bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven Low-energy Generic Generator for CMD-3
Authors: Korobov A A , Eidelman S I 
Published: July 2020

Abstract
A Monte Carlo generator to simulate events of single-photon annihilation to hadrons at center-of-mass energies below 2 GeV is described. The generator is based on existing data on cross sections of various exclusive channels of e+e− annihilation obtained in scan and ISR experiments. It is extensively used in the software packages for analysis of experiments at the Novosibirsk e+e− colliders VEPP-2000 and VEPP-4M aimed at high-precision measurements of hadronic cross sections with different applications, e.g. to calculations of the hadronic vacuum polarization for the muon anomalous magnetic moment.
Decoding the nature of Dark Matter at current and future experiments
Authors: Belyaev Alexander 
Published: July 2020

Abstract
Decoding the nature of Dark Matter (DM) as a crucial part of Beyond-the-Standard-Model (BSM) theory is one of the most important problems of modern particle physics. DM potentially provides unique signatures at collider and non-collider experiments. These signatures are quite generic, however their details could allow us to delineate various BSM models and the properties of DM. While there are many comprehensive studies of the phenomenology of various appealing BSM models, exhibiting “top-bottom” approach, there is no clear strategy for the reverse task of identifying the underlying theory from the new signatures. To solve this problem one should consider the comprehensive set of signatures, database of models and use modern methods, including machine learning and artificial intelligence, to decode the underlying theory from potential signals of new physics we are expecting from the coming experimental data. One of the tools which could be helpful to solve the problem is High Energy Physics Model Database (HEPMDB) which was created to make a step forward towards solving this problem. It is aimed to facilitate connection between HEP theory and experiment, to store, validate and explore BSM models and to collect their signatures. DM decoding is based on the unique complementarity of Large Hadron Collider (LHC) potential as well as on the potential DM direct and indirect detection experiments to probe DM. The combination of our knowledge on this complementarity, modern analysis methods, comprehensive database of BSM models and their signatures is the key point of decoding the nature of DM and the whole underlying theory of Nature.
Effective LHC measurements with matrix elements and machine learning
Authors: Brehmer J. , Cranmer K. , Espejo I. , Kling F. , Louppe G. , Pavez J. 
Published: July 2020

Abstract
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.
Performance evaluation of distributed file systems for the phase-II upgrade of the ATLAS experiment at CERN
Authors: Abed Abud Adam , Le Goff Fabrice , Avolio Giuseppe 
Published: July 2020

Abstract
Over the next few years, the LHC will prepare for the upcoming High-Luminosity upgrade in which it is expected to deliver ten times more pp collisions. This will create a harsher radiation environment and higher detector occupancy. In this context, the ATLAS experiment, one of the general purpose experiments at the LHC, plans substantial upgrades to the detectors and to the trigger system in order to efficiently select events. Similarly, the Data Acquisition System (DAQ) will have to redesign the data-flow architecture to accommodate for the large increase in event and data rates. The Phase-II DAQ design involves a large distributed storage system that buffers data read out from the detector, while a computing farm (Event Filter) analyzes and selects the most interesting events. This system will have to handle 5.2 TB/s of input data for an event rate of 1 MHz and provide access to 3 TB/s of these data to the filtering farm. A possible implementation for such a design is based on distributed file systems (DFS) which are becoming ubiquitous among the big data industry. Features of DFS such as replication strategies and smart placement policies match the distributed nature and the requirements of the new data-flow system. This paper presents an up-to-date performance evaluation of some of the DFS currently available: GlusterFS, HadoopFS and CephFS. After characterization of the future data-flow systems workload, we report on small-scale raw performance and scalability studies. Finally, we conclude on the suitability of such systems to the tight constraints expected for the ATLAS experiment in phase-II and, in general, what benefits the HEP community can take from these storage technologies.
Simulating Diverse HEP Workflows on Heterogeneous Architectures
Authors: Leggett Charles , Shapoval Illya , Clemencic Marco , Jones Christopher 
Published: July 2020

Abstract
We evaluate key patterns and estimate throughput bounds of simulated transformation of conventional high energy physics (HEP) data processing workflows to heterogeneous equivalents. The simulation parameter space includes the number of offloaded tasks, CPU/accelerator ratios of intra-task computations, offload latencies, and run time efficiency of offloaded computations. The simulation is performed for a diverse set of state-of-the-art event reconstruction scenarios from ATLAS, LHCb and CMS - the frontier HEP experiments of the Large Hadron Collider project.
JANA2: Multithreaded Event Reconstruction
Authors: Lawrence D , Boehnlein A , Brei N , Romanov D 
Published: July 2020

