Drift Intermittent Synchronization and Controllability in a Simple Model

A simple model of three coupled oscillators as an approximation of main modes behaviors in a spatial extended system is proposed. Multi-looping generalized synchronization and drift intermittent lag phase synchronization phenomena are found in this simple model. For a certain of parameters in which chaotic-like intermittent behavior exhibit the amplitudes and phases of three modes are controlled to be synchronized states via coupling them with an external periodic mode.

erent Phase Control in e-Ar Scattering in a Bichromatic Laser Field in the Second Born Approximation

We study the elastic scattering of atomic argon by an electron in the presence of a bichromatic laser field in the second Born approximation. The target atom is approximated by a simple screening potential. We explore the dependences of the differential cross sections on the relative phase φ between the two components of the radiation field and discuss the influence of the number of photons exchanged on the phase-dependence effect. Moreover, for different scattering angles and incident electron energies, the differential cross sections are notably different.

Towards an understanding of Type Ia supernovae from a synthesis of theory and observations

Motivated by the fact that calibrated light curves of Type Ia supernovae （SNe Ia） have become a major tool to determine the expansion history of the Universe, considerable attention has been given to, both, observations and models of these events over the past 15 years. Here, we summarize new observational constraints, address recent progress in modeling Type Ia supernovae by means of three-dimensional hydrodynamic simulations, and discuss several of the still open questions. It will be be shown that the new models have considerable predictive power which allows us to study observable properties such as light curves and spectra without adjustable non-physical parameters. This is a necessary requisite to improve our understanding of the explosion mechanism and to settle the question of the applicability of SNe Ia as distance indicators for cosmology. We explore the capabilities of the models by comparing them be applied to study the origin of the diversity with observations and we show how such models can of SNe Ia.

Uncertainty relations for general phase spaces

We describe a setup for obtain!ing uncertainty relations for arbitrary pairs of observables related by a Fourier transform. The physical examples discussed here are the standard position and momentum, number and angle, finite qudit systems, and strings of qubits for quantum information applications. The uncertainty relations allow for an arbitrary choice of metric for the outcome distance, and the choice of an exponent distinguishing, e.g., absolute and root mean square deviations. The emphasis of this article is on developing a unified treatment, in which one observable takes on values in an arbitrary locally compact Abelian group and the other in the dual group. In all cases, the phase space symmetry implies the eciuality of measurement and preparation uncertainty bounds. There is also a straightforward method for determining the optimal bounds.

Quark jet versus gluon jet: fully-connected neural networks with high-level features

Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit that essentially no physics input is required.Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks(FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum pTJbins;trained on a wide region of pTJ∈[200, 1000] GeV. We find their performances are almost the same. The performance is better when the pTJis larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic(SIC) from detector level data would at most decrease by 15%.We also test the performance of FNNs with the full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.