Quantum Zeno-like effect and spectra of particles in cascade transition

Shr\"odinger equation for two-step spontaneous cascade transition in a three-level quantum system is solved by means of Markovian approximation for non-Markovian integro-differential evolution equations for amplitudes of states. It is shown that both decay constant and radiation shift of initial level are affected by instability of intermediate level of the cascade. These phenomena are interpreted as the different manifestations of quantum Zeno-like effect. The spectra of particles emitted during the cascade transition are calculated in the general case and, in particular, for an unusual situation when the initial state is lower than the intermediate one. It is shown that the spectra of particles do not have a peak-like shape in the latter case.Comment: 13 pages, no figures, to be published in Physica

General equation for Zeno-like effects in spontaneous exponential decay

It was shown that different mechanisms of perturbation of spontaneous decay constant: inelastic interaction of emitted particles with particle detector, decay onto an unstable level, Rabi transition from the final state of decay (electromagnetic field domination) and some others are really the special kinds of one general effect - perturbation of decay constant by dissipation of the final state of decay. Such phenomena are considered to be Zeno-like effects and general formula for perturbed decay constant is deduced.Comment: LaTeX 2.09 file, 11 pages, no figures. Accepted in Physics Letters

Selective Nonparametric Regression via Testing

Prediction with the possibility of abstention (or selective prediction) is an important problem for error-critical machine learning applications. While well-studied in the classification setup, selective approaches to regression are much less developed. In this work, we consider the nonparametric heteroskedastic regression problem and develop an abstention procedure via testing the hypothesis on the value of the conditional variance at a given point. Unlike existing methods, the proposed one allows to account not only for the value of the variance itself but also for the uncertainty of the corresponding variance predictor. We prove non-asymptotic bounds on the risk of the resulting estimator and show the existence of several different convergence regimes. Theoretical analysis is illustrated with a series of experiments on simulated and real-world data

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires either some heuristics to sample a batch of design points at one active learning iteration, or retraining the neural network after adding each data point, which is computationally inefficient. Moreover, uncertainty estimates for neural networks sometimes are overconfident for the points lying far from the training sample. In this work we propose to approximate Bayesian neural networks (BNN) by Gaussian processes, which allows us to update the uncertainty estimates of predictions efficiently without retraining the neural network, while avoiding overconfident uncertainty prediction for out-of-sample points. In a series of experiments on real-world data including large-scale problems of chemical and physical modeling, we show superiority of the proposed approach over the state-of-the-art methods

HERO (High Energy Ray Observatory) optimization and current status

The High-Energy Ray Observatory (HERO) is a space experiment based on a heavy ionization calorimeter for direct study of cosmic rays. The effective geometrical factor of the apparatus varies from 12 to 60 m$^2$sr for protons depending on the weight of the calorimeter from 10 to 70 tons. During the exposure for $\sim$5 years this mission will make it possible to measure energy spectra of all abundant cosmic ray nuclei in the knee region ($\sim$3 PeV) with individual resolution of charges with energy resolution better than 30\% and provide useful information to solve the puzzle of the cosmic ray knee origin. HERO mission will make it also possible to measure energy spectra of cosmic rays nuclei for energies 1-1000 TeV with very high precision and energy resolution (up to 3\% for calorimeter 70 tons) and study the fine structure of the spectra. The planned experiment launch is no earlier than 2029.Comment: LaTeX,25 pages, 19 figure