78 citations · 682 across the 30 of their papers we have counts for
6 papers · 1 filter
Strong Lensing Model and Dust Extinction Maps of the Host Galaxy of Type Ia Supernova H0pe
A. Galan, S. Schuldt, G. B. Caminha +11
Strong gravitational lensing by massive galaxy clusters offers rare opportunities to observe multiple images of distant () Type Ia supernovae (SNe) and to resolve the…
Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Time delays & Hubble constant measurement
J. D. R. Pierel, E. E. Hayes, M. Millon +26
Multiply-imaged supernovae (SNe) provide a novel means of constraining the Hubble constant (). Such measurements require a combination of precise models of the lensing mass di…
Cosmology with supernova Encore in the strong lensing cluster MACS J0138-2155: Lens model comparison and H0 measurement
S. H. Suyu, A. Acebron, C. Grillo +28
MACS J0138-2155 is the only known cluster to strongly lens two supernovae (SNe), Requiem and Encore, from the same host galaxy at z=1.949. We present seven independent mass models…
A long-lasting eruption heralds SN 2023ldh, a clone of SN 2009ip
A. Pastorello, A. Reguitti, L. Tartaglia +46
We discuss the results of the spectroscopic and photometric monitoring of the type IIn supernova (SN) 2023ldh. Survey archive data show that the SN progenitor experienced some erra…
HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal
S. Schuldt, R. Cañameras, Y. Shu +6
We have carried out a systematic search for galaxy-scale lenses exploiting multi-band imaging data from the third public data release of the Hyper Suprime-Cam (HSC) survey with the…
Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
J. Gonzalez, P. Holloway, T. Collett +70
We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learnin…