Matrix element method at NLO for (anti-) 
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-jet algorithms

Kraus, Manfred; Martini, Till; Uwer, Peter

doi:10.1103/physrevd.100.076010

Cited by 8 publications

(13 citation statements)

References 105 publications

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“…This would also allow us to directly compare first-principles QCD predictions with modern LHC measurements. In addition, our fast inversion might help with advanced statistical techniques like the matrix element method [29][30][31][32][33][34].…”

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confidence: 99%

How to GAN away detector effects

et al. 2020

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LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.

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confidence: 99%

How to GAN away detector effects

et al. 2020

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“…It uses machine learning to approximate the full likelihood fully taking into account parton shower as well as detector effects, which are only approximated in similar approaches like the matrix element method (see e.g. [36,[57][58][59][60][61][62][63][64][65][66][67][68][69]) or the optimal observable approach [70][71][72].…”

Section: Jhep03(2022)017mentioning

confidence: 99%

Constraining $$ \mathcal{CP} $$-violation in the Higgs-top-quark interaction using machine-learning-based inference

Bahl

Braß

2022

J. High Energ. Phys.

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While $$ \mathcal{CP} $$ CP violation in the Higgs interactions with massive vector boson is already tightly constrained, the $$ \mathcal{CP} $$ CP nature of the Higgs interactions with fermions is far less constrained. In this work, we assess the potential of machine-learning-based inference methods to constrain $$ \mathcal{CP} $$ CP violation in the Higgs top-Yukawa coupling. This approach enables the use of the full available kinematic information. Concentrating on top-associated Higgs production with the Higgs decaying to two photons, we derive expected exclusion bounds for the LHC and the high-luminosity phase of the LHC. We also study the dependence of these bounds on the Higgs interaction with massive vector bosons and their robustness against theoretical uncertainties. In addition to deriving expected exclusion bounds, we discuss at which level a non-zero $$ \mathcal{CP} $$ CP -violating top-Yukawa coupling can be distinguished from the SM. Moreover, we analyze which kinematic distributions are most sensitive to a $$ \mathcal{CP} $$ CP -violating top-Yukawa coupling.

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“…Following the conventions of our GAN analysis and in analogy to Eqs. (6) to (8) we define the network input as a vector of hard process information x p ∈ R D p and the output at detector level via the vector…”

Section: Naive Innmentioning

confidence: 99%

“…The best way to compare two hypotheses is the log-likelihood ratio based on new physics and Standard Model predictions for the hard process. Using this ratio in the analysis is the idea behind the matrix element method [2][3][4][5][6][7], but usually this information is not available [8].…”

Section: Introductionmentioning

confidence: 99%

Invertible networks or partons to detector and back again

et al. 2020

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For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

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Matrix element method at NLO for (anti-) kt -jet algorithms

Cited by 8 publications

References 105 publications

How to GAN away detector effects

How to GAN away detector effects

Constraining $$ \mathcal{CP} $$-violation in the Higgs-top-quark interaction using machine-learning-based inference

Invertible networks or partons to detector and back again

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