We are non formal group of researchers of Sternberg astronomical institute, Laboratoire de Physique de Clermont, Space Research Institute joined together to solve the problem of detecting unusual objects in supernova datasets with machine learning methods. This is our first task but we hope to apply the skills and knowledge that we will get during this research to the new fascinating problems in astrophysics. We support any international exchanges and collaborations. We also welcome the students, postgraduates, and everyone who is willing to join us.
The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. The goal of this project is to develop a pipeline where human expertise and modern machine learning techniques can complement each other in the task of identifying unusual astronomical objects. Using supernovae as a case study, our proposal is divided in two parts: a first automatized fitting and screening based on Gaussian processes and one-class support vector machines — where anomalous objects are identified, and a second phase where such anomalous objects submitted to careful individual analysis. The strategy requires an initial data set for which spectroscopic is available for training purposes, but can be applied to a much larger data set for which we only have photometric observations. Enabling reliable anomaly/outlier detection based solely on photometric observations is one of the fundamental puzzles to be solved before we can convert the full potential of large-scale surveys into scientific results. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. Full description of the project (in Russian).
Ishida E.E.O., Kornilov M., Malanchev K. et al. "Active Anomaly Detection for time-domain discoveries", arXiv:1909.13260.
Pruzhinskaya M., Malanchev K., Kornilov M., Ishida E., Mondon F., Volnova A., Korolev V. "Anomaly Detection in the Open Supernova Catalog", Monthly Notices of the Royal Astronomical Society, Volume 489, Issue 3, Pages 3591-3608, 2019.
Malanchev K., Volnova A., Kornilov M. et al. "Use of Machine Learning for Anomaly Detection Problem in Large Astronomical Databases", Proceedings of the ХХI International Conference DAMDID / RCDL'2019, Pages 238-249, 2019.
Kornilov M.V., Pruzhinskaya M.V., Malanchev K.L. et al. "Machine learning techniques for analysis of photometric data from the Open Supernova catalog", in Proceedings of the International Conference "The multi-messenger astronomy: gamma-ray bursts, search for electromagnetic counterparts to neutrino events and gravitational waves", Publishing house SNEG Pyatigorsk, pp. 100-110, 2019, DOI:10.26119/SAO.2019.1.35517.
Pruzhinskaya M., Malanchev K., Kornilov M. et al. "Machine Learning Analysis of Supernova Light Curves", Proceedings of Science, vol. 342, 51, 2019.
Kornilov, M. et al. "Algorithms for the active anomaly detection in the era of wide-field astronomical surveys", Highlights of the Russian astrophysics 2019, Moscow, Russia, 16 December 2019.
Malanchev K., Volnova A. et al. "Use of machine learning for anomaly detection in large astronomical databases", XXI international conference "Data Analytics and Management in Data Intensive Domains" (DAMDID), Kazan, Russia, 15-18 October 2019 (slides).
Pruzhinskaya, M. et al. "Machine learning and new classes of astrophysical objects", 7th School-seminar "Magneto-Plasma Processes in Relativistic Astrophysics", Tarusa, Russia, 17-21 June 2019.
Kornilov, M. et al. "Anomaly detection in the Open Supernova Catalog by machine learning algorithms", Lomonosov conference 2019, Moscow, Russia, 8-12 April 2019.
Pruzhinskaya, M. et al. "Anomaly detection in the Open Supernova Catalog by machine learning algorithms", IAU100 Special Session "Women and Girls in Astronomy", Moscow, Russia, 11 Febrary 2019 (slides, in Russian).
Kornilov, M. et al. "Anomaly detection in the Open Supernova Catalog by machine learning algorithms", Highlights of the Russian astrophysics 2018, Moscow, Russia, 17 December 2018.
Kornilov, M. et al. "Machine learning techniques for analysis of photometric data from the Open Supernova catalog", The multi-messenger astronomy: gamma-ray bursts, search for electromagnetic counterparts to neutrino events and gravitational waves, Nizhnij Arkhyz (SAO), Russia, 7-14 October 2018 (slides).
Sternberg Astronomical Institute, Moscow State University, Universitetsky pr., 13, Moscow 119234, Russia