High Energy Physics Data Research Articles

Increasing Parallelism in the ROOT I/O Subsystem

When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exception. The recent parallelisation of LHC experiments’ software frameworks and the analysis of the ever increasing amount of collision data collected by experiments further emphasised this issue underlying the need of increasing the implicit parallelism expressed within the ROOT I/O.In this contribution we highlight the improvements of the ROOT I/O subsystem which targeted a satisfactory scaling behaviour in a multithreaded context. The effect of parallelism on the individual steps which are chained by ROOT to read and write data, namely (de)compression, (de)serialisation, access to storage backend, are discussed. Performance measurements are discussed through real life examples coming from CMS production workflows on traditional server platforms and highly parallel architectures such as Intel Xeon Phi.

End-to-End Event Classification of High-Energy Physics Data

Feature extraction algorithms, such as convolutional neural networks, have introduced the possibility of using deep learning to train directly on raw data without the need for rule-based feature engineering. In the context of particle physics, such end-to-end approaches can be used for event classification to learn directly from detector-level data in a way that is completely independent of the high-level physics reconstruction. We demonstrate a technique for building such end-to-end event classifiers to distinguish simulated electromagnetic decays in a high-fidelity model of the CMS Electromagnetic Calorimeter.

Renormalization of Wilson-line operators in the presence of nonzero quark masses

In this paper, we examine the effect of nonzero quark masses on the renormalization of gauge-invariant nonlocal quark bilinear operators, including a finite-length Wilson line (called Wilson-line operators). These operators are relevant to the definition of parton quasi-distribution functions, the calculation on the lattice of which allows the direct nonperturbative study of the corresponding physical parton distribution functions. We present our perturbative calculations of the bare Green's functions, the renormalization factors in RI' and MSbar schemes, as well as the conversion factors of these operators between the two renormalization schemes. Our computations have been performed in dimensional regularization at one-loop level, using massive quarks. The conversion factors can be used to convert the corresponding lattice nonperturbative results to the MSbar scheme, which is the most widely used renormalization scheme for the analysis of experimental data in high-energy physics. Also, our study is relevant for disentangling the additional operator mixing which occurs in the presence of nonzero quark masses, both on the lattice and in dimensional regularization.

Deep Learning and Its Application to LHC Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

Scidac-Data: Enabling Data Driven Modeling of Exascale Computing

The SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities.We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.

Graphical processors for HEP trigger systems

General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to employ GPUs as accelerators in offline computations. With the steady decrease of GPU latencies and the increase in link and memory throughputs, time is ripe for real-time applications using GPUs in high-energy physics data acquisition and trigger systems. We will discuss the use of online parallel computing on GPUs for synchronous low level trigger systems, focusing on tests performed on the trigger of the CERN NA62 experiment. Latencies of all components need analysing, networking being the most critical. To keep it under control, we envisioned NaNet, an FPGA-based PCIe Network Interface Card (NIC) enabling GPUDirect connection. Moreover, we discuss how specific trigger algorithms can be parallelised and thus benefit from a GPU implementation, in terms of increased execution speed. Such improvements are particularly relevant for the foreseen LHC luminosity upgrade where highly selective algorithms will be crucial to maintain sustainable trigger rates with very high pileup.

Graphics Processors in HEP Low-Level Trigger Systems

Usage of Graphics Processing Units (GPUs) in the so called general-purpose computing is emerging as an effective approach in several fields of science, although so far applications have been employing GPUs typically for offline computations. Taking into account the steady performance increase of GPU architectures in terms of computing power and I/O capacity, the real-time applications of these devices can thrive in high-energy physics data acquisition and trigger systems. We will examine the use of online parallel computing on GPUs for the synchronous low-level trigger, focusing on tests performed on the trigger system of the CERN NA62 experiment. To successfully integrate GPUs in such an online environment, latencies of all components need analysing, networking being the most critical. To keep it under control, we envisioned NaNet, an FPGA-based PCIe Network Interface Card (NIC) enabling GPUDirect connection. Furthermore, it is assessed how specific trigger algorithms can be parallelized and thus benefit from a GPU implementation, in terms of increased execution speed. Such improvements are particularly relevant for the foreseen Large Hadron Collider (LHC) luminosity upgrade where highly selective algorithms will be essential to maintain sustainable trigger rates with very high pileup.

