Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/27588| Title: | Stellar formation rates in galaxies using Machine Learning models | Authors: | DELLI VENERI, MICHELE CAVUOTI, STEFANO BRESCIA, Massimo RICCIO, GIUSEPPE Longo, Giuseppe |
Issue Date: | 2018 | Volume: | ESANN 2018 - Proceedings 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | First Page: | arXiv:1805.06338 | Abstract: | Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models. | Conference Name: | ESANN 2018 : European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | Conference Place: | Bruges | Conference Date: | 25-27April, 2018 | URI: | http://hdl.handle.net/20.500.12386/27588 | URL: | http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=67641©ownerid=82112 | ISBN: | 978-2-87587-048-3 | Bibcode ADS: | 2018arXiv180506338D | Fulltext: | open |
| Appears in Collections: | 3.01 Contributi in Atti di convegno |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| DelliVeneriEtAl-1805.06338.pdf | 389.65 kB | Adobe PDF | View/Open |
Page view(s)
16
checked on Oct 27, 2020
Download(s)
2
checked on Oct 27, 2020
Google ScholarTM
Check
Altmetric
Items in DSpace are published in Open Access, unless otherwise indicated.