Hasil untuk "hep-ex"

Christian W. Bauer. Zohreh Davoudi, A. Balantekin, Tanmoy Bhattacharya et al.

It is for the first time that Quantum Simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information Sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond.

Théo Reymermier, Oleg Brandt, Anna Mullin et al.

Despite the success of the Standard Model (SM) there remains behaviour it cannot describe, in particular the presence of non-interacting Dark Matter. Many models that describe dark matter can generically introduce exotic Long-Lived Particles (LLPs). The proposed ANUBIS experiment is designed to search for these LLPs within the ATLAS detector cavern, located approximately 20-30 m from the Interaction Point (IP). A prototype detector, proANUBIS, has taken data within the ATLAS detector cavern since 2024, corresponding to 104 $fb^{-1}$ of pp data. We report on the potential sensitivity of ANUBIS to a selection of LLP models, i.e. Higgs Portal and Heavy Neutral Leptons, as well as future planned studies. Additionally, we will show the first results of the proANUBIS demonstrator, and how it will be used to study the expected backgrounds for the ANUBIS detector.

K. Adamczyk, B. Badełek, T. Banaszkiewicz et al.

The Polish high energy physics (HEP) community fully recognizes the urgent need to host at CERN a flagship project implementing a broad, long-term, and comprehensive vision of particle physics research and pursuing technological advances. Thus, we give preference and declare willingness to actively engage and participate in every aspect of the FCC project (both FCC-ee and FCC-hh), particularly accelerator development, detector construction, theoretical calculations, and physics analyses. As the e+e- Higgs Factory is the top priority for our field, the proposal to build a linear collider facility at CERN, opening up complementary physics prospects, should be considered as the second option. Polish teams declare strong support and are fully committed to contribute to the full exploitation of all aspects of the physics potential of the LHC and the HL-LHC programmes. To ensure the long-term development of particle physics, we also support the continuation of the high-field magnet research programme, as well as investigating other scenarios including, in particular, linear acceleration techniques and new acceleration technologies such as plasma acceleration, the muon collider and Gamma Factory. In addition, CERN should continue to provide support to fixed-target programmes at SPS as well as other non-collider and non-accelerator experiments at CERN. Participation in major projects conducted in and outside Europe should also be fostered. Education, communication, and outreach of particle physics are of paramount importance for the future of our field. An increased effort coordinated at the European level and resources allocated in all Member States are essential to effectively support future large-scale particle physics projects.

Tim Voigtländer, Manuel Giffels, Günter Quast et al.

With the increasing usage of Machine Learning (ML) in High energy physics (HEP), there is a variety of new analyses with a large spread in compute resource requirements, especially when it comes to GPU resources. For institutes, like the Karlsruhe Institute of Technology (KIT), that provide GPU compute resources to HEP via their batch systems or the Grid, a high throughput, as well as energy efficient usage of their systems is essential. With low intensity GPU analyses specifically, inefficiencies are created by the standard scheduling, as resources are over-assigned to such workflows. An approach that is flexible enough to cover the entire spectrum, from multi-process per GPU, to multi-GPU per process, is necessary. As a follow-up to the techniques presented at ACAT 2022, this time we study NVIDIA's Multi-Process Service (MPS), its ability to securely distribute device memory and its interplay with the KIT HTCondor batch system. A number of ML applications were benchmarked using this approach to illustrate the performance implications in terms of throughput and energy efficiency.

Oz Amram, Luca Anzalone, Joschka Birk et al.

Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets dataset, consisting of approximately 178M high $p_T$ jets derived from CMS 2016 Open Data. We show how pre-training the OmniJet-$α$ foundation model on AspenOpenJets improves performance on generative tasks with significant domain shift: generating boosted top and QCD jets from the simulated JetClass dataset. In addition to demonstrating the power of pre-training of a jet-based foundation model on actual proton-proton collision data, we provide the ML-ready derived AspenOpenJets dataset for further public use.

Zihan Zhao, Farouk Mokhtar, Raghav Kansal et al.

