What is the machine learning?

Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

doi:10.1103/physrevd.97.056009

Cited by 94 publications

(79 citation statements)

References 24 publications

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“…For single variable classifier, this can be done analytically, which we review below. For multivariable classifiers, the decorrelation must be done numerically, which we study using two recently proposed methods: Planing [9] and PCA-based rescaling [7,28].…”

Section: Decorrelation Based On Data Augmentationmentioning

confidence: 99%

Mass agnostic jet taggers

Layne¹,

Mishra

Mitridate

et al. 2020

SciPost Phys.

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Distinguishing boosted objects in hadronic final states requires a combined understanding of robust distributions for signal and background events. Substructure based approaches can isolate signal events in hadronic channels but tend to distort defining features of the background to be more signal-like (such as a smoothly falling invariant mass distribution). Getting the most out of experimental efforts needs a balance between the two competing effects of signal identification and background distortion. In this work, we perform a systematic study of various jet tagging methods that aim for this balance. We explore both single variable and multivariate approaches. 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 NNs and uBoost, but are both easier to implement and computationally cheaper.

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Section: Decorrelation Based On Data Augmentationmentioning

confidence: 99%

Mass agnostic jet taggers

Layne¹,

Mishra

Mitridate

et al. 2020

SciPost Phys.

Self Cite

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“…While the benchmark points representing a cluster are isolated points in the parameter space, the procedure we propose here allows us to associate certain shapes more straightforwardly with distinct regions in the parameter space. The application of machine learning techniques in high energy physics, in particular to constrain the EFT/new physics parameter space, has been brought forward already some time ago [68][69][70][71], with successful applications in jet and top quark identification [72][73][74][75][76][77][78][79][80][81], new physics searches [70,71,[82][83][84][85][86][87][88][89][90] and PDFs [91]. Shape analysis with machine learning has been applied already to constrain anomalous Higgs-vector boson couplings in HZ production [92].…”

Section: Introductionmentioning

confidence: 99%

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Capozi

Heinrich

2020

J. High Energ. Phys.

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We classify shapes of Higgs boson pair invariant mass distributions m hh , calculated at NLO with full top quark mass dependence, and visualise how distinct classes of shapes relate to the underlying coupling parameter space. Our study is based on a five-dimensional parameter space relevant for Higgs boson pair production in a non-linear Effective Field Theory framework. We use two approaches: an analysis based on predefined shape types and a classification into shape clusters based on unsupervised learning. We find that our method based on unsupervised learning is able to capture shape features very well and therefore allows a more detailed study of the impact of anomalous couplings on the m hh shape compared to more conventional approaches to a shape analysis.

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“…To conclude this section, we note that φ 0 in the mass range of 1 − 10 GeV is notoriously challenging for experiments to discover. A direct detection will be impossible at the LHC [29]. However, the Higgs boson invisible decay can be searched for via H → φ 0 φ 0 , and the pair of φ 0 's will act as missing energy as noted before.…”

Section: χ As Warm Dark Mattermentioning

confidence: 98%

KeV scale new fermion from a hidden sector

Chang

2020

Phys. Rev. D

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We studied a simple model of hidden sector that consists of a Dirac fermion χ and a spontaneously broken U (1) s symmetry. The dark sector is connected to the Standard Model(SM) via three righthanded SM singlet neutrinos, N R 's, and the kinetic mixing between U (1) s and U (1) Y . A mixing between the scalar φ that breaks U (1) s and the SM Higgs boson, H, is implemented via the term φ † φH † H and this provides a third connection to the SM. Integrating out the N R at a high scale not only gives the active neutrinos, ν, masses but generates effective Dirac-type couplings between ν and χ. This changes the usual Type-I seesaw results for active neutrino masses and makes χ behave like a sterile neutrino even though its origin is in the hidden sector. Note that χ is also split into a pair of Majorana fermions. The amount of splitting depends on the parameters. If the lighter of the pair has a mass around keV, its lifetime is longer than the age of the Universe and it can be a warm dark matter candidate. Signatures of χ in high precision Kurie plots of nuclei β decays and low energy neutrino nuclei coherent scatterings are discussed. The model also induces new invisible Z decay modes that can be searched for in future Z factories. * Electronic address: wfchang@phys.nthu.edu.tw † Electronic address: misery@triumf.ca

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What is the machine learning?

Cited by 94 publications

References 24 publications

Mass agnostic jet taggers

Mass agnostic jet taggers

Exploring anomalous couplings in Higgs boson pair production through shape analysis

KeV scale new fermion from a hidden sector

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