Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics
ABSTRACT Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science...
Gespeichert in:
| Veröffentlicht in: | Monthly notices of the Royal Astronomical Society 2024-09, Vol.533 (2), p.2113-2132 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2132 |
|---|---|
| container_issue | 2 |
| container_start_page | 2113 |
| container_title | Monthly notices of the Royal Astronomical Society |
| container_volume | 533 |
| creator | Killestein, T L Kelsey, L Wickens, E Nuttall, L Lyman, J Krawczyk, C Ackley, K Dyer, M J Jiménez-Ibarra, F Ulaczyk, K O’Neill, D Kumar, A Steeghs, D Galloway, D K Dhillon, V S O’Brien, P Ramsay, G Noysena, K Kotak, R Breton, R P Pallé, E Pollacco, D Awiphan, S Belkin, S Chote, P Clark, P Coppejans, D Duffy, C Eyles-Ferris, R Godson, B Gompertz, B Graur, O Irawati, P Jarvis, D Julakanti, Y Kennedy, M R Kuncarayakti, H Levan, A Littlefair, S Magee, M Mandhai, S Mata Sánchez, D Mattila, S McCormac, J Mullaney, J Munday, J Patel, M Pursiainen, M Rana, J Sawangwit, U Stanway, E Starling, R Warwick, B Wiersema, K |
| description | ABSTRACT
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in 2023 July and received over 600 000 classifications from approximately 2000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a ‘gold-standard’ training set of 17 682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development. |
| doi_str_mv | 10.1093/mnras/stae1817 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3103064846</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/mnras/stae1817</oup_id><sourcerecordid>3103064846</sourcerecordid><originalsourceid>FETCH-LOGICAL-c226t-bbf6ac2026cf9c52f009d946a7b81112a16dd8c12fca627c3406b7ff9874e6693</originalsourceid><addsrcrecordid>eNqFkD1PwzAQhi0EEqWwMltiYkjrj_SSsKGKFkSlDpQNKXKcs-rSxMF2kcqvJ6UwM93p9Lx3p4eQa85GnBVy3LRehXGICnnOsxMy4BImiSgATsmAMTlJ8ozzc3IRwoYxlkoBA_L2bLeudZ-KviC-ow93NK6RzperJe2826CO1DhPPaptEm2DVNtov7ClQVtsNVLb0sM8qV2j-l6F6F233gerwyU5M2ob8Oq3Dsnr7GE1fUwWy_nT9H6RaCEgJlVlQGnBBGhT6IkwjBV1kYLKqpxzLhSHus41F0YrEJmWKYMqM6bIsxQBCjkkN8e9_ccfOwyx3Lidb_uTpeRMMkjzFHpqdKS0dyF4NGXnbaP8vuSsPBgsfwyWfwb7wO0x4Hbdf-w3gCF0xQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3103064846</pqid></control><display><type>article</type><title>Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics</title><source>Open Access: Oxford University Press Open Journals</source><source>DOAJ Directory of Open Access Journals</source><creator>Killestein, T L ; Kelsey, L ; Wickens, E ; Nuttall, L ; Lyman, J ; Krawczyk, C ; Ackley, K ; Dyer, M J ; Jiménez-Ibarra, F ; Ulaczyk, K ; O’Neill, D ; Kumar, A ; Steeghs, D ; Galloway, D K ; Dhillon, V S ; O’Brien, P ; Ramsay, G ; Noysena, K ; Kotak, R ; Breton, R P ; Pallé, E ; Pollacco, D ; Awiphan, S ; Belkin, S ; Chote, P ; Clark, P ; Coppejans, D ; Duffy, C ; Eyles-Ferris, R ; Godson, B ; Gompertz, B ; Graur, O ; Irawati, P ; Jarvis, D ; Julakanti, Y ; Kennedy, M R ; Kuncarayakti, H ; Levan, A ; Littlefair, S ; Magee, M ; Mandhai, S ; Mata Sánchez, D ; Mattila, S ; McCormac, J ; Mullaney, J ; Munday, J ; Patel, M ; Pursiainen, M ; Rana, J ; Sawangwit, U ; Stanway, E ; Starling, R ; Warwick, B ; Wiersema, K</creator><creatorcontrib>Killestein, T L ; Kelsey, L ; Wickens, E ; Nuttall, L ; Lyman, J ; Krawczyk, C ; Ackley, K ; Dyer, M J ; Jiménez-Ibarra, F ; Ulaczyk, K ; O’Neill, D ; Kumar, A ; Steeghs, D ; Galloway, D K ; Dhillon, V S ; O’Brien, P ; Ramsay, G ; Noysena, K ; Kotak, R ; Breton, R P ; Pallé, E ; Pollacco, D ; Awiphan, S ; Belkin, S ; Chote, P ; Clark, P ; Coppejans, D ; Duffy, C ; Eyles-Ferris, R ; Godson, B ; Gompertz, B ; Graur, O ; Irawati, P ; Jarvis, D ; Julakanti, Y ; Kennedy, M R ; Kuncarayakti, H ; Levan, A ; Littlefair, S ; Magee, M ; Mandhai, S ; Mata Sánchez, D ; Mattila, S ; McCormac, J ; Mullaney, J ; Munday, J ; Patel, M ; Pursiainen, M ; Rana, J ; Sawangwit, U ; Stanway, E ; Starling, R ; Warwick, B ; Wiersema, K</creatorcontrib><description>ABSTRACT
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in 2023 July and received over 600 000 classifications from approximately 2000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a ‘gold-standard’ training set of 17 682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.</description><identifier>ISSN: 0035-8711</identifier><identifier>EISSN: 1365-2966</identifier><identifier>DOI: 10.1093/mnras/stae1817</identifier><language>eng</language><publisher>London: Oxford University Press</publisher><subject>Algorithms ; Astrophysics ; Classification ; Kilonovae ; Machine learning ; Real time ; Telescopes ; Time domain analysis</subject><ispartof>Monthly notices of the Royal Astronomical Society, 2024-09, Vol.533 (2), p.2113-2132</ispartof><rights>2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. 2024</rights><rights>2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c226t-bbf6ac2026cf9c52f009d946a7b81112a16dd8c12fca627c3406b7ff9874e6693</cites><orcidid>0000-0003-3251-3583 ; 0000-0002-0629-8931 ; 0000-0001-6894-6044 ; 0000-0002-0440-9597 ; 0000-0001-9772-1071 ; 0000-0001-9233-2341 ; 0000-0002-4391-6137 ; 0000-0002-3464-0642 ; 0000-0001-5803-2038 ; 0000-0003-0313-0487 ; 0000-0002-5826-0548 ; 0000-0003-3665-5482 ; 0000-0001-8522-4983 ; 0000-0001-8722-9710 ; 0000-0002-9798-029X ; 0000-0003-0771-4746 ; 0000-0002-8775-2365 ; 0000-0002-8770-809X ; 0000-0001-6662-0200 ; 0000-0003-4236-9642</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,1598,27901,27902</link.rule.ids></links><search><creatorcontrib>Killestein, T L</creatorcontrib><creatorcontrib>Kelsey, L</creatorcontrib><creatorcontrib>Wickens, E</creatorcontrib><creatorcontrib>Nuttall, L</creatorcontrib><creatorcontrib>Lyman, J</creatorcontrib><creatorcontrib>Krawczyk, C</creatorcontrib><creatorcontrib>Ackley, K</creatorcontrib><creatorcontrib>Dyer, M J</creatorcontrib><creatorcontrib>Jiménez-Ibarra, F</creatorcontrib><creatorcontrib>Ulaczyk, K</creatorcontrib><creatorcontrib>O’Neill, D</creatorcontrib><creatorcontrib>Kumar, A</creatorcontrib><creatorcontrib>Steeghs, D</creatorcontrib><creatorcontrib>Galloway, D K</creatorcontrib><creatorcontrib>Dhillon, V S</creatorcontrib><creatorcontrib>O’Brien, P</creatorcontrib><creatorcontrib>Ramsay, G</creatorcontrib><creatorcontrib>Noysena, K</creatorcontrib><creatorcontrib>Kotak, R</creatorcontrib><creatorcontrib>Breton, R