DDAp/DDEIT and HIRSAP (Helmholtz International School for Astroparticle Physics and Enabling Technologies)
Dark matter has been an active field in cosmology and particle physics, involving astrophysical observations, experiments and theoretical modeling. As the Weakly Interacting Massive Particle paradigm became empirically disfavoured, a pandora box of Dark Matter models was opened, with candidates spanning 90 decades in mass. In this talk I will discuss one technique to explore Dark Matter properties, namely, strong gravitational lensing (SL). This effect produces highly distorted, magnified and/or multiple images of distant sources, whose light bundles are affected by the gravitational field of a foreground object acting as a lens. More generally, SL can be used to constrain cosmological parameters and to test modifications of General Relativity aimed at explaining Dark Energy. I briefly review the phenomenology of SL, from microlensing by stars and planets to giant arcs and Einstein rings from the lensing by galaxies and galaxy clusters. I will present recent results on using Einstein rings to constrain modified gravity and the prospects for upcoming wide-field optical surveys such as Rubin LSST. These surveys will require the use of machine learning both to find and to model the strong lenses. We shall also discuss the prospects of LSST for constraining the abundance of primordial black holes as Dark Matter.
The increasing depth and completeness of cosmological surveys is generating a wealth of data which is not trivial to unravel. New techniques using machine learning and advanced statistical methods will be necessary to make sense of that data, and to combine the different surveys. This vast wealth of information also imposes strict demands from simulations, which must cope with a unified description of large-scale structures not only in terms of different tracers, but also in different messengers -- including cosmic rays, gravitational waves and neutrinos. In this talk I will discuss some efforts in these directions.
In this talk I will present the last results in the development of the Digital Bakend (DB) for a read-out electronics, suitable for cryogenic particle sensors multiplexed in the frequency domain (FDM), with applicability in Cosmic Microwave Background Radiation (CMB) surveys and neutrino mass determination such as ECHo.