scholarly journals Double-charming Higgs boson identification using machine-learning assisted jet shapes

2018 ◽  
Vol 97 (1) ◽  
Author(s):  
Alexander Lenz ◽  
Michael Spannowsky ◽  
Gilberto Tetlalmatzi-Xolocotzi
Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1129
Author(s):  
Darius A. Faroughy ◽  
Blaž Bortolato ◽  
Jernej F. Kamenik ◽  
Nejc Košnik ◽  
Aleks Smolkovič

We summarize our recent proposals for probing the CP-odd iκ˜t¯γ5th interaction at the LHC and its projected upgrades directly using associated on-shell Higgs boson and top quark or top quark pair production. We first recount how to construct a CP-odd observable based on top quark polarization in Wb→th scattering with optimal linear sensitivity to κ˜. For the corresponding hadronic process pp→thj we then present a method of extracting the phase-space dependent weight function that allows to retain close to optimal sensitivity to κ˜. For the case of top quark pair production in association with the Higgs boson, pp→tt¯h, with semileptonically decaying tops, we instead show how one can construct manifestly CP-odd observables that rely solely on measuring the momenta of the Higgs boson and the leptons and b-jets from the decaying tops without having to distinguish the charge of the b-jets. Finally, we introduce machine learning (ML) and non-ML techniques to study the phase-space optimization of such CP-odd observables. We emphasize a simple optimized linear combination α·ω that gives similar sensitivity as the studied fully fledged ML models. Using α·ω we review sensitivity projections to κ˜ at HL-LHC, HE-LHC, and FCC-hh.


2020 ◽  
Vol 170 ◽  
pp. 1141-1146
Author(s):  
Mourad Azhari ◽  
Abdallah Abarda ◽  
Badia Ettaki ◽  
Jamal Zerouaoui ◽  
Mohamed Dakkon

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Christophe Grojean ◽  
Ayan Paul ◽  
Zhuoni Qian

Abstract The associated production of a $$ b\overline{b} $$ b b ¯ pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, yb. However, the signal is shrouded by several background processes including the irreducible Zh, Z →$$ b\overline{b} $$ b b ¯ background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the yb-sensitive production modes from both the irreducible and the QCD-QED backgrounds using the $$ b\overline{b}\gamma \gamma $$ b b ¯ γγ final state. We draw a page from game theory and use Shapley values to make Boosted Decision Trees interpretable in terms of kinematic measurables and provide physics insights into the variances in the kinematic shapes of the different channels that help us complete this feat. Adding interpretability to the machine learning algorithm opens up the black-box and allows us to cherry-pick only those kinematic variables that matter most in the analysis. We resurrect the hope of constraining the size and, possibly, the phase of yb using kinematic shape studies of $$ b\overline{b}h $$ b b ¯ h production with the full HL-LHC data and at FCC-hh.


2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Murat Abdughani ◽  
Daohan Wang ◽  
Lei Wu ◽  
Jin Min Yang ◽  
Jun Zhao

2019 ◽  
Vol 100 (11) ◽  
Author(s):  
K. Lasocha ◽  
E. Richter-Was ◽  
D. Tracz ◽  
Z. Was ◽  
P. Winkowska

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

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