This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the increasing computational cost of producing high-quality labeled simulation samples at the CERN LHC, we propose leveraging large volumes of unlabeled data to overcome the limitations of supervised learning methods, which heavily rely on detailed labeled simulations. By pretraining models on these vast, mostly untapped datasets, we aim to learn generic representations that can be finetuned with smaller quantities of labeled data. Our methodology employs contrastive learning with augmentations on jet datasets to teach the model to recognize common representations of jets, addressing the unique challenges of LHC physics. Building on the groundwork laid by previous studies, our work demonstrates the critical ability of SSL to utilize large-scale unlabeled data effectively. We showcase the scalability and effectiveness of our models by gradually increasing the size of the pretraining dataset and assessing the resultant performance enhancements. Our results, obtained from experiments on two datasets -- JetClass, representing unlabeled data, and Top Tagging, serving as labeled simulation data -- show significant improvements in data efficiency, computational efficiency, and overall performance. These findings suggest that SSL can greatly enhance the adaptability of ML models to the HEP domain. This work opens new avenues for the use of unlabeled data in HEP and contributes to a better understanding the potential of SSL for scientific discovery.

Franco Bedeschi

Vertex fitting code is commonly found within the analysis packages of several HEP experiments, unfortunately it usually deeply packaged inside their software infrastructure, making it cumbersome to use in the context of external applications. In this paper a totally independent package is described. The only dependencies being the ROOT libraries, making it easy to use in a wide range of applications. The code currently works with track trajectories that are either helices or straight lines, as generated by charged or neutral particles in a constant magnetic field, but is expandable in principle to other type of trajectories or parameterizations.

Anni Li, Venkat Krishnamohan, Raghav Kansal et al.

In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message-passing generative adversarial network (MPGAN) was the first model to simulate collisions as point, or ``particle'', clouds, with state-of-the-art results, but suffered from quadratic time complexity. Recently, generative adversarial particle transformers (GAPTs) were introduced to address this drawback; however, results did not surpass MPGAN. We introduce induced GAPT (iGAPT) which, by integrating ``induced particle-attention blocks'' and conditioning on global jet attributes, not only offers linear time complexity but is also able to capture intricate jet substructure, surpassing MPGAN in many metrics. Our experiments demonstrate the potential of iGAPT to simulate complex HEP data accurately and efficiently.

Zichun Hao, Raghav Kansal, Javier Duarte et al.

There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $\mathrm{SO}^+(3,1)$, with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

Pieter David

With the LHC continuing to collect more data and experimental analyses becoming increasingly complex, tools to efficiently develop and execute these analyses are essential. The bamboo framework defines a domain-specific language, embedded in python, that allows to concisely express the analysis logic in a functional style. The implementation based on ROOT's RDataFrame and cling C++ JIT compiler approaches the performance of dedicated native code. Bamboo is currently being used for several CMS Run 2 analyses that rely on the NanoAOD data format, which will become more common in Run 3 and beyond, and for which many reusable components are included, but it provides many possibilities for customisation, which allow for straightforward adaptation to other formats and workflows

Ning Li, Zhi-Feng Sun, Xiang Liu et al.

In 2012, we investigated the possible molecular states composed of two charmed mesons [Phys.Rev. D 88, 114008 (2013), arXiv:1211.5007 [hep-ph](2012)]. The $D^*D$ system with the quantum numbers of $I(J^P)=0(1^+)$ was found to be a good candidate of the loosely bound molecular state. This state is very close to the $D^*D$ threshold with a binding energy around 0.47 MeV. This prediction was confirmed by the new LHCb observation of $T_{cc}^+$ [see Franz Muheim's talk at the European Physical Society conference on high energy physics 2021].

