# MaxiMask and MaxiTrack: two new tools for identifying contaminants in astronomical images using convolutional neural networks

## TL;DR

This paper introduces two convolutional neural network tools, MaxiMask and MaxiTrack, for detecting various contaminants in astronomical images, improving accuracy and adaptability across different observational conditions.

## Contribution

The paper presents novel CNN-based classifiers, MaxiMask and MaxiTrack, capable of identifying diverse contaminants and tracking errors in astronomical images with state-of-the-art performance.

## Key findings

- MaxiMask achieves state-of-the-art cosmic ray hit detection.
- Both classifiers perform well across various detector types and conditions.
- Bayesian update mechanism allows tuning for specific science goals.

## Abstract

In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are able to identify various contaminants, such as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulous features, saturated pixels, diffraction spikes, and tracking errors in images. They encompass a broad range of ambient conditions, such as seeing, image sampling, detector type, optics, and stellar density. The first classifier, MaxiM ask , performs semantic segmentation and generates bad pixel maps for each contaminant, based on the probability that each pixel belongs to a given contaminant class. The second classifier, MaxiTrack , classifies entire images and mosaics, by computing the probability for the focal plane to be affected by tracking errors. We gathered training and testing data from real data originating from various modern charged-coupled devices and near-infrared cameras, that are augmented with image simulations. We quantified the performance of both classifiers and show that M axi M ask achieves state-of-the-art performance for the identification of cosmic ray hits. Thanks to a built-in Bayesian update mechanism, both classifiers can be tuned to meet specific science goals in various observational contexts.