bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation on properties of FeNi intermetallics under pressure by First-principles
Authors: Wang M J , Zhang G W , Xu H 
Published: July 2020

Abstract
In this paper, the structural stabilities, elastic, electronic and magnetic properties of three binary Fe-Ni intermetallics with different structure under pressure have been systematically investigated by first-principle method based on density functional theory. The results indicated that the lattice parameters and bulk modulus of Fe-Ni compounds at zero pressure match well with other experimental data and available theoretical calculated values. The calculated energy-volume and pressure-volume expressed that all the compounds are mechanical stability under pressure, and the volume variation increase with the increasing iron content under pressure. The shear deformation resistant and volume deformation resistant are estimated by elastic constant Cij and bulk modulus B. Meanwhile, applied pressure improved the ductility of the Fe-Ni compounds, elastic anisotropy of three compounds under pressure are arranged in the following order: Fe3Ni>FeNi>FeNi3. Debye temperature ΘD of compounds gradually increase with the increase of iron contents and pressure, the temperature dependence of the linear thermal expansion coefficient α and heat capacity (CV and CP) are also calculated based on quasi-harmonic Debye model under pressure from 0 to 50GPa and various temperature. The results gain a better understanding of iron-nickel alloys.
Study of the influence of electrical parameters on launch performance of projectile from the single-stage reluctance coil launcher
Authors: Li Zh Y , Shen Y L , Li H R , Song L N 
Published: July 2020

Abstract
To increase the exit velocity of projectile from single-stage reluctance coil launcher, the theoretical analysis is conducted for the projectile acceleration principle. The influence law of the electrical parameters on the projectile velocity is obtained, by the imitation research about the influence rule of the variation of electrical parameters (including capacitor voltage and capacitance) in a single-stage reluctance coil emitter on the transmitting performance, using the two-dimensional transient field solver in the finite element analysis software of Ansoft. At the same time, the single-stage reluctance coil launcher is also set up to conduct relevant tests, and the correctness of simulation analysis is verified. The results show that increasing the voltage and capacitance of the capacitor is beneficial to increase the exit velocity of the projectile, in the condition that the relative position of the projectile and driving coil is constant, The capacitor voltage is less than 1000V, and the capacitor capacitance is less than 1500μF. The simulation and experiment results provide guidance for the related analysis and study of multi-stage reluctance coil launcher.
Research on optimize application of Buckingham Pi theorem to wind tunnel test and its aerodynamic simulation verification
Authors: Hu Pengfei , Chang Chia-kan 
Published: July 2020

Abstract
In physical modeling, especially in the field of wind tunnel tests, dimensional analysis is essential, but the existing dimensional analysis methods are difficult to select the appropriate physical quantities. Therefore, based on the improvement of existing dimensional analysis methods for wind tunnel tests, a set of optimized dimensional analysis methods for wind tunnel tests based on Buckingham Pi theorem was derived in this paper, and then we put forward an aerodynamic simulation experiment based on fluent to verify this method.
Simulation analysis on explosion safety of intense light stun grenades
Authors: Ma Y Zh , Yang D Ch 
Published: July 2020

Abstract
To evaluate the explosion safety of intense light stun grenades, an explosion simulation model was established for their explosion bodies based on a LS-DYNA simulation platform. In this way, both the distribution rules and velocity variations of fragments can be analyzed. As indicated by the simulation results, their fragments are proven to be non-uniformly distributed as far as their sizes are concerned; and, low-velocity fragments with a large mass produced in the middle of the explosion body are the major sources of damage. Moreover, the corresponding radius of safety turns out to be 2.33m approximately. In a word, such an investigation provides a scientific basis for the R&D and applications of intense light stun grenades.
Three loop QCD corrections to heavy quark form factors
Authors: Ablinger J. , Blümlein J. , Marquard P. , Rana N. , Schneider C. 
Published: July 2020