A. Cuker, G. Arepally, M. Crowther et al.

Katarzyna Grebieszkow

The exploration of the QCD phase diagram is the most important task of present heavy ion experiments. In particular, we want to study the phase transition from hadronic to partonic matter and look for the critical point (CP) of strongly interacting matter. Fluctuations and correlations in kinematic characteristics and particle yields may help to locate the CP (in analogy to enlarged fluctuations due to critical opalescence close to a CP in a liquid/gas transition). The strong interactions program of the NA61/SHINE experiment may allow to discover or rule out the existence of the CP in the Super Proton Synchrotron energy domain. For this purpose we perform a two-dimensional scan by varying the energy ($5.1 < \sqrt{s_{NN}} < 16.8/17.3$ GeV) and the system size (p+p, p+Pb, Be+Be, Ar+Sc, Xe+La, Pb+Pb) of the collisions. In this report new NA61/SHINE results on fluctuations and correlations in p+p, Be+Be, and Ar+Sc collisions are presented. In particular, results on transverse momentum and multiplicity fluctuations, as well as higher order moments of net-charge fluctuations are discussed. The NA61/SHINE data are compared to predictions of string hadronic models and to NA49 results.

Boris Tuchming

We present the most recent and sensitive measurements of the top quark mass, performed by the Tevatron experiments, D0 and CDF, using ppbar collision data at sqrt(s) = 1.96 TeV. We also present the first measurement of the top quark polarization at Tevatron, obtained by the D0 Collaboration in the dilepton channels.

M. Mahler, M. Fritzler

Antinuclear antibodies (ANAs) are a serological hallmark in the diagnosis of systemic autoimmune rheumatic diseases (SARD). The indirect immunofluorescence (IIF) assay on HEp-2 cells is a commonly used test for the detection of ANA and has been recently recommended as the screening test of choice by a task force of the American College of Rheumatology. However, up to 20% of apparently healthy individuals (HI) have been reported to have a positive IIF ANA test, primarily related to autoantibodies that target the dense fine speckles 70 (DFS70) antigen. Even more important, the DFS IIF pattern has been reported in up to 33% of ANA positive HI, but not in ANA positive SARD sera. Since the intended use of the ANA HEp-2 test is to aid in the diagnosis and classification of SARD, the detection and reporting of anti-DFS70 antibodies and their associated pattern (DFS) as a positive test significantly reduce the specificity and the positive likelihood of the ANA test. This has significant implications for medical management and diagnostic algorithms involving the detection of ANA. Recently, a novel immunoadsorption method has been developed that specifically blocks anti-DFS70 antibodies and, therefore, significantly increases the specificity of the ANA test for SARD. This immunoadsorption method has the potential to overcome a significant limitation of the ANA HEp-2 assay. The present paper summarizes the current knowledge about anti-DFS70 antibodies and their clinical impact on ANA testing.

S. Tsai, Jai-Sing Yang, Shu‐Fen Peng et al.

J. Voigt, C. Krause, E. Rohwäder et al.

Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra- and interlaboratory variability.

Susan S. Copple, Si', S. Giles et al.

J. Devi, E. Thangam

Sulforaphane (SFN) an isothiocyanate formed by hydrolysis of glucosinolates found in Brassica oleraceae is reported to possess anticancer and antioxidant activities. In this study, we isolated SFN from red cabbage (Brassica oleraceae var rubra) and evaluated the comparative antiproliferative activity of various fractions (standard SFN, extract and purified SFN) by MTT assay in human epithelial carcinoma HEp -2 and and Vero cells. Probable apoptotic mechanisms mediated through p53, bax and bcl-2 were also examined. The SFN fraction was collected by HPLC, enriched for its SFN content and confirmed. Expression of apoptosis-related proteins was detected by western blotting and RT PCR. Results showed that Std SFN and purified SFN concentration found to have closer IC50 which is equal to 58.96 microgram/ml (HEp-2 cells), 61.2 microgram/ml (Vero cells) and less than the extract which is found to be 113 microgram/ml (HEp-2 cells) and 125 microgram/ml (Vero cells). Further studies on apoptotic mechanisms showed that purified SFN down-regulated the expression of bcl-2 (antiapoptotic), while up-regulating p53 and Bax (proapoptotic) proteins, as well as caspase-3. This study indicates that purified SFN possesses antiproliferative effects the same as Std SFN and its apoptotic mechanism in HEp-2 cells could be mediated through p53 induction, bax and bcl-2 signaling pathways.

Kuan Li, Jianping Yin, Zhi Lu et al.