P</creatorcontrib><creatorcontrib>Pallé, E</creatorcontrib><creatorcontrib>Pollacco, D</creatorcontrib><creatorcontrib>Awiphan, S</creatorcontrib><creatorcontrib>Belkin, S</creatorcontrib><creatorcontrib>Chote, P</creatorcontrib><creatorcontrib>Clark, P</creatorcontrib><creatorcontrib>Coppejans, D</creatorcontrib><creatorcontrib>Duffy, C</creatorcontrib><creatorcontrib>Eyles-Ferris, R</creatorcontrib><creatorcontrib>Godson, B</creatorcontrib><creatorcontrib>Gompertz, B</creatorcontrib><creatorcontrib>Graur, O</creatorcontrib><creatorcontrib>Irawati, P</creatorcontrib><creatorcontrib>Jarvis, D</creatorcontrib><creatorcontrib>Julakanti, Y</creatorcontrib><creatorcontrib>Kennedy, M R</creatorcontrib><creatorcontrib>Kuncarayakti, H</creatorcontrib><creatorcontrib>Levan, A</creatorcontrib><creatorcontrib>Littlefair, S</creatorcontrib><creatorcontrib>Magee, M</creatorcontrib><creatorcontrib>Mandhai, S</creatorcontrib><creatorcontrib>Mata Sánchez, D</creatorcontrib><creatorcontrib>Mattila, S</creatorcontrib><creatorcontrib>McCormac, J</creatorcontrib><creatorcontrib>Mullaney, J</creatorcontrib><creatorcontrib>Munday, J</creatorcontrib><creatorcontrib>Patel, M</creatorcontrib><creatorcontrib>Pursiainen, M</creatorcontrib><creatorcontrib>Rana, J</creatorcontrib><creatorcontrib>Sawangwit, U</creatorcontrib><creatorcontrib>Stanway, E</creatorcontrib><creatorcontrib>Starling, R</creatorcontrib><creatorcontrib>Warwick, B</creatorcontrib><creatorcontrib>Wiersema, K</creatorcontrib><title>Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics</title><title>Monthly notices of the Royal Astronomical Society</title><description>ABSTRACT
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in 2023 July and received over 600 000 classifications from approximately 2000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a ‘gold-standard’ training set of 17 682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.</description><subject>Algorithms</subject><subject>Astrophysics</subject><subject>Classification</subject><subject>Kilonovae</subject><subject>Machine learning</subject><subject>Real time</subject><subject>Telescopes</subject><subject>Time domain analysis</subject><issn>0035-8711</issn><issn>1365-2966</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqFkD1PwzAQhi0EEqWwMltiYkjrj_SSsKGKFkSlDpQNKXKcs-rSxMF2kcqvJ6UwM93p9Lx3p4eQa85GnBVy3LRehXGICnnOsxMy4BImiSgATsmAMTlJ8ozzc3IRwoYxlkoBA_L2bLeudZ-KviC-ow93NK6RzperJe2826CO1DhPPaptEm2DVNtov7ClQVtsNVLb0sM8qV2j-l6F6F233gerwyU5M2ob8Oq3Dsnr7GE1fUwWy_nT9H6RaCEgJlVlQGnBBGhT6IkwjBV1kYLKqpxzLhSHus41F0YrEJmWKYMqM6bIsxQBCjkkN8e9_ccfOwyx3Lidb_uTpeRMMkjzFHpqdKS0dyF4NGXnbaP8vuSsPBgsfwyWfwb7wO0x4Hbdf-w3gCF0xQ</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Killestein, T L</creator><creator>Kelsey, L</creator><creator>Wickens, E</creator><creator>Nuttall, L</creator><creator>Lyman, J</creator><creator>Krawczyk, C</creator><creator>Ackley, K</creator><creator>Dyer, M J</creator><creator>Jiménez-Ibarra, F</creator><creator>Ulaczyk, K</creator><creator>O’Neill, D</creator><creator>Kumar, A</creator><creator>Steeghs, D</creator><creator>Galloway, D K</creator><creator>Dhillon, V S</creator><creator>O’Brien, P</creator><creator>Ramsay, G</creator><creator>Noysena, K</creator><creator>Kotak, R</creator><creator>Breton, R P</creator><creator>Pallé, E</creator><creator>Pollacco, D</creator><creator>Awiphan, S</creator><creator>Belkin, S</creator><creator>Chote, P</creator><creator>Clark, P</creator><creator>Coppejans, D</creator><creator>Duffy, C</creator><creator>Eyles-Ferris, R</creator><creator>Godson, B</creator><creator>Gompertz, B</creator><creator>Graur, O</creator><creator>Irawati, P</creator><creator>Jarvis, D</creator><creator>Julakanti, Y</creator><creator>Kennedy, M