Scientific Series

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.

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.

I’ll discuss the issue of how we can tell which quantum state might be the “right “ one for inflationary quantum fluctuations. I’ll then use a new class of states that entangle curvature fluctuations with those of a spectator scalar field and discuss potential observational signatures of such states.

One of the leading hypotheses for dark matter is that it consists of bosonic particles with masses below the eV scale, such as axions, moduli and dark photons. Unlike spin-0 particles, spin-1 particles do not have a misalignment mechanism to produce the desired abundance of dark matter, and population of light dark photon dark matter has been an open question in cosmology. I will present a novel mechanism to produce light spin-1 dark matter in cosmology. The dark matter energy density is initially stored in an axion-like field which is misaligned from its minimum during inflation. This energy density is efficiently transferred to dark photons using tachyonic particle production. This mechanism opens up extensive parameter space for dark photon dark matter.

The appearance of scale invariance and diverging response functions in many-body systems is inseparably linked to the presence of a critical point and spontaneous symmetry breaking. In thermal equilibrium critical points mostly correspond to isolated spots in parameter space, which require rather strong fine tuning of e.g., the temperature of magnetic fields, in order to be reached. Pushing systems away from thermal equilibrium, e.g., by exposing them to external drive fields or dissipation, can give rise to more unconventional forms of criticality. External driving and dissipation may even turn critical points robust and attractive, imposing scale invariance for a large variety of external parameters, a phenomenon known as self-organized criticality (SOC).

I will discuss the manifestation of SOC in driven dissipative cold atom ensembles, for which a direct mapping of their microscopic parameters to an SOC field theory exists. This yields an opportunity to study SOC, typically associated with hard-to-control, large scale non-equilibrium setups, such as earthquakes, solar flares, or neural networks, under very controlled conditions. In addition, I will briefly explain the theoretical challenges for a quantum theory of self-organized criticality and point out current steps towards their realization.

Statistical evidence has previously suggested that the Galactic Center GeV Excess (GCE) originates largely from point sources, and not from annihilating dark matter. In this talk, I will discuss the impact of unmodeled source populations on identifying the true origin of the GCE. In a proof-of-principle example with simulated data, I will demonstrate that unmodeled sources in the Fermi Bubbles can lead to a dark matter signal being misattributed to point sources. Furthermore, I will show there is striking behavior consistent with a mismodeling effect in the real Fermi data, finding that large artificial injected dark matter signals are completely misattributed to point sources. Consequently, I will conclude that dark matter may provide a dominant contribution to the GCE after all, and discuss future directions.

It is generally believed that modification of general relativity inevitably introduce extra physical degree(s) of freedom.
In this talk I argue that this is not the case by constructing modified gravity theories with two local physical degrees of freedom. After classifying such theories into two types, I show explicit examples and discuss their cosmology and phenomenology.