Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Department of Computer Science University of California Irvine（加利福尼亚大学尔湾分校计算机科学系）；Université Clermont Auvergne CNRS/IN2P3, LPC Clermont Ferrand（克莱蒙费朗大学（Université Clermont Auvergne）、法国国家科学研究中心/法国国家核物理与粒子物理研究所（CNRS/IN2P3）、克莱蒙费朗物理实验室（LPC Clermont Ferrand））；CENTRA-Centro de Astrofísica e Gravitação Universidade de Lisboa（里斯本大学（Universidade de Lisboa）中心天体物理与引力中心（CENTRA-Centro de Astrofísica e Gravitação））；Shanghai Astronomical Observatory Chinese Academy of Sciences（中国科学院上海天文台）；Berkeley Center for Cosmological Physics University of California Berkeley（加利福尼亚大学伯克利分校宇宙物理伯克利中心）；Department of Computer Science University of Houston（休斯顿大学计算机科学系）；Department of Physics and Astronomy The Johns Hopkins University（约翰斯·霍普金斯大学物理与天文系）；Department of Informatics University of California Irvine（加利福尼亚大学尔湾分校信息学系）；NSF’s NOIRLab（美国国家科学基金会国家光学红外天文学研究实验室（NOIRLab））；Gemini Observatory（双子座天文台）
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander Ihler, Kara Ponder, Ricardo Vilalta, Anais Moller, David O. Jones, … 展开作者
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan, Rafael S. de Souza, Alexander Ihler, Kara Ponder, Ricardo Vilalta, Anais Moller, David O. Jones, Mi Dai, Alberto Krone-Martins, Bruno Quint, Sreevarsha Sreejith, Alex I. Malz, Lluis Galbany
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Accepted to the 2020 IEEE Symposium Series on Computational Intelligence
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 follow-up (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.
Machine Learning Assisted NEO Discovery and Polarimetric Characterisation with Astronomical Surveys
University of Groningen（格罗宁根大学）；Stellenbosch University（斯泰伦博斯大学）；ESA Near-Earth Object Coordination Centre（欧洲空间局近地天体协调中心）；Lunar and Planetary Exploration, LPE/TUM（月球与行星探索，LPE/慕尼黑工业大学）；Space Exploration Institute (SPACE-X)（太空探索研究所（SPACE-X））；Observatoire Astronomique de Strasbourg（斯特拉斯堡天文台）；Delft University of Technology（代尔夫特理工大学）；Universidad de Alicante（阿利坎特大学）；Universitat de Barcelona（巴塞罗那大学）；Armagh Observatory and Planetarium（阿尔马赫天文台和天文馆）；Division of Space Technology, Luleå University of Technology（鲁勒理工学院空间技术系）；Laboratory of Astrophysics, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院天体物理学实验室）
Archives of Photographic PLates for Astronomical USE (APPLAUSE) Digitisation of astronomical plates and their integration into the International Virtual Observatory
SNGuess: A method for the selection of young extragalactic transients
Humboldt-Universität zu Berlin（柏林洪堡大学）；Stockholm University（斯德哥尔摩大学）；California Institute of Technology（加州理工学院）；Deutsches Elektronen-Synchrotron（德国电子同步加速器研究所）
Classification of compact radio sources in the Galactic plane with supervised machine learning
INAF - Osservatorio Astrofisico di Catania（意大利国家天体物理研究所卡塔尼亚天体观测台）；Department of Physics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校物理系）；Western Sydney University（西悉尼大学）；CSIRO Space & Astronomy（澳大利亚联邦科学与工业研究组织空间与天文分部）；Thüringer Landessternwarte Tautenburg (TLS)（图林根州陶滕堡天文台）