R</creator><creator>Kuncarayakti, H</creator><creator>Levan, A</creator><creator>Littlefair, S</creator><creator>Magee, M</creator><creator>Mandhai, S</creator><creator>Mata Sánchez, D</creator><creator>Mattila, S</creator><creator>McCormac, J</creator><creator>Mullaney, J</creator><creator>Munday, J</creator><creator>Patel, M</creator><creator>Pursiainen, M</creator><creator>Rana, J</creator><creator>Sawangwit, U</creator><creator>Stanway, E</creator><creator>Starling, R</creator><creator>Warwick, B</creator><creator>Wiersema, K</creator><general>Oxford University Press</general><scope>TOX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-3251-3583</orcidid><orcidid>https://orcid.org/0000-0002-0629-8931</orcidid><orcidid>https://orcid.org/0000-0001-6894-6044</orcidid><orcidid>https://orcid.org/0000-0002-0440-9597</orcidid><orcidid>https://orcid.org/0000-0001-9772-1071</orcidid><orcidid>https://orcid.org/0000-0001-9233-2341</orcidid><orcidid>https://orcid.org/0000-0002-4391-6137</orcidid><orcidid>https://orcid.org/0000-0002-3464-0642</orcidid><orcidid>https://orcid.org/0000-0001-5803-2038</orcidid><orcidid>https://orcid.org/0000-0003-0313-0487</orcidid><orcidid>https://orcid.org/0000-0002-5826-0548</orcidid><orcidid>https://orcid.org/0000-0003-3665-5482</orcidid><orcidid>https://orcid.org/0000-0001-8522-4983</orcidid><orcidid>https://orcid.org/0000-0001-8722-9710</orcidid><orcidid>https://orcid.org/0000-0002-9798-029X</orcidid><orcidid>https://orcid.org/0000-0003-0771-4746</orcidid><orcidid>https://orcid.org/0000-0002-8775-2365</orcidid><orcidid>https://orcid.org/0000-0002-8770-809X</orcidid><orcidid>https://orcid.org/0000-0001-6662-0200</orcidid><orcidid>https://orcid.org/0000-0003-4236-9642</orcidid></search><sort><creationdate>20240901</creationdate><title>Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics</title><author>Killestein, T L ; Kelsey, L ; Wickens, E ; Nuttall, L ; Lyman, J ; Krawczyk, C ; Ackley, K ; Dyer, M J ; Jiménez-Ibarra, F ; Ulaczyk, K ; O’Neill, D ; Kumar, A ; Steeghs, D ; Galloway, D K ; Dhillon, V S ; O’Brien, P ; Ramsay, G ; Noysena, K ; Kotak, R ; Breton, R P ; Pallé, E ; Pollacco, D ; Awiphan, S ; Belkin, S ; Chote, P ; Clark, P ; Coppejans, D ; Duffy, C ; Eyles-Ferris, R ; Godson, B ; Gompertz, B ; Graur, O ; Irawati, P ; Jarvis, D ; Julakanti, Y ; Kennedy, M R ; Kuncarayakti, H ; Levan, A ; Littlefair, S ; Magee, M ; Mandhai, S ; Mata Sánchez, D ; Mattila, S ; McCormac, J ; Mullaney, J ; Munday, J ; Patel, M ; Pursiainen, M ; Rana, J ; Sawangwit, U ; Stanway, E ; Starling, R ; Warwick, B ; Wiersema, K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c226t-bbf6ac2026cf9c52f009d946a7b81112a16dd8c12fca627c3406b7ff9874e6693</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Astrophysics</topic><topic>Classification</topic><topic>Kilonovae</topic><topic>Machine learning</topic><topic>Real time</topic><topic>Telescopes</topic><topic>Time domain analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Killestein, T L</creatorcontrib><creatorcontrib>Kelsey, L</creatorcontrib><creatorcontrib>Wickens, E</creatorcontrib><creatorcontrib>Nuttall, L</creatorcontrib><creatorcontrib>Lyman, J</creatorcontrib><creatorcontrib>Krawczyk, C</creatorcontrib><creatorcontrib>Ackley, K</creatorcontrib><creatorcontrib>Dyer, M