Relevant bibliographies by topics / Machine Learning,High Energy Physics,CMS,Top physics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Machine Learning,High Energy Physics,CMS,Top physics'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Machine Learning,High Energy Physics,CMS,Top physics"

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Andrews, Michael, Bjorn Burkle, Shravan Chaudhari, et al. "Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data." EPJ Web of Conferences 251 (2021): 03057. http://dx.doi.org/10.1051/epjconf/202125103057.

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Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.
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Kiehn, Moritz, Sabrina Amrouche, Paolo Calafiura, et al. "The TrackML high-energy physics tracking challenge on Kaggle." EPJ Web of Conferences 214 (2019): 06037. http://dx.doi.org/10.1051/epjconf/201921406037.

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The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.
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Qasim, Shah Rukh, Kenneth Long, Jan Kieseler, Maurizio Pierini, and Raheel Nawaz. "Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks." EPJ Web of Conferences 251 (2021): 03072. http://dx.doi.org/10.1051/epjconf/202125103072.

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The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.
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Blance, Andrew, and Michael Spannowsky. "Quantum machine learning for particle physics using a variational quantum classifier." Journal of High Energy Physics 2021, no. 2 (2021). http://dx.doi.org/10.1007/jhep02(2021)212.

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Abstract Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.
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Finke, Thorben, Michael Krämer, Alessandro Morandini, Alexander Mück, and Ivan Oleksiyuk. "Autoencoders for unsupervised anomaly detection in high energy physics." Journal of High Energy Physics 2021, no. 6 (2021). http://dx.doi.org/10.1007/jhep06(2021)161.

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Abstract Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations. As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images. Although we reproduce the positive results from the literature, we show that the standard autoencoder setup cannot be considered as a model-independent anomaly tagger by inverting the task: due to the sparsity and the specific structure of the jet images, the autoencoder fails to tag QCD jets if it is trained on top jets even in a semi-supervised setup. Since the same autoencoder architecture can be a good tagger for a specific example of an anomaly and a bad tagger for a different example, we suggest improved performance measures for the task of model-independent anomaly detection. We also improve the capability of the autoencoder to learn non-trivial features of the jet images, such that it is able to achieve both top jet tagging and the inverse task of QCD jet tagging with the same setup. However, we want to stress that a truly model-independent and powerful autoencoder-based unsupervised jet tagger still needs to be developed.
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Kuznetsov, Valentin, Luca Giommi, and Daniele Bonacorsi. "MLaaS4HEP: Machine Learning as a Service for HEP." Computing and Software for Big Science 5, no. 1 (2021). http://dx.doi.org/10.1007/s41781-021-00061-3.

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AbstractMachine Learning (ML) will play a significant role in the success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. An unprecedented amount of data at the exascale will be collected by LHC experiments in the next decade, and this effort will require novel approaches to train and use ML models. In this paper, we discuss a Machine Learning as a Service pipeline for HEP (MLaaS4HEP) which provides three independent layers: a data streaming layer to read High-Energy Physics (HEP) data in their native ROOT data format; a data training layer to train ML models using distributed ROOT files; a data inference layer to serve predictions using pre-trained ML models via HTTP protocol. Such modular design opens up the possibility to train data at large scale by reading ROOT files from remote storage facilities, e.g., World-Wide LHC Computing Grid (WLCG) infrastructure, and feed the data to the user’s favorite ML framework. The inference layer implemented as TensorFlow as a Service (TFaaS) may provide an easy access to pre-trained ML models in existing infrastructure and applications inside or outside of the HEP domain. In particular, we demonstrate the usage of the MLaaS4HEP architecture for a physics use-case, namely, the $$t{\bar{t}}$$ t t ¯ Higgs analysis in CMS originally performed using custom made Ntuples. We provide details on the training of the ML model using distributed ROOT files, discuss the performance of the MLaaS and TFaaS approaches for the selected physics analysis, and compare the results with traditional methods.
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Flesher, Forrest, Katherine Fraser, Charles Hutchison, Bryan Ostdiek, and Matthew D. Schwartz. "Parameter inference from event ensembles and the top-quark mass." Journal of High Energy Physics 2021, no. 9 (2021). http://dx.doi.org/10.1007/jhep09(2021)058.

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Abstract One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.
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Khalek, Rabah Abdul, Jacob J. Ethier, Juan Rojo, and Gijs van Weelden. "nNNPDF2.0: quark flavor separation in nuclei from LHC data." Journal of High Energy Physics 2020, no. 9 (2020). http://dx.doi.org/10.1007/jhep09(2020)183.