AbstractHigher order calculations in perturbative Quantum Field Theories often produce coupled linear systems of differential equations which factorize to first order. Here we present an algorithm to solve such systems in terms of iterated integrals over an alphabet the structure of which is implied by the coefficient matrix of the given system. We apply this method to calculate the master integrals in the color–planar and complete light quark contributions to the three-loop massive form factors.
CutLang: a cut-based HEP analysis description language and runtime interpreter
Authors: Unel G. , Sekmen S. , Toon A.M. 
Published: July 2020

Abstract
We present CutLang, an analysis description language and runtime interpreter for high energy collider physics data analyses. An analysis description language is a declerative domain specific language that can express all elements of a data analysis in an easy and unambiguous way. A full-fledged human readable analysis description language, incorporating logical and mathematical expressions, would eliminate many programming difficulties and errors, consequently allowing the scientist to focus on the goal, but not on the tool. In this paper, we discuss the guiding principles and scope of the CutLang language, implementation of the CutLang runtime interpreter and the CutLang framework, and demonstrate an example of top pair reconstruction.
The Controls and Configuration Software of the Atlas Data Acquisition System for LHC Run 2
Authors: Kazarov A , Aleksandrov I , Avolio G , Caprini M , Chitan A , Corso Radu A , Kazymov A , Lehmann Miotto G , Mineev M , Santos A , Soloviev I , Vasile M , Unel G 
Published: July 2020

Abstract
The ATLAS experiment at the Large Hadron Collider (LHC) operated very successfully in the years 2008 to 2013, a period identified as Run 1. ATLAS achieved an overall data-taking efficiency of 94%, largely constrained by the irreducible dead-time introduced to accommodate the limitations of the detector read-out electronics. Out of the 6% dead-time only about 15% could be attributed to the central trigger and DAQ system, and out of these, a negligible fraction was due to the Control and Configuration sub-system. Despite these achievements, and in order to improve the efficiency of the whole DAQ system in Run 2 (2015-2018), the first long LHC shutdown (2013-2014) was used to carry out a complete revision of the control and configuration software. The goals were three-fold: properly accommodate additional requirements that could not be seamlessly included during steady operation of the system; re-factor software that had been repeatedly modified to include new features, thus becoming less maintainable; and seize the opportunity of modernizing software written even before Run 1, thus profiting from the rapid evolution in IT technologies. This upgrade was carried out retaining the important constraint of minimally impacting the mode of operation of the system and public APIs, in order to maximize the acceptance of the changes by the large user community. This paper presents, using a few selected examples, how the work was approached and which new technologies were introduced into the ATLAS DAQ system, and how they were performing in course of Run 2. Despite these being specific to this system, many solutions can be considered and adapted to different distributed DAQ systems.
Heterogeneous computing for the local reconstruction algorithms of the CMS calorimeters
Authors: Massironi Andrea , Khristenko Viktor , DAlfonso Mariarosaria 
Published: July 2020

Abstract
The increasing LHC luminosity in Run III and, consequently, the increased number of simultaneous proton-proton collisions (pile-up) pose significant challenges for the CMS experiment. These challenges will affect not only the data taking conditions, but also the data processing environment of CMS, which requires an improvement in the online triggering system to match the required detector performance. In order to mitigate the increasing collision rates and complexity of a single event, various approaches are being investigated. Heterogenous computing resources, recently becoming prominent and abundant, may be significantly better performing for certain types of workflows. In this work, we investigate implementations of common algorithms targeting heterogenous platforms, such as GPUs and FPGAs. The local reconstruction algorithms of the CMS calorimeters, given their granularity and intrinsic parallelizability, are among the first candidates considered for implementation in such heterogenous platforms. We will present the current development status and preliminary performance results. Challenges and various obstacles related to each platform, together with the integration into CMS experiments framework, will be further discussed.
Automated and Intelligent Data Migration Strategy in High Energy Physical Storage Systems
Authors: Cheng Zhenjing , Wang Lu , Cheng Yaodong , Chen Gang 
Published: July 2020

