3.1k citations · 30.9k across the 143 of their papers we have counts for
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Lagrangian displacement field estimators in cosmology
Atsuhisa Ota, Hee-Jong Seo, Shun Saito +1
The late-time nonlinear Lagrangian displacement field is highly correlated with the initial field, so reconstructing it could enable us to extract primordial cosmological informati…
Constraining νΛCDM with density-split clustering
Enrique Paillas, Carolina Cuesta-Lazaro, Pauline Zarrouk +6
The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from galaxy surveys. The two-point correlation function (2…
pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology
Minas Karamanis, David Nabergoj, Florian Beutler +2
pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial g…
Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
Minas Karamanis, Florian Beutler, John A. Peacock +2
We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial g…
Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument
B. Abareshi, J. Aguilar, S. Ahlen +264
The Dark Energy Spectroscopic Instrument (DESI) has embarked on an ambitious five-year survey to explore the nature of dark energy with spectroscopy of 40 million galaxies and quas…
II: Accelerating Effective Field Theory Analyses of the Galaxy Power Spectrum
Jamie Donald-McCann, Kazuya Koyama, Florian Beutler
In this paper we present an extension to the suite of neural-network-based emulators. The new editions have been developed to accelerate EFTofLSS analyses of…