Abstract
JANA2 is a multi-threaded event reconstruction framework being developed for Experimental Nuclear Physics. It is an LDRD1 funded project that will be the successor of the original JANA framework. JANA2 is a near complete rewrite emphasizing C++ language features that have only become available since the introduction of the C++11 standard. Features such as shared pointers, language native threading, and atomics are employed. This paper outlines the status of the project.
Design Pattern for Analysis Automation on Distributed Resources using Luigi Analysis Workflows
Authors: Rieger Marcel 
Published: July 2020

Abstract
In particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo event generation. However, physicists performing data analyses are usually required to steer their individual workflows manually, which is time-consuming and often leads to undocumented relations between particular workloads. We present the Luigi Analysis Workflows (Law) Python package, which is based on the open-source pipelining tool Luigi, originally developed by Spotify. It establishes a generic design pattern for analyses of arbitrary scale and complexity, and shifts the focus from executing to defining the analysis logic. Law provides the building blocks to seamlessly integrate interchangeable remote resources without, however, limiting itself to a specific choice of infrastructure. In particular, it encourages and enables the separation of analysis algorithms on the one hand, and run locations, storage locations, and software environments on the other hand. To cope with the sophisticated demands of end-to-end HEP analyses, Law supports job execution on WLCG infrastructure (ARC, gLite) as well as on local computing clusters (HTCondor, LSF), remote file access via most common protocols through the GFAL2 library, and an environment sandboxing mechanism with support for Docker and Singularity containers. Moreover, the novel approach ultimately aims for analysis preservation out-of-the-box. Law is entirely experiment independent and developed open-source.
Faster RooFitting: Automated parallel calculation of collaborative statistical models
Authors: Patrick Bos E G , Burgard Carsten D , Croft Vincent A , Pelupessy Inti , Attema Jisk J , Verkerke Wouter 
Published: July 2020

Abstract
RooFit [1,2] is the main statistical modeling and fitting package used to extract physical parameters from reduced particle collision data, e.g. the Higgs boson experiments at the LHC [3,4]. RooFit aims to separate particle physics model building and fitting (the users’ goals) from their technical implementation and optimization in the back-end. In this paper, we outline our efforts to further optimize this back-end by automatically running parts of user models in parallel on multi-core machines. A major challenge is that RooFit allows users to define many different types of models, with different types of computational bottlenecks. Our automatic parallelization framework must then be flexible, while still reducing run-time by at least an order of magnitude, preferably more. We have performed extensive benchmarks and identified at least three bottlenecks that will benefit from parallelization. We designed a parallelization layer that allows us to parallelize existing classes with minimal effort, but with high performance and retaining as much of the existing class’s interface as possible. The high-level parallelization model is a task-stealing approach. Preliminary results show speed-ups of factor 2 to 20, depending on the exact model and parallelization strategy.
ROOT I/O compression algorithms and their performance impact within Run 3
Authors: Shadura O , Bockelman B 
Published: July 2020

Abstract
The LHC’s Run3 will push the envelope on data-intensive workflows and, since at the lowest level this data is managed using the ROOT software framework, preparations for managing this data are starting already. At the beginning of LHC Run 1, all ROOT data was compressed with the ZLIB algorithm; since then, ROOT has added support for additional algorithms such as LZMA and LZ4, each with unique strengths. This work must continue as industry introduces new techniques - ROOT can benefit saving disk space or reducing the I/O and bandwidth for online and offline needs of experiments by introducing better compression algorithms. In addition to alternate algorithms, we have been exploring alternate techniques to improve parallelism and apply pre-conditioners to the serialized data.
We have performed a survey of the performance of the new compression techniques. Our survey includes various use cases of data compression of ROOT files provided by different LHC experiments. We also provide insight into solutions applied to resolve bottlenecks in compression algorithms, resulting in improved ROOT performance.
Evolution of ROOT package management
Authors: Shadura O , Bockelman B , Vassilev V 
Published: July 2020

