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  1. Article ; Online: Learning to simulate high energy particle collisions from unlabeled data.

    Howard, Jessica N / Mandt, Stephan / Whiteson, Daniel / Yang, Yibo

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 7567

    Abstract: In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often ... ...

    Abstract In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder's latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.
    Language English
    Publishing date 2022-05-09
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-10966-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

    Liu, Junze / Ghosh, Aishik / Smith, Dylan / Baldi, Pierre / Whiteson, Daniel

    2023  

    Abstract: Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its ... ...

    Abstract Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. We develop a $\textit{geometry-aware}$ autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over $50\%$ in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability
    Subject code 670
    Publishing date 2023-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Enhanced Higgs boson to τ(+)τ(-) search with deep learning.

    Baldi, P / Sadowski, P / Whiteson, D

    Physical review letters

    2015  Volume 114, Issue 11, Page(s) 111801

    Abstract: The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to ... ...

    Abstract The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.
    Language English
    Publishing date 2015-03-20
    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.114.111801
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Efficient sampling of constrained high-dimensional theoretical spaces with machine learning

    Hollingsworth, Jacob / Ratz, Michael / Tanedo, Philip / Whiteson, Daniel

    2021  

    Abstract: Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental constraints project onto a subspace of viable parameters, but ... ...

    Abstract Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental constraints project onto a subspace of viable parameters, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.

    Comment: 10 pages, 6 figures
    Keywords High Energy Physics - Theory ; High Energy Physics - Phenomenology
    Subject code 621
    Publishing date 2021-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Geometry-aware Autoregressive Models for Calorimeter Shower Simulations

    Liu, Junze / Ghosh, Aishik / Smith, Dylan / Baldi, Pierre / Whiteson, Daniel

    2022  

    Abstract: Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors. A lot of effort is currently spent on optimizing generative architectures for specific detector geometries, which generalize poorly. We develop a ... ...

    Abstract Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors. A lot of effort is currently spent on optimizing generative architectures for specific detector geometries, which generalize poorly. We develop a geometry-aware autoregressive model on a range of calorimeter geometries such that the model learns to adapt its energy deposition depending on the size and position of the cells. This is a key proof-of-concept step towards building a model that can generalize to new unseen calorimeter geometries with little to no additional training. Such a model can replace the hundreds of generative models used for calorimeter simulation in a Large Hadron Collider experiment. For the study of future detectors, such a model will dramatically reduce the large upfront investment usually needed to generate simulations.

    Comment: This paper was submitted to NeurIPS Machine Learning and the Physical Sciences Workshop 2022
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability
    Subject code 612
    Publishing date 2022-12-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

    Shmakov, Alexander / Greif, Kevin / Fenton, Michael / Ghosh, Aishik / Baldi, Pierre / Whiteson, Daniel

    2023  

    Abstract: High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or ... ...

    Abstract High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions or measurements from other detectors. Methods to solve this \textit{inverse problem} of mapping detector observations to theoretical quantities of the underlying collision are essential parts of many physics analyses at the LHC. We investigate and compare various generative deep learning methods to approximate this inverse mapping. We introduce a novel unified architecture, termed latent variation diffusion models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. We demonstrate the effectiveness of this approach for reconstructing global distributions of theoretical kinematic quantities, as well as for ensuring the adherence of the learned posterior distributions to known physics constraints. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline and 3 times less than traditional latent diffusion models.
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-05-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Searching for exotic particles in high-energy physics with deep learning.

    Baldi, P / Sadowski, P / Whiteson, D

    Nature communications

    2014  Volume 5, Page(s) 4308

    Abstract: Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches ... ...

    Abstract Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. Standard approaches have relied on 'shallow' machine-learning models that have a limited capacity to learn complex nonlinear functions of the inputs, and rely on a painstaking search through manually constructed nonlinear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Here, using benchmark data sets, we show that deep-learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep-learning approaches can improve the power of collider searches for exotic particles.
    Language English
    Publishing date 2014-07-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2041-1723
    ISSN (online) 2041-1723
    DOI 10.1038/ncomms5308
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Machine-Learning Compression for Particle Physics Discoveries

    Collins, Jack H. / Huang, Yifeng / Knapen, Simon / Nachman, Benjamin / Whiteson, Daniel

    2022  

    Abstract: In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store the complete ... ...

    Abstract In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis. Typically, algorithms select individual collision events for preservation and store the complete experimental response. A relatively new alternative strategy is to additionally save a partial record for a larger subset of events, allowing for later specific analysis of a larger fraction of events. We propose a strategy that bridges these paradigms by compressing entire events for generic offline analysis but at a lower fidelity. An optimal-transport-based $\beta$ Variational Autoencoder (VAE) is used to automate the compression and the hyperparameter $\beta$ controls the compression fidelity. We introduce a new approach for multi-objective learning functions by simultaneously learning a VAE appropriate for all values of $\beta$ through parameterization. We present an example use case, a di-muon resonance search at the Large Hadron Collider (LHC), where we show that simulated data compressed by our $\beta$-VAE has enough fidelity to distinguish distinct signal morphologies.

    Comment: 9 pages, 3 figures
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability
    Subject code 006
    Publishing date 2022-10-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks

    Fenton, Michael James / Shmakov, Alexander / Okawa, Hideki / Li, Yuji / Hsiao, Ko-Yang / Hsu, Shih-Chieh / Whiteson, Daniel / Baldi, Pierre

    2023  

    Abstract: Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention mechanism, ... ...

    Abstract Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention mechanism, symmetry preserving attention networks (Spa-Net), has been previously applied to top quark pair decays at the Large Hadron Collider which produce only hadronic jets. Here we extend the Spa-Net architecture to consider multiple input object types, such as leptons, as well as global event features, such as the missing transverse momentum. In addition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of Spa-Net in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: a search for ttH, a measurement of the top quark mass, and a search for a heavy Z' decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case.

    Comment: Submitted to Nature Communications
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology
    Subject code 539
    Publishing date 2023-09-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: SPANet

    Shmakov, Alexander / Fenton, Michael James / Ho, Ta-Wei / Hsu, Shih-Chieh / Whiteson, Daniel / Baldi, Pierre

    Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention

    2021  

    Abstract: The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities ... ...

    Abstract The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19\% - 35\% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are avaiable at https://github.com/Alexanders101/SPANet

    Comment: published in SciPost
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology
    Subject code 006
    Publishing date 2021-06-07
    Publishing country us
    Document type Book ; Online
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