2023 Abstract from Sofia and Jerry

Identifying Double-Source Plane Lenses for Cosmological Studies

Students: Sofia Grimm, Jerry Zhou

Scientist Mentors: Brian Nord, Aleksandra Ciprijanovic

Double-Source Plane Lenses (DSPLs) are a form of strong gravitational lensing that can be used to uniquely constrain cosmological parameters like the dark energy equation of state. These objects are exceedingly rare: to date, only four have been discovered in astronomical surveys, like the Sloan Digital Sky Survey and the HyperSuprimeCam Survey. Additionally, the main features of these objects are typically co-located sets of arcs, which look like Einstein Rings of single-source galaxy-galaxy lenses. Larger astronomical surveys typically have low image resolution, which significantly exacerbates issues when trying to find DSPLs. Future surveys are likely to include many more of these objects and have higher image resolution. Traditional methods for finding strong lenses struggle to identify simple galaxy-galaxy lenses. Recently, neural networks have become the standard for lens identification, but these tools have not yet been used to search for DSPLs. In this work, we use the software deeplenstronomy to simulate images of DSPLs and Einstein Rings primarily varying the redshift of the sources and lenses. The first dataset contains simulations of DSPLs and Einstein Rings with no noise, low seeing, and very high resolution, which represents a baseline high-quality data set. The second dataset consists of simulations with conditions that mimic modern cosmological surveys like the Dark Energy Survey (DES).  We create these two datasets to explore the morphological differences between DSPLs and ERs in the contexts of noise-free and noisy images.  This results in a benchmark dataset of 40,000 total images.  These datasets will be used to develop machine learning models to automatically discern between DSPLs and ERs.