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  1. Book ; Online: Processing Columnar Collider Data withGPU-Accelerated Kernels

    Pata, Joosep / Spiropulu, Maria

    2019  

    Abstract: At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such ... ...

    Abstract At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query languages allow, such that custom numerical codes are used. At present, these codes mostly operate on individual event records and are parallelized in multi-step data reduction workflows using batch jobs across CPU farms. Based on a simplified top quark pair analysis with CMS Open Data, we demonstrate that it is possible to carry out significant parts of a collider analysis at a rate of around a million events per second on a single multicore server with optional GPU acceleration. This is achieved by representing HEP event data as memory-mappable sparse arrays of columns, and by expressing common analysis operations as kernels that can be used to process the event data in parallel. We find that only a small number of relatively simple functional kernels are needed for a generic HEP analysis. The approach based on columnar processing of data could speed up and simplify the cycle for delivering physics results at HEP experiments. We release the \texttt{hepaccelerate} prototype library as a demonstrator of such methods.
    Keywords Physics - Data Analysis ; Statistics and Probability ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Physics - Computational Physics
    Subject code 006
    Publishing date 2019-06-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

    Liu, Ryan / Gandrakota, Abhijith / Ngadiuba, Jennifer / Spiropulu, Maria / Vlimant, Jean-Roch

    2023  

    Abstract: Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a ... ...

    Abstract Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.

    Comment: 7 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Publishing date 2023-11-28
    Publishing country us
    Document type Book ; Online
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  3. Book ; Online: Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation

    Liu, Ryan / Gandrakota, Abhijith / Ngadiuba, Jennifer / Spiropulu, Maria / Vlimant, Jean-Roch

    2023  

    Abstract: The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low ... ...

    Abstract The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low computational complexity that have weak inductive bias are feasible. To address this issue, we utilize knowledge distillation to leverage both the performance of large models and the reduced computational complexity of small ones. In this paper, we present an implementation of knowledge distillation, demonstrating an overall boost in the student models' performance for the task of classifying jets at the LHC. Furthermore, by using a teacher model with a strong inductive bias of Lorentz symmetry, we show that we can induce the same inductive bias in the student model which leads to better robustness against arbitrary Lorentz boost.

    Comment: 7 pages, 3 figures, accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-23
    Publishing country us
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  4. Article ; Online: Traversable wormhole dynamics on a quantum processor.

    Jafferis, Daniel / Zlokapa, Alexander / Lykken, Joseph D / Kolchmeyer, David K / Davis, Samantha I / Lauk, Nikolai / Neven, Hartmut / Spiropulu, Maria

    Nature

    2022  Volume 612, Issue 7938, Page(s) 51–55

    Abstract: The holographic principle, theorized to be a property of quantum gravity, postulates that the description of a volume of space can be encoded on a lower-dimensional boundary. The anti-de Sitter (AdS)/conformal field theory correspondence or ... ...

    Abstract The holographic principle, theorized to be a property of quantum gravity, postulates that the description of a volume of space can be encoded on a lower-dimensional boundary. The anti-de Sitter (AdS)/conformal field theory correspondence or duality
    Language English
    Publishing date 2022-11-30
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/s41586-022-05424-3
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  5. Article: Supersymmetry and the crisis in physics.

    Lykken, Joseph / Spiropulu, Maria

    Scientific American

    2014  Volume 310, Issue 5, Page(s) 34–39

    Language English
    Publishing date 2014-03-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 246-x
    ISSN 1946-7087 ; 0036-8733
    ISSN (online) 1946-7087
    ISSN 0036-8733
    DOI 10.1038/scientificamerican0514-34
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    In stock of ZB MED Cologne/Königswinter

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  6. Book ; Online: Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders

    Moreno, Eric A. / Vlimant, Jean-Roch / Spiropulu, Maria / Borzyszkowski, Bartlomiej / Pierini, Maurizio

    2021  

    Abstract: We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an ... ...