Abstract
As a data-intensive computing application, high-energy physics requires storage and computing for large amounts of data at the PB level. IHEP computing center is beginning to use tiered storage architectures, such as tape, disk or solid state drives to reduce hardware purchase costs and power consumption. At present, automatic data migration strategies are mainly used to resolve data migration between memory and disk. So the rules are relatively simple. This paper attempted to use the deep learning algorithm model to predict the evolution trend of data access heat as the basis for data migration. The implementation of some initial parts of the system were discussed, as well as the file trace collector and the LSTM model. At last some preliminary experiments are conducted with these parts.
In-Memory Parallel Computing for Partial Wave Analysis
Authors: Wei Zhanchen , Huang Qiulan , Sun Gongxing , Liu Xiaoyu 
Published: July 2020

Abstract
The traditional partial wave analysis (PWA) algorithm is designed to process data serially which requires a large amount of memory that may exceed the memory capacity of one single node to store runtime data. It is quite necessary to parallelize this algorithm in a distributed data computing framework to improve its performance. Within an existing production-level Hadoop cluster, we implement PWA algorithm on top of Spark to process data storing on low-level storage system HDFS. But in this case, sharing data through HDFS or internal data communication mechanism of Spark is extremely inefficient. In order to solve this problem, this paper presents an in-memory parallel computing method for PWA algorithm. With this system, we can easily share runtime data in parallel algorithms. We can ensure complete data locality to keep compatibility with the traditional data input/output way and cache most repeatedly used data in memory to improve the performance, owe to the data management mechanism of Alluxio.
Real-time cluster finding for LHCb silicon pixel VELO detector using FPGA
Authors: Lazzari Federico , Bassi Giovanni , Cenci Riccardo , Morello Michael J , Punzi Giovanni 
Published: July 2020

Abstract
In the Run-3 of LHCb, the High Level Trigger will have to process events at full LHC collision rate (30 MHz). This is a very challenging goal, and delegating some low-level tasks to FPGA accelerators can be very helpful by saving precious computing time. In particular, the 2D pixel geometry of the new LHCb VELO detector makes the cluster-finding process particularly CPU-time demanding. We realized and tested a highly parallel FPGA-based clustering algorithm, capable of performing this reconstruction in real time at 30 MHz event rate using a modest amount of hardware resources, that can be a viable alternative solution.
Speeding HEP Analysis with ROOT Bulk I/O
Authors: Bockelman B , Zhang Z , Shadura O 
Published: July 2020

Abstract
Distinct HEP workflows have distinct I/O needs; while ROOT I/O excels at serializing complex C++ objects common to reconstruction, analysis workflows typically have simpler objects and can sustain higher event rates. To meet these workflows, we have developed a “bulk I/O” interface, allowing multiple events’ data to be returned per library call. This reduces ROOT-related overheads and increases event rates – orders-of-magnitude improvements are shown in microbenchmarks.
Unfortunately, this bulk interface is difficult to use as it requires users to identify when it is applicable and they still “think” in terms of events, not arrays of data. We have integrated the bulk I/O interface into the new RDataFrame analysis framework inside ROOT. As RDataFrame’s interface can provide improved type information, the framework itself can determine what data is readable via the bulk IO and automatically switch between interfaces. We demonstrate how this can improve event rates when reading analysis data formats, such as CMS’s NanoAOD.
Towards a coherent Data Life Cycle in Astroparticle Physics
Authors: Tokareva V , Haungs A , Kang D , Kostunin D , Polgart F , Wochele D , Wochele J 
Published: July 2020

