AutoSourceID-FeatureExtractor

Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation

¹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
e-mail: f.stoppa@astro.ru.nl
² Instituto de Física Corpuscular, IFIC-UV/CSIC, Valencia, Spain
³ High Energy Physics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
⁴ Nikhef, Science Park 105, 1098 XG Amsterdam, The Netherlands
⁵ Center for Astrophysics and Cosmology, University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia
⁶ Department of Mathematics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
⁷ SRON, Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, The Netherlands
⁸ Institute of Astronomy, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
⁹ Institute for Fundamental Physics of the Universe, Via Beirut 2, 34151 Trieste, Italy
¹⁰ Dipartimento di Fisica, Università di Trieste, 34127 Trieste, Italy
¹¹ Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, 34127 Trieste, Italy
¹² Department of Astronomy and Inter-University Institute for Data Intensive Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
¹³ South African Astronomical Observatory, PO Box 9, Observatory 7935, South Africa

Received: 23 May 2023
Accepted: 17 October 2023

Abstract

Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources’ features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.

Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.

Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.