pith. sign in

arxiv: 2606.01423 · v1 · pith:FID7QK5Tnew · submitted 2026-05-31 · ✦ hep-ex

Search for single production of a vector-like B' quark decaying to a top quark and a W boson in the single-lepton final state in proton-proton collisions at sqrt{s} = 13 TeV

CMS Collaboration This is my paper

Pith reviewed 2026-06-28 15:45 UTC · model grok-4.3

classification ✦ hep-ex
keywords vector-like quarksB' quarksingle productiontop quarkW bosonCMS experimentLHC13 TeV collisions
0
0 comments X

The pith

CMS excludes narrow vector-like B' quarks with masses from 0.8 to 1.23 TeV

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports a search for single production of a narrow-width vector-like B' quark that decays to a top quark and a W boson, focusing on the single-lepton final state. Using 138 fb^{-1} of 13 TeV proton-proton collision data collected by CMS from 2016 to 2018, the analysis reconstructs candidate events with a lepton, missing transverse momentum, and jets. A neural-network tagger identifies the origin of large-radius jets while a neural autoregressive flow network models the dominant backgrounds directly from data. No excess is observed over the predicted background, which sets the most stringent limits to date on such particles.

Core claim

This search excludes singlet B' quarks with relative width 5 percent for masses between 0.8 and 1.23 TeV at 95 percent confidence level. Limits are also set on the production cross section for B' quarks produced in association with top quarks and on the B' coupling strength to electroweak bosons.

What carries the argument

Reconstruction of the B' candidate from a lepton, missing transverse momentum, one large-radius jet, and one small-radius jet, with neural-network jet tagging and neural autoregressive flow network for background modeling.

If this is right

  • No evidence for single B' production is found in the analyzed dataset.
  • Upper limits are placed on the production cross section of single B' quarks produced with top quarks.
  • Constraints are derived on the coupling factor of the B' quark to electroweak bosons.
  • The result improves the sensitivity reach for narrow-width vector-like B' quarks relative to earlier searches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The data-driven neural flow background technique could be adapted to improve precision in other rare-process searches at the LHC.
  • Models predicting vector-like quarks with different widths or mixing patterns would face tighter constraints if future data extend the mass reach.
  • These limits restrict the parameter space of Standard Model extensions that introduce heavy quarks coupling preferentially to third-generation fermions.

Load-bearing premise

The dominant background contributions are modeled from data using a neural autoregressive flow network; systematic misestimation of this background in the signal region would shift the exclusion limits.

What would settle it

An observed excess of events above the modeled background whose mass and kinematic distributions match the expected B' signal at a mass between 0.8 and 1.23 TeV would indicate the presence of the quark.

Figures

Figures reproduced from arXiv: 2606.01423 by CMS Collaboration.

Figure 1
Figure 1. Figure 1: Tree-level Feynman diagram showing the single production of a B [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of the reconstructed B′ quark mass, mtW (upper left), the scalar pT sum (ST ) of small-radius jets, lepton, and p miss T (upper right), and the reconstruction case (lower) for all selected events. The observed data are shown as black markers. Predicted bqB′ quark sig￾nals with masses of 1.0 and 1.4 TeV are shown as solid and dashed lines, respectively, normal￾ized to a cross section of 1 pb f… view at source ↗
Figure 3
Figure 3. Figure 3: Distributions of b-tagged jet multiplicity (left) and forward jet multiplicity (right) for [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Diagrams illustrating the definitions and labels of the CRs and SR (left), and the [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distributions of [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distributions of [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Distributions of mtW for VR events in Case 1 (upper left) through Case 4 (lower right). Observed data are shown as black markers. Predicted bqB′ quark signals with masses of 1.0 and 1.4 TeV are shown as solid and dashed lines, respectively, normalized to the predicted cross section for singlet bqB′ production with Γ/mB′ = 5%. Background estimates are displayed as filled histograms, with the ABCDnn predicti… view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of mtW for events in the SR in Case 1 (upper left) through Case 4 (lower right). The observed data are shown as black markers. Predicted bqB′ quark signals with masses of 1.0 and 1.4 TeV are shown as the solid and dashed lines, respectively, normalized to the predicted cross section for singlet bqB′ production with Γ/mB′ = 5%. The best-fit back￾ground prediction from a background-only fit to … view at source ↗
Figure 9
Figure 9. Figure 9: Observed (solid black lines) and expected (dashed lines) 95% CL upper limits on the [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Observed (solid black lines) and expected (dashed lines) 95% CL upper limits on the [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
read the original abstract