Abstract
ROOT is a large code base with a complex set of build-time dependencies; there is a significant difference in compilation time between the “core” of ROOT and the full-fledged deployment. We present results on a “delayed build” for internal ROOT packages and external packages. This gives the ability to offer a “lightweight” core of ROOT, later extended by building additional modules to extend the functionality of ROOT. As a part of this work, we have improved the separation of ROOT code into distinct modules and packages with minimal dependencies. This approach gives users better flexibility and the possibility to combine various build features without rebuilding from scratch.
Dependency hell is a common problem found in software and particularly in HEP software ecosystem. We would like to discuss an improvement of artifact management (“lazy-install”) system as a solution to the “dependency hell” problem.
HEP software stack usually consists of multiple sub-projects with dependencies. The development model is often distributed, independent and non-coherent among the sub-projects. We believe that software should be designed to take advantage of other software components that are already available, or have already been designed and implemented for use elsewhere rather than “reinventing the wheel”.
The main idea is to build the ROOT project and all of its dependencies recursively and incrementally, making it fundamentally different than just adding one external project and rebuilding from scratch. In addition, this allows to keep a list of dependencies to be able to resolve possible incompatibility of transitive dependencies caused by the versions conict.
In our contribution, we will present our approach to artifact management system of ROOT together with a set of examples and use cases.
Nested data structures in array frameworks
Authors: Pivarski Jim , Lange David , Elmer Peter 
Published: July 2020

Abstract
The need for nested data structures and combinatorial operations on arbitrary length lists has prevented particle physicists from adopting array-based data analysis frameworks, such as R, MATLAB, Numpy, and Pandas. These array frameworks work well for purely rectangular tables and hypercubes, but arrays of variable length arrays, called “jagged arrays,” are out of their scope. However, jagged arrays are a fundamental feature of particle physics data, as well as combining them to search for particle decays. To bridge this gap, we developed the awkward-array library, and in this paper we present feedback from some of the first physics groups using it for their analyses. They report similar computational performance to analysis code written in C++, but are split on the ease-of-use of array syntax. In a series of four phone interviews, all users noted how different array programming is from imperative programming, but whereas some found it easier in all aspects, others said it was more difficult to write, yet easier to read.
Vectorization of random number generation and reproducibility of concurrent particle transport simulation
Authors: Jun S Y , Canal P , Apostolakis J , Gheata A , Moneta L 
Published: July 2020

Abstract
Efficient random number generation with high quality statistical properties and exact reproducibility of Monte Carlo simulations are important requirements in many areas of computational science. VecRNG is a package providing pseudo-random number generation (pRNG) in the context of a new library VecMath. This library bundles up several general-purpose mathematical utilities, data structures, and algorithms having both SIMD and SIMT (GPUs) support based on VecCore. Several state-of-the-art RNG algorithms are implemented as kernels supporting parallel generation of random numbers in scalar, vector, and Cuda workflows. In this report, we will present design considerations, implementation details, and computing performance of parallel pRNG engines on both CPU and GPU. Reproducibility of propagating multiple particles in parallel for HEP event simulation is demonstrated, using GeantV-based examples, for both sequential and fine-grain track-level concurrent simulation workflows. Strategies for efficient uses of vectorized pRNG and non-overlapping streams of random number sequences in concurrent computing environments is discussed as well.
Using DODAS as deployment manager for smart caching of CMS data management system
Authors: Tracolli M. , Antonacci M. , Boccali T. , Bonacorsi D. , Ciangottini D , Donvito G. , Duma C. , Gaido L. , Salomoni D. , Spiga D. , Kuznetsov V. 
Published: July 2020

Abstract
DODAS stands for Dynamic On Demand Analysis Service and is a Platform as a Service toolkit built around several EOSC-hub services designed to instantiate and configure on-demand container-based clusters over public or private Cloud resources. It automates the whole workflow from service provisioning to the configuration and setup of software applications. Therefore, such a solution allows using “any cloud provider”, with almost zero effort. In this paper, we demonstrate how DODAS can be adopted as a deployment manager to set up and manage the compute resources and services required to develop an AI solution for smart data caching. The smart caching layer may reduce the operational cost and increase flexibility with respect to regular centrally managed storage of the current CMS computing model. The cache space should be dynamically populated with the most requested data. In addition, clustering such caching systems will allow to operate them as a Content Delivery System between data providers and end-users. Moreover, a geographically distributed caching layer will be functional also to a data-lake based model, where many satellite computing centers might appear and disappear dynamically. In this context, our strategy is to develop a flexible and automated AI environment for smart management of the content of such clustered cache system. In this contribution, we will describe the identified computational phases required for the AI environment implementation, as well as the related DODAS integration. Therefore we will start with the overview of the architecture for the pre-processing step, based on Spark, which has the role to prepare data for a Machine Learning technique. A focus will be given on the automation implemented through DODAS. Then, we will show how to train an AI-based smart cache and how we implemented a training facility managed through DODAS. Finally, we provide an overview of the inference system, based on the CMS-TensorFlow as a Service and also deployed as a DODAS service.
Hardware Accelerated ATLAS Workloads on the WLCG Grid
Authors: Forti A C , Heinrich L , Guth M 
Published: July 2020

