The ZTF Source Classification Project. I. Methods and Infrastructure
The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5 σ for 30 s exposure) in the g , r , and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epoch...
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| Veröffentlicht in: | Astron.J 2021-06, Vol.161 (6), p.267 |
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| creator | van Roestel, Jan Duev, Dmitry A. Mahabal, Ashish A. Coughlin, Michael W. Mróz, Przemek Burdge, Kevin Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne Bellm, Eric C. Kupfer, Thomas Delacroix, Alexandre Fremling, C. Golkhou, V. Zach Hale, David Laher, Russ R. Masci, Frank J. Riddle, Reed Rosnet, Philippe Rusholme, Ben Smith, Roger Soumagnac, Maayane T. Walters, Richard Prince, Thomas A. Kulkarni, S. R. |
| description | The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5
σ
for 30 s exposure) in the
g
,
r
, and
i
filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in
g
and
r
, and fewer in
i
. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated. |
| doi_str_mv | 10.3847/1538-3881/abe853 |
| format | Article |
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σ
for 30 s exposure) in the
g
,
r
, and
i
filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in
g
and
r
, and fewer in
i
. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. 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All rights reserved.</rights><rights>Copyright IOP Publishing Jun 2021</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c477t-f28ab9f6f7b595f48c1737a59a3e0b8565a9ff862b54aec9bdd58a0373f59523</citedby><cites>FETCH-LOGICAL-c477t-f28ab9f6f7b595f48c1737a59a3e0b8565a9ff862b54aec9bdd58a0373f59523</cites><orcidid>0000-0001-8205-2506 ; 0000-0001-8018-5348 ; 0000-0002-0387-370X ; 0000-0003-2242-0244 ; 0000-0002-8532-9395 ; 0000-0002-7226-836X ; 0000-0001-5060-8733 ; 0000-0001-5390-8563 ; 0000-0002-4223-103X ; 0000-0001-7062-9726 ; 0000-0002-3168-0139 ; 0000-0001-6753-1488 ; 0000-0002-2626-2872 ; 0000-0001-7648-4142 ; 0000-0002-8262-2924 ; 0000-0001-7016-1692 ; 0000-0003-2451-5482 ; 0000-0002-6099-7565 ; 0000-0002-8850-3627 ; 0000-0002-6540-1484 ; 0000000170629726 ; 0000000288503627 ; 0000000170161692 ; 0000000324515482 ; 000000024223103X ; 0000000226262872 ; 0000000285329395 ; 000000020387370X ; 0000000150608733 ; 0000000265401484 ; 0000000167531488 ; 0000000260997565 ; 0000000282622924 ; 0000000322420244 ; 0000000231680139 ; 0000000176484142 ; 0000000180185348 ; 0000000153908563 ; 000000027226836X ; 0000000182052506</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.3847/1538-3881/abe853/pdf$$EPDF$$P50$$Giop$$H</linktopdf><link.rule.ids>230,314,780,784,885,27924,27925,38868,38890,53840,53867</link.rule.ids><linktorsrc>$$Uhttps://iopscience.iop.org/article/10.3847/1538-3881/abe853$$EView_record_in_IOP_Publishing$$FView_record_in_$$GIOP_Publishing</linktorsrc><backlink>$$Uhttps://hal.science/hal-03229363$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1865077$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>van Roestel, Jan</creatorcontrib><creatorcontrib>Duev, Dmitry A.</creatorcontrib><creatorcontrib>Mahabal, Ashish A.</creatorcontrib><creatorcontrib>Coughlin, Michael W.</creatorcontrib><creatorcontrib>Mróz, Przemek</creatorcontrib><creatorcontrib>Burdge, Kevin</creatorcontrib><creatorcontrib>Drake, Andrew</creatorcontrib><creatorcontrib>Graham, Matthew J.</creatorcontrib><creatorcontrib>Hillenbrand, Lynne</creatorcontrib><creatorcontrib>Bellm, Eric C.</creatorcontrib><creatorcontrib>Kupfer, Thomas</creatorcontrib><creatorcontrib>Delacroix, Alexandre</creatorcontrib><creatorcontrib>Fremling, C.</creatorcontrib><creatorcontrib>Golkhou, V. 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σ
