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Found 10 papers, 3 papers with code
Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information.
Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Strong gravitational lensing has emerged as a promising approach for probing dark matter models on sub-galactic scales.
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads in many corners of cosmology.
Image segmentation for analyzing galaxy-galaxy strong lensing systems
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
Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
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.
Flow-Based Likelihoods for Non-Gaussian Inference
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.
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis
We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation.
Circumventing Lens Modeling to Detect Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
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
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Gravitational Lensing and the Power Spectrum of Dark Matter Substructure: Insights from the ETHOS N-body Simulations
Comparing the amplitude and slope of the power spectrum on scales $0. 1 \lesssim k/$kpc$^{-1} \lesssim 10$ from lenses at different redshifts can help us distinguish between cold dark matter and other exotic dark matter scenarios that alter the abundance and central densities of subhalos.
Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
