Vacuum breakdown in magnetic dipole wave by 10-PW class lasers.

The vacuum breakdown by 10-PW-class lasers is studied in the optimal configuration of laser beams in the form of an m-dipole wave, which maximizes the magnetic field. Using 3D PIC simulations we calculated the threshold of vacuum breakdown, which is about 10 PW. We examined in detail the dynamics of particles and identified particle trajectories which contribute the most to vacuum breakdown in such highly inhomogeneous fields.

Particle trajectories, gamma-ray emission, and anomalous radiative trapping effects in magnetic dipole wave.

In studies of interaction of matter with laser fields of extreme intensity there are two limiting cases of a multibeam setup maximizing either the electric field or the magnetic field. In this work attention is paid to the optimal configuration of laser beams in the form of an m-dipole wave, which maximizes the magnetic field. We consider in such highly inhomogeneous fields the advantages and specific features of laser-matter interaction, which stem from individual particle trajectories that are strongly affected by gamma photon emission.

Machine learning approach to the Floquet-Lindbladian problem.

Similar to its classical version, quantum Markovian evolution can be either time-discrete or time-continuous. Discrete quantum Markovian evolution is usually modeled with completely positive trace-preserving maps, while time-continuous evolution is often specified with superoperators referred to as "Lindbladians." Here, we address the following question: Being given a quantum map, can we find a Lindbladian that generates an evolution identical-when monitored at discrete instances of time-to the one induced by the map? It was demonstrated that the problem of getting the answer to this question can be reduced to an NP-complete (in the dimension N of the Hilbert space, the evolution takes place in) problem.

Towards ML-Based Diagnostics of Laser-Plasma Interactions.

The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, the differences between the two can pose an obstacle to reliable data extraction. Here we report on the development of ML-based diagnostics for experiments on high-intensity laser-matter interactions.

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy.

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments.