45 citations · 84 across the 4 of their papers we have counts for
4 papers
Investigating Deep Learning Methods for Obtaining Photometric Redshift Estimations from Images
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed,…
Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks
Adam Shandonay, Robert Morgan, Keith Bechtol +11
Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact detection algor…
Benchmarking and Scalability of Machine Learning Methods for Photometric Redshift Estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam +2
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift es…
Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
B. Henghes, O. Lahav, D. W. Gerdes +56
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data wh…