Graphics Processing Units for HEP trigger systems

General-purpose computing on GPUs (Graphics Processing Units) is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerator in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughput, the use of such devices for real-time applications in high-energy physics data acquisition and trigger systems is becoming ripe. We will discuss the use of online parallel computing on GPU for synchronous low level trigger, focusing on CERN NA62 experiment trigger system. The use of GPU in higher level trigger system is also briefly considered.

BESIII Physics Data Storing and Processing on HBase and MapReduce

In the past years, we have successfully applied Hadoop to high-energy physics analysis. Although, it has not only improved the efficiency of data analysis, but also reduced the cost of cluster building so far, there are still some spaces to be optimized, like inflexible pre-selection, low-efficient random data reading and I/O bottleneck caused by Fuse that is used to access HDFS. In order to change this situation, this paper presents a new analysis platform for high-energy physics data storing and analysing. The data structure is changed from DST tree-like files to HBase according to the features of the data itself and analysis processes, since HBase is more suitable for processing random data reading than DST files and enable HDFS to be accessed directly. A few of optimization measures are taken for the purpose of getting a good performance. A customized protocol is defined for data serializing and desterilizing for the sake of decreasing the storage space in HBase. In order to make full use of locality of data storing in HBase, utilizing a new MapReduce model and a new split policy for HBase regions are proposed in the paper. In addition, a dynamic pluggable easy-to-use TAG (event metadata) based pre-selection subsystem is established. It can assist physicists even to filter out 999%o uninterested data, if the conditions are set properly. This means that a lot of I/O resources can be saved, the CPU usage can be improved and consuming time for data analysis can be reduced. Finally, several use cases are designed, the test results show that the new platform has an excellent performance with 3.4 times faster with pre-selection and 20% faster without preselection, and the new platform is stable and scalable as well.

Using wavelet analysis to compare the QCD prediction and experimental data on $$R_{e^+e^-}$$ R e + e - and to determine parameters of the charmonium states above the $$D\bar{D}$$ D D ¯ threshold

The first part of our analysis uses the wavelet method to compare the quantum chromodynamic (QCD) prediction for the ratio of hadronic to muon cross sections in electron-positron collisions, R, with experimental data for R over a center of mass energy range up to about 7 GeV. A direct comparison of the raw experimental data and the QCD prediction is difficult because the data have a wide range of structures and large statistical errors and the QCD description contains sharp quark-antiquark thresholds. However, a meaningful comparison can be made if a type of “smearing” procedure is used to smooth out rapid variations in both the theoretical and experimental values of R. A wavelet analysis (WA) can be used to achieve this smearing effect. The second part of the analysis concentrates on the 3.0–6.0 GeV energy region which includes the relatively wide charmonium resonances $$\psi (1^-)$$ . We use the wavelet methodology to distinguish these resonances from experimental noise, background and from each other, allowing a reliable determination of the parameters of these states. Both analyses are examples of the usefulness of WA in extracting information in a model independent way from high energy physics data.

Successes, Challenges and Future Outlook of Multivariate Analysis In HEP

Multivariate techniques using machine learning algorithms have become an integral part in many High Energy Physics data analyses. This article is intended to sketch how this development took place by pointing out a few analyses that pushed forward the exploitation of these powerful analysis techniques. This article does not focus on controversial issues like for example how systematic uncertainties can be dealt with when using such techniques, which have been widely discussed previously by other authors. The main purpose here is to point to the gain in physics reach of the physics experiments due to the adaptation of machine learning techniques and to the challenges the HEP community faces in the light a rapid development in the field of machine learning if we want to make successful use of these powerful selection and reconstruction techniques.

