K. Lannon

Machine learning is an important applied research area in particle physics, 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 in machine learning in particle physics with 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.

The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop and operate the software and computing resources necessary to process CMS data expeditiously and to enable U.S. physicists to fully participate in the physics of CMS. We have developed a strategic plan to prioritize R&D efforts to reach this goal for the HL-LHC. This plan includes four grand challenges: modernizing physics software and improving algorithms, building infrastructure for exabyte-scale datasets, transforming the scientific data analysis process and transitioning from R&D to operations. We are involved in a variety of R&D projects that fall within these grand challenges. In this talk, we will introduce our four grand challenges and outline the R&D program of the U.S. CMS Software & Computing Operations Program.

An increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters—such as predictions coming from an effective field theory (EFT) framework—and the observed data. Traditionally, such characterizations take the form of a negative log likelihood (NLL) function, which can only be evaluated numerically. The lack of a closed-form description of the NLL function makes it difficult to convey results of the statistical analysis. Typical results are limited to extracting “best fit” values of the model parameters and 1D intervals or 2D contours extracted from scanning the higher dimensional parameter space. It is desirable to explore these high-dimensional model parameter spaces in more sophisticated ways. One option for overcoming this challenge is to use a neural network to approximate the NLL function. This approach has the advantage of being continuous and differentiable by construction, which are essential properties for an NLL function and may also provide useful handles in exploring the NLL as a function of the model parameters. In this talk, we describe the advantages and limitations of this approach in the context of applying it to a CMS data analysis using the framework of EFT.

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.

During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 μs on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and system performance studies are presented.

The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High-Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment.A central component to allow efficient operation under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardwarebased trigger system.There are many challenges involved in achieving this: a large input data rate of about 20-40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles ("stubs"); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 µs.This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution.The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.

With the full Tevatron Run II and early LHC data samples, the opportunity for furthering our understanding of the properties of the top quark has never been more promising. Although the current knowledge of the top quark comes largely from Tevatron measurements, the experiments at the LHC are poised to probe top-quark production and decay in unprecedented regimes. Although no current top quark measurements conclusively contradict predictions from the standard model, the precision of most measurements remains statistically limited. Additionally, some measurements, most notably A FB in top quark pair production, show tantalizing hints of beyond-the-Standard-Model dynamics. The top quark sample is growing rapidly at the LHC, with initial results now public. This review examines the current status of top quark measurements in the particular light of searching for evidence of new physics, either through direct searches for beyond the standard model phenomena or indirectly via precise measurements of standard model top quark properties.

The high energy physics (HEP) community relies upon a global network of computing and data centers to analyze data produced by multiple experiments at the Large Hadron Collider (LHC). However, this global network does not satisfy all research needs. Ambitious researchers often wish to harness computing resources that are not integrated into the global network, including private clusters, commercial clouds, and other production grids. To enable these use cases, we have constructed Lobster, a system for deploying data intensive high throughput applications on non-dedicated clusters. This requires solving multiple problems related to non-dedicated resources, including work decomposition, software delivery, concurrency management, data access, data merging, and performance troubleshooting. With these techniques, we demonstrate Lobster running effectively on 10k cores, producing throughput at a level comparable with some of the largest dedicated clusters in the LHC infrastructure.

Reproducibility is an essential component of the scientific method. In order to validate the correctness or facilitate the extension of a computational result, it should be possible to re-run a published result and verify that the same results are produced. However, reproducing a computational result is surprisingly difficult: non-determinism and other factors may make it impossible to get the same result, even when running the same code on the same machine on the same day. We explore this problem in the context of HEP codes and data, showing three high level methods for dealing with non-determinism in general: 1) Domain specific methods; 2) Domain specific comparisons; and 3) Virtualization adjustments. Using a CMS workflow with output data stored in ROOT files, we use these methods to prevent, detect, and eliminate some sources of non-determinism. We observe improved determinism using pre-determined random seeds, a predictable progression of system timestamps, and fixed process identifiers. Unfortunately, sources of non-determinism continue to exist despite the combination of all three methods. Hierarchical data comparisons also allow us to appropriately ignore some non-determinism when it is unavoidable. We conclude that there is still room for improvement, and identify directions that can be taken in each method to make an experiment more reproducible.

