Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements
Active Regions (ARs) are regions of strong magnetic flux in the solar atmosphere. Understanding the global evolution of ARs is critical for solar magnetism as well as for accurate space-weather forecasting. We present the first far-side AR data set based on EUV observation and helioseismic measureme...
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description | Active Regions (ARs) are regions of strong magnetic flux in the solar atmosphere. Understanding the global evolution of ARs is critical for solar magnetism as well as for accurate space-weather forecasting. We present the first far-side AR data set based on EUV observation and helioseismic measurements. For the EUV observations, we use synchronic maps at 304 Å comprised of observations from the Solar Dynamics Observatory/Atmospheric Imaging Assembly and the Solar TErrestrial RElations Observatory/Extreme UltraViolet Imager, in heliocentric orbit with direct vantages into the Sun’s far hemisphere. We used the brightening of the solar transition region in EUV/304 Å maps as a proxy for the magnetic regions. For the far-side helioseismic measurements, we used seismic phase-shift maps of the Sun’s far hemisphere, computed from helioseismic Dopplergrams observed by NSO/Global Oscillations Network Group (GONG). In this study, we present the first global EUV AR data set of the whole Sun, providing several basic parameters, such as location, area, tilt angle, EUV brightness, and latitudinal/longitudinal extents of the identified ARs. We also present a similar data set for the far-side GONG ARs where the helioseismic phase shift parameters are included. Helioseismic far-side GONG ARs are examined, and their success at predicting strong ARs is assessed. We discuss the temporal and spatial evolution for the EUV AR signatures and far-side GONG AR signatures during the ascending and maximum phases of Solar Cycle 24 (2010 May–2016 May). We examine the correlation between the helioseismic signatures and the respective EUV source distributions in the Sun’s far hemisphere. We present the first far-side AR butterfly diagram based on helioseismic measurements. |
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Understanding the global evolution of ARs is critical for solar magnetism as well as for accurate space-weather forecasting. We present the first far-side AR data set based on EUV observation and helioseismic measurements. For the EUV observations, we use synchronic maps at 304 Å comprised of observations from the Solar Dynamics Observatory/Atmospheric Imaging Assembly and the Solar TErrestrial RElations Observatory/Extreme UltraViolet Imager, in heliocentric orbit with direct vantages into the Sun’s far hemisphere. We used the brightening of the solar transition region in EUV/304 Å maps as a proxy for the magnetic regions. For the far-side helioseismic measurements, we used seismic phase-shift maps of the Sun’s far hemisphere, computed from helioseismic Dopplergrams observed by NSO/Global Oscillations Network Group (GONG). In this study, we present the first global EUV AR data set of the whole Sun, providing several basic parameters, such as location, area, tilt angle, EUV brightness, and latitudinal/longitudinal extents of the identified ARs. We also present a similar data set for the far-side GONG ARs where the helioseismic phase shift parameters are included. Helioseismic far-side GONG ARs are examined, and their success at predicting strong ARs is assessed. We discuss the temporal and spatial evolution for the EUV AR signatures and far-side GONG AR signatures during the ascending and maximum phases of Solar Cycle 24 (2010 May–2016 May). We examine the correlation between the helioseismic signatures and the respective EUV source distributions in the Sun’s far hemisphere. We present the first far-side AR butterfly diagram based on helioseismic measurements.</description><identifier>ISSN: 0004-637X</identifier><identifier>EISSN: 1538-4357</identifier><identifier>DOI: 10.3847/1538-4357/ad8636</identifier><language>eng</language><publisher>Philadelphia: The American Astronomical Society</publisher><subject>Brightening ; Datasets ; Evolution ; Helioseismology ; Machine learning ; Magnetic flux ; Magnetism ; Observatories ; Orbital mechanics ; Parameter identification ; Phase shift ; Signatures ; Solar active regions ; Solar activity ; Solar atmosphere ; Solar cycle ; Solar extreme ultraviolet emission ; Solar magnetism ; Solar observatories ; Solar orbits ; Solar oscillations ; Solar physics ; Solar transition region ; Space weather ; Sun ; Ultraviolet imagery ; Weather forecasting</subject><ispartof>The Astrophysical journal, 2024-12, Vol.977 (1), p.85</ispartof><rights>2024. The Author(s). 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We present the first far-side AR butterfly diagram based on helioseismic measurements.