Abstract
The German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) aims to develop a data life cycle (DLC), namely a clearly defined and maximally automated data processing pipeline for a combined analysis of data from the experiment KASCADE-Grande (Karlsruhe, Germany) and experiments installed at the Tunka Valley in Russia (TAIGA). The important features of such an astroparticle DLC include scalability for handling large amounts of data, heterogeneous data integration, and exploiting parallel and distributed computing at every possible stage of the data processing. In this work we provide an overview of the technical challenges and solutions worked out so far by the GRADLCI group in the framework of a far-reaching analysis and data center. We will touch the peculiarities of data management in astroparticle physics and employing distributed computing for simulations and physics analyses in this field.
Explore cloud solutions for ATLAS with $250,000 AWS cloud credits
Authors: Bawa H. , Gao Y. , Moss J. , Grimm K. 
Published: July 2020

Abstract
Maintaining the huge grid computing facilities for LHC experiments and replacing their hardware every few years has been very expensive. The California State University (CSU) ATLAS group recently received $250,000 AWS cloud credit from the CSU Chancellor’s Office to build the first virtual US ATLAS Tier 3 to explore cloud solutions for ATLAS. We will use this award to set up full ATLAS computing environments on the cloud for ATLAS physics analysis frame works, MC generation, simulation and production. We will also develop policies for ATLAS members to submit jobs to the cloud and develop an economic model focused especially on the cost effectiveness of cloud solutions for ATLAS through extensive real user experience. The results will help ATLAS computing and physics communities decide future directions with incoming LHC upgrades.
Incorporation of systematic uncertainties in the training of multivariate methods
Authors: Alef Thomas , Toerne Eckhard von 
Published: July 2020

Abstract
Due to the large size of datasets accumulated at the LHC, analysis results are often limited by systematic effects. The application of multivariate analysis techniques such as Boosted Decision Trees (BDTs) or artificial neural nets typically maximises the statistical significance of the results while ignoring systematic effects. There is a known strategy to mitigate systematic effects for neural nets but no firmly established procedure for BDTs. We present a method to incorporate systematic uncertainties into a BDT, the systematics-aware BDT (saBDT). We evaluate our method on open data of the ATLAS Higgs machine learning challenge and compare our results to neural nets trained with an adversary.
Machine Learning on sWeighted data
Authors: Borisyak M , Kazeev N 
Published: July 2020

Abstract
Data analysis in high energy physics has to deal with data samples produced from different sources. One of the most widely used ways to unfold their contributions is the sPlot technique. It uses the results of a maximum likelihood fit to assign weights to events. Some weights produced by sPlot are by design negative. Negative weights make it difficult to apply machine learning methods. The loss function becomes unbounded. This leads to divergent neural network training. In this paper we propose a mathematically rigorous way to transform the weights obtained by sPlot into class probabilities conditioned on observables, thus enabling to apply any machine learning algorithm out-of-the-box.
Automated performance tuning of distributed storage system based on deep reinforcement learning
Authors: Wang Lu , Zhang Wentao , Cheng Yaodong 
Published: July 2020

Abstract
Automated performance tuning is a tricky task for a large scale storage system. Traditional methods highly reply on experience of system administrators and cannot adapt to changes of working load and system configurations. Reinforcement learning is a promising machine learning paradigm which learns an optimized strategy from the trials and errors between agents and environments. Combining with the strong feature learning capability of deep learning, deep reinforcement learning has showed its success in many fields. We implemented a performance parameter tuning engine based on deep reinforcement learning for Lustre file system, a distributed file system widely used in HEP data centres. Three reinforcement learning algorithms: Deep Q-learning, A2C, and PPO are enabled in the tuning engine. Experiments show that, in a small test bed, with IOzone workload, this method can increase the random read throughput by about 30% compared to default settings of Lustre. In the future, it is possible to apply this method to other parameter tuning use cases of data centre operations.
Deep Learning applied to hit classification for the BESIII drift chamber
Authors: Zhang Yao , Yuan Ye , Ma Qiumei 
Published: July 2020

