BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Instituto de Astrofísica de Andalucía - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.iaa.csic.es
X-WR-CALDESC:Eventos para Instituto de Astrofísica de Andalucía
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260407T123000
DTEND;TZID=Europe/Paris:20260407T133000
DTSTAMP:20260404T003809
CREATED:20260302T152934Z
LAST-MODIFIED:20260318T143449Z
UID:30044-1775565000-1775568600@www.iaa.csic.es
SUMMARY:SO Colloquium - New optical diagnostics and Machine Learning classification of emission-line nebulae: Toward larger and more complete samples of supernova remnants in nearby galaxies
DESCRIPTION:Supernova remnants (SNRs) provide valuable insights into supernova (SN) environments and the long-term influence of SN-driven shocks on the interstellar medium (ISM). By heating\, compressing\, and chemically enriching the ISM\, SNRs trace how different explosion environments shape galaxy evolution. However\, classical optical criteria\, particularly the [S II]/Hα ratio\, miss a significant fraction of low-excitation or evolved SNRs\, biasing current extragalactic SNR samples. \nTo mitigate these biases\, we develop new optical diagnostics based on two- and three-line emission ratios combined with a Support Vector Machine classifier. This method reliably separates SNRs from contaminating H II regions and recovers up to ~35% of remnants overlooked by traditional cuts\, enabling more complete SNR samples. \nWe apply these tools to MUSE IFU observations of nearby galaxies\, identifying previously unrecognized SNR populations and characterizing their physical properties and luminosity functions. Using complementary Chandra observations\, we also detect new X-ray SNRs and investigate links between their optical and X-ray emission. We find that remnants located in higher pre-shock densities tend to be more X-ray luminous\, and that optical SNRs with X-ray counterparts commonly show enhanced [O III] λ5007 emission\, consistent with faster shock velocities. \nFinally\, we present extended diagnostics that incorporate a broader set of emission lines and physical parameters\, enabling the robust identification and classification of emission-line nebulae\, including SNRs\, H II regions\, and planetary nebulae. These tools provide a general framework for detecting and characterizing previously unrecognized nebular populations in nearby galaxies. \n  \nFecha y lugar: 07/04/2026 – 12:30 | Salón de ActosMaria KopsacheiliICE-CSIC \n  \n 
URL:https://www.iaa.csic.es/evento/so-new-optical-diagnostics-and-machine-learning-classification-of-emission-line-nebulae/
LOCATION:IAA – CSIC\, Glorieta de la Astronomía\, Granada\, España
CATEGORIES:Seminarios
ATTACH;FMTTYPE=image/png:https://www.iaa.csic.es/wp-content/uploads/2025/07/SO-COLLOQUIUM.png
END:VEVENT
END:VCALENDAR