Transfer of "dark hydrogen" by atomic matter. Methods of diagnostics of "dark hydrogen"

This work experimentally shown that traces found on track detectors during the study of low-energy nuclear reactions are also formed in the course of many widely used technical processes (combustion of hydrocarbons, operation of internal combustion engines, physicochemical processes accompanying the process of charging smartphone batteries). This coincidence of the track pattern allows us to consider low-energy nuclear reactions as a significant environmental factor, and indicates the important role of “dark hydrogen” in nature. The paper shows the convective transfer of “dark hydrogen” from the discharge zone along the path of the air-water mixture. Using the theoretical model of “dark hydrogen”, fundamentally new, less laborious, in comparison with track, methods of its registration have been developed and described: 1) measurement of the charge of a copper box with its irradiation with “dark hydrogen”, 2) measurement of pressure in a closed volume when irradiated with “dark hydrogen”, 3) the use of a torsion balance with a nickel plate with magnets when irradiated with “dark hydrogen”.

Development of a track-pattern-based medium-range tropical cyclone forecasting system for the western North Pacific

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the present document we describe new algorithms that can handle complex realistic detectors. The new algorithms are implemented in ACTS, a common framework for tracking software. This work aims at implementing a realistic GNN-based algorithm that can be deployed in an HL-LHC experiment.

Dominant Influence of ENSO-Like and Global Sea Surface Temperature Patterns on Changes in Prevailing Boreal Summer Tropical Cyclone Tracks over the Western North Pacific

A conventional empirical orthogonal function (EOF) analysis is performed on summertime (May–October) western North Pacific (WNP) tropical cyclone (TC) track density anomalies during 1970–2012. The first leading EOF mode is characterized by a consistent spatial distribution across the WNP basin, which is closely related to an El Niño–Southern Oscillation (ENSO)-like pattern that prevails on both interannual and interdecadal time scales. The second EOF mode is represented by a tripole pattern with consistent changes in westward and recurving tracks but with an opposite change for west-northwestward TC tracks. This second EOF pattern is dominated by consistent global sea surface temperature anomaly (SSTA) patterns on interannual and interdecadal time scales, along with a long-term increasing global temperature trend. Observed WNP TC tracks have three distinct interdecadal epochs (1970–86, 1987–97, and 1998–2012) based on EOF analyses. The interdecadal change is largely determined by the changing impact of ENSO-like and consistent global SSTA patterns. When global SSTAs are cool (warm) during 1970–86 (1998–2012), these SSTAs exert a dominant impact and generate a tripole track pattern that is similar to the positive (negative) second EOF mode. In contrast, a predominately El Niño–like SSTA pattern during 1987–97 contributed to increasing TC occurrences across most of the WNP during this 11-yr period. These findings are consistent with long-term trends in TC tracks, with a tripole track pattern observed as global SSTs increase. This study reveals the potential large-scale physical mechanisms driving the changes of WNP TC tracks in association with climate change.

Quantum annealing algorithms for track pattern recognition

The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.

Quantum Associative Memory in Hep Track Pattern Recognition

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

Statistical–Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors

A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004–12 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%–25% between 24- and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.