no code implementations • 14 Sep 2020 • Bryan Ostdiek, Ana Diaz Rivero, Cora Dvorkin
The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
no code implementations • 14 Sep 2020 • Bryan Ostdiek, Ana Diaz Rivero, Cora Dvorkin
Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio.
no code implementations • 10 Jul 2020 • Ana Diaz Rivero, Cora Dvorkin
We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned.
no code implementations • 17 Oct 2019 • Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero, Cora Dvorkin
We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation.
no code implementations • 30 Sep 2019 • Ana Diaz Rivero, Cora Dvorkin
Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos.
Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Data Analysis, Statistics and Probability