arXiv subjects

Jim Pivarski

Publications and source records attributed to Jim Pivarski.

33 records · Page 2Linked to original sources

arXiv2020

The Scikit HEP Project -- overview and prospects

Scikit-HEP is a community-driven and community-oriented project with the goal of providing an ecosystem for particle physics data analysis in Python. Scikit-HEP is a toolset of approximately twenty packages and a few "affiliated" packages. It expands the typical Python data analysis tools for particle physicists. Each package focuses on a particular topic, and interacts with other packages in the toolset, where appropriate. Most of the packages are easy to install in many environments; much work has been done this year to provide binary "wheels" on PyPI and conda-forge packages. The Scikit-HEP project has been gaining interest and momentum, by building a user and developer community engaging collaboration across experiments. Some of the packages are being used by other communities, including the astroparticle physics community. An overview of the overall project and toolset will be presented, as well as a vision for development and sustainability.

physics.comp-ph↗

arXiv2020

Awkward Arrays in Python, C++, and Numba

The Awkward Array library has been an important tool for physics analysis in Python since September 2018. However, some interface and implementation issues have been raised in Awkward Array's first year that argue for a reimplementation in C++ and Numba. We describe those issues, the new architecture, and present some examples of how the new interface will look to users. Of particular importance is the separation of kernel functions from data structure management, which allows a C++ implementation and a Numba implementation to share kernel functions, and the algorithm that transforms record-oriented data into columnar Awkward Arrays.

cs.MS↗

arXiv2019

Machine Learning in High Energy Physics Community White Paper

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

physics.comp-ph↗

arXiv2019

Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.

cs.DC↗

arXiv2018

HEP Software Foundation Community White Paper Working Group -- Data Organization, Management and Access (DOMA)

Without significant changes to data organization, management, and access (DOMA), HEP experiments will find scientific output limited by how fast data can be accessed and digested by computational resources. In this white paper we discuss challenges in DOMA that HEP experiments, such as the HL-LHC, will face as well as potential ways to address them. A research and development timeline to assess these changes is also proposed.

physics.comp-ph↗

arXiv2018

HEP Software Foundation Community White Paper Working Group --- Visualization

In modern High Energy Physics (HEP) experiments visualization of experimental data has a key role in many activities and tasks across the whole data chain: from detector development to monitoring, from event generation to reconstruction of physics objects, from detector simulation to data analysis, and all the way to outreach and education. In this paper, the definition, status, and evolution of data visualization for HEP experiments will be presented. Suggestions for the upgrade of data visualization tools and techniques in current experiments will be outlined, along with guidelines for future experiments. This paper expands on the summary content published in the HSF \emph{Roadmap} Community White Paper~\cite{HSF-CWP-2017-01}

physics.comp-ph↗

arXiv2018

HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse

In this chapter of the High Energy Physics Software Foundation Community Whitepaper, we discuss the current state of infrastructure, best practices, and ongoing developments in the area of data and software preservation in high energy physics. A re-framing of the motivation for preservation to enable re-use is presented. A series of research and development goals in software and other cyberinfrastructure that will aid in the enabling of reuse of particle physics analyses and production software are presented and discussed.

physics.comp-ph↗

arXiv2018

HEP Software Foundation Community White Paper Working Group - Data Analysis and Interpretation

At the heart of experimental high energy physics (HEP) is the development of facilities and instrumentation that provide sensitivity to new phenomena. Our understanding of nature at its most fundamental level is advanced through the analysis and interpretation of data from sophisticated detectors in HEP experiments. The goal of data analysis systems is to realize the maximum possible scientific potential of the data within the constraints of computing and human resources in the least time. To achieve this goal, future analysis systems should empower physicists to access the data with a high level of interactivity, reproducibility and throughput capability. As part of the HEP Software Foundation Community White Paper process, a working group on Data Analysis and Interpretation was formed to assess the challenges and opportunities in HEP data analysis and develop a roadmap for activities in this area over the next decade. In this report, the key findings and recommendations of the Data Analysis and Interpretation Working Group are presented.

physics.comp-ph↗

arXiv2017

Toward real-time data query systems in HEP

Exploratory data analysis tools must respond quickly to a user's questions, so that the answer to one question (e.g. a visualized histogram or fit) can influence the next. In some SQL-based query systems used in industry, even very large (petabyte) datasets can be summarized on a human timescale (seconds), employing techniques such as columnar data representation, caching, indexing, and code generation/JIT-compilation. This article describes progress toward realizing such a system for High Energy Physics (HEP), focusing on the intermediate problems of optimizing data access and calculations for "query sized" payloads, such as a single histogram or group of histograms, rather than large reconstruction or data-skimming jobs. These techniques include direct extraction of ROOT TBranches into Numpy arrays and compilation of Python analysis functions (rather than SQL) to be executed very quickly. We will also discuss the problem of caching and actively delivering jobs to worker nodes that have the necessary input data preloaded in cache. All of these pieces of the larger solution are available as standalone GitHub repositories, and could be used in current analyses.

