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Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology…
Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems. For many ground states of gapped Hamiltonians, generative models can learn from measurements of a single quantum state to…
Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine…
We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from…
The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential.…
Modern coaxial and planar HPGe detectors allow a precise determination of the energies and trajectories of the impinging gamma-rays. This entails the location of the gamma interactions inside the crystal from the shape of the delivered…
We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in…
We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method…
With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the scope of our understanding about Nature without relying on…
We present a general method of associating next-to-leading order weights to leading order phase space configurations at hadron colliders. The method relies on a re-organization of phase space for the real radiation contributions, defining a…
The axial anomaly in the divergence of the singlet axial current in QCD $+$ QED leads to low-energy theorems for the matrix element of this operator equation over vacuum and two--photon states and for the matrix element over vacuum and…
We propose a new method for neutrino mass generation through a modified Higgs mechanism by introducing an additional tiny vacuum breaking to the charged Higgs field. With identical particle spectrum as standard electroweak model, this…
A nonlinear Helmholtz equation (NLH) with high wave number and Sommerfeld radiation condition is approximated by the perfectly matched layer (PML) technique and then discretized by the linear finite element method (FEM).…
A new method to construct event-generators based on next-to-leading order QCD matrix-elements and leading-logarithmic parton showers is proposed. Matrix elements of loop diagram as well as those of a tree level can be generated using an…
A finite element methodology for large classes of variational boundary value problems is defined which involves discretizing two linear operators: (1) the differential operator defining the spatial boundary value problem; and (2) a Riesz…
The presence of non-zero helicity in intergalactic magnetic fields is a smoking gun for their primordial origin since they have to be generated by processes that break CP invariance. As an experimental signature for the presence of helical…
This publication presents the combination of the one-loop matrix-element generator Recola with the multipurpose Monte Carlo program Sherpa. Since both programs are highly automated, the resulting Sherpa+Recola framework allows for the…
A Standard Model extension with two Majorana neutrinos can explain the measured neutrino masses and mixings, and also account for the matter-antimatter asymmetry in a region of parameter space that could be testable in future experiments.…
We review the modelling of multiple interactions in the event generator Herwig++ and study implications of recent tuning efforts to Tevatron and LHC data. It is often said that measurements of the effective cross section for double-parton…