for 30 s exposure) in the
g
,
r
, and
i
filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in
g
and
r
, and fewer in
i
. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.</description><subject>Astronomy</subject><subject>ASTRONOMY AND ASTROPHYSICS</subject><subject>celestial objects catalog</subject><subject>Celestial objects catalogs</subject><subject>Classification</subject><subject>Infrastructure</subject><subject>Intrinsic variable stars</subject><subject>Light curve</subject><subject>Machine learning</subject><subject>Northern sky</subject><subject>Physics</subject><subject>Surveys</subject><subject>Training</subject><subject>Variable stars</subject><issn>0004-6256</issn><issn>1538-3881</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kMFLwzAUh4MoOKd3j0VPgt2SpmnS45jODSYK9uQlpGlCM2Yzk1TwvzelMk-eHjy-34_3PgCuEZxhltM5IpilmDE0F7ViBJ-AyXF1CiYQwjwtMlKcgwvvdxAixGA-AQ9Vq5L3apW82d5JlSz3wnujjRTB2C55dXanZJglm1nyrEJrG5-Irkk2nXbCB9fL0Dt1Cc602Ht19TunoFo9Vst1un152iwX21TmlIZUZ0zUpS40rUlJdM4kopgKUgqsYM1IQUSpNSuymuRCybJuGsIExBTryGd4Cm7GWuuD4V6aoGQrbdfFCzliBYGURuhuhFqx5wdnPoT75lYYvl5s-bCDOMtKXOAvFNnbkT04-9krH_guWujiCzwjGctLliMWKThS0lnvndLHWgT54J4Povkgmo_uY-R-jBh7-Ov8F_8BmaaCVQ</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>van Roestel, Jan</creator><creator>Duev, Dmitry A.</creator><creator>Mahabal, Ashish A.</creator><creator>Coughlin, Michael W.</creator><creator>Mróz, Przemek</creator><creator>Burdge, Kevin</creator><creator>Drake, Andrew</creator><creator>Graham, Matthew J.</creator><creator>Hillenbrand, Lynne</creator><creator>Bellm, Eric C.</creator><creator>Kupfer, Thomas</creator><creator>Delacroix, Alexandre</creator><creator>Fremling, C.</creator><creator>Golkhou, V. 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Zach</au><au>Hale, David</au><au>Laher, Russ R.</au><au>Masci, Frank J.</au><au>Riddle, Reed</au><au>Rosnet, Philippe</au><au>Rusholme, Ben</au><au>Smith, Roger</au><au>Soumagnac, Maayane T.</au><au>Walters, Richard</au><au>Prince, Thomas A.</au><au>Kulkarni, S. R.</au><aucorp>Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The ZTF Source Classification Project. I. Methods and Infrastructure</atitle><jtitle>Astron.J</jtitle><stitle>AJ</stitle><addtitle>Astron. J</addtitle><date>2021-06-01</date><risdate>2021</risdate><volume>161</volume><issue>6</issue><spage>267</spage><pages>267-</pages><issn>0004-6256</issn><eissn>1538-3881</eissn><abstract>The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5
σ
for 30 s exposure) in the
g
,
r
, and
i
filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in
g
and
r
, and fewer in
i
. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.</abstract><cop>Madison</cop><pub>The American Astronomical Society</pub><doi>10.3847/1538-3881/abe853</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-8205-2506</orcidid><orcidid>https://orcid.org/0000-0001-8018-5348</orcidid><orcidid>https://orcid.org/0000-0002-0387-370X</orcidid><orcidid>https://orcid.org/0000-0003-2242-0244</orcidid><orcidid>https://orcid.org/0000-0002-8532-9395</orcidid><orcidid>https://orcid.org/0000-0002-7226-836X</orcidid><orcidid>https://orcid.org/0000-0001-5060-8733</orcidid><orcidid>https://orcid.org/0000-0001-5390-8563</orcidid><orcidid>https://orcid.org/0000-0002-4223-103X</orcidid><orcidid>https://orcid.org/0000-0001-7062-9726</orcidid><orcidid>https://orcid.org/0000-0002-3168-0139</orcidid><orcidid>https://orcid.org/0000-0001-6753-1488</orcidid><orcidid>https://orcid.org/0000-0002-2626-2872</orcidid><orcidid>https://orcid.org/0000-0001-7648-4142</orcidid><orcidid>https://orcid.org/0000-0002-8262-2924</orcidid><orcidid>https://orcid.org/0000-0001-7016-1692</orcidid><orcidid>https://orcid.org/0000-0003-2451-5482</orcidid><orcidid>https://orcid.org/0000-0002-6099-7565</orcidid><orcidid>https://orcid.org/0000-0002-8850-3627</orcidid><orcidid>https://orcid.org/0000-0002-6540-1484</orcidid><orcidid>https://orcid.org/0000000170629726</orcidid><orcidid>https://orcid.org/0000000288503627</orcidid><orcidid>https://orcid.org/0000000170161692</orcidid><orcidid>https://orcid.org/0000000324515482</orcidid><orcidid>https://orcid.org/000000024223103X</orcidid><orcidid>https://orcid.org/0000000226262872</orcidid><orcidid>https://orcid.org/0000000285329395</orcidid><orcidid>https://orcid.org/000000020387370X</orcidid><orcidid>https://orcid.org/0000000150608733</orcidid><orcidid>https://orcid.org/0000000265401484</orcidid><orcidid>https://orcid.org/0000000167531488</orcidid><orcidid>https://orcid.org/0000000260997565</orcidid><orcidid>https://orcid.org/0000000282622924</orcidid><orcidid>https://orcid.org/0000000322420244</orcidid><orcidid>https://orcid.org/0000000231680139</orcidid><orcidid>https://orcid.org/0000000176484142</orcidid><orcidid>https://orcid.org/0000000180185348</orcidid><orcidid>https://orcid.org/0000000153908563</orcidid><orcidid>https://orcid.org/000000027226836X</orcidid><orcidid>https://orcid.org/0000000182052506</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0004-6256 |
| ispartof | Astron.J, 2021-06, Vol.161 (6), p.267 |
| issn | 0004-6256 1538-3881 |
| language | eng |
| recordid | cdi_proquest_journals_2528498418 |
| source | Institute of Physics Open Access Journal Titles |
| subjects | Astronomy ASTRONOMY AND ASTROPHYSICS celestial objects catalog Celestial objects catalogs Classification Infrastructure Intrinsic variable stars Light curve Machine learning Northern sky Physics Surveys Training Variable stars |
| title | The ZTF Source Classification Project. I. Methods and Infrastructure |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T08%3A30%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_O3W&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20ZTF%20Source%20Classification%20Project.%20I.%20Methods%20and%20Infrastructure&rft.jtitle=Astron.J&rft.au=van%20Roestel,%20Jan&rft.aucorp=Lawrence%20Berkeley%20National%20Lab.%20(LBNL),%20Berkeley,%20CA%20(United%20States)&rft.date=2021-06-01&rft.volume=161&rft.issue=6&rft.spage=267&rft.pages=267-&rft.issn=0004-6256&rft.eissn=1538-3881&rft_id=info:doi/10.3847/1538-3881/abe853&rft_dat=%3Cproquest_O3W%3E2528498418%3C/proquest_O3W%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2528498418&rft_id=info:pmid/&rfr_iscdi=true |