Zobrazeno 1 - 10
of 794
pro vyhledávání: '"A. T. Lucas"'
We present a way to capture high-information posteriors from training sets that are sparsely sampled over the parameter space for robust simulation-based inference. In physical inference problems, we can often apply domain knowledge to define traditi
Externí odkaz:
http://arxiv.org/abs/2410.07548
Autor:
Pandey, Shivam, Modi, Chirag, Wandelt, Benjamin D., Bartlett, Deaglan J., Bayer, Adrian E., Bryan, Greg L., Ho, Matthew, Lavaux, Guilhem, Makinen, T. Lucas, Villaescusa-Navarro, Francisco
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle i
Externí odkaz:
http://arxiv.org/abs/2409.09124
Autor:
Makinen, T. Lucas, Heavens, Alan, Porqueres, Natalia, Charnock, Tom, Lapel, Axel, Wandelt, Benjamin D.
In inference problems, we often have domain knowledge which allows us to define summary statistics that capture most of the information content in a dataset. In this paper, we present a hybrid approach, where such physics-based summaries are augmente
Externí odkaz:
http://arxiv.org/abs/2407.18909
Autor:
Lanzieri, Denise, Zeghal, Justine, Makinen, T. Lucas, Boucaud, Alexandre, Starck, Jean-Luc, Lanusse, François
Traditionally, weak lensing cosmological surveys have been analyzed using summary statistics motivated by their analytically tractable likelihoods, or by their ability to access higher-order information, at the cost of requiring Simulation-Based Infe
Externí odkaz:
http://arxiv.org/abs/2407.10877
Autor:
Ho, Matthew, Bartlett, Deaglan J., Chartier, Nicolas, Cuesta-Lazaro, Carolina, Ding, Simon, Lapel, Axel, Lemos, Pablo, Lovell, Christopher C., Makinen, T. Lucas, Modi, Chirag, Pandya, Viraj, Pandey, Shivam, Perez, Lucia A., Wandelt, Benjamin, Bryan, Greg L.
Publikováno v:
2024 OJA, Vol. 7
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for im
Externí odkaz:
http://arxiv.org/abs/2402.05137
Set-based learning is an essential component of modern deep learning and network science. Graph Neural Networks (GNNs) and their edge-free counterparts Deepsets have proven remarkably useful on ragged and topologically challenging datasets. The key t
Externí odkaz:
http://arxiv.org/abs/2310.03812
We construct a field-based Bayesian Hierarchical Model for cosmic shear that includes, for the first time, the important astrophysical systematics of intrinsic alignments and baryon feedback, in addition to a gravity model. We add to the BORG-WL fram
Externí odkaz:
http://arxiv.org/abs/2304.04785
Autor:
Makinen, T. Lucas, Charnock, Tom, Lemos, Pablo, Porqueres, Natalia, Heavens, Alan, Wandelt, Benjamin D.
We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter constraints using mock dark matter halo catalogues. We employ Informatio
Externí odkaz:
http://arxiv.org/abs/2207.05202
Autor:
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
Publikováno v:
The Open Journal of Astrophysics, Vol 7 (2024)
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for im
Externí odkaz:
https://doaj.org/article/af69423f4173436cad76953bf39249be
Atmospheric mass-loss is known to play a leading role in sculpting the demographics of small, close-in exoplanets. Knowledge of how such planets evolve allows one to ``rewind the clock'' to infer the conditions in which they formed. Here, we explore
Externí odkaz:
http://arxiv.org/abs/2110.15162