cs.DC↗

arXiv2017

Optimizing ROOT IO For Analysis

The ROOT I/O (RIO) subsystem is foundational to most HEP experiments - it provides a file format, a set of APIs/semantics, and a reference implementation in C++. It is often found at the base of an experiment's framework and is used to serialize the experiment's data; in the case of an LHC experiment, this may be hundreds of petabytes of files! Individual physicists will further use RIO to perform their end-stage analysis, reading from intermediate files they generate from experiment data. RIO is thus incredibly flexible: it must serve as a file format for archival (optimized for space) and for working data (optimized for read speed). To date, most of the technical work has focused on improving the former use case. We present work designed to help improve RIO for analysis. We analyze the real-world impact of LZ4 to decrease decompression times (and the corresponding cost in disk space). We introduce new APIs that read RIO data in bulk, removing the per-event overhead of a C++ function call. We compare the performance with the existing RIO APIs for simple structure data and show how this can be complimentary with efforts to improve the parallelism of the RIO stack.

cs.DC↗

arXiv2017

Fast Access to Columnar, Hierarchically Nested Data via Code Transformation

Big Data query systems represent data in a columnar format for fast, selective access, and in some cases (e.g. Apache Drill), perform calculations directly on the columnar data without row materialization, avoiding runtime costs. However, many analysis procedures cannot be easily or efficiently expressed as SQL. In High Energy Physics, the majority of data processing requires nested loops with complex dependencies. When faced with tasks like these, the conventional approach is to convert the columnar data back into an object form, usually with a performance price. This paper describes a new technique to transform procedural code so that it operates on hierarchically nested, columnar data natively, without row materialization. It can be viewed as a compiler pass on the typed abstract syntax tree, rewriting references to objects as columnar array lookups. We will also present performance comparisons between transformed code and conventional object-oriented code in a High Energy Physics context.

cs.PL↗

arXiv2017

CMS Analysis and Data Reduction with Apache Spark

Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called "Big Data" technologies have emerged from industry and open source projects to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and tools, promising a fresh look at analysis of very large datasets that could potentially reduce the time-to-physics with increased interactivity. Moreover these new tools are typically actively developed by large communities, often profiting of industry resources, and under open source licensing. These factors result in a boost for adoption and maturity of the tools and for the communities supporting them, at the same time helping in reducing the cost of ownership for the end-users. In this talk, we are presenting studies of using Apache Spark for end user data analysis. We are studying the HEP analysis workflow separated into two thrusts: the reduction of centrally produced experiment datasets and the end-analysis up to the publication plot. Studying the first thrust, CMS is working together with CERN openlab and Intel on the CMS Big Data Reduction Facility. The goal is to reduce 1 PB of official CMS data to 1 TB of ntuple output for analysis. We are presenting the progress of this 2-year project with first results of scaling up Spark-based HEP analysis. Studying the second thrust, we are presenting studies on using Apache Spark for a CMS Dark Matter physics search, comparing Spark's feasibility, usability and performance to the ROOT-based analysis.

cs.DC↗

arXiv2017

Big Data in HEP: A comprehensive use case study

Experimental Particle Physics has been at the forefront of analyzing the worlds largest datasets for decades. The HEP community was the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems collectively called Big Data technologies have emerged to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and promise a fresh look at analysis of very large datasets and could potentially reduce the time-to-physics with increased interactivity. In this talk, we present an active LHC Run 2 analysis, searching for dark matter with the CMS detector, as a testbed for Big Data technologies. We directly compare the traditional NTuple-based analysis with an equivalent analysis using Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the analysis with the official experiment data formats and produce publication physics plots. We will discuss advantages and disadvantages of each approach and give an outlook on further studies needed.

cs.DC↗

arXiv2016

The Matsu Wheel: A Cloud-based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery. A particular focus is the development of applications for detecting fires and floods to help support natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce, Storm and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most of the data, in a database or data repository. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The resultant products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

cs.DC↗

arXiv2010

LHC discovery potential of the lightest NMSSM Higgs in the h1 -> a1 a1 -> 4 muons channel

We explore the potential of the Large Hadron Collider to observe the h1 -> a1 a1 -> 4 muons signal from the lightest scalar Higgs boson (h1) decaying into the two lightest pseudoscalar Higgs bosons (a1), followed by their decays into four muons in the Next-to-Minimal Supersymmetric Standard Model (NMSSM). The signature under study applies to the region of the NMSSM parameter space in which m_a1 < 2 m_tau, which has not been studied previously. In such a scenario, the suggested strategy of searching for a four-muon signal with the appropriate background suppression would provide a powerful method to discover the lightest CP-even and CP-odd NMSSM Higgs bosons h1 and a1.

hep-ph↗