AutoSourceID-Classifier. Star-Galaxy Classification using a
  Convolutional Neural Network with Spatial Information
Aims. Traditional star-galaxy classification techniques often rely on feature
estimation from catalogues, a process susceptible to introducing inaccuracies,
thereby potentially jeopardizing the classification's reliability. Certain
galaxies, especially those not manifesting as extended sources, can be
misclassified when their shape parameters and flux solely drive the inference.
We aim to create a robust and accurate classification network for identifying
stars and galaxies directly from astronomical images. By leveraging
convolutional neural networks (CNN) and additional information about the source
position, we aim to accurately classify all stars and galaxies within a survey,
particularly those with a signal-to-noise ratio (S/N) near the detection limit.
Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed here uses
32x32 pixel single filter band source cutouts generated by the previously
developed ASID-L code. ASID-C utilizes CNNs to distinguish these cutouts into
stars or galaxies, leveraging their strong feature-learning capabilities.
Subsequently, we employ a modified Platt Scaling calibration for the output of
the CNN. This technique ensures that the derived probabilities are effectively
calibrated, delivering precise and reliable results. Results. We show that
ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera
Legacy Survey (DECaLS) morphological classification, outperforms similar codes
like SourceExtractor. ASID-C opens up new possibilities for accurate celestial
object classification, especially for sources with a S/N near the detection
limit. Potential applications of ASID-C, like real-time star-galaxy
classification and transient's host identification, promise significant
contributions to astronomical research.
F. Stoppa, S. Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, G. Zaharijas, S. Bloemen, G. Principe, D. Malyshev, V. Vodeb, P. J. Groot, E. Cator, G. Nelemans
# AutoSourceID-Classifier

###### Abstract

Aims:Traditional star-galaxy classification techniques often rely on feature estimation from catalogues, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. By leveraging convolutional neural networks (CNN) and additional information about the source position, we aim to accurately classify all stars and galaxies within a survey, particularly those with a signal-to-noise ratio (S/N) near the detection limit.

Methods: The AutoSourceID-Classifier (ASID-C) algorithm developed here uses 32x32 pixel single filter band source cutouts generated by the previously developed ASID-L code. ASID-C utilizes CNNs to distinguish these cutouts into stars or galaxies, leveraging their strong feature-learning capabilities. Subsequently, we employ a modified Platt Scaling calibration for the output of the CNN. This technique ensures that the derived probabilities are effectively calibrated, delivering precise and reliable results.

Results: We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECALS) morphological classification, outperforms similar codes like SourceExtractor. ASID-C opens up new possibilities for accurate celestial object classification, especially for sources with a S/N near the detection limit. Potential applications of ASID-C, like real-time star-galaxy classification and transient's host identification, promise significant contributions to astronomical research.

## I Introduction

Current and future large-scale astronomical photometric surveys are amassing, and will continue to amass, vast quantities of photometric data, including millions of images containing billions of stars and galaxies. This data influx necessitates a series of processing steps, such as source localization, anomaly detection, feature extraction, and star-galaxy classification, to be further optimized for efficiency in terms of both time and computational resources. With the advent of CMOS detectors capable of capturing large-format images of the night sky at cadences exceeding 1Hz, traditional methods and software for data processing will be inadequate to keep pace with the data acquisition rate.

To address this challenge, our research aims to enhance and streamline existing methods using machine learning tools. In AstroSourceID-Light (ASID-L, Stoppa _et al._ 2022), we demonstrated the potential for rapid and accurate source identification on images, achieving this in a fraction of the time required by currently used methods. Building on this foundation, we now focus on star-galaxy classification, a fundamental data-processing task and often the initial step for the scientific exploitation of survey data. Despite advances in source localization and feature extraction algorithms, there remains significant potential for improvement in star-galaxy classification methods, which are often applied from catalogues rather than directly from source images (Weir _et al._ 1995, Ball _et al._ 2006, Vasconcellos _et al._ 2011, Sevilla-Noarbe _et al._ 2018). Current classification methods, including classification tree methods based on the morphological features of the sources and Bayesian approaches that integrate available source information with prior knowledge about nearby star and galaxy