arXiv research

Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $ρ(p_T,Φ)$. High-level correlations of $ρ(p_T,Φ)$ learned by the neural network act a…

Geometric QCD framework establishes stable vacuum for quark confinement.

Study finds almost contact structures in thermal QCD-like theories at intermediate coupling.

Study of ${\cal M}$-theory dual of thermal QCD-like theories at intermediate coupling.

We show that the baryon number of N=2 supersymmetric QCD can be twisted in order to couple the topological field theory of non-abelian monopoles to $Spin^c$-structures. To motivate the construction, we also consider some aspects of the twisting procedure as a gauging of global currents in two and four dimensions, in pa…

Compactification of AdS5 allows studying meson behavior in QCD.

In this paper the dynamics of the classical chiral $QCD_{2}$ currents is studied. We describe how the dynamics of the theory can be summarized in an equation of the Lax form, thereby demonstrating the existence of an infinite set of conserved quantities. Next, the $r$ matrix of a fundamental Poisson relation is obtaine…

New method uses neural maps to efficiently sample lattice QCD distributions.

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair …

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering…

Geometric QHD tests improve hub detection in correlated data.

We obtain the best known quantitative estimates for the $L^p$-Poincaré and log-Sobolev inequalities on domains in various sub-Riemannian manifolds, including ideal Carnot groups and in particular ideal generalized H-type Carnot groups and the Heisenberg groups, corank $1$ Carnot groups, the Grushin plane, and various H…

Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.

Many four-dimensional supersymmetric compactifications of F-theory contain gauge groups that cannot be spontaneously broken through geometric deformations. These "non-Higgsable clusters" include realizations of $SU(3)$, $SU(2)$, and $SU(3) \times SU(2)$, but no $SU(n)$ gauge groups or factors with $n> 3$. We study poss…

Deep learning enhances Hamiltonian Monte Carlo for sampling gauge field configurations.

We study the path integral of a twisted $N=2$ supersymmetric Yang-Mills theory coupled with hypermultiplet having the bare mass. We explicitly compute the topological correlation functions for the $SU(2)$ theory on a compact oriented simply connected simple type Riemann manifold with $b_2^+ \geq 3$. As the corollaries,…

Non-trivial obstructions found for topological solitons in Yang-Mills-Chern-Simons theories.

Anomaly Awareness detects anomalies in particle physics and computer vision.

Study topological twists of massive SQCD with gauge group SU(2) and 3 or fewer fundamental hypermultiplets.

New quantum integrals discovered for a spin chain model.

We study solutions of the Bogomolny equation on R^2\times S^1$ with prescribed singularities. We show that Nahm transform establishes a one-to-one correspondence between such solutions and solutions of the Hitchin equations on a punctured cylinder with the eigenvalues of the Higgs field growing at infinity in a particu…

Foundation models trained on collider data improve jet generation tasks.

Improved diffusion models for manifold learning.

Study evaluates topological contributions in massive SQCD on compact 4-manifolds.

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for…

Optimizes latency and false alarm probability in change detection problems.

Lossy compression of statistical data using quantum annealing.

VAE improves anomaly detection for jet tagging at the LHC.

Interprets AI model for identifying boosted H → b̄b jets.

New construction reveals SU(2)-flavor fields in heterotic M5-brane model.