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  1. AU="Golling, T."
  2. AU="Young, Jonathan R"
  3. AU="Lee, Jae Young"
  4. AU="Deng, Xing-Wang"
  5. AU="Wiarda, Grant"
  6. AU="Pereira, Naveen"
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  15. AU="Vachiraarunwong, Arpamas"
  16. AU="Mohammad-Najar, Narges"
  17. AU="Sarica, Kemal"
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  1. Book ; Online: Decorrelation with conditional normalizing flows

    Klein, Samuel / Golling, Tobias

    2022  

    Abstract: The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we show ...

    Abstract The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we show that a normalizing flow conditioned on the protected attributes can be used to find a decorrelated representation for any discriminant. As a normalizing flow is invertible the separation power of the resulting discriminant will be unchanged at any fixed value of the protected attributes. We demonstrate the efficacy of our approach by building supervised jet taggers that produce almost no sculpting in the mass distribution of the background.
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning
    Publishing date 2022-11-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.

    Raine, John Andrew / Klein, Samuel / Sengupta, Debajyoti / Golling, Tobias

    Frontiers in big data

    2023  Volume 6, Page(s) 899345

    Abstract: We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band ... ...

    Abstract We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using Curtains, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the Curtains background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that Curtains matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.
    Language English
    Publishing date 2023-03-21
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2023.899345
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

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  3. Book ; Online: Decorrelation using Optimal Transport

    Algren, Malte / Raine, John Andrew / Golling, Tobias

    2023  

    Abstract: Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots) ... ...

    Abstract Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots) that is able to decorrelate a continuous feature space against protected attributes with optimal transport. We demonstrate how well it performs in the context of jet classification in high energy physics, where classifier scores are desired to be decorrelated from the mass of a jet. The decorrelation achieved in binary classification approaches the levels achieved by the state-of-the-art using conditional normalising flows. When moving to multiclass outputs the optimal transport approach performs significantly better than the state-of-the-art, suggesting substantial gains at decorrelating multidimensional feature spaces.
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 006
    Publishing date 2023-07-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Article: The interplay of machine learning-based resonant anomaly detection methods.

    Golling, Tobias / Kasieczka, Gregor / Krause, Claudius / Mastandrea, Radha / Nachman, Benjamin / Raine, John Andrew / Sengupta, Debajyoti / Shih, David / Sommerhalder, Manuel

    The European physical journal. C, Particles and fields

    2024  Volume 84, Issue 3, Page(s) 241

    Abstract: Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, ... ...

    Abstract Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD methods that has received significant attention is resonant anomaly detection, where the BSM physics is assumed to be localized in at least one known variable. While there have been many methods proposed to identify such a BSM signal that make use of simulated or detected data in different ways, there has not yet been a study of the methods' complementarity. To this end, we address two questions. First, in the absence of any signal, do different methods pick the same events as signal-like? If not, then we can significantly reduce the false-positive rate by comparing different methods on the same dataset. Second, if there is a signal, are different methods fully correlated? Even if their maximum performance is the same, since we do not know how much signal is present, it may be beneficial to combine approaches. Using the Large Hadron Collider (LHC) Olympics dataset, we provide quantitative answers to these questions. We find that there are significant gains possible by combining multiple methods, which will strengthen the search program at the LHC and beyond.
    Language English
    Publishing date 2024-03-08
    Publishing country France
    Document type Journal Article
    ZDB-ID 1459069-4
    ISSN 1434-6052 ; 1434-6044
    ISSN (online) 1434-6052
    ISSN 1434-6044
    DOI 10.1140/epjc/s10052-024-12607-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Order via subito

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  5. Book ; Online: CURTAINs Flows For Flows

    Sengupta, Debajyoti / Klein, Samuel / Raine, John Andrew / Golling, Tobias

    Constructing Unobserved Regions with Maximum Likelihood Estimation

    2023  

    Abstract: Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. We introduce a major improvement to the CURTAINs method by training the ... ...

    Abstract Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. We introduce a major improvement to the CURTAINs method by training the conditional normalizing flow between two side-band regions using maximum likelihood estimation instead of an optimal transport loss. The new training objective improves the robustness and fidelity of the transformed data and is much faster and easier to train. We compare the performance against the previous approach and the current state of the art using the LHC Olympics anomaly detection dataset, where we see a significant improvement in sensitivity over the original CURTAINs method. Furthermore, CURTAINsF4F requires substantially less computational resources to cover a large number of signal regions than other fully data driven approaches. When using an efficient configuration, an order of magnitude more models can be trained in the same time required for ten signal regions, without a significant drop in performance.

    Comment: 19 pages, 10 figures, 4 tables
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 621
    Publishing date 2023-05-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: $\nu^2$-Flows

    Raine, John Andrew / Leigh, Matthew / Zoch, Knut / Golling, Tobias

    Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows

    2023  

    Abstract: In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired ... ...

    Abstract In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.

    Comment: 24 pages, 19 figures, 6 tables
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 530
    Publishing date 2023-07-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  7. Book ; Online: Flowification

    Máté, Bálint / Klein, Samuel / Golling, Tobias / Fleuret, François

    Everything is a Normalizing Flow

    2022  

    Abstract: The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood of data points as samples are propagated along the network. Recently, ... ...

    Abstract The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood of data points as samples are propagated along the network. Recently, multiple generalizations of normalizing flows have been introduced that relax these two conditions. On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution. In this paper we argue that certain neural network architectures can be enriched with a stochastic inverse pass and that their likelihood contribution can be monitored in a way that they fall under the generalized notion of a normalizing flow mentioned above. We term this enrichment flowification. We prove that neural networks only containing linear layers, convolutional layers and invertible activations such as LeakyReLU can be flowified and evaluate them in the generative setting on image datasets.

    Comment: NeurIPS 2022
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2022-05-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  8. Book ; Online: FETA

    Golling, Tobias / Klein, Samuel / Mastandrea, Radha / Nachman, Benjamin

    Flow-Enhanced Transportation for Anomaly Detection

    2022  

    Abstract: Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background ... ...

    Abstract Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data. The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region. To illustrate this approach, we use simulated collisions from the Large Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed background method has competitive sensitivity with other recent proposals and can therefore provide complementary information to improve future searches.

    Comment: 13 pages, 11 figures. minor updates, v2 (published version)
    Keywords High Energy Physics - Phenomenology ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability
    Subject code 621
    Publishing date 2022-12-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Book ; Online: \nu-Flows

    Leigh, Matthew / Raine, John Andrew / Zoch, Knut / Golling, Tobias

    Conditional Neutrino Regression

    2022  

    Abstract: We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full ... ...

    Abstract We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.

    Comment: 26 pages, 15 figures
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Publishing date 2022-07-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  10. Book ; Online: PC-JeDi

    Leigh, Matthew / Sengupta, Debajyoti / Quétant, Guillaume / Raine, John Andrew / Zoch, Knut / Golling, Tobias

    Diffusion for Particle Cloud Generation in High Energy Physics

    2023  

    Abstract: In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle ...

    Abstract In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons.

    Comment: 29 pages, 25 figures, 5 tables
    Keywords High Energy Physics - Phenomenology ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 541
    Publishing date 2023-03-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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