteningly fast, and given that it does multiple steps at one go, the advantages for multi-CCD, fast surveys like ZTF and LSST are obvious.