SKA Science Data Challenge 2: analysis and results
Abstract
The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarise the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral hydrogen (Hi) sources in a simulated data product representing a 2000~h SKA MID spectral line observation from redshifts 0.25 to 0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, `reproducibility awards' were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy -- which combined predictions from two independent machine learning techniques to yield a 20 percent improvement in overall performance -- underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical datasets.
Cite
@article{arxiv.2303.07943,
title = {SKA Science Data Challenge 2: analysis and results},
author = {P. Hartley and A. Bonaldi and R. Braun and J. N. H. S. Aditya and S. Aicardi and L. Alegre and A. Chakraborty and X. Chen and S. Choudhuri and A. O. Clarke and J. Coles and J. S. Collinson and D. Cornu and L. Darriba and M. Delli Veneri and J. Forbrich and B. Fraga and A. Galan and J. Garrido and F. Gubanov and H. Håkansson and M. J. Hardcastle and C. Heneka and D. Herranz and K. M. Hess and M. Jagannath and S. Jaiswal and R. J. Jurek and D. Korber and S. Kitaeff and D. Kleiner and B. Lao and X. Lu and A. Mazumder and J. Moldón and R. Mondal and S. Ni and M. Önnheim and M. Parra and N. Patra and A. Peel and P. Salomé and S. Sánchez-Expósito and M. Sargent and B. Semelin and P. Serra and A. K. Shaw and A. X. Shen and A. Sjöberg and L. Smith and A. Soroka and V. Stolyarov and E. Tolley and M. C. Toribio and J. M. van der Hulst and A. Vafaei Sadr and L. Verdes-Montenegro and T. Westmeier and K. Yu and L. Yu and L. Zhang and X. Zhang and Y. Zhang and A. Alberdi and M. Ashdown and C. R. Bom and M. Brüggen and J. Cannon and R. Chen and F. Combes and J. Conway and F. Courbin and J. Ding and G. Fourestey and J. Freundlich and L. Gao and C. Gheller and Q. Guo and E. Gustavsson and M. Jirstrand and M. G. Jones and G. Józsa and P. Kamphuis and J. -P. Kneib and M. Lindqvist and B. Liu and Y. Liu and Y. Mao and A. Marchal and I. Márquez and A. Meshcheryakov and M. Olberg and N. Oozeer and M. Pandey-Pommier and W. Pei and B. Peng and J. Sabater and A. Sorgho and J. L. Starck and C. Tasse and A. Wang and Y. Wang and H. Xi and X. Yang and H. Zhang and J. Zhang and M. Zhao and S. Zuo},
journal= {arXiv preprint arXiv:2303.07943},
year = {2023}
}
Comments
Under review by MNRAS; 28 pages, 16 figures