J</creatorcontrib><creatorcontrib>Jiménez-Ibarra, F</creatorcontrib><creatorcontrib>Ulaczyk, K</creatorcontrib><creatorcontrib>O’Neill, D</creatorcontrib><creatorcontrib>Kumar, A</creatorcontrib><creatorcontrib>Steeghs, D</creatorcontrib><creatorcontrib>Galloway, D K</creatorcontrib><creatorcontrib>Dhillon, V S</creatorcontrib><creatorcontrib>O’Brien, P</creatorcontrib><creatorcontrib>Ramsay, G</creatorcontrib><creatorcontrib>Noysena, K</creatorcontrib><creatorcontrib>Kotak, R</creatorcontrib><creatorcontrib>Breton, R P</creatorcontrib><creatorcontrib>Pallé, E</creatorcontrib><creatorcontrib>Pollacco, D</creatorcontrib><creatorcontrib>Awiphan, S</creatorcontrib><creatorcontrib>Belkin, S</creatorcontrib><creatorcontrib>Chote, P</creatorcontrib><creatorcontrib>Clark, P</creatorcontrib><creatorcontrib>Coppejans, D</creatorcontrib><creatorcontrib>Duffy, C</creatorcontrib><creatorcontrib>Eyles-Ferris, R</creatorcontrib><creatorcontrib>Godson, B</creatorcontrib><creatorcontrib>Gompertz, B</creatorcontrib><creatorcontrib>Graur, O</creatorcontrib><creatorcontrib>Irawati, P</creatorcontrib><creatorcontrib>Jarvis, D</creatorcontrib><creatorcontrib>Julakanti, Y</creatorcontrib><creatorcontrib>Kennedy, M R</creatorcontrib><creatorcontrib>Kuncarayakti, H</creatorcontrib><creatorcontrib>Levan, A</creatorcontrib><creatorcontrib>Littlefair, S</creatorcontrib><creatorcontrib>Magee, M</creatorcontrib><creatorcontrib>Mandhai, S</creatorcontrib><creatorcontrib>Mata Sánchez, D</creatorcontrib><creatorcontrib>Mattila, S</creatorcontrib><creatorcontrib>McCormac, J</creatorcontrib><creatorcontrib>Mullaney, J</creatorcontrib><creatorcontrib>Munday, J</creatorcontrib><creatorcontrib>Patel, M</creatorcontrib><creatorcontrib>Pursiainen, M</creatorcontrib><creatorcontrib>Rana, J</creatorcontrib><creatorcontrib>Sawangwit, U</creatorcontrib><creatorcontrib>Stanway, E</creatorcontrib><creatorcontrib>Starling, R</creatorcontrib><creatorcontrib>Warwick, B</creatorcontrib><creatorcontrib>Wiersema, K</creatorcontrib><collection>Open Access: Oxford University Press Open Journals</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Monthly notices of the Royal Astronomical Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Killestein, T L</au><au>Kelsey, L</au><au>Wickens, E</au><au>Nuttall, L</au><au>Lyman, J</au><au>Krawczyk, C</au><au>Ackley, K</au><au>Dyer, M J</au><au>Jiménez-Ibarra, F</au><au>Ulaczyk, K</au><au>O’Neill, D</au><au>Kumar, A</au><au>Steeghs, D</au><au>Galloway, D K</au><au>Dhillon, V S</au><au>O’Brien, P</au><au>Ramsay, G</au><au>Noysena, K</au><au>Kotak, R</au><au>Breton, R P</au><au>Pallé, E</au><au>Pollacco, D</au><au>Awiphan, S</au><au>Belkin, S</au><au>Chote, P</au><au>Clark, P</au><au>Coppejans, D</au><au>Duffy, C</au><au>Eyles-Ferris, R</au><au>Godson, B</au><au>Gompertz, B</au><au>Graur, O</au><au>Irawati, P</au><au>Jarvis, D</au><au>Julakanti, Y</au><au>Kennedy, M R</au><au>Kuncarayakti, H</au><au>Levan, A</au><au>Littlefair, S</au><au>Magee, M</au><au>Mandhai, S</au><au>Mata Sánchez, D</au><au>Mattila, S</au><au>McCormac, J</au><au>Mullaney, J</au><au>Munday, J</au><au>Patel, M</au><au>Pursiainen, M</au><au>Rana, J</au><au>Sawangwit, U</au><au>Stanway, E</au><au>Starling, R</au><au>Warwick, B</au><au>Wiersema, K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics</atitle><jtitle>Monthly notices of the Royal Astronomical Society</jtitle><date>2024-09-01</date><risdate>2024</risdate><volume>533</volume><issue>2</issue><spage>2113</spage><epage>2132</epage><pages>2113-2132</pages><issn>0035-8711</issn><eissn>1365-2966</eissn><abstract>ABSTRACT
Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratized, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery – with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the Kilonova Seekers citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. Kilonova Seekers launched in 2023 July and received over 600 000 classifications from approximately 2000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a ‘gold-standard’ training set of 17 682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development.</abstract><cop>London</cop><pub>Oxford University Press</pub><doi>10.1093/mnras/stae1817</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-3251-3583</orcidid><orcidid>https://orcid.org/0000-0002-0629-8931</orcidid><orcidid>https://orcid.org/0000-0001-6894-6044</orcidid><orcidid>https://orcid.org/0000-0002-0440-9597</orcidid><orcidid>https://orcid.org/0000-0001-9772-1071</orcidid><orcidid>https://orcid.org/0000-0001-9233-2341</orcidid><orcidid>https://orcid.org/0000-0002-4391-6137</orcidid><orcidid>https://orcid.org/0000-0002-3464-0642</orcidid><orcidid>https://orcid.org/0000-0001-5803-2038</orcidid><orcidid>https://orcid.org/0000-0003-0313-0487</orcidid><orcidid>https://orcid.org/0000-0002-5826-0548</orcidid><orcidid>https://orcid.org/0000-0003-3665-5482</orcidid><orcidid>https://orcid.org/0000-0001-8522-4983</orcidid><orcidid>https://orcid.org/0000-0001-8722-9710</orcidid><orcidid>https://orcid.org/0000-0002-9798-029X</orcidid><orcidid>https://orcid.org/0000-0003-0771-4746</orcidid><orcidid>https://orcid.org/0000-0002-8775-2365</orcidid><orcidid>https://orcid.org/0000-0002-8770-809X</orcidid><orcidid>https://orcid.org/0000-0001-6662-0200</orcidid><orcidid>https://orcid.org/0000-0003-4236-9642</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0035-8711 |
| ispartof | Monthly notices of the Royal Astronomical Society, 2024-09, Vol.533 (2), p.2113-2132 |
| issn | 0035-8711 1365-2966 |
| language | eng |
| recordid | cdi_proquest_journals_3103064846 |
| source | Open Access: Oxford University Press Open Journals; DOAJ Directory of Open Access Journals |
| subjects | Algorithms Astrophysics Classification Kilonovae Machine learning Real time Telescopes Time domain analysis |
| title | Kilonova Seekers: the GOTO project for real-time citizen science in time-domain astrophysics |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T08%3A31%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Kilonova%20Seekers:%20the%20GOTO%20project%20for%20real-time%20citizen%20science%20in%20time-domain%20astrophysics&rft.jtitle=Monthly%20notices%20of%20the%20Royal%20Astronomical%20Society&rft.au=Killestein,%20T%20L&rft.date=2024-09-01&rft.volume=533&rft.issue=2&rft.spage=2113&rft.epage=2132&rft.pages=2113-2132&rft.issn=0035-8711&rft.eissn=1365-2966&rft_id=info:doi/10.1093/mnras/stae1817&rft_dat=%3Cproquest_cross%3E3103064846%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3103064846&rft_id=info:pmid/&rft_oup_id=10.1093/mnras/stae1817&rfr_iscdi=true |