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Characterizing Sparse Asteroid Light Curves with Gaussian Processes

Lindberg, Christina Willecke and Huppenkothen, Daniela and Jones, R. Lynne and Bolin, Bryce T. and Jurić, Mario and Golkhou, V. Zach and Bellm, Eric C. and Drake, Andrew J. and Graham, Matthew J. and Laher, Russ R. and Mahabal, Ashish A. and Masci, Frank J. and Riddle, Reed and Shin, Kyung Min (2022) Characterizing Sparse Asteroid Light Curves with Gaussian Processes. Astronomical Journal, 163 (1). Art. No. 29. ISSN 0004-6256. doi:10.3847/1538-3881/ac3079.

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In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory’s Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian processes to enable Bayesian parameter inference of asteroid time-series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light-curve amplitude, changing pulse profile, and magnitude changes due to the phase-angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast but very limited-period detection algorithms and much more comprehensive but computationally expensive shape-modeling based on ray-tracing codes.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Lindberg, Christina Willecke0000-0003-0588-7360
Huppenkothen, Daniela0000-0002-1169-7486
Jones, R. Lynne0000-0001-5916-0031
Bolin, Bryce T.0000-0002-4950-6323
Jurić, Mario0000-0003-1996-9252
Golkhou, V. Zach0000-0001-8205-2506
Bellm, Eric C.0000-0001-8018-5348
Graham, Matthew J.0000-0002-3168-0139
Laher, Russ R.0000-0003-2451-5482
Mahabal, Ashish A.0000-0003-2242-0244
Masci, Frank J.0000-0002-8532-9395
Riddle, Reed0000-0002-0387-370X
Shin, Kyung Min0000-0002-1486-3582
Additional Information:© 2021. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 April 5; revised 2021 September 23; accepted 2021 October 15; published 2021 December 21. The authors wish to thank Josef Ďurech for helpful discussion in accessing the DAMIT asteroid model database in generating the synthetic asteroid light curves used in this study as well as help with the DAMIT synthetic asteroid light curve code. Based on observations obtained with the Samuel Oschin Telescope 48 inch and the 60 inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. ZTF is supported by the National Science Foundation under grant No. AST-1440341 and a collaboration including Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories, the TANGO Consortium of Taiwan, the University of Wisconsin at Milwaukee, and Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and UW. D.H. is supported by the Women In Science Excel (WISE) program of the Netherlands Organisation for Scientific Research (NWO). D.H. acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. This work was supported by a Data Science Environments project award from the Gordon and Betty Moore Foundation (Award #2013-10-29) and the Alfred P. Sloan Foundation (Award #3835) to the University of Washington eScience Institute and by the eScience Institute. This research has made use of NASA's Astrophysics Data System Bibliographic Services. Software: numpy (Harris et al. 2020), matplotlib (Hunter 2007), george (Ambikasaran et al. 2015), emcee (Foreman-Mackey et al. 2013), scipy (Virtanen et al. 2020), pandas (pandas development team 2020), h5py (Collette 2013), corner (Foreman-Mackey 2016).
Group:Infrared Processing and Analysis Center (IPAC), Zwicky Transient Facility
Funding AgencyGrant Number
ZTF partner institutionsUNSPECIFIED
Women In Science Excel (WISE)UNSPECIFIED
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)UNSPECIFIED
Charles and Lisa Simonyi Fund for Arts and SciencesUNSPECIFIED
Washington Research FoundationUNSPECIFIED
Gordon and Betty Moore Foundation2013-10-29
Alfred P. Sloan Foundation3835
Subject Keywords:Asteroids; Gaussian Processes regression; Bayesian statistics; Time series analysis; Period search
Issue or Number:1
Classification Code:Unified Astronomy Thesaurus concepts: Asteroids (72); Gaussian Processes regression (1930); Bayesian statistics (1900); Time series analysis (1916); Period search (1955)
Record Number:CaltechAUTHORS:20211221-470304000
Persistent URL:
Official Citation:Christina Willecke Lindberg et al 2022 AJ 163 29
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112608
Deposited By: George Porter
Deposited On:21 Dec 2021 22:14
Last Modified:25 Jul 2022 23:15

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