The computing needs of high energy physics experiments like the Compact Muon Solenoid experiment at the Large Hadron Collider currently exceed the available dedicated computational resources, hence motivating a push to leverage opportunistic resources. However, access to opportunistic resources faces many obstacles, not the least of which is making available the complex software stack typically associated with such computations. This paper describes a framework constructed using existing software packages to distribute the needed software to opportunistic resources without the need for the job to have root-level privileges. Preliminary tests with this framework have demonstrated the feasibility of the approach and identified bottlenecks as well as reliability issues which must be resolved in order to make this approach viable for broad use.

The upgrades of the Compact Muon Solenoid particle physics experiment at CERN's Large Hadron Collider provide a major challenge for the real-time collision data selection. This paper presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The challenges include a large input data rate of about 20 to 40 Tbps, processing a new batch of input data every 25 ns, each consisting of about 10,000 precise position measurements of particles (`stubs'), perform the pattern recognition on these stubs to find the trajectories, and produce the list of parameters describing these trajectories within 4 μs. A proposed solution to this problem is described, in particular, the implementation of the pattern recognition and particle trajectory determination using an all-FPGA system. The results of an end-to-end demonstrator system based on Xilinx Virtex-7 FPGAs that meets timing and performance requirements are presented.

A bstract In this paper, we explore the impact of extra radiation on predictions of $$ pp\to \mathrm{t}\overline{\mathrm{t}}\mathrm{X},\mathrm{X}=\mathrm{h}/{\mathrm{W}}^{\pm }/\mathrm{Z} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>pp</mml:mi> <mml:mo>→</mml:mo> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> <mml:mo>,</mml:mo> <mml:mi>X</mml:mi> <mml:mo>=</mml:mo> <mml:mi>h</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mi>W</mml:mi> <mml:mo>±</mml:mo> </mml:msup> <mml:mo>/</mml:mo> <mml:mi>Z</mml:mi> </mml:math> processes within the dimension-6 SMEFT framework. While full next-to-leading order calculations are of course preferred, they are not always practical, and so it is useful to be able to capture the impacts of extra radiation using leading-order matrix elements matched to the parton shower and merged. While a matched/merged leading-order calculation for $$ \mathrm{t}\overline{\mathrm{t}}\mathrm{X} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> </mml:math> is not expected to reproduce the next-to-leading order inclusive cross section precisely, we show that it does capture the relative impact of the EFT effects by considering the ratio of matched SMEFT inclusive cross sections to Standard Model values, $$ {\sigma}_{\mathrm{SM}\mathrm{EFT}}\left(\mathrm{t}\overline{\mathrm{t}}\mathrm{X}+\mathrm{j}\right)/{\sigma}_{\mathrm{SM}}\left(\mathrm{t}\overline{\mathrm{t}}\mathrm{X}+\mathrm{j}\right)\equiv \mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>σ</mml:mi> <mml:mtext>SMEFT</mml:mtext> </mml:msub> <mml:mfenced> <mml:mrow> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> <mml:mo>+</mml:mo> <mml:mi>j</mml:mi> </mml:mrow> </mml:mfenced> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>σ</mml:mi> <mml:mi>SM</mml:mi> </mml:msub> <mml:mfenced> <mml:mrow> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> <mml:mo>+</mml:mo> <mml:mi>j</mml:mi> </mml:mrow> </mml:mfenced> <mml:mo>≡</mml:mo> <mml:mi>μ</mml:mi> </mml:math> . Furthermore, we compare leading order calculations with and without extra radiation and find several cases, such as the effect of the operator $$ \left({\varphi}^{\dagger }i{\overleftrightarrow{D}}_{\mu}\varphi \right)\left(\overline{t}{\gamma}^{\mu }t\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:mrow> <mml:msup> <mml:mi>φ</mml:mi> <mml:mo>†</mml:mo> </mml:msup> <mml:mi>i</mml:mi> <mml:msub> <mml:mover> <mml:mi>D</mml:mi> <mml:mo>↔</mml:mo> </mml:mover> <mml:mi>μ</mml:mi> </mml:msub> <mml:mi>φ</mml:mi> </mml:mrow> </mml:mfenced> <mml:mfenced> <mml:mrow> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:msup> <mml:mi>γ</mml:mi> <mml:mi>μ</mml:mi> </mml:msup> <mml:mi>t</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> on $$ \mathrm{t}\overline{\mathrm{t}}\mathrm{h} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>h</mml:mi> </mml:math> and $$ \mathrm{t}\overline{\mathrm{t}}\mathrm{W} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>W</mml:mi> </mml:math> , for which the relative cross section prediction increases by more than 10% — significantly larger than the uncertainty derived by varying the input scales in the calculation, including the additional scales required for matching and merging. Being leading order at heart, matching and merging can be applied to all operators and processes relevant to $$ pp\to \mathrm{t}\overline{\mathrm{t}}\mathrm{X},\mathrm{X}=\mathrm{h}/{\mathrm{W}}^{\pm }/\mathrm{Z}+\mathrm{jet} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>pp</mml:mi> <mml:mo>→</mml:mo> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> <mml:mo>,</mml:mo> <mml:mi>X</mml:mi> <mml:mo>=</mml:mo> <mml:mi>h</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mi>W</mml:mi> <mml:mo>±</mml:mo> </mml:msup> <mml:mo>/</mml:mo> <mml:mi>Z</mml:mi> <mml:mo>+</mml:mo> <mml:mi>jet</mml:mi> </mml:math> , is computationally fast and not susceptible to negative weights. Therefore, it is a useful approach in $$ \mathrm{t}\overline{\mathrm{t}}\mathrm{X} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>t</mml:mi> <mml:mover> <mml:mi>t</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mi>X</mml:mi> </mml:math> + jet studies where complete next-to-leading order results are currently unavailable or unwieldy.

The processing needs for the High Luminosity (HL) upgrade for the LHC require the CMS collaboration to harness the computational power available on non-CMS resources, such as High-Performance Computing centers (HPCs). These sites often limit the external network connectivity of their computational nodes. In this paper we describe a strategy in which all network connections of CMS jobs inside a facility are routed to a single point of external network connectivity using a Virtual Private Network (VPN) server by creating virtual network interfaces in the computational nodes. We show that when the computational nodes and the host running the VPN server have the namespaces capability enabled, the setup can run entirely on user space with no other root permissions required. The VPN server host may be a privileged node inside the facility configured for outside network access, or an external service that the nodes are allowed to contact. When namespaces are not enabled at the client side, then the setup falls back to using a SOCKS server instead of virtual network interfaces. We demonstrate the strategy by executing CMS Monte Carlo production requests on opportunistic non-CMS resources at the University of Notre Dame. For these jobs, cvmfs support is tested via fusermount (cvmfsexec), and the native fuse module.

Analysis of high energy physics experiments using the Compact Muon Solenoid (CMS) at the Large Hadron Collider (LHC) can be limited by availability of computing resources. As a joint effort involving computer scientists and CMS physicists at Notre Dame, we have developed an opportunistic workflow management tool, Lobster, to harvest available cycles from university campus computing pools. Lobster consists of a management server, file server, and worker processes which can be submitted to any available computing resource without requiring root access.