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Abstract We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure functions used in our previous analysis, nNNPDF1.0, are complemented by inclusive and charm-tagged cross-sections from charged-current scattering. Furthermore, we include all available measurements of W and Z leptonic rapidity distributions in proton-lead collisions from ATLAS and CMS at $$ \sqrt{s} $$ s = 5.02 TeV and 8.16 TeV. The resulting nPDF determination, nNNPDF2.0, achieves a good description of all datasets. In addition to quantifying the nuclear modifications affecting individual quarks and antiquarks, we examine the implications for strangeness, assess the role that the momentum and valence sum rules play in nPDF extractions, and present predictions for representative phenomenological applications. Our results, made available via the LHAPDF library, highlight the potential of high-energy collider measurements to probe nuclear dynamics in a robust manner.
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Amacker, Jacob, William Balunas, Lydia Beresford, et al. "Higgs self-coupling measurements using deep learning in the $$ b\overline{b}b\overline{b} $$ final state." Journal of High Energy Physics 2020, no. 12 (2020). http://dx.doi.org/10.1007/jhep12(2020)115.

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Abstract Measuring the Higgs trilinear self-coupling λhhh is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in the four b-quark channel (hh → 4b), extending current methods in several directions. We perform deep learning to suppress the formidable multijet background with dedicated optimisation for BSM λhhh scenarios. We compare the λhhh constraining power of events using different multiplicities of large radius jets with a two-prong structure that reconstruct boosted h → bb decays. We show that current uncertainties in the SM top Yukawa coupling yt can modify λhhh constraints by ∼ 20%. For SM yt, we find prospects of −0.8 <$$ {\lambda}_{hhh}/{\lambda}_{hhh}^{\mathrm{SM}} $$ λ hhh / λ hhh SM < 6.6 at 68% CL under simplified assumptions for 3000 fb−1 of HL-LHC data. Our results provide a careful assessment of di-Higgs identification and machine learning techniques for all-hadronic measurements of the Higgs self-coupling and sharpens the requirements for future improvement.
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Araz, Jack Y., and Michael Spannowsky. "Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States." Journal of High Energy Physics 2021, no. 8 (2021). http://dx.doi.org/10.1007/jhep08(2021)112.

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Abstract Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniques, thereby facilitating an improved interpretability of neural networks. This study presents the discrimination of top quark signal over QCD background processes using a Matrix Product State classifier. We show that entanglement entropy can be used to interpret what a network learns, which can be used to reduce the complexity of the network and feature space without loss of generality or performance. For the optimisation of the network, we compare the Density Matrix Renormalization Group (DMRG) algorithm to stochastic gradient descent (SGD) and propose a joined training algorithm to harness the explainability of DMRG with the efficiency of SGD.
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Dissertations / Theses on the topic "Machine Learning,High Energy Physics,CMS,Top physics"

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Giommi, Luca. "Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15803/.

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Big volumes of data are collected and analysed by LHC experiments at CERN. The success of this scientific challenges is ensured by a great amount of computing power and storage capacity, operated over high performance networks, in very complex LHC computing models on the LHC Computing Grid infrastructure. Now in Run-2 data taking, LHC has an ambitious and broad experimental programme for the coming decades: it includes large investments in detector hardware, and similarly it requires commensurate investment in the R&D in software and com- puting to acquire, manage, process, and analyse the shear amounts of data to be recorded in the High-Luminosity LHC (HL-LHC) era. The new rise of Artificial Intelligence - related to the current Big Data era, to the technological progress and to a bump in resources democratization and efficient allocation at affordable costs through cloud solutions - is posing new challenges but also offering extremely promising techniques, not only for the commercial world but also for scientific enterprises such as HEP experiments. Machine Learning and Deep Learning are rapidly evolving approaches to characterising and describing data with the potential to radically change how data is reduced and analysed, also at LHC. This thesis aims at contributing to the construction of a Machine Learning “as a service” solution for CMS Physics needs, namely an end-to-end data-service to serve Machine Learning trained model to the CMS software framework. To this ambitious goal, this thesis work contributes firstly with a proof of concept of a first prototype of such infrastructure, and secondly with a specific physics use-case: the Signal versus Background discrimination in the study of CMS all-hadronic top quark decays, done with scalable Machine Learning techniques.
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Zoch, Knut. "Cross-section measurements of top-quark pair production in association with a hard photon at 13 TeV with the ATLAS detector." Doctoral thesis, 2020. http://hdl.handle.net/21.11130/00-1735-0000-0005-1440-C.

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Conference papers on the topic "Machine Learning,High Energy Physics,CMS,Top physics"

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Bhattacharya, Soham, Monoranjan Guchait, and Aravind H. Vijay. "Boosted Top Quark Tagging and Polarization 2 Measurement using Machine Learning." In 40th International Conference on High Energy physics. Sissa Medialab, 2021. http://dx.doi.org/10.22323/1.390.0318.

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