activity
20152022
most citedRecurrent galactic cosmic-ray flux modulation in L1 and geomagnetic activity during the declining phase of the solar cycle 24

15 citations · 50 across the 8 of their papers we have counts for

collaborators

11 papers

gr-qc20214 cited

Including topology change in Loop Quantum Gravity with topspin network formalism with application to homogeneous and isotropic cosmology

Mattia Villani

We apply topspin network formalism to Loop Quantum Gravity in order to include in the theory the possibility of changes in the topology of spacetime. We apply this formalism to thr…

physics.space-ph202012 cited

Low-energy electromagnetic processes affecting free-falling test-mass charging for LISA and future space interferometers

Catia Grimani, Andrea Cesarini, Michele Fabi +1

Galactic cosmic rays and solar energetic particles charge gold-platinum, free-falling test masses (TMs) on board interferometers for the detection of gravitational waves in space.…

astro-ph.EP202015 cited

Recurrent galactic cosmic-ray flux modulation in L1 and geomagnetic activity during the declining phase of the solar cycle 24

Catia Grimani, Andrea Cesarini, Michele Fabi +3

Galactic cosmic-ray (GCR) flux short-term variations (1 month) in the inner heliosphere are mainly associated with the passage of high-speed solar wind streams (HSS) and interpl…

math.ST2020

When are Bayesian model probabilities overconfident?

Oscar Oelrich, Shutong Ding, Måns Magnusson +2

Bayesian model comparison is often based on the posterior distribution over the set of compared models. This distribution is often observed to concentrate on a single model even wh…

stat.ME2019

Anatomically informed Bayesian spatial priors for fMRI analysis

David Abramian, Per Sidén, Hans Knutsson +2

Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical bounda…

stat.ME20196 cited

Spectral Subsampling MCMC for Stationary Time Series

Robert Salomone, Matias Quiroz, Robert Kohn +2

Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relativel…