Main belt asteroids taxonomical information from dark energy survey data

V. Carruba; J. I.B. Camargo; S. Aljbaae; F. S. Ferreira; E. Lin; V. Figueiredo-Peixoto; M. V. Banda-Huarca; A. Pieres; R. C. Boufleur; L. N. Da Costa; T. M.C. Abbott; M. Aguena; S. S. Allam; O. Alves; P. H. Bernardinelli; E. Bertin; D. Brooks; A. Carnero Rosell; J. Carretero; M. E.S. Pereira; T. M. Davis; J. De Vicente; S. Desai; P. Doel; I. Ferrero; D. Friedel; J. Frieman; J. García-Bellido; M. Gatti; G. Giannini; D. Gruen; R. A. Gruendl; K. Herner; S. R. Hinton; D. L. Hollowood; D. J. James; S. Kent; K. Kuehn; O. Lahav; J. L. Marshall; J. Mena-Fernández; R. Miquel; A. Palmese; A. A. Plazas Malagón; M. Rodríguez-Monroy; E. Sanchez; B. Santiago; M. Schubnell; M. Smith; E. Suchyta; M. E.C. Swanson; G. Tarle; A. R. Walker; N. Weaverdyck; P. Wiseman

doi:10.1093/mnras/stad3466

Main belt asteroids taxonomical information from dark energy survey data

V. Carruba, J. I.B. Camargo, S. Aljbaae, F. S. Ferreira, E. Lin, V. Figueiredo-Peixoto, M. V. Banda-Huarca, A. Pieres, R. C. Boufleur, L. N. Da Costa, T. M.C. Abbott, M. Aguena, S. S. Allam, O. Alves, P. H. Bernardinelli, E. Bertin, D. Brooks, A. Carnero Rosell, J. Carretero, M. E.S. PereiraT. M. Davis, J. De Vicente, S. Desai, P. Doel, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, M. Gatti, G. Giannini, D. Gruen, R. A. Gruendl, K. Herner, S. R. Hinton, D. L. Hollowood, D. J. James, S. Kent, K. Kuehn, O. Lahav, J. L. Marshall, J. Mena-Fernández, R. Miquel, A. Palmese, A. A. Plazas Malagón, M. Rodríguez-Monroy, E. Sanchez, B. Santiago, M. Schubnell, M. Smith, E. Suchyta, M. E.C. Swanson, G. Tarle, A. R. Walker, N. Weaverdyck, P. Wiseman

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Abstract

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i - z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.

Original language	English
Pages (from-to)	6495-6505
Number of pages	11
Journal	Monthly Notices of the Royal Astronomical Society
Volume	527
Issue number	3
DOIs	https://doi.org/10.1093/mnras/stad3466
State	Published - Jan 1 2024

Funding

We are grateful to an anonymous reviewer for insightful and constructive comments that greatly improved the quality of this work. We would like to thank the Brazilian National Research Council (CNPq, grant 304168/2021-1). JIBC acknowledges grants 305917/2019-6, 306691/2022-1 (CNPq) and 201.681/2019 (Rio de Janeiro State Research Support Foundation, FAPERJ). This research used computational resources from the Interinstitucional Laboratory of e-Astronomy (LIneA) with financial support from the National Institute of Science and Technology (INCT) of the e-Universo (Process number 465376/2014-2). FSF acknowledges the support of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. VF acknowledges a CNPq support, PIBIC/ON (process 143944/2022-3). Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by the Spanish Ministry of Science and Innovation , MICINN, under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include European Regional Development Funds, ERDF, funds from the European Union. IFAE is partially funded by the Centres de Recerca de Catalunya, CERCA, program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including European Research Council, ERC, grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).

Funders	Funder number
Brazilian Instituto Nacional de Ciência e Tecnologia
Collaborating Institutions in the Dark Energy Survey
Fermi Research Alliance, LLC	DE-AC02-07CH11359
Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University
PIBIC/ON	143944/2022-3
Rio de Janeiro State Research Support Foundation
Science and Technology Facilities Council of the United Kingdom
National Science Foundation	AST-1138766, AST-1536171
U.S. Department of Energy
Office of Science
High Energy Physics
Ohio State University
University of Chicago
National Centre for Supercomputing Applications
Seventh Framework Programme
Higher Education Funding Council for England
Centres de Recerca de Catalunya
European Commission
European Research Council	240672, 306478, 291329
Deutsche Forschungsgemeinschaft
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Ulsan National Institute of Science and Technology
Generalitat de Catalunya
Ministério da Ciência, Tecnologia e Inovação
Conselho Nacional de Desenvolvimento Científico e Tecnológico	465376/2014-2, 306691/2022-1, 304168/2021-1, 201.681/2019, 305917/2019-6
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Financiadora de Estudos e Projetos
Ministerio de Ciencia e Innovación	SEV-2016-0588, SEV-2016-0597, MDM-2015-0509, PGC2018-094773, PGC2018-102021, ESP2017-89838
Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção
Ministry of Education and Science of Ukraine
European Regional Development Fund

Keywords

catalogues
celestial mechanics
minor planets, asteroids: general

Access to Document

10.1093/mnras/stad3466

Cite this

Carruba, V., Camargo, J. I. B., Aljbaae, S., Ferreira, F. S., Lin, E., Figueiredo-Peixoto, V., Banda-Huarca, M. V., Pieres, A., Boufleur, R. C., Da Costa, L. N., Abbott, T. M. C., Aguena, M., Allam, S. S., Alves, O., Bernardinelli, P. H., Bertin, E., Brooks, D., Carnero Rosell, A., Carretero, J., ... Wiseman, P. (2024). Main belt asteroids taxonomical information from dark energy survey data. Monthly Notices of the Royal Astronomical Society, 527(3), 6495-6505. https://doi.org/10.1093/mnras/stad3466

