A5015938482- Dark Matter and Cosmic Phenomena
- Galaxies: Formation, Evolution, Phenomena
- Cosmology and Gravitation Theories
- Scientific Research and Discoveries
- Astronomy and Astrophysical Research
- Stellar, planetary, and galactic studies
- Adaptive optics and wavefront sensing
- Astrophysics and Cosmic Phenomena
- Neutrino Physics Research
- CCD and CMOS Imaging Sensors
- Gamma-ray bursts and supernovae
- Relativity and Gravitational Theory
- Advanced Semiconductor Detectors and Materials
- Infrared Target Detection Methodologies
- Health, Environment, Cognitive Aging
- Climate Change and Health Impacts
- Advanced Fluorescence Microscopy Techniques
- Calibration and Measurement Techniques
- Gaussian Processes and Bayesian Inference
- Advanced Mathematical Theories and Applications
- Blind Source Separation Techniques
- Spectroscopy and Laser Applications
- Data-Driven Disease Surveillance
- Advanced Thermodynamics and Statistical Mechanics
Harvard University
2018-2022
Harvard University Press
2019-2020
Recent advances in cosmic observations have brought us to the verge of discovery absolute scale neutrino masses. Nonzero masses are known evidence new physics beyond Standard Model. Our understanding clustering matter presence massive neutrinos has significantly improved over past decade, yielding cosmological constraints that tighter than any laboratory experiment, and which will improve next resulting a guaranteed detection mass scale.
Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks analyze each system. We use machine learning circumvent need for develop neural network both locate subhalos in an image as well determine their mass using technique segmentation. The trained with single subhalo located near Einstein ring across...
Aims. The goal of this Letter is to develop a machine learning model analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images strongly lensed galaxies. Methods. Using technique image segmentation, we turn task identifying subhalos into classification problem, where label each pixel in an as coming from lens, subhalo binned mass range, or neither. Our network only trained on with single smooth either zero one near Einstein ring. Results. On...
Studying the smallest self-bound dark matter structure in our Universe can yield important clues about fundamental particle nature of matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize substructures at cosmological distances from Milky Way. Within cold (CDM) paradigm, number low-mass subhalos within lens galaxies is expected be large, implying that their contribution convergence field approximately Gaussian could thus described by power...
Strong gravitational lensing has been identified as a promising astrophysical probe to study the particle nature of dark matter. In this paper we present detailed power spectrum projected mass density (convergence) field substructure in Milky Way-sized halo. This suggested key observable that can be extracted from strongly-lensed images and yield important clues about matter distribution within lens galaxy. We use two different $N$-body simulations ETHOS framework: one with cold another...
Astrophysical and cosmological observations currently provide the only robust, empirical measurements of dark matter. Future with Large Synoptic Survey Telescope (LSST) will necessary guidance for experimental matter program. This white paper represents a community effort to summarize science case studying fundamental physics LSST. We discuss how LSST inform our understanding properties matter, such as particle mass, self-interaction strength, non-gravitational couplings Standard Model,...
Strong gravitational lensing is a promising way of uncovering the nature dark matter, by finding perturbations to images that cannot be accounted for well modeling lens galaxy without additional structure, it subhalos (smaller halos within smooth lens) or line-of-sight (LOS) halos. We present results attempting infer presence substructure from requiring an intermediate step in which model has subtracted, using simple convolutional neural network (CNN). find only able with greater than 75%...
Galaxy-galaxy strong gravitational lenses have become a popular probe of dark matter (DM) by providing window into structure formation on the smallest scales. In particular, convergence power spectrum subhalos within lensing galaxies has been suggested as promising observable to study DM. However, distances involved in strong-lensing systems are vast, and we expect relevant volume contain line-of-sight (LOS) halos that not associated with main lens. We develop formalism calculate effect LOS...
Abstract Strong lensing is a sensitive probe of the small-scale density fluctuations in Universe. We implement pipeline to model strongly lensed systems using probabilistic cataloging , which transdimensional, hierarchical, and Bayesian framework sample from metamodel (union models with different dimensionality) consistent observed photon count maps. Probabilistic allows one robustly characterize modeling covariances within across lens numbers subhalos. Unlike traditional subhalos, it does...
We investigate the bounds on sum of neutrino masses in a cosmic-acceleration scenario where equation state $w(z)$ dark energy (DE) is constructed model-independent way, using basis principal components (PCs) that are allowed to cross phantom barrier $w(z)=\ensuremath{-}1$. find additional freedom provided means DE can undo changes background expansion induced by massive neutrinos at low redshifts. This has two significant consequences: (1) it leads substantial increase upper bound for...
We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that true likelihood data Gaussian. In particular, we suggest using optimization targets flow-based generative models, class models can capture complex distributions by transforming simple base distribution through layers nonlinearities. call these (FBL). analyze accuracy and precision reconstructed on mock Gaussian data, show simply gauging quality samples drawn from...
ABSTRACT We present a novel technique for cosmic microwave background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to generalized morphological component analysis (GMCA), we introduce hierarchical GMCA (HGMCA), Bayesian graphical model test our method Nside = 256 simulated sky maps that include dust, synchrotron, free–free, and anomalous emission, show HGMCA reduces contamination $25{{\ \rm per\...
Astrophysical observations currently provide the only robust, empirical measurements of dark matter. In coming decade, astrophysical will guide other experimental efforts, while simultaneously probing unique regions matter parameter space. This white paper summarizes that can constrain fundamental physics in era LSST. We describe how inform our understanding properties matter, such as particle mass, self-interaction strength, non-gravitational interactions with Standard Model, and compact...