Abstract
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.
Highlights
In 2012, Particle Physics entered a new age
We constrain ourselves in this paper to High Energy Physics (HEP), where known particles are made to collide at the highest energies possible, the underlying data organization holds for all sub-fields of particle physics
We present an active Large Hadron Collider (LHC) Run 2 analysis, searching for dark matter with the Compact Muon Solenoid (CMS) detector, as a testbed for the application of Big Data technologies to a HEP analysis problem
Summary
With the discovery of the Higgs Boson, the Standard Model was extended with the missing mechanism that gives rise to particle masses. This theory, developed since the 1960’s and constrained by numerous experiments before discovery, followed a predictable path. We constrain ourselves in this paper to High Energy Physics (HEP), where known particles are made to collide at the highest energies possible, the underlying data organization holds for all sub-fields of particle physics. The most basic concept of how data in HEP is organized is an event: all detector signals associated with a single beam crossing and highenergy collision. Events are the atomic unit of HEP data and may be processed separately, which is why the computational problems of particle physics can be parallelized
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