</description><subject>Brightening</subject><subject>Datasets</subject><subject>Evolution</subject><subject>Helioseismology</subject><subject>Machine learning</subject><subject>Magnetic flux</subject><subject>Magnetism</subject><subject>Observatories</subject><subject>Orbital mechanics</subject><subject>Parameter identification</subject><subject>Phase shift</subject><subject>Signatures</subject><subject>Solar active regions</subject><subject>Solar activity</subject><subject>Solar atmosphere</subject><subject>Solar cycle</subject><subject>Solar extreme ultraviolet emission</subject><subject>Solar magnetism</subject><subject>Solar observatories</subject><subject>Solar orbits</subject><subject>Solar oscillations</subject><subject>Solar physics</subject><subject>Solar transition region</subject><subject>Space weather</subject><subject>Sun</subject><subject>Ultraviolet imagery</subject><subject>Weather forecasting</subject><issn>0004-637X</issn><issn>1538-4357</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>DOA</sourceid><recordid>eNptkc1v1DAQxSMEEkvpnaMlOBLqr8QOt6W0tNIWJEoRN2tiT7Ze7drBzhb1yH-Ol1Rw6WVGM_q9pxm9qnrF6DuhpTphjdC1FI06Aadb0T6pFv9WT6sFpVTWrVA_nlcvct4cRt51i-r3OaQ6e4dkaSd_h-Qrrn0MmXyAjI7EQC5w62NGn3feEgiOnN18J1cIeZ9wh2HK78mSfMZf5CNMQK5xIkNMD6rxFlNRXYG99QHJCiEFH9Zk6e4g2Fn-sno2wDbj8UM_qm7Oz76dXtSrL58uT5er2rGuZbXkvZVNO7CeK0655E4hohrk0GgArlEoZsvDQruOFbQU3YKUVHTMaifFUXU5-7oIGzMmv4N0byJ483cR09pAmrzdoulbq7XttJSOy6Hte11O6FWPKHtnORSv17PXmOLPPebJbOI-hXK-EUwyqhradoV6O1M-jv8BRs0hMHNIxxzSMXNgBX_zCA7jxnSq4EY3ZnSD-AO2FZSI</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Hamada, Amr</creator><creator>Jain, Kiran</creator><creator>Lindsey, Charles</creator><creator>Creelman, Mitchell</creator><creator>Oien, Niles</creator><general>The American Astronomical Society</general><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0000-5113-2757</orcidid><orcidid>https://orcid.org/0000-0002-8900-8011</orcidid><orcidid>https://orcid.org/0009-0008-2557-3848</orcidid><orcidid>https://orcid.org/0000-0002-5658-5541</orcidid><orcidid>https://orcid.org/0000-0002-1905-1639</orcidid></search><sort><creationdate>20241201</creationdate><title>Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements</title><author>Hamada, Amr ; Jain, Kiran ; Lindsey, Charles ; Creelman, Mitchell ; Oien, Niles</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d1961-42bc456f1b2720242d7eee7f4f58aa28e371c53838d91bc491b86a440391c8d43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Brightening</topic><topic>Datasets</topic><topic>Evolution</topic><topic>Helioseismology</topic><topic>Machine learning</topic><topic>Magnetic flux</topic><topic>Magnetism</topic><topic>Observatories</topic><topic>Orbital mechanics</topic><topic>Parameter identification</topic><topic>Phase shift</topic><topic>Signatures</topic><topic>Solar active regions</topic><topic>Solar activity</topic><topic>Solar atmosphere</topic><topic>Solar cycle</topic><topic>Solar extreme ultraviolet emission</topic><topic>Solar magnetism</topic><topic>Solar observatories</topic><topic>Solar orbits</topic><topic>Solar oscillations</topic><topic>Solar physics</topic><topic>Solar transition region</topic><topic>Space weather</topic><topic>Sun</topic><topic>Ultraviolet imagery</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hamada, Amr</creatorcontrib><creatorcontrib>Jain, Kiran</creatorcontrib><creatorcontrib>Lindsey, Charles</creatorcontrib><creatorcontrib>Creelman, Mitchell</creatorcontrib><creatorcontrib>Oien, Niles</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>The Astrophysical journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hamada, Amr</au><au>Jain, Kiran</au><au>Lindsey, Charles</au><au>Creelman, Mitchell</au><au>Oien, Niles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements</atitle><jtitle>The Astrophysical journal</jtitle><stitle>APJ</stitle><addtitle>Astrophys. 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For the far-side helioseismic measurements, we used seismic phase-shift maps of the Sun’s far hemisphere, computed from helioseismic Dopplergrams observed by NSO/Global Oscillations Network Group (GONG). In this study, we present the first global EUV AR data set of the whole Sun, providing several basic parameters, such as location, area, tilt angle, EUV brightness, and latitudinal/longitudinal extents of the identified ARs. We also present a similar data set for the far-side GONG ARs where the helioseismic phase shift parameters are included. Helioseismic far-side GONG ARs are examined, and their success at predicting strong ARs is assessed. We discuss the temporal and spatial evolution for the EUV AR signatures and far-side GONG AR signatures during the ascending and maximum phases of Solar Cycle 24 (2010 May–2016 May). We examine the correlation between the helioseismic signatures and the respective EUV source distributions in the Sun’s far hemisphere. 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subjects | Brightening Datasets Evolution Helioseismology Machine learning Magnetic flux Magnetism Observatories Orbital mechanics Parameter identification Phase shift Signatures Solar active regions Solar activity Solar atmosphere Solar cycle Solar extreme ultraviolet emission Solar magnetism Solar observatories Solar orbits Solar oscillations Solar physics Solar transition region Space weather Sun Ultraviolet imagery Weather forecasting |
title | Far-side Active Regions Based on Helioseismic and EUV Measurements: A New Data Set for Heliospheric Machine Learning Advancements |
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