Talks

Neutrinos are a key (although implicit) ingredient of the standard cosmological model, LambdaCDM. Firstly, neutrinos directly participate in neutron freeze out during BBN, and secondly, they represent 40% of the energy density of the Universe after electron positron annihilation up to almost matter radiation equality. The latter fact makes neutrinos a necessary element to understand CMB observations. 

In this talk, I will review the cosmological implications of neutrinos. I will explain how current cosmological observations can be used to constrain their masses, their abundances, and their properties -- such as their interaction rate with other species. In particular, I will highlight that the typically very stringent constraint on their masses can be substantially relaxed if neutrinos decay on cosmological timescales. I will illustrate the implications of neutrino decays in cosmology with a few well-motivated neutrino mass models in which neutrinos can decay. I will then show that Planck CMB observations are a powerful tool to constraint neutrino interactions with neutrinophilic bosons. In particular, I will demonstrate that Planck legacy constraints neutrinophilic bosons with couplings as small as 10^{-13} with neutrinos for boson masses in the 0.1 eV < m < 300 eV range. I will finish by reviewing the role neutrinos can play with regards to the outstanding Hubble tension. I will show that pseudogoldstone bosons (majorons) interacting with neutrinos right before recombination represent a well motivated possibility to ameliorate (and potentially solve) the Hubble tension.

Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to predict the phases of the one-dimensional extended spinless Fermi-Hubbard model at half-filling. Results provide strong evidence that the NN correctly learns an order parameter describing the quantum transition. Moreover, we demonstrate that influence functions not only allow to check that the network, trained to recognize known quantum phases, can predict new unknown ones but even guide physicists in understanding patterns responsible for the phase transition. This method requires no a priori knowledge on the order parameter, the system itself, or even the architecture of the ML model.