The CDF II eXtremely Fast Tracker (XFT) is the trigger processor which reconstructs charged particle tracks in the CDF II central outer tracking chamber. The XFT tracks are also extrapolated to the electromagnetic calorimeter and muon chambers and are associated to electromagnetic clusters and muon stubs to generate trigger electron and muon candidates. The steady increase of the Tevatron instantaneous luminosity and the resulting higher occupancy of the chamber demanded an upgrade of the original system, which performed tracking only in the transverse plane of the chamber and was consequently affected by a significant level of fake tracks. In the upgraded XFT, tracking is reinforced by using additional data from the stereo layers of the chamber to reduce the level of fake tracks and to perform three-dimensional track reconstruction. A review of this upgrade is presented.

The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission of the U.S. CMS Software & Computing Operations Program is to develop and operate the software and computing resources necessary to process CMS data expeditiously and to enable U.S. physicists to fully participate in the physics of CMS. We have developed a strategic plan to prioritize R&D efforts to reach this goal for the HL-LHC. This plan includes four grand challenges: modernizing physics software and improving algorithms, building infrastructure for exabyte-scale datasets, transforming the scientific data analysis process and transitioning from R&D to operations. We are involved in a variety of R&D projects that fall within these grand challenges. In this talk, we will introduce our four grand challenges and outline the R&D program of the U.S. CMS Software & Computing Operations Program.

Intellectual disability (ID) is a chronic neurodevelopmental condition characterised by limitations in intelligence and adaptive skills with an onset prior to the age of 18 years. People with ID have complex healthcare needs and are more likely than the general population to experience multiple comorbidities and polypharmacy, with subsequent increased risk of adverse medication effects. The aim of this scoping review is to characterise rating scales used to measure adverse effects of medication in people with ID.Four online databases (PsycINFO, Medline, Web of Science and OpenGrey) were searched in April 2020. Studies were assessed for inclusion against pre-specified eligibility criteria. Reference lists of included studies were hand searched. Data extraction was carried out by two independent reviewers and key findings were tabulated for consideration. Studies were assessed for quality using the Mixed Methods Appraisal Tool.The search resulted in 512 unique records, of which fifteen met the inclusion criteria. Fourteen scales were identified. All scales assessed adverse effects of psychotropics only. Of the scales, only one, the Matson Evaluation of Drug Side Effects, which focuses on psychotropic medications, was originally developed for use in a population with ID.The Matson Evaluation of Drug Side Effects scale appears to be the most reliable and well-researched scale in people with ID. However, a scale which measures adverse effects across multiple medication classes would be valuable for use in this population.

We previously described Lobster, a workflow management tool for exploiting volatile opportunistic computing resources for computation in HEP. We will discuss the various challenges that have been encountered while scaling up the simultaneous CPU core utilization and the software improvements required to overcome these challenges.

The CMS experiment collects and analyzes large amounts of data coming from high energy particle collisions produced by the Large Hadron Collider (LHC) at CERN. This involves a huge amount of real and simulated data processing that needs to be handled in batch-oriented platforms. The CMS Global Pool of computing resources provide +100K dedicated CPU cores and another 50K to 100K CPU cores from opportunistic resources for these kind of tasks and even though production and event processing analysis workflows are already managed by existing tools, there is still a lack of support to submit final stage condor-like analysis jobs familiar to Tier-3 or local Computing Facilities users into these distributed resources in an integrated (with other CMS services) and friendly way. CMS Connect is a set of computing tools and services designed to augment existing services in the CMS Physics community focusing on these kind of condor analysis jobs. It is based on the CI-Connect platform developed by the Open Science Grid and uses the CMS GlideInWMS infrastructure to transparently plug CMS global grid resources into a virtual pool accessed via a single submission machine. This paper describes the specific developments and deployment of CMS Connect beyond the CI-Connect platform in order to integrate the service with CMS specific needs, including specific Site submission, accounting of jobs and automated reporting to standard CMS monitoring resources in an effortless way to their users.

