Zobrazeno 1 - 10
of 107
pro vyhledávání: '"A. Ćiprijanović"'
Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. W
Externí odkaz:
http://arxiv.org/abs/2411.08587
Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the
Externí odkaz:
http://arxiv.org/abs/2411.03334
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of
Externí odkaz:
http://arxiv.org/abs/2410.16347
In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial ins
Externí odkaz:
http://arxiv.org/abs/2407.17292
Autor:
Roncoli, Andrea, Ćiprijanović, Aleksandra, Voetberg, Maggie, Villaescusa-Navarro, Francisco, Nord, Brian
Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and
Externí odkaz:
http://arxiv.org/abs/2311.01588
Autor:
Savić, Đorđe V., Jankov, Isidora, Yu, Weixiang, Petrecca, Vincenzo, Temple, Matthew J., Ni, Qingling, Shirley, Raphael, Kovacevic, Andjelka B., Nikolic, Mladen, Ilic, Dragana, Popovic, Luka C., Paolillo, Maurizio, Panda, Swayamtrupta, Ciprijanovic, Aleksandra, Richards, Gordon T.
Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Scie
Externí odkaz:
http://arxiv.org/abs/2307.04072
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, suc
Externí odkaz:
http://arxiv.org/abs/2302.02005
The study of quasar light curves poses two problems: inference of the power spectrum and interpolation of an irregularly sampled time series. A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in
Externí odkaz:
http://arxiv.org/abs/2211.10305
With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amor
Externí odkaz:
http://arxiv.org/abs/2211.09126
Autor:
Poh, Jason, Samudre, Ashwin, Ćiprijanović, Aleksandra, Nord, Brian, Khullar, Gourav, Tanoglidis, Dimitrios, Frieman, Joshua A.
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) will i
Externí odkaz:
http://arxiv.org/abs/2211.05836