Search Talks

The stability of dark matter and its interactions with the Standard Model remain some of the biggest mysteries of our time. Instead of inventing ad hoc stabilization symmetries I propose to economically use the existing baryon and lepton number symmetries of the SM. This can lead to interesting signatures in terrestrial experiments, shower us in anti-helium, and keep neutron stars warm.

We introduce two new effective quantities for the study of comoving curvature perturbations ζ: the space dependent effective sound speed (SESS) and the momentum dependent effective sound speed (MESS) . We use the SESS and the MESS to derive a new set of equations,  not involving explicitly entropy or anisotropies, which can be applied to any system described by an effective stress-energy-momentum tensor (EST), including any multi-fields systems, supergravity and modified gravity theories.

The MESS is the natural quantity to parametrize in a model independent way the effects produced on curvature perturbations by multi-fields systems, particle production and modified gravity theories and could be conveniently used in the analysis of LSS observations, such as the ones from the upcoming EUCLID mission or CMB radiation measurements. It can be also useful to study in a model independent way the production of primordial black holes.

Beside the degeneracy due to different theoretical scenarios producing the same MESS, we show that in absence of entropy or effective anisotropic stress there is an additional degeneracy related to the freedom in the choice of the initial energy scale of inflation, or to the sign of the Hubble parameter.  This implies the existence of an infinite family of dual slow-roll parameters histories which can produce the same spectrum of comoving curvature perturbations, implying  that in general there is no one-to-one correspondence between the spectrum and higher order correlation functions. Bounce models are  examples of  the members of this infinite class of dual models.

The combined analysis of data from future CMB and gravitational wave experiments could allow to distinguish between dual models  because the primordial tensor perturbations spectra of dual models are in general different.

Why is the future different from the past? The most poignant property about the world around us is that time is always moving forward, yet, as odd as it may seem, our current theories of physics cannot account for this property. In all of fundamental physics, the future and the past are entirely similar. We select time increasing solutions by hand and call on extremely unlikely initial conditions at the big bang to justify this choice. I will address this question and describe attempts to restore the irreversibility of time as the building block of our understanding of nature.

This talk was delivered at ISSYP 2019, Perimeter's high school summer program.

Canonical quantization is not well suited to quantize gravity, while affine quantization is. For those unfamiliar with affine quantization the talk will include a primer. This procedure is then applied to deal with three nonrenormalizable, field theoretical, problems of increasing difficulty, the last one being general relativity itself.

Self-learning Monte Carlo (SLMC) method is a general-purpose numerical method to simulate many-body systems. SLMC can efficiently cure the critical slowing down in both bosonic and fermionic systems. Moreover, for fermionic systems, SLMC can generally reduce the computational complexity and speed up simulations even away from the critical points. For example, SLMC is more than 1000 times faster than the conventional method for the double exchange model in 8*8*8 cubic lattice. In addition, SLMC also provides a general framework to naturally integrate the advanced machine learning techniques into Monte Carlo. In this talk, I will give an introduction about the background, basic idea and the design principle of SLMC. Later, I will explicitly show how to use SLMC and its great accelerations in classic systems, free fermions coupled with classical spins systems, and interacting fermion systems. At the end, I will talk our recent developed a new type of neural network, which is motivated by the mean field theory and could be used to represent many physical Hamiltonians. We believe the new structural network would be highly efficient to identify different phases and accelerate the numerical simulations for even complex models.