A Validation Framework for the Long Term Preservation of High Energy Physics Data

The study group on data preservation in high energy physics, DPHEP, is moving to a new collaboration structure, which will focus on the implementation of preservation projects, such as those described in the group's large scale report published in 2012. One such project is the development of a validation framework, which checks the compatibility of evolving computing environments and technologies with the experiments software for as long as possible, with the aim of substantially extending the lifetime of the analysis software, and hence of the usability of the data. The framework is designed to automatically test and validate the software and data of an experiment against changes and upgrades to the computing environment, as well as changes to the experiment software itself. Technically, this is realised using a framework capable of hosting a number of virtual machine images, built with different configurations of operating systems and the relevant software, including any necessary external dependencies.

BESIU Physical Analysis on Hadoop Platform

In the past 20 years, computing cluster has been widely used for High Energy Physics data processing. The jobs running on the traditional cluster with a Data-to-Computing structure, have to read large volumes of data via the network to the computing nodes for analysis, thereby making the I/O latency become a bottleneck of the whole system. The new distributed computing technology based on the MapReduce programming model has many advantages, such as high concurrency, high scalability and high fault tolerance, and it can benefit us in dealing with Big Data. This paper brings the idea of using MapReduce model to do BESIII physical analysis, and presents a new data analysis system structure based on Hadoop platform, which not only greatly improve the efficiency of data analysis, but also reduces the cost of system building. Moreover, this paper establishes an event pre-selection system based on the event level metadata(TAGs) database to optimize the data analyzing procedure.

GPUs for real-time processing in HEP trigger systems

We describe a pilot project for the use of Graphics Processing Units (GPUs) for online triggering applications in High Energy Physics (HEP) experiments. Two major trends can be identified in the development of trigger and DAQ systems for HEP experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a pure software selection system (trigger-less). The very innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software both at low- and high-level trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerator in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high-energy physics data acquisition and trigger systems is becoming very attractive. We discuss in details the use of online parallel computing on GPUs for synchronous low-level trigger with fixed latency. In particular we show preliminary results on a first test in the NA62 experiment at CERN. The use of GPUs in high-level triggers is also considered, the ATLAS experiment (and in particular the muon trigger) at CERN will be taken as a study case of possible applications.

GPUs for real-time processing in HEP trigger systems

We describe a pilot project (GAP – GPU Application Project) for the use of GPUs (Graphics processing units) for online triggering applications in High Energy Physics experiments. Two major trends can be identified in the development of trigger and DAQ systems for particle physics experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a fully software data selection system ("trigger-less"). The innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software not only in high level trigger levels but also in early trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerators in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high energy physics data acquisition and trigger systems is becoming relevant. We discuss in detail the use of online parallel computing on GPUs for synchronous low-level triggers with fixed latency. In particular we show preliminary results on a first test in the CERN NA62 experiment. The use of GPUs in high level triggers is also considered, the CERN ATLAS experiment being taken as a case study of possible applications.

Simultaneous least squares fitter based on the Lagrange multiplier method

We developed a least squares fitter used for extracting expected physics parameters from the correlated experimental data in high energy physics. This fitter considers the correlations among the observables and handles the nonlinearity using linearization during the χ2 minimization. This method can naturally be extended to the analysis with external inputs. By incorporating with Lagrange multipliers, the fitter includes constraints among the measured observables and the parameters of interest. We applied this fitter to the study of the D0-D̄0 mixing parameters as the test-bed based on MC simulation. The test results show that the fitter gives unbiased estimators with correct uncertainties and the approach is credible.

