This note summarizes the lectures given in the tutorial session of the Introduction to the Terascale school at DESY on March 2023. The target audience are advanced bachelor and master physics students. The tutorial aims to best prepare the students for starting an LHC experimental physics thesis. The cross section of the top quark pair production is detailed alongside with the reconstruction of the invariant masses of the top quark as well as of the $W$ and $Z$ bosons. The tutorial uses ideas and CMS open data files from the CMS HEP Tutorial written by C. Sander and A. Schmidt, but is entirely rewritten so that it can be run in Google Colab Cloud in a columnar style of analysis with python. In addition, a minimal C/C++ version of a simple event-loop analysis relying on ROOT is exampled. The code is kept as short as possible with emphasis on the transparency of the analysis steps, rather than the elegance of the software, having in mind that the students will in any case need to rewrite their own custom analysis framework.
Lorenzo Borella, Alberto Coppi, Jacopo Pazzini
et al.
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learning techniques. In this work, we study the use of Tree Tensor Networks (TTNs) in high-frequency real-time applications by exploiting the low-latency hardware of the Field-Programmable Gate Array (FPGA) technology. We present different implementations of TTN classifiers, capable of performing inference on classical ML datasets as well as on complex physics data. A preparatory analysis of bond dimensions and weight quantization is realized in the training phase, together with entanglement entropy and correlation measurements, that help setting the choice of the TTN architecture. The generated TTNs are then deployed on a hardware accelerator; using an FPGA integrated into a server, the inference of the TTN is completely offloaded. Eventually, a classifier for High Energy Physics (HEP) applications is implemented and executed fully pipelined with sub-microsecond latency.
The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming more widely available. To facilitate the efficient use of these architectures in a heterogeneous computing environment, several programming models are available to enable portability and performance across different computing systems, such as Kokkos, SYCL, OpenMP and others. As part of the High Energy Physics Center for Computational Excellence (HEP-CCE) project, we investigate if and how these different programming models may be suitable for experimental HEP workflows through a few representative use cases. One of such use cases is the Liquid Argon Time Projection Chamber (LArTPC) simulation which is essential for LArTPC detector design, validation and data analysis. Following up on our previous investigations of using Kokkos to port LArTPC simulation in the Wire-Cell Toolkit (WCT) to GPUs, we have explored OpenMP and SYCL as potential portable programming models for WCT, with the goal to make diverse computing resources accessible to the LArTPC simulations. In this work, we describe how we utilize relevant features of OpenMP and SYCL for the LArTPC simulation module in WCT. We also show performance benchmark results on multi-core CPUs, NVIDIA and AMD GPUs for both the OpenMP and the SYCL implementations. Comparisons with different compilers will also be given where appropriate.
Andre Sailer, Benedikt Hegner, Clement Helsens
et al.
The Key4hep project aims to provide a turnkey software solution for the full experiment lifecycle, based on established community tools. Several future collider communities (CEPC, CLIC, EIC, FCC, and ILC) have joined to develop and adapt their workflows to use the common data model EDM4hep and common framework. Besides sharing of existing experiment workflows, one focus of the Key4hep project is the development and integration of new experiment independent software libraries. Ongoing collaborations with projects such as ACTS, CLUE, PandoraPFA and the OpenDataDector show the potential of Key4hep as an experiment-independent testbed and development platform. In this talk, we present the challenges of an experiment-independent framework along with the lessons learned from discussions of interested communities (such as LUXE) and recent adopters of Key4hep in order to discuss how Key4hep could be of interest to the wider HEP community while staying true to its goal of supporting future collider designs studies.
Miguel Caçador Peixoto, Nuno Filipe Castro, Miguel Crispim Romão
et al.
Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.
The Super Tau Charm Facility (STCF) is a proposed electron-positron collider working at $\sqrt{s}=2\sim 7$ GeV, and the peak luminosity is designed to be above $0.5 \times 10^{35}cm^{-2}s^{-1}$. The huge amount of scientific data brings great challenges to the offline data processing software, including the Monte Carlo simulation, calibration, reconstruction as well as the data analysis. To facilitate efficient offline data processing, the offline software of Super Tau Charm Facility (OSCAR) is developed based on SNiPER, a lightweight framework designed for HEP experiments, as well as a few state-of-art software in the HEP community, such as podio and DD4hep. This paper describes the design and implementation of the core software of the OSCAR, which provides the foundation for the development of complex algorithms to be applied on the large data sets produced by STCF, particularly the way to integrate the common HEP software toolkits, such as Geant4, DD4hep and podio, into SNiPER. The software framework also provides a potential solution for other lightweight HEP experiments as well.
This Letter considers the design for computing facilities that are complementary to the leadership class High Performance Computing (HPC) facilities. This design envisions a future where funding agencies are allocating greater resources for leadership class facilities and these facilities will provide a significant part of the total compute cycles for HEP Experiments. While a leadership class facility (LCF) may provide cycles and advanced architectures, the facility does not necessarily provide all of the services needed to help HEP users make the best use of the HPC facility, as well as the services needed to provide computing for workflows that are not a good fit for the HPC facilities. This Letter outlines some of the necessary components of a facility designed to provide those services and capabilities.
G. N. Zeminiani, J. J. Cobos-Martínez, K. Tsushima
[1]Abstract: This is a contribution for the PANIC 2021 Proceedings based on the articles, Eur. Phys. J. A 57, 259 (2021) and the accompanied article $[$arXiv:2109.08636 $[$hep-ph$]]$ (Hadron 2021 contribution). We have estimated for the first time the mass shifts of the $Υ$ and $η_b$ mesons in symmetric nuclear matter by an SU(5) flavor symmetric effective Lagrangian approach, as well as the in-medium mass of $B^*$ meson by the quark-meson coupling (QMC) model. The attractive potentials for the $Υ$- and $η_b$-nuclear matter are obtained, and one can expect for these mesons to form nuclear bound states. We have indeed found such nuclear bound states with $^{12}$C nucleus, where the results for the $^{12}$C nucleus bound state energies are new, and we report here for the first time. [2]Abstract: We estimate for the first time the mass shifts (scalar potentials) in symmetric nuclear matter of the $Υ$ and $η_b$ mesons using an effective Lagrangian approach, as well as the in-medium mass of the $B^*$ meson by the quark-meson coupling model. The attractive potentials of both $Υ$ and $η_b$ are expected to be strong enough for these mesons to be bound to the $^4$He nucleus, and we have obtained such nuclear bound state energies.
The discovery of QGP phenomena in small collision systems like pp and p-Pb collisions have challenged the basic paradigms of heavy-ion and high-energy physics. These proceedings give a brief overview of the key findings and their implications, as well as today's experimental and theoretical situation. An outlook of future measurement is made.