672 research outputs found

    On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

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    Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered in the domain of high-energy physics, using a well-studied dataset: the 2014 Higgs ML Kaggle dataset. The advantages are evaluated in terms of both performance metrics and the time required to train and apply the resulting models. Techniques examined include domain-specific data-augmentation, learning rate and momentum scheduling, (advanced) ensembling in both model-space and weight-space, and alternative architectures and connection methods. Following the investigation, we arrive at a model which achieves equal performance to the winning solution of the original Kaggle challenge, whilst being significantly quicker to train and apply, and being suitable for use with both GPU and CPU hardware setups. These reductions in timing and hardware requirements potentially allow the use of more powerful algorithms in HEP analyses, where models must be retrained frequently, sometimes at short notice, by small groups of researchers with limited hardware resources. Additionally, a new wrapper library for PyTorch called LUMIN is presented, which incorporates all of the techniques studied.Comment: Preprint V4: Fixing typographical error and correcting two plots. Mach. Learn.: Sci. Technol (2020

    Computing the Expected Value of Sample Information Efficiently : Practical Guidance and Recommendations for Four Model-Based Methods

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    Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst’s expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods’ use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems

    Evidence for the associated production of a single top quark and a photon in proton-proton collisions at s=\sqrt{s}= 13 TeV

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    The first evidence of events consistent with the production of a single top quark in association with a photon is reported. The analysis is based on proton-proton collisions at s=13  TeV and recorded by the CMS experiment in 2016, corresponding to an integrated luminosity of 35.9  fb-1. Events are selected by requiring the presence of a muon (ÎŒ), a photon (Îł), an imbalance in transverse momentum from an undetected neutrino (Îœ), and at least two jets (j) of which exactly one is identified as associated with the hadronization of a b quark. A multivariate discriminant based on topological and kinematic event properties is employed to separate signal from background processes. An excess above the background-only hypothesis is observed, with a significance of 4.4 standard deviations. A fiducial cross section is measured for isolated photons with transverse momentum greater than 25 GeV in the central region of the detector. The measured product of the cross section and branching fraction is σ(pp→tÎłj)B(t→ΌΜb)=115±17(stat)±30(syst)  fb, which is consistent with the standard model prediction.Peer Reviewe

    Search for pair production of excited top quarks in the lepton + jets final state

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    A search is performed for the pair production of spin-3/2 excited top quarks, each decaying to a top quark and a gluon. The search uses the data collected with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 inverse femtobarns. Events are selected by requiring an isolated muon or electron, an imbalance in the transverse momentum, and at least six jets of which exactly two must be compatible with originating from the fragmentation of a bottom quark. No significant excess over the standard model predictions is found. A lower limit of 1.2 TeV is set at 95% confidence level on the mass of the spin-3/2 excited top quark in an extension of the Randall-Sundrum model, assuming a 100% branching fraction of its decay into a top quark and a gluon. These are the best limits to date in a search for excited top quarks and the first at 13 TeV.Peer Reviewe

    Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

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    Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (Ό̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ÂŻ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ÂŻ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),Ό̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

    An embedding technique to determine ττ backgrounds in proton-proton collision data