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pro vyhledávání: '"Ana Diaz Rivero"'
Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05ea757baacb0f70b3ccd94fd7622513
http://arxiv.org/abs/2009.06639
http://arxiv.org/abs/2009.06639
Galaxy-galaxy strong gravitational lenses have become a popular probe of dark matter (DM) by providing a window into structure formation on the smallest scales. In particular, the convergence power spectrum of subhalos within lensing galaxies has bee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e0402f536389934ebf0ef30c865af65
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. Using the technique of image segmentation, we turn the t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7c0fad414d18e99c53960636ec61195
Autor:
Ana Diaz Rivero, Cora Dvorkin
We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eced2f176a993d35caec52fdaca731c
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e1aa9f8fa98b0938ca7a06ebfb12865
Autor:
Cora Dvorkin, Ana Diaz Rivero
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 with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::533ec323a79fe985b53611a742def393
Strong gravitational lensing has been identified as a promising astrophysical probe to study the particle nature of dark matter. In this paper we present a detailed study of the power spectrum of the projected mass density (convergence) field of subs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f21b7343390b9eff9c0c989ab257629
Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark mat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f0dffae68c42994e3c6f48978367921
Strong lensing is a sensitive probe of the small-scale density fluctuations in the Universe. We implement a novel approach to modeling strongly lensed systems using probabilistic cataloging, which is a transdimensional, hierarchical, and Bayesian fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7bd8f455ea9e9d621e9a8306756017e
Autor:
Dvorkin, Cora, Gerbino, Martina, Alonso, David, Battaglia, Nicholas, Bird, Simeon, Ana Diaz Rivero, Font-Ribera, Andreu, Fuller, George, Lattanzi, Massimiliano, Loverde, Marilena, Muñoz, Julian B., Sherwin, Blake, Slosar, Anže, Villaescusa-Navarro, Francisco
Publikováno v:
INSPIRE-HEP
Recent advances in cosmic observations have brought us to the verge of discovery of the absolute scale of neutrino masses. Nonzero neutrino masses are known evidence of new physics beyond the Standard Model. Our understanding of the clustering of mat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee4827e57a7df8410eee268d52545d96
http://inspirehep.net/record/1724447
http://inspirehep.net/record/1724447