Abstract
Drift chamber is the main tracking detector for high energy physics experiment BESIII. Due to the high luminosity and high beam intensity, the BESIII drift chamber is suffered from the beam background and electronics noise which represent a computing challenge to the reconstruction software. Deep learning developments in the last few years have shown tremendous improvements in the analysis of data especially for object classification. Here we present a first study of deep learning architectures applied to the real data of BESIII drift chamber to accomplish the hit classification of the background and signal.
Electromagnetic shower generation with Graph Neural Networks
Authors: Belavin V. , Ustyuzhanin A. 
Published: July 2020

Abstract
In this work, we propose an approach for electromagnetic shower generation on a track level. Currently, Monte Carlo simulation occupies 50-70% of total computing resources that are used by physicists experiments worldwide. Thus, speedup of the simulation step allows to reduce simulation cost and accelerate synthetic experiments. In this paper, we suggest dividing the problem of shower generation into two separate issues: graph generation and tracks features generation. Both these problems can be efficiently solved with a cascade of deep autoregressive generative network and graph convolution network. The novelty of the proposed approach lies in the application of graph neural networks to the generation of a complex recursive physical process.
Generalization of Homogeneity Tests for Weighted Samples and Their Implementation in ROOT
Authors: Trusina Jakub , Franc Jiří , Novotný Adam 
Published: July 2020

Abstract
In High Energy Physics, tests of homogeneity are used primarily in two cases: for verification that data sample does not differ significantly from numerically produced Monte Carlo sample and for verifying separation of signal from background. Since Monte Carlo samples are usually weighted, it is necessary to modify classical homogeneity tests in order to apply them to weighted samples. In ROOT, the only homogeneity tests that allow testing weighted samples are implemented for binned data. However, after the data are binned the full information is lost. Therefore we compare these ordinary tests with modified versions of the Kolmogorov-Smirnov, Anderson-Darling, and Cramér-von Mises tests that use full sample information. The proposed tests are compared by estimating a probability of type-I error which is crucial for a test’s reliability.
Impact of climate change on rainfall over Chennai
Authors: Athira T , Agilan V 
Published: July 2020

Abstract
Global warming and climate change are currently topics of importance in environmental studies. Climate change is predominantly contributed by human activities and it presents a serious threat to nature and people. Greenhouse gasses such as carbon dioxide trap heat in the atmosphere and regulate climate. In addition, rapid urbanization and industrialization add more greenhouse gases to atmosphere and increase overall global temperature and further change the weather patterns. The unexpected weather conditions in Chennai has gained wide attention, especially after 2015 flood, Vardah and Ocki cyclones. In this study, the possible impact of climate change on rainfall over Chennai city is analysed under various Representative Concentration Pathway (RCP) scenarios. Simulations of a Coordinated Regional Climate Downscaling Experiment (CORDEX) Regional Climate Model (RCM) is downscaled using change factor method. The study results show that the intensity and frequency of extreme rainfall over Chennai is likely to increase.
Analytical evidence for deep ocean warming trend in tropical Indian Ocean
Authors: Malavika A R , Pai Maya L , Saji P K 
Published: July 2020

AbstractThe present climate change has a warming impact on the Earth’s climate system, hence called as global warming. Study about oceans is relevant to the current scenario of changing climate and many studies show significant warming trends in the Sea Surface Temperature (SST) in the ocean during the past several decades. Extensive warming is found in deeper layers of oceans too. In this study, the monthly mean deep ocean temperature data which was spatially averaged over the tropical Indian Ocean (IO) during the period 1950 to 2012 was subjected to a statistical EMD analysis to estimate the impact of warming to deeper IO. The temperature signal is decomposed into components called as Intrinsic Mode Functions (IMFs) along with a residual part. The IMFs represent the temperature fluctuations resulting from inter-annual oscillations such as ENSO (El-Nino Southern Oscillation) and the residual part can be considered as the long-term trend. The IMFs obtained at each depth showed variabilities in QBO, ENSO and tidal frequencies. Residual part of the signal too had significant magnitudes, indicating the impact of global warming signals at deeper oceans.