Related papers: Statistical analysis of electron bunch longitudina…
Neutrinos, dark matter, and long-lived neutral particles traverse the particle detectors unnoticed, carrying away information about their parent particles and interaction sources needed to reconstruct key variables like resonance peaks in…
The description of physical processes with many-particle systems is a key approach to the modeling of countless physical systems. In storage rings, where ultrarelativistic particles are agglomerated in dense bunches, the measurement of…
Spectral reconstruction is a well studied numerically ill-posed problem which arises due to the relation of the Euclidean correlator to the spectral function via an inhomogeneous Fredholm equation of the first kind. Several different…
Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable…
As charged particle bunches become shorter and more intense, the effects of nonlinear intra-bunch collective interactions such as space charge forces and bunch-to-bunch influences such as wakefields and coherent synchrotron radiation also…
We presented a novel concept of longitudinal bunch train compression capable of manipulating relativistic electron beam in range of hundreds of meters. This concept has the potential to compress the electron beam with a high ratio and raise…
We introduce a systematic approach that enables two robust methods for performing terahertz time-domain spectroscopy in reflection geometry. Using the Kramers-Kronig relations in connection to accurate experimental measurements of the…
Plasma accelerators driven by intense laser or particle beams provide gigavolt-per-meter accelerating fields, promising to drastically shrink particle accelerators for high-energy physics and photon science. Applications such as linear…
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise…
When passing through an undulator in a Free Electron Laser, dense bunches of relativistic electrons split into micro-bunches, attaining a periodic space-time structure. We show that the field of such periodically modulated bunches is…
Using the multilayer convolutional neural network (CNN), we can detect the quantum phases in random electron systems, and phase diagrams of two and higher dimensional Anderson transitions and quantum percolations as well as disordered…
High energy physics experiments, in particular experiments at the LHC, require the reconstruction of charged particle trajectories. Methods of reconstructing such trajectories have been known for decades, yet the applications at High…
Plasma wakefield acceleration holds remarkable promise for future advanced accelerators. The design and optimization of plasma-based accelerators typically require particle-in-cell simulations, which can be computationally intensive and…
Long term behavior of nonlinear deterministic continuous time signals can be studied in terms of their reconstructed attractors. Reconstructed attractors of a continuous signal are meant to be topologically equivalent representations of the…
Laser wakefield acceleration modeling using the Lorentz-boosted frame technique in the particle-in-cell code has demonstrated orders of magnitude speedups. A convergence study was previously conducted in cases with external injection in the…
This study investigates the convergence properties of a collection of iterative electron ptychography methods, under low electron doses ($<$ 10$^3$ $e^-/A^2$) and gives particular attention to the impact of the user-defined update…
Accurate computational ptychographic phase reconstructions are enabled by fast direct-electron cameras with high dynamic ranges used for four-dimensional scanning transmission electron microscopy (4D-STEM). The availability of open software…
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in…
We propose an L-BFGS optimization algorithm on Riemannian manifolds using minibatched stochastic variance reduction techniques for fast convergence with constant step sizes, without resorting to linesearch methods designed to satisfy Wolfe…
An adaptive kernel method in the Bayesian framework together with a new simulation program for Rutherford backscattering spectroscopy (RBS) have been applied to the analysis of RBS data. Even in the case of strongly overlapping RBS peaks a…