Speaker
Description
Strongly lensed supernovae (SNe) hold great promise for measuring the expansion rate of the Universe and studying SN progenitors and environments. However, finding lensed SNe resembles searching for the needle in a haystack, and following them up is very demanding and costly due to their faintness. For the best possible use of available resources, it is thus essential that we can securely identify lensed systems to beat down the large number of false alerts expected in the era of LSST, and predict the number and time delays of trailing images promptly to optimise the follow-up. In this talk, I will present some of the steps taken by the HOLISMOKES team in this direction, including a fast machine-learning-assisted extraction of lens parameters and a comparison of spectroscopic and lens-modelling-based techniques for confirming candidate systems as genuine strong lenses.
| Abstract title | Lens confirmation and machine-learning-assisted lens modelling as milestones for an effective lensed-SN follow-up |
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