Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
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AuteurKennamer, Noble; Galbany González, Lluis; LSST Dark Energy Science Collaboration; COIN Collaboration
Active learningMachine learningAstrostatistics
Published version: N. Kennamer... [et al.], "Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients," 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020, pp. 3115-3124, doi: [10.1109/SSCI47803.2020.9308300]
PatrocinadorHPI Research Center in Machine Learning and Data Science at UC Irvine; CNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky Surveys; FCT under Project CRISP PTDC/FIS-AST-31546/2017; Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston; Gordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa Cruz; Space Telescope Science Institute; National Aeronautics & Space Administration (NASA) HF2-51462.001 NAS5-26555; International Gemini Observatory, a program of NSF's NOIRLab; National Science Foundation (NSF); Max Planck Society; Foundation CELLEX; Alexander von Humboldt Foundation; European Commission 839090; Spanish grant within the European Funds for Regional Development (FEDER) PGC2018-095317-B-C21
The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.