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SUMMARY:GausSN: Bayesian Time Delay Estimation for Strongly Lensed Superno
 vae - Erin Hayes
DTSTART:20231108T134000Z
DTEND:20231108T140500Z
UID:TALK208168@talks.cam.ac.uk
CONTACT:Hannah Uebler
DESCRIPTION:Time delay cosmography with strongly lensed supernovae (SNe) i
 s an exciting local probe of H0 that is independent of the local distance 
 ladder. One of the most important ingredients in H0 estimates from strongl
 y lensed SNe is the time delay between the appearance of the multiple imag
 es of the SN. In this talk\, I describe GausSN – a new method for extrac
 ting time delays from multi-band photometric observations of resolved lens
 ed SNe images using Gaussian Processes. Our methodology improves upon exis
 ting time delay estimation methods by including a fully Bayesian explorati
 on of the parameter space and a novel treatment of microlensing\, all with
  minimal assumptions about the underlying shape of the light curve. We dem
 onstrate the ability of GausSN to recover accurate and precise time delays
  using simulations of lensed SNe data as expected from the Roman Space Tel
 escope. Compared to existing methods\, GausSN recovers time delays which a
 re on average as close to the truth with better calibrated uncertainties. 
 With the upcoming Rubin Observatory’s Legacy Survey of Space and Time an
 d Roman Space Telescope\, we expect to discover tens to hundreds of lensed
  SNe\, which\, with tools like GausSN\, will provide important independent
  evidence for investigating the Hubble tension in the coming decade.
LOCATION:The Hoyle Lecture Theatre + Zoom 
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