Mass Agnostic Jet Taggers
TL;DR: In this article, the authors performed a systematic study of both single variable and multivariate jet tagging methods that aim to find a balance between signal identification and background distortion, and showed that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost.
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Abstract: Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.
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Citations
Mass Unspecific Supervised Tagging (MUST) for boosted jets
TL;DR: In this article, the authors introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wide ranges as input variables - together with jet substructure observables - of a multivariate tool.
A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
Benjamin Nachman
- 23 Jun 2020
TL;DR: Nachman et al. as discussed by the authors present a review of how to optimally integrate information with deep learning and explicitly describes the corresponding sources of uncertainty, which can be applied in practice.
Jet Substructure from Dark Sector Showers
TL;DR: In this paper, the robustness of collider phenomenology predictions for a dark sector scenario with QCD-like properties was examined, and the uncertainties inherent to modeling dark sector hadronization were investigated.
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Enhancing searches for resonances with machine learning and moment decomposition
TL;DR: A new set of tools using a novel moment loss function (Moment Decomposition or MoDe) which relax the assumption of independence without creating structures in the background to enhance the sensitivity to new physics without compromising the fidelity of the background estimation.
Enhancing searches for resonances with machine learning and moment decomposition
TL;DR: In this article, a moment loss function (Moment Decomposition or MoDe) is proposed to relax the assumption of independence without creating structures in the background, which can enhance the sensitivity to new physics without compromising the fidelity of the background estimation.
References
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
•Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
•Posted Content
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Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
Johan Alwall,Rikkert Frederix,Stefano Frixione,Valentin Hirschi,Fabio Maltoni,Olivier Mattelaer,Hua-Sheng Shao,Tim Stelzer,Paolo Torrielli,Marco Zaro,Marco Zaro +10 more
TL;DR: MadGraph5 aMC@NLO as discussed by the authors is a computer program capable of handling all these computations, including parton-level fixed order, shower-matched, merged, in a unified framework whose defining features are flexibility, high level of parallelisation and human intervention limited to input physics quantities.