    Abstract We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trained on noise data, this class of algorithms could detect signals using an unsupervised strategy, i.e., without targeting a specific kind of source. We develop a custom architecture to analyze the data from two interferometers. We compare the obtained performance to that obtained with other autoencoder architectures and with a convolutional classifier. The unsupervised nature of the proposed strategy comes with a cost in terms of accuracy, when compared to more traditional supervised techniques. On the other hand, there is a qualitative gain in generalizing the experimental sensitivity beyond the ensemble of pre-computed signal templates. The recurrent autoencoder outperforms other autoencoders based on different architectures. The class of recurrent autoencoders presented in this paper could complement the search strategy employed for gravitational wave detection and extend the reach of the ongoing detection campaigns.

    Comment: 16 pages, 6 figures
    Keywords General Relativity and Quantum Cosmology ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; Physics - Data Analysis ; Statistics and Probability ; Physics - Instrumentation and Detectors
    Subject code 006
    Publishing date 2021-07-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: MLPF

    Pata, Joosep / Duarte, Javier / Vlimant, Jean-Roch / Pierini, Maurizio / Spiropulu, Maria

    Efficient machine-learned particle-flow reconstruction using graph neural networks

    2021  

    Abstract: In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector ... ...

    Abstract In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton-proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural networks optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

    Comment: 15 pages, 10 figures
    Keywords Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Instrumentation and Detectors ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2021-01-21
    Publishing country us
    Document type Book ; Online
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  8. Article ; Online: Solving a Higgs optimization problem with quantum annealing for machine learning.

    Mott, Alex / Job, Joshua / Vlimant, Jean-Roch / Lidar, Daniel / Spiropulu, Maria

    Nature

    2017  Volume 550, Issue 7676, Page(s) 375–379

    Abstract: The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect ... ...

    Abstract The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.
    Language English
    Publishing date 2017-10-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 120714-3
    ISSN 1476-4687 ; 0028-0836
    ISSN (online) 1476-4687
    ISSN 0028-0836
    DOI 10.1038/nature24047
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  9. Book ; Online: Quantum adiabatic machine learning with zooming

    Zlokapa, Alexander / Mott, Alex / Job, Joshua / Vlimant, Jean-Roch / Lidar, Daniel / Spiropulu, Maria

    2019  

    Abstract: Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, a novel algorithm ... ...

    Abstract Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, a novel algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the ROC curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

    Comment: 9 pages, 5 figures
    Keywords Quantum Physics ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology
    Subject code 006
    Publishing date 2019-08-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Golden Probe of Electroweak Symmetry Breaking.

    Chen, Yi / Lykken, Joe / Spiropulu, Maria / Stolarski, Daniel / Vega-Morales, Roberto

    Physical review letters

    2016  Volume 117, Issue 24, Page(s) 241801

    Abstract: The ratio of the Higgs couplings to WW and ZZ pairs, λ_{WZ}, is a fundamental parameter in electroweak symmetry breaking as well as a measure of the (approximate) custodial symmetry possessed by the gauge boson mass matrix. We show that Higgs decays to ... ...

    Abstract The ratio of the Higgs couplings to WW and ZZ pairs, λ_{WZ}, is a fundamental parameter in electroweak symmetry breaking as well as a measure of the (approximate) custodial symmetry possessed by the gauge boson mass matrix. We show that Higgs decays to four leptons are sensitive, via tree level or one-loop interference effects, to both the magnitude and, in particular, overall sign of λ_{WZ}. Determining this sign requires interference effects, as it is nearly impossible to measure with rate information. Furthermore, simply determining the sign effectively establishes the custodial representation of the Higgs boson. We find that h→4ℓ (4ℓ≡2e2μ, 4e, 4μ) decays have excellent prospects of directly establishing the overall sign at a high luminosity 13 TeV LHC. We also examine the ultimate LHC sensitivity in h→4ℓ to the magnitude of λ_{WZ}. Our results are independent of other measurements of the Higgs boson couplings and, in particular, largely free of assumptions about the top quark Yukawa couplings which also enter at one loop. This makes h→4ℓ a unique and independent probe of electroweak symmetry breaking and custodial symmetry.
    Language English
    Publishing date 2016-12-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.117.241801
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