Recently, the CDF experiment at the Fermilab Tevatron has used complementary methods to make multiple measurements of the singly produced top quark cross section. All analyses use the same dataset with more than 2 fb^-1 of CDF data and event selection based on W+2 or W+3 jet events with at least one b-tagged jet. However, due to differences in analysis techniques these results are not fully correlated and a combination provides improved experimental precision. Two independent methods are used to combine the results. This combination results in an improved measurement of the single top production cross section and also the CKM matrix element V_tb.

The CDF II level 1 track trigger system reconstructs charged tracks in the plane transverse to the beam direction. The track trigger electronics uses the hit data from the 4 axial layers of the CDF II central outer tracking chamber, and has been recently upgraded to include the complementary information from the 3 stereo layers. Together with the existing system it provides improved fake track rejection at level 1. In addition, the high resolution segment information is delivered to the Level 2 processors, where software algorithms perform three-dimensional stereo track reconstruction. The 3D-tracks are further extrapolated to the electromagnetic calorimeter towers and muon chambers to generate trigger electron and muon candidates. The invariant mass of track pairs and track isolations are also calculated and used in the level 2 trigger decision. We describe the hardware and software for the level 2 part of the track trigger upgrade as well as the performance of the new track trigger algorithms.

We present recent results from CDF in the search for new phenomena appearing in the top quark samples. These results use data from pp̄ collisions at s = 1.96 TeV corresponding to an integrated luminosity ranging from 195 pb−1 to 760 pb−1. No deviations are observed from the Standard Model expectations, so upper limits on the size of possible new phenomena are set.

The research performed revolves around creating tracking algorithms for the proposed ten-year upgrade to the silicon tracker for the Compact Muon Solenoid (CMS), one of two main detectors for the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland. The proposed upgrade to the silicon tracker for CMS will use high-speed electronics to trace particle trajectories so that they can be used immediately in a trigger system. The additional information will be combined with other sub-detectors in CMS to distinguish interesting events from background, enabling the good events to be read-out by the detector. The algorithms would be implemented directly into the Level-1 trigger, i.e. the first trigger in a two-trigger system, to be used in real time. Specifically, by analyzing computer generated stable particles over various ranges of transverse momentum and the various tracks they produce, we created and tested various simulated trigger algorithms that would be hopefully used in hardware. As one algorithm has proved very effective, the next step is to this algorithm against simulated events with an environment equivalent to SLHC luminosities.

The University of Notre Dame (ND) CMS group operates a modest-sized Tier-3 site suitable for local, final-stage analysis of CMS data. However, through the ND Center for Research Computing (CRC), Notre Dame researchers have opportunistic access to roughly 25k CPU cores of computing and a 100 Gb/s WAN network link. To understand the limits of what might be possible in this scenario, we undertook to use these resources for a wide range of CMS computing tasks from user analysis through large-scale Monte Carlo production (including both detector simulation and data reconstruction.) We will discuss the challenges inherent in effectively utilizing CRC resources for these tasks and the solutions deployed to overcome them.

