Center for Theoretical Physics

A high energy muon collider can provide new and complementary discovery potential to the LHC or future hadron colliders. Leptoquarks are a motivated class of exotic new physics models, with distinct production channels at hadron and lepton machines. We study a vector leptoquark model at a muon collider with \(\sqrt{s} = 3, 14\) TeV within a set of both UV and phenomenologically motivated flavor scenarios. We compute which production mechanism has the greatest reach for various values of the leptoquark mass and the coupling between leptoquark and Standard Model fermions. We find that we can probe leptoquark masses up to an order of magnitude beyond \(\sqrt{s}\) with perturbative couplings. Additionally, we can also probe regions of parameter space unavailable to flavor experiments. In particular, all of the parameter space of interest to explain recent low-energy anomalies in B meson decays would be covered even by a \(\sqrt{s} = 3\) TeV collider.

We study the contributions of supersymmetric models with a \(U(1)\) horizontal symmetry and only spontaneous CP breaking to various lepton flavor observables, such as \(\mu \to e\gamma\) and the electron electric dipole moment. We show that both a horizontal symmetry and a lack of explicit CP violation can alleviate the existing bounds from such observables. The undetermined \(\mathcal{O}(1)\) coefficients in such mass matrix models muddle the interpretation of the bounds from various flavor observables. To overcome this, we define a new fine-tuning measure for different observables in such setups. This allows us to study how naturally the observed IR flavor observables can emerge from a given mass matrix model. We use our flavor-naturalness measure in study of our supersymmetric models and quantify the degree of fine tuning required by the bounds from various lepton flavor observables at each mass scale of sleptons, neutralinos, and charginos. We also comment on implications of the observed discrepancy in the anomalous magnetic moment of the muon for our supersymmetric models.

We discuss why AI is hard and why physics is simple. We discuss how physical intuition and the approach of theoretical physics can be brought to bear on the field of artificial intelligence and specifically machine learning. We suggest that the underlying project of machine learning and the underlying project of physics are strongly coupled through the principle of sparsity, and we call upon theoretical physicists to work on AI as physicists. As a first step in that direction, we discuss an upcoming book on the principles of deep learning theory that attempts to realize this approach.

We study the effect of a first-order phase transition in a confining \(SU(N)\) dark sector with heavy dark quarks. The baryons of this sector are the dark matter candidate. During the confinement phase transition the heavy quarks are trapped inside isolated, contracting pockets of the deconfined phase, giving rise to a second stage of annihilation that dramatically suppresses the dark quark abundance. The surviving abundance is determined by the local accidental asymmetry in each pocket. The correct dark matter abundance is obtained for \(\mathcal{O}(1-100)\) PeV dark quarks, above the usual unitarity bound.