Theory Seminar

Unsupervised Machine Learning in Astrophysical Data

Large astrophysical surveys provide us with a wealth of data that has important implications for fundamental physics questions. Fully exploiting these datasets requires new techniques and approaches, including machine learning. However, it is …

B anomalies and the flavor of SMEFT

Recent data in B-meson decays indicate a coherent pattern of deviations from the Standard Model predictions. These 'anomalies' provide a perfect playground for using the Standard Model Effective Field Theory (SMEFT) in a bottom-up approach, not only …

Venturing into the neutrino fog

The last few years have seen the largest underground dark matter searches rapidly approach their purported ultimate sensitivity limit, which is increasingly being referred to as the "neutrino fog". An experiment reaches the neutrino fog went it …

Forward and Inverse LHC Simulations with Neural Networks

LHC physics is a unique field in the sense that we compare vast and highly complex data sets with precise first-principles predictions. Generative neural networks can supplement these simulations and come with conceptional advantages. I will show …

Gravitational instantons, CP asymmetry, and axions

Quantum gravity effects are typically considered irrelevant for particle physics and its phenomenology. Contrary to this, I will argue that non-perturbative quantum gravity effects associated with charged gravitational instantons have significant …

Dark Matter Puzzles from Indirect Searches

The nature and origin of dark matter is one of the key unresolved questions of fundamental physics. Astrophysical and cosmological data provide powerful probes of dark matter properties, although to date no signal has been confirmed. I will discuss a …

FASER and Forward Physics at the LHC

For decades, the focus of searches for new particles at the energy frontier has been on heavy particles and high $p_T$. Recently, however, new ideas have led to novel opportunities for discovery in the forward region with relatively fast, small, and …

Dark Matter Capture in Stars

The capture of dark matter in stars provides a cosmic laboratory in which to study the nature of dark matter particles and their interactions. We outline an improved treatment of the dark matter capture process, which correctly incorporates a number …

Direct detection of dark matter - a theorist’s perspective

Measurement of a signal in direct detection of a hypothetical particle constituting the dark matter in the Universe would provide arguably the most convincing proof of its nature. The problem is that we do not really know what the dark matter is. …

New Paradigms for New Physics Searches at the LHC with Machine Learning

In recent years, there has been growing interest and many new ideas for anomaly detection and model-independent new physics searches at the LHC, enabled by powerful advances in deep learning. I will give an overview of some of the recent progress in …