A search is presented for the single production of a narrow-width vector-like B' quark that decays to a t quark and a W boson, with one of the decay products yielding an electron or muon. The data were collected from 2016 to 2018 by the CMS experiment at the LHC in proton-proton collisions at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-2}$. The search is performed in a single-lepton final state, where the B' quark candidate is reconstructed from an electron or muon, missing transverse momentum, one large-radius jet, and one small-radius jet if the t quark decays leptonically. The originating particles of large-radius jets are identified using a neural-network-based tagger, and the dominant background contributions are modeled from data using a neural autoregressive flow network. This search is the most sensitive to date to the single production of narrow-width B' quarks, excluding singlet B' quarks with $\Gamma/m_\mathrm{B'}$ = 5% for masses between 0.8 and 1.23 TeV. Limits are also placed on the production cross section of single B' quarks produced in association with t quarks, and on the coupling factor of the B' quark to electroweak bosons.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a search by the CMS experiment for single production of narrow-width vector-like B' quarks decaying to tW in the single-lepton final state, using 138 fb^{-1} of 13 TeV pp collision data. The analysis reconstructs B' candidates from a lepton, MET, one large-R jet (tagged via neural network), and optionally a small-R jet; dominant backgrounds are estimated via a neural autoregressive flow network trained on data. The central result is an exclusion of singlet B' quarks with Γ/m_B' = 5% for masses 0.8–1.23 TeV, stated to be the most sensitive limit to date, together with cross-section and coupling limits.

Significance. If the background modeling holds, the result provides the strongest constraints to date on narrow-width single B' production and improves the reach of vector-like quark searches at the LHC. The data-driven flow-network approach and neural tagger are positive technical elements when accompanied by robust validation.

major comments (2)
  1. [Background estimation] Background estimation section: the neural autoregressive flow network is used to model the dominant backgrounds in the high-p_T signal region. The manuscript must provide quantitative closure tests (e.g., predicted vs. observed yields and shapes) in sidebands and control regions that match the signal-region jet multiplicity, large-R jet mass, and MET kinematics; without these, any unmodeled correlation between substructure variables and MET directly affects the extracted upper limits.
  2. [Results] Results section (limit extraction): the quoted exclusion 0.8–1.23 TeV for Γ/m_B' = 5% relies on the background prediction in the highest-mass bins. The paper should report the systematic uncertainty assigned to the flow-network extrapolation (including training-sample statistics and kinematic extrapolation) and show how it propagates into the limit; the current abstract claim of “most sensitive to date” cannot be assessed without this breakdown.
minor comments (2)
  1. [Introduction] The notation Γ/m_B' is used without an explicit definition of the width parameterization in the signal model; a short clarification in the introduction or theory section would help readers.
  2. [Object identification] Figure captions for the neural-network tagger performance plots should state the working point efficiency and mis-tag rate used in the analysis.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and agree to incorporate additional validation material and uncertainty details in a revised version.

read point-by-point responses

  1. Referee: [Background estimation] Background estimation section: the neural autoregressive flow network is used to model the dominant backgrounds in the high-p_T signal region. The manuscript must provide quantitative closure tests (e.g., predicted vs. observed yields and shapes) in sidebands and control regions that match the signal-region jet multiplicity, large-R jet mass, and MET kinematics; without these, any unmodeled correlation between substructure variables and MET directly affects the extracted upper limits.

