Fetching the paper…

Reading the bibliography…

Benchmarking simplified template cross sections in $WH$ production · Around

2019

Benchmarking simplified template cross sections in $WH$ production

Brehmer, Johann, Dawson, Sally, Homiller, Samuel et al.

Original
PDF
Post on X

Understand

Simplified template cross sections define a framework for the measurement and dissemination of kinematic information in Higgs measurements.

We benchmark the currently proposed setup in an analysis of dimension-6 effective field theory operators for $WH$ production.
Calculating the Fisher information allows us to quantify the sensitivity of this framework to new physics and study its dependence on phase space.
New machine-learning techniques let us compare the simplified template cross section framework to the full, high-dimensional kinematic information.

Built on

F. F. Freitas, C. K. Khosa and V. Sanz, Exploring SMEFT in VH with Machine Learning , 1902.05803
Original
1902
Earlier work this paper cites.
E. Rodrigues, The Scikit-HEP Project , in 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) Sofia, Bulgaria, July 9-13, 2018 , 2019, 1905.00002
Original
1905
Earlier work this paper cites.
N. Berger et al., Simplified Template Cross Sections - Stage 1.1 , 1906.02754
Original
1906
Earlier work this paper cites.
J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe and J. Pavez, Effective LHC measurements with matrix elements and machine learning , in 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: Empowering the revolution: Bringing Machine Learning to High Performance Computing (ACAT 2019) Saas-Fee, Switzerland, March 11-15, 2019 , 2019, 1906.01578
Original
1906
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo and K. Cranmer, MadMiner: Machine learning-based inference for particle physics , 1907.10621
Original
1907
Earlier work this paper cites.
R. A. Fisher, The detection of linkage with “dominant” abnormalities , Annals of Eugenics 6
1935
Earlier work this paper cites.
S. S. Wilks, The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , Annals Math. Statist. 9
1938
Earlier work this paper cites.
A. Wald, Tests of statistical hypotheses concerning several parameters when the number of observations is large , Transactions of the American Mathematical Society 54
1943
Earlier work this paper cites.
C. N. Leung, S. T. Love and S. Rao, Low-Energy Manifestations of a New Interaction Scale: Operator Analysis , Z. Phys. C31
1986
Earlier work this paper cites.
W. Buchmuller and D. Wyler, Effective Lagrangian Analysis of New Interactions and Flavor Conservation , Nucl. Phys. B268
1986
Earlier work this paper cites.
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models , J. Phys. Soc. Jap. 57
1988
Earlier work this paper cites.
D. Atwood and A. Soni, Analysis for magnetic moment and electric dipole moment form-factors of the top quark via e + e − → t t ¯ e^{+}e^{-}\to t\bar{t} , Phys. Rev. D45
1992
Earlier work this paper cites.
M. Davier, L. Duflot, F. Le Diberder and A. Rouge, The Optimal method for the measurement of tau polarization , Phys. Lett. B306
1993
Earlier work this paper cites.
M. Diehl and O. Nachtmann, Optimal observables for the measurement of three gauge boson couplings in e + e − → W + W − e^{+}e^{-}\to W^{+}W^{-} , Z. Phys. C62
1994
Earlier work this paper cites.
G. Van Rossum and F. L. Drake Jr, Python tutorial . Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995
1995
Earlier work this paper cites.
M. C. Gonzalez-Garcia, Anomalous Higgs couplings , Int. J. Mod. Phys. A14
1999
Earlier work this paper cites.
T. Oliphant, “NumPy: A guide to NumPy.” USA: Trelgol Publishing, 2006–
2006
Earlier work this paper cites.
K. Cranmer and T. Plehn, Maximum significance at the LHC and Higgs decays to muons , Eur. Phys. J. C51
2007
Earlier work this paper cites.
J. D. Hunter, Matplotlib: A 2d graphics environment , Computing in Science & Engineering 9
2007
Earlier work this paper cites.
B. Grzadkowski, M. Iskrzynski, M. Misiak and J. Rosiek, Dimension-Six Terms in the Standard Model Lagrangian , JHEP 10
Original
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in Python , Journal of Machine Learning Research 12
2011
Earlier work this paper cites.

Similar

T. Corbett, O. J. P. Eboli, J. Gonzalez-Fraile and M. C. Gonzalez-Garcia, Robust Determination of the Higgs Couplings: Power to the Data , Phys. Rev. D87
Original
2013
Cited alongside, same era.
S. Banerjee, S. Mukhopadhyay and B. Mukhopadhyaya, Higher dimensional operators and the LHC Higgs data: The role of modified kinematics , Phys. Rev. D89
Original
2014
Cited alongside, same era.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations , JHEP 07
Original
2014
Cited alongside, same era.
D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization , arXiv e-prints (2014) arXiv:1412.6980 [ 1412.6980 ]
Original
2014
Cited alongside, same era.