The CDF II eXtremely Fast Tracker (XFT) is a trigger processor which reconstructs charged particle tracks in the transverse plane of the central tracking chamber. The XFT tracks are also extrapolated to the electromagnetic calorimeter and muon chambers to generate trigger electron and muon candidates. The XFT is crucial for the entire CDF II physics program: it detects high Pt lepton from W/Z and heavy flavors decay and, in conjunction with the level 2 processor, it identifies secondary vertices from beauty decay. The XFT has thus been crucial for the recent measurement of the Bs0 oscillation and Σb. The increase of the Tevatron instantaneous luminosity demanded an upgrade of the system to cope with the higher occupancy of the chamber. In the upgraded XFT, three-dimensional tracking reduces the level of fake tracks and measures the longitudinal track parameters, which strongly reinforce the trigger selection. This allows to maintain the trigger perfectly efficient at the record luminosities 2–3×1032cm-2s-1 and to maintain intact the CDF II high luminosity physics program, which includes the Higgs search. In this paper we review the architecture, the used technology, the performance and the impact of the upgraded XFT on the entire CDF II trigger strategy.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <?Pub Dtl=""?>The CDF II detector uses dedicated hardware to identify charged tracks that are used in an important class of level 1 trigger decisions. Until now, this hardware identified track segments based on patterns of hits on only the axial sense wires in the tracking chamber and determined the transverse momentum of track candidates from patterns of track segments. This identification is efficient but produces trigger rates that grow rapidly with increasing instantaneous luminosity. High trigger rates are a consequence of the large numbers of low momentum tracks produced in inelastic <formula formulatype="inline"> <tex>$p\bar p$</tex></formula> collisions which generate overlapping patterns of hits that match those expected for high-momentum tracks. A recently completed upgrade to the level 1 track trigger system makes use of information from stereo wires in the tracking chamber to reduce the rate of false triggers while maintaining high efficiency for real high momentum particles. We describe the new electronics used to instrument the additional sense wires, identify track segments and correlate these with the track candidates found by the original track trigger system. The performance of this system is characterized in terms of the efficiency for identifying charged particles and the improved rejection of axial track candidates that do not correspond to real particles. </para>

Analysis cyberinfrastructure refers to the combination of software and computer hardware used to support late-stage data analysis in High Energy Physics (HEP). For the purposes of this white paper, late-stage data analysis refers specifically to the step of transforming the most reduced common data format produced by a given experimental collaboration (for example, nanoAOD for the CMS experiment) into histograms. In this white paper, we reflect on observations gathered from a recent experience with data analysis using a recent, python-based analysis framework, and extrapolate these experiences though the High-Luminosity LHC era as way of highlighting potential R\&D topics in analysis cyberinfrastructure.

Distributed data analysis frameworks are widely used for processing large datasets generated by instruments in scientific fields such as astronomy, genomics, and particle physics. Such frameworks partition petabyte-size datasets into chunks and execute many parallel tasks to search for common patterns, locate unusual signals, or compute aggregate properties. When well-configured, such frameworks make it easy to churn through large quantities of data on large clusters. However, configuring frameworks presents a challenge for end users, who must select a variety of parameters such as the blocking of the input data, the number of tasks, the resources allocated to each task, and the size of nodes on which they run. If poorly configured, the result may perform many orders of magnitude worse than optimal, or the application may even fail to make progress at all. Even if a good configuration is found through painstaking observations, the performance may change drastically when the input data or analysis kernel changes. This paper considers the problem of automatically configuring a data analysis application for high energy physics (TopEFT) built upon standard frameworks for physics analysis (Coffea) and distributed tasking (Work Queue). We observe the inherent variability within the application, demonstrate the problems of poor configuration, and then develop several techniques for automatically sizing tasks to meet goals of resource consumption, and overall application completion.

The CDF II detector uses dedicated hardware to identify charged tracks that are used in an important class of Level 1 trigger decisions. Until now, this hardware identified track segments based on patterns of hits on only the axial sense wires in the tracking chamber and determined the transverse momentum of track candidates from patterns of track segments. This identification is efficient but produces trigger rates that grow rapidly with increasing instantaneous luminosity. High trigger rates are a consequence of the large numbers of low momentum tracks produced in inelastic pp macr collisions which generate overlapping patterns of hits that match those expected for high-momentum tracks. A recently completed upgrade to the Level 1 track trigger system makes use of information from stereo wires in the tracking chamber to reduce the rate of false triggers while maintaining high efficiency for real high momentum particles. We describe the new electronics used to instrument the additional sense wires, identify track segments and correlate these with the track candidates found by the original track trigger system. The performance of this system is characterized in terms of the efficiency for identifying charged particles and the improved rejection of axial track candidates that do not correspond to real particles.

