Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Alessio Spurio Mancini"'
Publikováno v:
The Open Journal of Astrophysics, Vol 7 (2024)
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. Specifically, we combine (i) e
Externí odkaz:
https://doaj.org/article/667fddd3b3364ce2b806376959bcc953
Autor:
Alessio Spurio Mancini, Benjamin Bose
Publikováno v:
The Open Journal of Astrophysics, Vol 6 (2023)
Modelling nonlinear structure formation is essential for current and forthcoming cosmic shear experiments. We combine the halo model reaction formalism, implemented in the REACT code, with the COSMOPOWER machine learning emulation platform, to develo
Externí odkaz:
https://doaj.org/article/3d9a57fc75ed40d984b5865ec729734d
Autor:
Matthew A. Price, Matthijs Mars, Matthew M. Docherty, Alessio Spurio Mancini, Augustin Marignier, Jason D. McEwen
Publikováno v:
The Open Journal of Astrophysics, Vol 6 (2023)
Cosmic strings are linear topological defects that may have been produced during symmetry-breaking phase transitions in the very early Universe. In an expanding Universe the existence of causally separate regions prevents such symmetries from being b
Externí odkaz:
https://doaj.org/article/568b69a15d7e455685994672d1d77ecd
Autor:
Davide Piras, Alessio Spurio Mancini
Publikováno v:
The Open Journal of Astrophysics, Vol 6 (2023)
We present CosmoPower-JAX, a JAX-based implementation of the CosmoPower framework, which accelerates cosmological inference by building neural emulators of cosmological power spectra. We show how, using the automatic differentiation, batch evaluation
Externí odkaz:
https://doaj.org/article/6f620497eba343d8992c2eaef5dbe0fa
Bayesian inference provides a pathway toward accurate predictions of source parameters (e.g., location and moment tensor), along with principled, well-calibrated uncertainty estimates. Unfortunately, standard Bayesian inference techniques can often r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a452e298e4d86b1afcf8c2e210240734
https://doi.org/10.5194/egusphere-egu23-7939
https://doi.org/10.5194/egusphere-egu23-7939
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 511:1771-1788
We present $\it{CosmoPower}$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Backgroun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39b84bc92e7d24f26ff2dc21e507890a
https://doi.org/10.1093/mnras/stac064
https://doi.org/10.1093/mnras/stac064
Publikováno v:
Mancini, A S & Pourtsidou, A 2022, ' KiDS-1000 Cosmology: machine learning-accelerated constraints on Interacting Dark Energy with COSMOPOWER ', Monthly Notices of the Royal Astronomical Society, vol. 512, no. 1, pp. L44-L48 . https://doi.org/10.1093/mnrasl/slac019
We derive constraints on a coupled quintessence model with pure momentum exchange from the public $\sim$1000 deg$^2$ cosmic shear measurements from the Kilo-Degree Survey and the $\it{Planck}$ 2018 Cosmic Microwave Background data. We compare this mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f33d75c6fd723b537002eeaca227ddc
Publikováno v:
Modified Gravity and Cosmology ISBN: 9783030837143
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::230896c62fc1313d95808f1fa5631e65
https://doi.org/10.1007/978-3-030-83715-0_35
https://doi.org/10.1007/978-3-030-83715-0_35