Then

I. Brivio, Y. Jiang and M. Trott, The SMEFTsim package, theory and tools , JHEP 12
Original
2017
Later among the works it cites.
F. Kling, T. Plehn and P. Schichtel, Maximizing the significance in Higgs boson pair analyses , Phys. Rev. D95
Original
2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito et al., Automatic differentiation in pytorch , in NIPS-W , 2017
2017
Later among the works it cites.
S. Di Vita, G. Durieux, C. Grojean, J. Gu, Z. Liu, G. Panico et al., A global view on the Higgs self-coupling at lepton colliders , JHEP 02
Original
2018
Later among the works it cites.
J. Ellis, C. W. Murphy, V. Sanz and T. You, Updated Global SMEFT Fit to Higgs, Diboson and Electroweak Data , JHEP 06

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

T. Plehn, P. Schichtel and D. Wiegand, Where boosted significances come from , Phys. Rev. D89

2014

Cited alongside, same era.

2014

Cited alongside, same era.

K. Cranmer, S. Kreiss, D. Lopez-Val and T. Plehn, Decoupling Theoretical Uncertainties from Measurements of the Higgs Boson , Phys. Rev. D91

2015

Cited alongside, same era.

V. Hirschi and O. Mattelaer, Automated event generation for loop-induced processes , JHEP 10

2015

Cited alongside, same era.

T. Sjstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten et al., An Introduction to PYTHIA 8.2 , Comput. Phys. Commun. 191

2015

Cited alongside, same era.

A. Butter, O. J. P. Eboli, J. Gonzalez-Fraile, M. C. Gonzalez-Garcia, T. Plehn and M. Rauch, The Gauge-Higgs Legacy of the LHC Run I , JHEP 07

2016

Cited alongside, same era.

I. Brivio, J. Gonzalez-Fraile, M. C. Gonzalez-Garcia and L. Merlo, The complete HEFT Lagrangian after the LHC Run I , Eur. Phys. J. C76

2016

Cited alongside, same era.

L. Berthier, M. Bjorn and M. Trott, Incorporating doubly resonant W ± W^{\pm} data in a global fit of SMEFT parameters to lift flat directions , JHEP 09

2016

Cited alongside, same era.

2018

Later among the works it cites.

J. Brehmer, F. Kling, T. Plehn and T. M. P. Tait, Better Higgs-CP Tests Through Information Geometry , Phys. Rev. D97

2018

Later among the works it cites.

J. Brehmer, K. Cranmer, G. Louppe and J. Pavez, A Guide to Constraining Effective Field Theories with Machine Learning , Phys. Rev. D98

2018

Later among the works it cites.

J. Brehmer, K. Cranmer, G. Louppe and J. Pavez, Constraining Effective Field Theories with Machine Learning , Phys. Rev. Lett. 121

2018

Later among the works it cites.

I. Brivio and M. Trott, Scheming in the SMEFT… and a reparameterization invariance! , JHEP 07

2018

Later among the works it cites.

J. Haller, A. Hoecker, R. Kogler, K. Monig, T. Peiffer and J. Stelzer, Update of the global electroweak fit and constraints on two-Higgs-doublet models , Eur. Phys. J. C78

2018

Later among the works it cites.

G. Panico, F. Riva and A. Wulzer, Diboson Interference Resurrection , Phys. Lett. B776

2018

Later among the works it cites.

R. Franceschini, G. Panico, A. Pomarol, F. Riva and A. Wulzer, Electroweak Precision Tests in High-Energy Diboson Processes , JHEP 02

2018

Later among the works it cites.

10.5281/zenodo.1217032

Lukas, lukasheinrich/pylhe v0.0.4 , Apr., 2018 · 2018

Later among the works it cites.

S. Dawson, C. Englert and T. Plehn, Higgs Physics: It ain’t over till it’s over , Phys. Rept. 816

2019

Closest in time.

I. Brivio and M. Trott, The Standard Model as an Effective Field Theory , Phys. Rept. 793

2019

Closest in time.

E. da Silva Almeida, A. Alves, N. Rosa Agostinho, O. J. P. Éboli and M. C. Gonzalez-Garcia, Electroweak Sector Under Scrutiny: A Combined Analysis of LHC and Electroweak Precision Data , Phys. Rev. D99

Original

2019

Closest in time.

A. Biekötter, T. Corbett and T. Plehn, The Gauge-Higgs Legacy of the LHC Run II , SciPost Phys. 6

2019

Closest in time.

C. Grojean, M. Montull and M. Riembau, Diboson at the LHC vs LEP , JHEP 03

2019

Closest in time.

I. Brivio, S. Bruggisser, F. Maltoni, R. Moutafis, T. Plehn, E. Vryonidou et al., Another Fit of the Dimension-6 Top Sector , in preparation (2019)

2019

Closest in time.

10.5281/zenodo.3256257

J. Pivarski, P. Das, D. Smirnov, C. Burr, M. Feickert, N. Biederbeck et al., scikit-hep/uproot: 3.7.2 , June, 2019 · 2019

Closest in time.