An international team of researchers discovered 335 new strong lensing candidates based on a deep dive into data collected for a U.S. Department of Energy-supported telescope project in Arizona called the Dark Energy Spectroscopic Instrument (DESI).
The study relied on a machine learning algorithm that won an international science competition.
The team used Cori, a supercomputer at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center, to compare imaging data from the Dark Energy Camera Legacy Survey with a training sample of 423 known lenses and 9,451 non-lenses.
The gravitational lensing candidates were identified with the assistance of a neural network originally developed for The Strong Gravitational Lens Finding Challenge programming competition.
From Berkeley Lab News Center
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