Computational particle physics for event generators and data analysis

High-energy physics data analysis relies heavily on the comparison between experimental and simulated data as stressed lately by the Higgs search at LHC and the recent identification of a Higgs-like new boson. The first link in the full simulation chain is the event generation both for background and for expected signals. Nowadays event generators are based on the automatic computation of matrix element or amplitude for each process of interest.Moreover, recent analysis techniques based on the matrix element likelihood method assign probabilities for every event to belong to any of a given set of possible processes. This method originally used for the top mass measurement, although computing intensive, has shown its efficiency at LHC to extract the new boson signal from the background.Serving both needs, the automatic calculation of matrix element is therefore more than ever of prime importance for particle physics. Initiated in the 80's, the techniques have matured for the lowest order calculations (tree-level), but become complex and CPU time consuming when higher order calculations involving loop diagrams are necessary like for QCD processes at LHC. New calculation techniques for next-to-leading order (NLO) have surfaced making possible the generation of processes with many final state particles (up to 6). If NLO calculations are in many cases under control, although not yet fully automatic, even higher precision calculations involving processes at 2-loops or more remain a big challenge.After a short introduction to particle physics and to the related theoretical framework, we will review some of the computing techniques that have been developed to make these calculations automatic. The main available packages and some of the most important applications for simulation and data analysis, in particular at LHC will also be summarized (see CCP2012 slides [1]).

Information and Perception of Meaningful Patterns

The visual system needs to extract the most important elements of the external world from a large flux of information in a short time for survival purposes. It is widely believed that in performing this task, it operates a strong data reduction at an early stage, by creating a compact summary of relevant information that can be handled by further levels of processing. In this work we formulate a model of early vision based on a pattern-filtering architecture, partly inspired by high-speed digital data reduction in experimental high-energy physics (HEP). This allows a much stronger data reduction than models based just on redundancy reduction. We show that optimizing this model for best information preservation under tight constraints on computational resources yields surprisingly specific a-priori predictions for the shape of biologically plausible features, and for experimental observations on fast extraction of salient visual features by human observers. Interestingly, applying the same optimized model to HEP data acquisition systems based on pattern-filtering architectures leads to specific a-priori predictions for the relevant data patterns that these devices extract from their inputs. These results suggest that the limitedness of computing resources can play an important role in shaping the nature of perception, by determining what is perceived as “meaningful features” in the input data.

Dark matter, baryogenesis and neutrino oscillations from right-handed neutrinos

We show that, leaving aside accelerated cosmic expansion, all experimental data in high energy physics that are commonly agreed to require physics beyond the Standard Model can be explained when completing it by three right handed neutrinos that can be searched for using current day experimental techniques. The model that realises this scenario is known as Neutrino Minimal Standard Model (\nu MSM). In this article we give a comprehensive summary of all known constraints in the \nu MSM, along with a pedagogical introduction to the model. We present the first complete quantitative study of the parameter space of the model where no physics beyond the \nu MSM is needed to simultaneously explain neutrino oscillations, dark matter and the baryon asymmetry of the universe. This requires to track the time evolution of left and right handed neutrino abundances from hot big bang initial conditions down to temperatures below the QCD scale. We find that the interplay of resonant amplifications, CP-violating flavour oscillations, scatterings and decays leads to a number of previously unknown constraints on the sterile neutrino properties. We furthermore re-analyse bounds from past collider experiments and big bang nucleosynthesis in the face of recent evidence for a non-zero neutrino mixing angle \theta_{13}. We combine all our results with existing constraints on dark matter properties from astrophysics and cosmology. Our results provide a guideline for future experimental searches for sterile neutrinos. A summary of the constraints on sterile neutrino masses and mixings has appeared in arXiv:1204.3902 [hep-ph]. In this article we provide all details of our calculations and give constraints on other model parameters.

Data preservation and long term accessibility in high energy physics at the 2nd International Conference on Frontiers in Diagnostic Technologies in Frascati, Italy

Recently there have been significant developments in high energy physics (HEP) data preservation projects while other large data volume experiments are now also facing for the first time insuring the long term accessibility of their data. In this presentation, the status of advanced HEP data preservation projects as well as the challenges and plans for projects newly facing this task will be presented. The continuing efforts to develop guidelines and tools, the justifications for these efforts and the state of discussions of more open accessibility of the data and how this is foreseen to be managed are included.