Search
Now showing items 1-10 of 17
Isothermal dust models of Herschel-ATLAS galaxies
(Oxford University Press, 2013)
We use galaxies from the Herschel-ATLAS survey, and a suite of ancillary simulations based on an isothermal dust model, to study our ability to determine the effective dust temperature, luminosity and emissivity index of ...
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 ...
Hiflow: Generating diverse hi maps and inferring cosmology while marginalizing over astrophysics using normalizing flows
(IOP Publishing, 2022)
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale
surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract ...
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 ...