The detection rate of early UV emission from supernovae: A dedicated GALEX/PTF survey and calibrated theoretical estimates
Noam Ganot (1), Avishay Gal-Yam (1), Eran O. Ofek (1), Ilan Sagiv (1),, Eli Waxman (1), Ofer Lapid (1), Shrinivas R. Kulkarni (2), Sagi Ben-Ami (3),, Mansi M. Kasliwal (2), Doron Chelouche (4), Stephen Rafter (4), Ehud Behar, (5), Ari Laor (5), Dovi Poznanski (6), Udi Nakar (6)

TL;DR
This study combines GALEX and PTF data to detect early UV emission from supernovae, validating theoretical models and highlighting the importance of UV observations for understanding supernova progenitors and explosion characteristics.
Contribution
First simultaneous GALEX/PTF survey providing empirical detection rates of early UV supernova emission, confirming theoretical predictions and emphasizing UV's role in progenitor analysis.
Findings
Detected 6 Type II SNe and 1 SLSN-II in GALEX data.
Observed detection rates align with models assuming RSG progenitors.
Projected ULTRASAT will detect over 100 SNe annually in UV.
Abstract
The radius and surface composition of an exploding massive star,as well as the explosion energy per unit mass, can be measured using early UV observations of core collapse supernovae (SNe). We present the first results from a simultaneous GALEX/PTF search for early UV emission from SNe. Six Type II SNe and one Type II superluminous SN (SLSN-II) are clearly detected in the GALEX NUV data. We compare our detection rate with theoretical estimates based on early, shock-cooling UV light curves calculated from models that fit existing Swift and GALEX observations well, combined with volumetric SN rates. We find that our observations are in good agreement with calculated rates assuming that red supergiants (RSGs) explode with fiducial radii of 500 solar, explosion energies of 10^51 erg, and ejecta masses of 10 solar masses. Exploding blue supergiants and Wolf-Rayet stars are poorly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
