Search for: All records

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. It is an experiment-agnostic service around a workflow-oriented structure to work with existing and emerging use cases in ATLAS and other experiments. Here we will present the motivation for iDDS, its design schema and architecture, use cases and current status, and plans for the future. 

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that are the hallmark of a particle physics analysis: selection, filtering, basic vector operations, and filling histograms. The High Luminosity run of the Large Hadron Collider (HL-LHC), scheduled to start in 2026, will require physicists to regularly skim datasets that are over a PB in size, and repeatedly run over datasets that are 100’s of TB’s – too big to fit in memory. Declarative programming techniques are a way of separating the intent of the physicist from the mechanics of finding the data and using distributed computing to process and make histograms. This paper describes a library that implements a declarative distributed framework based on array programming. This prototype library provides a framework for different sub-systems to cooperate in producing plots via plug-in’s. This prototype has a ServiceX data-delivery sub-system and an awkward array sub-system cooperating to generate requested data or plots. The ServiceX system runs against ATLAS xAOD data and flat ROOT TTree’s and awkward on the columnar data produced by ServiceX. 

The CMS experiment at CERN has released research-quality data from particle collisions at the LHC since 2014. Almost all data from the first LHC run in 2010–2012 with the corresponding simulated samples are now in the public domain, and several scientific studies have been performed using these data. This paper summarizes the available data and tools, reviews the challenges in using them in research, and discusses measures to improve their usability. 

The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the “toy Monte Carlo” originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments. 

We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We also review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how the dynamic programming and reinforcement learning can be used to find the maximum likelihood clusterinng in this enormous search space. This work builds bridges with work in hierarchical clustering, statistics, combinatorial optmization, and reinforcement learning. 

File formats for generic data structures, such as ROOT, Avro, and Parquet, pose a problem for deserialization: it must be fast, but its code depends on the type of the data structure, not known at compile-time. Just-in-time compilation can satisfy both constraints, but we propose a more portable solution: specialized virtual machines. AwkwardForth is a Forth-driven virtual machine for deserializing data into Awkward Arrays. As a language, it is not intended for humans to write, but it loosens the coupling between Uproot and Awkward Array. AwkwardForth programs for deserializing record-oriented formats (ROOT and Avro) are about as fast as C++ ROOT and 10–80× faster than fastavro. Columnar formats (simple TTrees, RNTuple, and Parquet) only require specialization to interpret metadata and are therefore faster with precompiled code. 

The traditional approach in HEP analysis software is to loop over every event and every object via the ROOT framework. This method follows an imperative paradigm, in which the code is tied to the storage format and steps of execution. A more desirable strategy would be to implement a declarative language, such that the storage medium and execution are not included in the abstraction model. This will become increasingly important to managing the large dataset collected by the LHC and the HL-LHC. A new analysis description language (ADL) inspired by functional programming, FuncADL, was developed using Python as a host language. The expressiveness of this language was tested by implementing example analysis tasks designed to benchmark the functionality of ADLs. Many simple selections are expressible in a declarative way with FuncADL, which can be used as an interface to retrieve filtered data. Some limitations were identified, but the design of the language allows for future extensions to add missing features. FuncADL is part of a suite of analysis software tools being developed by the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP). These tools will be available to develop highly scalable physics analyses for the LHC. 

The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software framework A Common Tracking Software (ACTS). ACTS aims to provide an experimentindependent and framework-independent tracking software designed for modern computing architectures. We show that our optimization algorithms find highly performing configurations in ACTS without hand-tuning. These techniques can be applied to other reconstruction tasks, improving performance and reducing the need for laborious hand-tuning of parameters. 

The cabinetry library provides a Python-based solution for building and steering binned template fits. It tightly integrates with the pythonic High Energy Physics ecosystem, and in particular with pyhf for statistical inference. cabinetry uses a declarative approach for building statistical models, with a JSON schema describing possible configuration choices. Model building instructions can additionally be provided via custom code, which is automatically executed when applicable at key steps of the workflow. The library implements interfaces for performing maximum likelihood fitting, upper parameter limit determination, and discovery significance calculation. cabinetry also provides a range of utilities to study and disseminate fit results. These include visualizations of the fit model and data, visualizations of template histograms and fit results, ranking of nuisance parameters by their impact, a goodness-of-fit calculation, and likelihood scans. The library takes a modular approach, allowing users to include some or all of its functionality in their workflow. 

High Energy Physics (HEP) experiments generally employ sophisticated statistical methods to present results in searches of new physics. In the problem of searching for sterile neutrinos, likelihood ratio tests are applied to short-baseline neutrino oscillation experiments to construct confidence intervals for the parameters of interest. The test statistics of the form Δχ 2 is often used to form the confidence intervals, however, this approach can lead to statistical inaccuracies due to the small signal rate in the region-of-interest. In this paper, we present a computational model for the computationally expensive Feldman-Cousins corrections to construct a statistically accurate confidence interval for neutrino oscillation analysis. The program performs a grid-based minimization over oscillation parameters and is written in C++. Our algorithms make use of vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by employing DIY. We demonstrate the strong scaling of the application at High-Performance Computing (HPC) sites. We utilize HDF5 along with HighFive to write the results of the calculation to file.