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AutoSourceID-FeatureExtractor : optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, Saptashwa Bhattacharyya, S. Caron, S. Bloemen, Gabrijela Zaharijas, G. Principe, Veronika Vodeb, P. J. Groot, E. Cator, G. Nelemans, 2023, izvirni znanstveni članek

Opis: 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.
Ključne besede: data analysis, image processing, astronomical databases
Objavljeno v RUNG: 08.11.2023; Ogledov: 837; Prenosov: 8
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2.
AutoSourceID-Light : Fast optical source localization via U-Net and Laplacian of Gaussian
F. Stoppa, P. Vreeswijk, S. Bloemen, Saptashwa Bhattacharyya, S Caron, G. Jóhannesson, R. Ruiz de Austri, C. Van den Oetelaar, Gabrijela Zaharijas, P.J. Groot, E. Cator, G. Nelemans, 2022, izvirni znanstveni članek

Opis: Aims: With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods: We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results: Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method rapidly detects more sources not only in low and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Ključne besede: astronomical databases, data analysis, image processing
Objavljeno v RUNG: 23.01.2023; Ogledov: 1600; Prenosov: 0
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