Related papers: Fast simulation of muons produced at the SHiP expe…

I train a Generative Adversarial Network to produce realistic seismic wave speed models. I integrate the generator network into seismic Full-Waveform Inversion to reduce the number of model parameters and restrict the inverted models to…

The Muon g-2 Experiment plans to use the Fermilab Recycler Ring for forming the proton bunches that hit its production target. The proposed scheme uses one RF system, 80 kV of 2.5 MHz RF. In order to avoid bunch rotations in a mismatched…

The presented results are part of a feasibility study of a Super- heated Superconducting Granule (SSG) device for weakly interacting massive particles (WIMPs) detection. The sensitivity of SSG to nuclear recoils has been explored…

Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e.g., adversarial examples. State-of-art attack methods can generate attack images by…

Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy computation and storage cost. Traditional network compression…

This paper develops a generative deep learning model for the synthesis of multiple-input multiple-output (MIMO) active sensing waveforms with desired properties, including constant modulus and a user-defined beampattern. The proposed…

Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation…

Remote sensing change detection between bi-temporal images receives growing concentration from researchers. However, comparing two bi-temporal images for detecting changes is challenging, as they demonstrate different appearances. In this…

We have calculated cross sections for the production of lepton pairs by a neutrino incident on a nucleus using both the equivalent photon approximation, and deep inelastic formalism. We find that production of mixed flavour lepton pairs can…

In order to study the interactions and structure of various types of matter one typically needs to carry out scattering experiments utilizing many different particles as projectiles. Whereas beams of $e^\pm$, $\mu^\pm$, $\pi^\pm$, $K^\pm$,…

A new muon beamline, muon science innovative channel (MuSIC), was set up at the Research Centre for Nuclear Physics (RCNP), Osaka University, in Osaka, Japan, using the 392 MeV proton beam impinging on a target. The production of an intense…

This thesis investigates the application of state-of-the-art advances in generative neural networks for fast simulation of the Zero Degree Calorimeter (ZDC) neutron detector in the ALICE experiment at CERN. Traditional simulation methods…

We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST…

Recently, generative machine-learning models have gained popularity in physics, driven by the goal of improving the efficiency of Markov chain Monte Carlo techniques and of exploring their potential in capturing experimental data…

Single-pixel imaging is a novel imaging scheme that has gained popularity due to its huge computational gain and potential for a low-cost alternative to imaging beyond the visible spectrum. The traditional reconstruction methods struggle to…

Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelerators. Recently, the HEP community has suggested Generative…

In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message-passing generative adversarial network (MPGAN) was the first…

We study axionlike particles (ALPs) in beam dump experiments, focusing on the Search for Hidden Particles (SHiP, at CERN) experiment and the Beam Dump eXperiment (BDX, at JLab). Many existing projections for sensitivity to ALPs in beam dump…

Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an access to samples which have high quality, and advanced…

We consider laboratory experiments that can detect stable, neutral strongly interacting massive particles (SIMPs). We explore the SIMP annihilation cross section from its minimum value (restricted by cosmological bounds) to the barn range,…