910 citations · 1.4k across the 13 of their papers we have counts for
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HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
R. Canameras, S. Schuldt, Y. Shu +10
While supervised neural networks have become state of the art for identifying the rare strong gravitational lenses from large imaging data sets, their selection remains significant…
HOLISMOKES. VI. New galaxy-scale strong lens candidates from the HSC-SSP imaging survey
R. Canameras, S. Schuldt, Y. Shu +8
We have carried out a systematic search for galaxy-scale strong lenses in multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated pipeline, based on realistic stro…
Photometric Redshift Estimation with a Convolutional Neural Network: NetZ
S. Schuldt, S. H. Suyu, R. Cañameras +4
The redshifts of galaxies are a key attribute that is needed for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, m…
HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
S. Schuldt, S. H. Suyu, T. Meinhardt +4
Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^…
HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
R. Canameras, S. Schuldt, S. H. Suyu +6
We present a systematic search for wide-separation (Einstein radius >1.5"), galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on the Northern sky. With l…