Hintze Centre for Astrophysical Surveys

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AutoSourceID-Classifier

Astronomy & Astrophysics EDP Sciences 680 (2023) A109-A109

Authors:

F Stoppa, S Bhattacharyya, R Ruiz de Austri, P Vreeswijk, S Caron, G Zaharijas, S Bloemen, G Principe, D Malyshev, V Vodeb, PJ Groot, E Cator, G Nelemans

Abstract:

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, 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.Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were 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, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
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AutoSourceID-FeatureExtractor

Astronomy & Astrophysics EDP Sciences 680 (2023) A108-A108

Authors:

F Stoppa, R Ruiz de Austri, P Vreeswijk, S Bhattacharyya, S Caron, S Bloemen, G Zaharijas, G Principe, V Vodeb, PJ Groot, E Cator, G Nelemans

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 theZwickyTransient Facility (ZTF) to test its transfer learning abilities.
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Minutes-duration optical flares with supernova luminosities

Nature Springer Nature 623:7989 (2023) 927-931

Authors:

Anna YQ Ho, Daniel A Perley, Ping Chen, Steve Schulze, Vik Dhillon, Harsh Kumar, Aswin Suresh, Vishwajeet Swain, Michael Bremer, Stephen J Smartt, Joseph P Anderson, GC Anupama, Supachai Awiphan, Sudhanshu Barway, Eric C Bellm, Sagi Ben-Ami, Varun Bhalerao, Thomas de Boer, Thomas G Brink, Rick Burruss, Poonam Chandra, Ting-Wan Chen, Wen-Ping Chen, Jeff Cooke, Michael W Coughlin, Kaustav K Das, Andrew J Drake, Alexei V Filippenko, James Freeburn, Christoffer Fremling, Michael D Fulton, Avishay Gal-Yam, Lluís Galbany, Hua Gao, Matthew J Graham, Mariusz Gromadzki, Claudia P Gutiérrez, K-Ryan Hinds, Cosimo Inserra, Nayana A J, Viraj Karambelkar, Mansi M Kasliwal, Shri Kulkarni, Tomás E Müller-Bravo, Eugene A Magnier, Ashish A Mahabal, Thomas Moore, Chow-Choong Ngeow, Matt Nicholl, Eran O Ofek, Conor MB Omand, Francesca Onori, Yen-Chen Pan, Priscila J Pessi, Glen Petitpas, David Polishook, Saran Poshyachinda, Miika Pursiainen, Reed Riddle, Antonio C Rodriguez, Ben Rusholme, Enrico Segre, Yashvi Sharma, Ken W Smith, Jesper Sollerman, Shubham Srivastav, Nora Linn Strotjohann, Mark Suhr, Dmitry Svinkin, Yanan Wang, Philip Wiseman, Avery Wold, Sheng Yang, Yi Yang, Yuhan Yao, David R Young, WeiKang Zheng
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The dense and non-homogeneous circumstellar medium revealed in radio wavelengths around the Type Ib SN 2019oys

(2023)

Authors:

Itai Sfaradi, Assaf Horesh, Jesper Sollerman, Rob Fender, Lauren Rhodes, David RA Williams, Joe Bright, Dave A Green, Steve Schulze, Avishay Gal-Yam
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WISDOM Project -- XVI. The link between circumnuclear molecular gas reservoirs and active galactic nucleus fuelling

(2023)

Authors:

Jacob S Elford, Timothy A Davis, Ilaria Ruffa, Martin Bureau, Michele Cappellari, Jindra Gensior, Satoru Iguchi, Fu-Heng Liang, Lijie Liu, Anan Lu, Thomas G Williams
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