scholarly journals Large-scale simulations of antihelium production in cosmic-ray interactions

2021 ◽  
Author(s):  
Anirvan Shukla ◽  
Amaresh Datta ◽  
Philip von Doetinchem ◽  
Diego-Mauricio Gomez-Coral ◽  
Carina Kanitz
Keyword(s):  
Large Scale ◽  
Cosmic Ray ◽  
Cosmic Ray Interactions ◽  
Large Scale Simulations

scholarly journals Large-scale simulations of antihelium production in cosmic-ray interactions

2020 ◽  
Vol 102 (6) ◽  
Author(s):  
Anirvan Shukla ◽  
Amaresh Datta ◽  
Philip von Doetinchem ◽  
Diego-Mauricio Gomez-Coral ◽  
Carina Kanitz
Keyword(s):  
Large Scale ◽  
Cosmic Ray ◽  
Cosmic Ray Interactions ◽  
Large Scale Simulations

An HPC Framework for Large Scale Simulations and Visualizations of Oil Spill Trajectories

2013 ◽  
Author(s):  
Jian Tao ◽  
Werner Benger ◽  
Kelin Hu ◽  
Edwin Mathews ◽  
Marcel Ritter ◽  
...  
Keyword(s):  
Oil Spill ◽  
Large Scale ◽  
Large Scale Simulations

scholarly journals Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips
Keyword(s):  
Large Scale ◽  
Research Question ◽  
Group Differences ◽  
Regularity Index ◽  
Wake Patterns ◽  
Health Related ◽  
Phase Deviation ◽  
Unbiased Estimates ◽  
Large Scale Simulations ◽  
Larger Sample

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4804
Author(s):  
Marcin Piekarczyk ◽  
Olaf Bar ◽  
Łukasz Bibrzycki ◽  
Michał Niedźwiecki ◽  
Krzysztof Rzecki ◽  
...  
Keyword(s):  
Wavelet Transforms ◽  
Exploratory Study ◽  
Large Scale ◽  
Morphological Difference ◽  
Cosmic Ray ◽  
Input Image ◽  
Image Features ◽  
The Earth ◽  
Cmos Sensor ◽  
Competition Process

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

scholarly journals Gamma-ray astrophysics in the MeV range

2021 ◽  
Author(s):  
Alessandro De Angelis ◽  
Vincent Tatischeff ◽  
Andrea Argan ◽  
Søren Brandt ◽  
Andrea Bulgarelli ◽  
...  
Keyword(s):  
High Efficiency ◽  
Gamma Ray ◽  
Earth Science ◽  
Technological Development ◽  
Cosmic Ray ◽  
Space Mission ◽  
Compact Objects ◽  
Medium Size ◽  
Cosmic Ray Interactions ◽  
Silicon Microstrip Detectors

AbstractThe energy range between about 100 keV and 1 GeV is of interest for a vast class of astrophysical topics. In particular, (1) it is the missing ingredient for understanding extreme processes in the multi-messenger era; (2) it allows localizing cosmic-ray interactions with background material and radiation in the Universe, and spotting the reprocessing of these particles; (3) last but not least, gamma-ray emission lines trace the formation of elements in the Galaxy and beyond. In addition, studying the still largely unexplored MeV domain of astronomy would provide for a rich observatory science, including the study of compact objects, solar- and Earth-science, as well as fundamental physics. The technological development of silicon microstrip detectors makes it possible now to detect MeV photons in space with high efficiency and low background. During the last decade, a concept of detector (“ASTROGAM”) has been proposed to fulfil these goals, based on a silicon hodoscope, a 3D position-sensitive calorimeter, and an anticoincidence detector. In this paper we stress the importance of a medium size (M-class) space mission, dubbed “ASTROMEV”, to fulfil these objectives.

Brain stem – from general view to computational model based on switchboard rules of operation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Włodzisław Duch ◽  
Dariusz Mikołajewski
Keyword(s):  
Brain Stem ◽  
Large Scale ◽  
Ionic Channels ◽  
General View ◽  
The Central Nervous System ◽  
Network States ◽  
Large Scale Simulations ◽  
Influence Of Noise ◽  
The Brain ◽  
Sufficient Precision

Abstract Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

scholarly journals High energy cosmic ray interactions and UHECR composition problem

2019 ◽  
Vol 210 ◽  
pp. 02001
Author(s):  
Sergey Ostapchenko
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Cosmic Rays ◽  
Cosmic Ray ◽  
High Energy ◽  
Ultra High Energy ◽  
Hadronic Interactions ◽  
High Energy Cosmic Rays ◽  
Cosmic Ray Interactions ◽  
Composition Problem

The differences between contemporary Monte Carlo generators of high energy hadronic interactions are discussed and their impact on the interpretation of experimental data on ultra-high energy cosmic rays (UHECRs) is studied. Key directions for further model improvements are outlined. The prospect for a coherent interpretation of the data in terms of the UHECR composition is investigated.

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