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title = "Main belt asteroids taxonomical information from dark energy survey data",

abstract = "While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i - z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.",

keywords = "catalogues, celestial mechanics, minor planets, asteroids: general",

author = "V. Carruba and Camargo, {J. I.B.} and S. Aljbaae and Ferreira, {F. S.} and E. Lin and V. Figueiredo-Peixoto and Banda-Huarca, {M. V.} and A. Pieres and Boufleur, {R. C.} and {Da Costa}, {L. N.} and Abbott, {T. M.C.} and M. Aguena and Allam, {S. S.} and O. Alves and Bernardinelli, {P. H.} and E. Bertin and D. Brooks and {Carnero Rosell}, A. and J. Carretero and Pereira, {M. E.S.} and Davis, {T. M.} and {De Vicente}, J. and S. Desai and P. Doel and I. Ferrero and D. Friedel and J. Frieman and J. Garc{\'i}a-Bellido and M. Gatti and G. Giannini and D. Gruen and Gruendl, {R. A.} and K. Herner and Hinton, {S. R.} and Hollowood, {D. L.} and James, {D. J.} and S. Kent and K. Kuehn and O. Lahav and Marshall, {J. L.} and J. Mena-Fern{\'a}ndez and R. Miquel and A. Palmese and {Plazas Malag{\'o}n}, {A. A.} and M. Rodr{\'i}guez-Monroy and E. Sanchez and B. Santiago and M. Schubnell and M. Smith and E. Suchyta and Swanson, {M. E.C.} and G. Tarle and Walker, {A. R.} and N. Weaverdyck and P. Wiseman",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.",

year = "2024",

month = jan,

day = "1",

doi = "10.1093/mnras/stad3466",

language = "English",

volume = "527",

pages = "6495--6505",

journal = "Monthly Notices of the Royal Astronomical Society",

issn = "0035-8711",

publisher = "Oxford University Press",

number = "3",

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Carruba, V, Camargo, JIB, Aljbaae, S, Ferreira, FS, Lin, E, Figueiredo-Peixoto, V, Banda-Huarca, MV, Pieres, A, Boufleur, RC, Da Costa, LN, Abbott, TMC, Aguena, M, Allam, SS, Alves, O, Bernardinelli, PH, Bertin, E, Brooks, D, Carnero Rosell, A, Carretero, J, Pereira, MES, Davis, TM, De Vicente, J, Desai, S, Doel, P, Ferrero, I, Friedel, D, Frieman, J, García-Bellido, J, Gatti, M, Giannini, G, Gruen, D, Gruendl, RA, Herner, K, Hinton, SR, Hollowood, DL, James, DJ, Kent, S, Kuehn, K, Lahav, O, Marshall, JL, Mena-Fernández, J, Miquel, R, Palmese, A, Plazas Malagón, AA, Rodríguez-Monroy, M, Sanchez, E, Santiago, B, Schubnell, M, Smith, M, Suchyta, E, Swanson, MEC, Tarle, G, Walker, AR, Weaverdyck, N & Wiseman, P 2024, 'Main belt asteroids taxonomical information from dark energy survey data', Monthly Notices of the Royal Astronomical Society, vol. 527, no. 3, pp. 6495-6505. https://doi.org/10.1093/mnras/stad3466

TY - JOUR

T1 - Main belt asteroids taxonomical information from dark energy survey data

AU - Carruba, V.

AU - Camargo, J. I.B.

AU - Aljbaae, S.

AU - Ferreira, F. S.

AU - Lin, E.

AU - Figueiredo-Peixoto, V.

AU - Banda-Huarca, M. V.

AU - Pieres, A.

AU - Boufleur, R. C.

AU - Da Costa, L. N.

AU - Abbott, T. M.C.

AU - Aguena, M.

AU - Allam, S. S.

AU - Alves, O.

AU - Bernardinelli, P. H.

AU - Bertin, E.

AU - Brooks, D.

AU - Carnero Rosell, A.

AU - Carretero, J.

AU - Pereira, M. E.S.

AU - Davis, T. M.

AU - De Vicente, J.

AU - Desai, S.

AU - Doel, P.

AU - Ferrero, I.

AU - Friedel, D.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gatti, M.

AU - Giannini, G.

AU - Gruen, D.

AU - Gruendl, R. A.

AU - Herner, K.

AU - Hinton, S. R.

AU - Hollowood, D. L.

AU - James, D. J.

AU - Kent, S.

AU - Kuehn, K.

AU - Lahav, O.

AU - Marshall, J. L.

AU - Mena-Fernández, J.

AU - Miquel, R.

AU - Palmese, A.

AU - Plazas Malagón, A. A.

AU - Rodríguez-Monroy, M.

AU - Sanchez, E.

AU - Santiago, B.

AU - Schubnell, M.

AU - Smith, M.

AU - Suchyta, E.

AU - Swanson, M. E.C.

AU - Tarle, G.

AU - Walker, A. R.

AU - Weaverdyck, N.

AU - Wiseman, P.

PY - 2024/1/1

Y1 - 2024/1/1

N2 - While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i - z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.

AB - While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i - z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.

KW - catalogues

KW - celestial mechanics

KW - minor planets, asteroids: general

UR - http://www.scopus.com/inward/record.url?scp=85180005444&partnerID=8YFLogxK

U2 - 10.1093/mnras/stad3466

DO - 10.1093/mnras/stad3466

M3 - Article

AN - SCOPUS:85180005444

SN - 0035-8711

VL - 527

SP - 6495

EP - 6505

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 3

ER -

Main belt asteroids taxonomical information from dark energy survey data

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