The CDF II eXtremely Fast Tracker is the trigger track processor which reconstructs charged particle tracks in the transverse plane of the CDF II central outer tracking chamber. The system is now being upgraded to perform a three dimensional track reconstruction. A review of the upgrade is presented here.

A new event data format has been designed and prototyped by the CMS collaboration to satisfy the needs of a large fraction of physics analyses (at least 50%) with a per event size of order 1 kB. This new format is more than a factor of 20 smaller than the MINIAOD format and contains only top level information typically used in the last steps of the analysis. The talk will review the current analysis strategy from the point of view of event format in CMS (both skims and formats such as RECO, AOD, MINIAOD, NANOAOD) and will describe the design guidelines for the new NANOAOD format.

CMS Tier 3 centers, frequently located at universities, play an important role in the physics analysis of CMS data. Although different computing resources are often available at universities, meeting all requirements to deploy a valid Tier 3 able to run CMS workflows can be challenging in certain scenarios. For instance, providing the right operating system (OS)with access to the CERNVM File System (CVMFS) on the worker nodes or having a Compute Element (CE) on the submit host is not always allowed or possible due to e.g: lack of root access to the nodes, TCP port network policies, maintenance of a C,etc. The Notre Dame group operates a CMS Tier 3 with 1K cores. In addition to this, researchers have access to an opportunistic pool with +25K cores that are used via lobster for CMS jobs, but cannot be used with other standard CMS submission tools on the grid like CRAB, as these resources are not part of the Tier 3 due to its opportunistic nature. This work describes the use of VC3, a service for automating the deployment of virtual cluster infrastructures, in order to provide the environment (user-space CVMFS access and customized OS via singularity containers) needed for CMS workflows to work. Also, we describe its integration with the OSG Hosted CE service, to add these resources to CMS as part of our existing Tier 3 in a seamless way.

Deep neural networks (DNNs) have been applied to the fields of computer vision and natural language processing with great success in recent years. The success of these applications has hinged on the development of specialized DNN architectures that take advantage of specific characteristics of the problem to be solved, namely convolutional neural networks for computer vision and recurrent neural networks for natural language processing. This research explores whether a neural network architecture specific to the task of identifying t → Wb decays in particle collision data yields better performance than a generic, fully-connected DNN. Although applied here to resolved top quark decays, this approach is inspired by an DNN technique for tagging boosted top quarks, which consists of defining custom neural network layers known as the combination and Lorentz layers. These layers encode knowledge of relativistic kinematics applied to combinations of particles, and the output of these specialized layers can then be fed into a fully connected neural network to learn tasks such as classification. This research compares the performance of these physics inspired networks to that of a generic, fully-connected DNN, to see if there is any advantage in terms of classification performance, size of the network, or ease of training.

The CDF Detector at the Tevatron currently uses an online track trigger, known as the XFT, to identify charged tracks with P/sub T/ > 1.5 GeV/c which are then utilized in a number of ways to produce an event-by-event trigger decision. The tracks found by the XFT are utilized in approximately 80 percent of the physics triggers, including identification of high energy leptons (e, /spl mu/, /spl tau/), events containing heavy, flavor (c, b, t) and events with interesting topologies in for searches for new phenomena. The XFT is functioning well in the current system. As the Tevatron luminosity grows, occupancy in the tracking chamber increases from multiple proton-antiproton interactions. In the trigger, this additional occupancy will cause the tracking resolution to degrade and the rate of fake tracks to grow. We propose to upgrade the existing system to mitigate these effects and allow the CDF detector to operate at its fullest capacity at the highest possible luminosity.

We present an unbinned maximum likelihood fit as an alternative to the currently used fit for the CP conserving couplings W plus photon production studied at CDF. We show that a four parameter double exponential fits the E{sub T} spectrum of the photon very well. We also show that the fit parameters can be related to and by a second order polynomial. Finally, we discuss various conclusions we have reasoned from our results to the fit so far.