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X-ORIGINAL-URL:https://www.oact.inaf.it
X-WR-CALDESC:Eventi per Osservatorio Astrofisico di Catania
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20220530T090000
DTEND;TZID=UTC:20220601T180000
DTSTAMP:20260416T040914
CREATED:20220114T213419Z
LAST-MODIFIED:20220114T214220Z
UID:8968-1653901200-1654106400@www.oact.inaf.it
SUMMARY:Machine Learning for Astrophysics - ML4Astro
DESCRIPTION:Bring together researchers actively involved in the fields of machine learning applications to astrophysics use cases is the aim of the International Conference on Machine Learning for Astrophysics – ML4Astro. \nIn the view of the forthcoming Big Data era for the astronomy community\, the conference focuses on challenges coming from the application of ML/DL methods to open problems in astrophysics: novel AI techniques will be presented and joint discussions on their use with observational data will be fostered. A special session on the Square Kilometre Array and its precursors/pathfinders is foreseen. \nParticipants are invited to submit an abstract for an Oral Presentation or a Poster Presentation on the topics of interest for the conference. \nDATE: 30 May (09:00) – 1 Jun (18:00) \nOrganizers & Sponsors: The ML4ASTRO Conference is organized by the National Institute for Astrophysics (INAF) in collaboration with the University of Catania\, the University of Milano Bicocca\, the Consorzio COMETA and the University of Malta. The event is sponsored by the H2020 NEANIAS project\, the INAF PRIN CIRASA project and the MOSAICO project. \nScientific Organizing Committee (SOC): Filomena Bufano (INAF)\, co-chair; Simone Riggi (INAF)\, co-chair; Eva Sciacca (INAF)\, co-chair; Ugo Becciani (INAF); Andrea De Marco (University of Malta); Giuseppe Vizzari (Universita’ Milano Bicocca) \nLocal Organizing Committee (LOC): Cristobal Bordiu (INAF); Giulia Miceli (INAF); Teresa Pulvirenti (INAF); Fabio Vitello (INAF) \nTOPICS: Supervised/Unsupervised/Semi-supervised Learning – Deep learning – Active Learning – Image segmentation\, object detection and classification – Anomaly discovery – Data preparation\, generation and augmentation – Time series analysis\, transients –  Classification and regression – Data mining – Software tools and services for machine learning – Computing infrastructures and devices for Artificial Intelligence \nSCIENTIFIC DOMAINS: Radioastronomy – Observational Astrophysics – Time Domain Astronomy – High-Energy Astrophysics – Astroparticle Physics – Galactic and Extragalactic Science – Cosmology and numerical simulations \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				Registration\n			\n			\n				\n				\n				\n				\n				\n				Call for Abstracts
URL:https://www.oact.inaf.it/event/machine-learning-for-astrophysics-ml4astro/
LOCATION:Il Principe Hotel\, Via Alessi\, 24\, Catania\, 95124\, Italia
CATEGORIES:Eventi & Seminari
ATTACH;FMTTYPE=image/png:https://www.oact.inaf.it/wp-content/uploads/2022/01/Schermata-2022-01-14-alle-11.53.33.png
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DTSTART;TZID=UTC:20220630T110000
DTEND;TZID=UTC:20220630T110000
DTSTAMP:20260416T040914
CREATED:20220622T201909Z
LAST-MODIFIED:20220622T201913Z
UID:10112-1656586800-1656586800@www.oact.inaf.it
SUMMARY:The Gaia DR3 2M catalogue of stellar chromospheric activity
DESCRIPTION:DATE: Thursday\, June 30\, 11 a.m. \nORGANIZER: INAF OACT \nLOCATION: AULA OVEST (INAF-OACT) – Audience in-person max 20 \nLink for the remote audience:https://meet.google.com/wvf-uwxo-oyj \nSPEAKER: Prof. Alessandro Lanzafame (Università di Catania) \nABSTRACT:We present the method devised for inferring the Gaia stellar activity index from the analysis of the Ca II infrared triplet (IRT) at 850.03\, 854.44\, and 866.45 nm in the Gaia Radial Velocity Spectrometer (RVS) spectrum\, an overview of the content of the chromospheric activity catalogue published in DR3\, and its scientific validation. A sample of well studied PMS stars is considered to identify the regime in which the Gaia stellar activity index may be affected by mass accretion. The correlation with the amplitude of the photometric rotational modulation is also discussed. Three regimes of the chromospheric stellar activity are identified\, confirming suggestions made by previous authors on much smaller chromospheric activity indices datasets. The highest stellar activity regime is associated with PMS stars and RS CVn systems\, in which activity is enhanced by tidal interaction. Some evidence of a bimodal distribution in MS stars with Teff > 5000 K is also found\, which defines the two other regimes\, without a clear gap in between. Stars with 3500 K < Teff < 5000 K are found to be either very active PMS stars or active MS stars with a unimodal distribution in chromospheric activity. A dramatic change in the activity distribution is found for Teff < 3500 K\, with a dominance of low activity stars close to the transition between partially- and fully-convective stars and a rise in activity down into the fully-convective regime. Overall\, the 2M catalogue of chromospheric activity in Gaia DR3 represents a gold mine for studies related to stellar magnetic activity and mass accretion in the solar vicinity. \n  \nA few rules:— access the virtual room with the microphone and camera switched off.— for questions leave a message in the chat and the answers at the end of the webinar.
URL:https://www.oact.inaf.it/event/the-gaia-dr3-2m-catalogue-of-stellar-chromospheric-activity/
CATEGORIES:Eventi & Seminari
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