Abstract
In recent years the usage of machine learning techniques within data-intensive sciences in general and high-energy physics in particular has rapidly increased, in part due to the availability of large datasets on which such algorithms can be trained, as well as suitable hardware, such as graphic or tensor processing units, which greatly accelerate the training and execution of such algorithms. Within the HEP domain, the development of these techniques has so far relied on resources external to the primary computing infrastructure of the WLCG (Worldwide LHC Computing Grid). In this paper we present an integration of hardware-accelerated workloads into the Grid through the declaration of dedicated queues with access to hardware accelerators and the use of Linux container images holding a modern data science software stack. A frequent use-case in the development of machine learning algorithms is the optimization of neural networks through the tuning of their Hyper Parameters (HP). For this often a large range of network variations must be trained and compared, which for some optimization schemes can be performed in parallel – a workload well suited for Grid computing. An example of such a hyper-parameter scan on Grid resources for the case of flavor tagging within ATLAS is presented.
Using Continous Deployment techniques to manage software change at a WLCG Tier-2
Authors: Roy G , Simili E , Stewart G , Skipsey S C , Britton D 
Published: July 2020

Abstract
Continuous Integration (CI) and Continuous Development (CD) are common techniques in software development. Continuous Integration is the practice of bringing together code from multiple developers into a single repository, while Continuous Development is the process by which new releases are automatically created and tested. CI/CD pipelines are available in popular automation tools such as GitLab, and act to enhance and accelerate the software development process. Continuous Deployment, in which automation is employed to push new software releases into the production environment, follows naturally from CI/CD, but is not as well established due to business and legal requirements. Such requirements do not exist in the Worldwide LHC Compute Gird (WLCG), making the use of continuous deployment to simplify the management of grid resources an attractive proposition. We have developed work presented previously on containerised worker node environments by introducing continuous deployment techniques and tooling, and show how these, in conjunction with CI/CD, can reduce the management burden at a WLCG Tier-2 resource. In particular, benefits include reduced downtime as a result of code changes and middleware updates.
Federated data storage evolution in HENP: data lakes and beyond
Authors: Zarochentsev Andrey , Espinal Xavier , Kiryanov Andrey , Schovancová Jaroslava 
Published: July 2020

Abstract
Storage has been identified as the main challenge for the future distributed computing infrastructures: Particle Physics (HL-LHC, DUNE, Belle-II), Astrophysics and Cosmology (SKA, LSST). In particular, the High Luminosity LHC (HL-LHC) will begin operations in the year of 2026 with expected data volumes to increase by at least an order of magnitude as compared with the present systems. Extrapolating from existing trends in disk and tape pricing, and assuming flat infrastructure budgets, the implications for data handling for end-user analysis are significant. HENP experiments need to manage data across a variety of mediums based on the types of data and its uses: from tapes (cold storage) to disks and solid state drives (hot storage) to caches (including world wide access data in clouds and “data lakes”). The DataLake R&D project aims at exploring an evolution of distributed storage while bearing in mind very high demands of the HL-LHC era. Its primary objective is to optimize hardware usage and operational costs of a storage system deployed across distributed centers connected by fat networks and operated as a single service. Such storage would host a large fraction of the data and optimize the cost, eliminating inefficiencies due to fragmentation. In this talk we will highlight current status of the project, its achievements, interconnection with other research activities in this field like WLCG-DOMA and ATLAS-Google DataOcean, and future plans.
Denis Perret-Gallix (1949-2018)
Authors: Werlen Monique 
Published: July 2020

Abstract
Denis Perret-Gallix was the founder of the AIHENP-ACAT workshop series in 1990 and has chaired its international advisory committee until June 2018. He passed away on June 28 during one of the mountain bike climbs he used to enjoy. He was a high energy experimental physicist affiliated to IN2P3-CNRS (research director). He worked at Rutherford Lab., SLAC (DELCO experiment), CERN (CHARM and L3 experiments) and KEK developing low temperature dark matter detectors and event generators for the collider (LEP, LHC, ILC) physics simulations. He was the director of the CNRS Tokyo office from 2000 to 2004 and played a leading role in the France-Japan Particle Physics Laboratory which was established in 2006.