BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Instituto de Astrofísica de Andalucía - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Instituto de Astrofísica de Andalucía
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:20260219T123000
DTEND;TZID=Europe/Paris:20260226T133000
DTSTAMP:20260404T101059
CREATED:20260113T105214Z
LAST-MODIFIED:20260213T142747Z
UID:29108-1771504200-1772112600@www.iaa.csic.es
SUMMARY:Deep Learning for Small Astrophysical Datasets: From Cosmology to Exoplanet Detection
DESCRIPTION:Deep learning is increasingly revolutionizing astrophysics by enabling advanced analysis of complex and high-dimensional datasets. However\, the application of these methods is often limited by data availability and noise\, requiring careful model design\, optimization\, and regularization to balance performance and robustness. \nThis talk presents recent research addressing these challenges\, including deep learning techniques for cosmological parameter reconstruction\, the use of genetic algorithms to optimize neural networks and improve precision\, and the combination of these methods to accelerate Bayesian inference. It then focuses on applications to the detection of Earth-like exoplanets using stellar radial-velocity measurements\, where stellar activity and instrumental effects dominate the signal. In particular\, deep learning models trained directly on real high-resolution stellar spectra are shown to recover tiny Doppler shifts in unseen data\, approaching the precision required for the detection of terrestrial planets. \nThe seminar aims to provide practical insights into the effective use of deep learning for astrophysical problems in which data are limited\, and precision is crucial. \nFecha y lugar: 19/02/2026 – 12:30 | Salón de ActosIsidro Gómez VargasIAA-CSIC \n  \n 
URL:https://www.iaa.csic.es/evento/deep-learning-for-small-astrophysical-datasets-from-cosmology-to-exoplanet-detection/
LOCATION:IAA – CSIC\, Glorieta de la Astronomía\, Granada\, España
CATEGORIES:Seminarios
ATTACH;FMTTYPE=image/png:https://www.iaa.csic.es/wp-content/uploads/2023/11/seminarscover-e1752660087307.png
END:VEVENT
END:VCALENDAR