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Consequences of using a smooth cosmic distance in a lumpy Universe. I.
(American Physical Society, 2022)
How do we appropriately fit a model based on an idealised Friedmann-Lemaˆıtre Robertson-Walker
spacetime to observations made from a single location in a lumpy Universe? We address this question
for surveys that measure ...
Inferring halo masses with graph neural networks
(Institute of Physics, 2022)
Understanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and
properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions,
...
Ultralarge-scale approximations and galaxy clustering: Debiasing constraints on cosmological parameters
(Oxford University Press, 2022)
Upcoming galaxy surveys will allow us to probe the growth of the cosmic large-scale structure with improved sensitivity
compared to current missions, and will also map larger areas of the sky. This means that in addition ...
Antenna beam characterization for the global 21-cm experiment LEDA and its impact on signal model parameter reconstruction
(Oxford University Press, 2022)
Cosmic dawn, the onset of star formation in the early universe, can in principle be studied via the 21-cm transition of neutral hydrogen, for which a sky-averaged absorption signal, redshifted to MHz frequencies, is predicted ...
Constraining the neutrino mass using a multitracer combination of two galaxy surveys and cosmic microwave background lensing
(Oxford University Press, 2022)
Measuring the total neutrino mass is one of the most exciting opportunities available with next-generation cosmological data
sets. We study the possibility of detecting the total neutrino mass using large-scale clustering ...
Gaussian process regression for foreground removal in hi intensity mapping experiments
(Oxford University Press, 2022)
We apply for the first time Gaussian Process Regression (GPR) as a foreground removal technique in the context of single-dish, low redshift H I intensity mapping, and present an open-source PYTHON toolkit for doing so. We ...
Probabilistic learning for pulsar classification
(IOP Publishing, 2022)
In this work, we explore the possibility of using probabilistic learning to identify
pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning
(DKL). Trained on a balanced training set in ...
On the primordial origin of the smoothing excess in the Planck temperature power spectrum in light of LSS data
(IOP Publishing, 2022)
The Planck DR3 measurements of the temperature and polarization anisotropies power spectra of the cosmic microwave background (CMB) show an excess of smoothing of the acoustic peaks with respect to ΛCDM, often quantified ...
Measurements of one-point statistics in 21-cm intensity maps via foreground avoidance strategy
(Oxford University Press, 2022)
Measurements of the one-point probability distribution function and higher-order moments (variance, skewness, and kurtosis) of
the high-redshift 21-cm fluctuations are among the most directstatistical probes of the ...
Designing an optimal LSST deep drilling program for cosmology with type Ia supernovae
(American Astronomical Society, 2023)
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) is forecast to collect a large sample of
Type Ia supernovae (SNe Ia) expected to be instrumental in unveiling the nature of dark energy. The feat, ...