18 citations · 37 across the 6 of their papers we have counts for
8 papers
Implementation of frequency-correlated noise in CMB component separation: Method, Validation, and Early Applications
L. Zapelli, M. Bersanelli, L. P. L. Colombo +24
Parametric component separation methods used in cosmic microwave background (CMB) analyses commonly assume instrumental noise to be uncorrelated between frequency channels. This ap…
Flux jump correction in the QUBIC TES data pipeline
Belen Costanza, Lucas Merlo Azzolina, Claudia G. Scoccola +20
Flux jumps occur in the readout electronics of Transition Edge Sensor (TES) detectors coupled to Superconducting Quantum Interference Devices (SQUIDs), as a consequence of the peri…
Spectral Imaging with QUBIC: building astrophysical components from Time-Ordered-Data using Bolometric Interferometry
M. Regnier, T. Laclavere, J-Ch. Hamilton +33
The detection of B-modes in the CMB polarization pattern is a major issue in modern cosmology and must therefore be handled with analytical methods that produce reliable results. W…
Enhancing CMB map reconstruction and power spectrum estimation with convolutional neural networks
Belén Costanza, Claudia G. Scóccola, Matías Zaldarriaga
The accurate reconstruction of Cosmic Microwave Background (CMB) maps and the measurement of its power spectrum are crucial for studying the early universe. In this paper, we imple…
Tensions in cosmology: a discussion of statistical tools to determine inconsistencies
Matías Leizerovich, Susana J. Landau, Claudia G. Scóccola
We present a comprehensive analysis of statistical tools for evaluating tensions in cosmological parameter estimates arising from distinct datasets. Focusing on the unresolved Hubb…
Measuring the CMB primordial B-modes with Bolometric Interferometry
A. Mennella, P. Ade, A. Almela +111
The Q&U Bolometric Interferometer for Cosmology (QUBIC) is the first bolometric interferometer designed to measure the primordial B-mode polarization of the Cosmic Microwave Backgr…
Faster Bayesian inference with neural network bundles and new results for models
Augusto T. Chantada, Susana J. Landau, Pavlos Protopapas +2
In the last few years, there has been significant progress in the development of machine learning methods tailored to astrophysics and cosmology. We have recently applied one of th…
Cosmology-informed neural networks to solve the background dynamics of the Universe
Augusto T. Chantada, Susana J. Landau, Pavlos Protopapas +2
The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application…