    Authors: We agree that explicit quantitative closure tests are required to demonstrate the robustness of the neural autoregressive flow network. The revised manuscript will include additional tables and figures presenting predicted versus observed yields and kinematic shapes in multiple sidebands and control regions selected to match the signal-region jet multiplicity, large-R jet mass, and MET distributions. These tests confirm agreement within the assigned uncertainties and will be accompanied by a discussion of any residual correlations. revision: yes

  2. Referee: [Results] Results section (limit extraction): the quoted exclusion 0.8–1.23 TeV for Γ/m_B' = 5% relies on the background prediction in the highest-mass bins. The paper should report the systematic uncertainty assigned to the flow-network extrapolation (including training-sample statistics and kinematic extrapolation) and show how it propagates into the limit; the current abstract claim of “most sensitive to date” cannot be assessed without this breakdown.

    Authors: We will expand the results section to report the systematic uncertainty on the flow-network extrapolation, explicitly separating contributions from training-sample statistics and kinematic extrapolation. This uncertainty will be propagated through the limit-setting procedure, with its effect on the final exclusion limits shown in a dedicated table or plot. A brief comparison with prior results will also be added to support the statement that the search is the most sensitive to date for narrow-width single B' production in this channel. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental limit from collision data

full rationale

The paper reports an LHC search for B' production with limits extracted from 138 fb^{-1} of collision data. The background is modeled via a neural autoregressive flow trained on data, but this is a standard data-driven technique whose validity is tested in control regions and sidebands (standard in CMS analyses). No derivation reduces by construction to a fitted input, no self-citation chain carries the central claim, and no ansatz or uniqueness theorem is invoked. The result is an observed upper limit on a cross section, externally falsifiable by future data or independent analyses, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on standard LHC experimental assumptions (detector response, parton shower modeling for signal) and the validity of the neural flow background model. No free parameters are fitted to the signal; the 5% width is an input model choice. The B' quark is a postulated entity from theory, not invented here.

axioms (1)
  • domain assumption Standard Model backgrounds and detector simulation are sufficiently accurate outside the signal region for the flow network to extrapolate correctly.
    Invoked when the abstract states backgrounds are modeled from data using the flow network.

pith-pipeline@v0.9.1-grok · 5781 in / 1272 out tokens · 23893 ms · 2026-06-28T15:45:43.303331+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

69 extracted references · 57 canonical work pages · 34 internal anchors

  1. [1]

    Light custodians in natural composite Higgs models

    R. Contino, L. Da Rold, and A. Pomarol, “Light custodians in natural composite Higgs models”,Phys. Rev. D75(Mar, 2007) 055014,doi:10.1103/PhysRevD.75.055014, arXiv:hep-ph/0612048. 24

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.75.055014 2007

  2. [2]

    Warped/Composite Phenomenology Simplified

    R. Contino, T. Kramer, M. Son, and R. Sundrum, “Warped/composite phenomenology simplified”,JHEP05(2007) 074,doi:10.1088/1126-6708/2007/05/074, arXiv:hep-ph/0612180

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1126-6708/2007/05/074 2007

  3. [3]

    A handbook of vector-like quarks: mixing and single production

    J. A. Aguilar-Saavedra, R. Benbrik, S. Heinemeyer, and M. P ´erez-Victoria, “Handbook of vectorlike quarks: Mixing and single production”,Phys. Rev. D88(2013) 094010, doi:10.1103/PhysRevD.88.094010,arXiv:1306.0572

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.88.094010 2013

  4. [4]

    A First Top Partner Hunter's Guide

    A. De Simone, O. Matsedonskyi, R. Rattazzi, and A. Wulzer, “A first top partner hunter’s guide”,JHEP04(2013) 004,doi:10.1007/JHEP04(2013)004,arXiv:1211.5663 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep04(2013)004 2013

  5. [5]

    Search for single production of vector-like quarks decaying to a top quark and a W boson in proton-proton collisions at $\sqrt{s} =$ 13 TeV

    CMS Collaboration, “Search for single production of vector-like quarks decaying to a top quark and a W boson in proton-proton collisions at √s=13 TeV”,Eur. Phys. J. C79 (2019) 90,doi:10.1140/epjc/s10052-019-6556-3,arXiv:1809.08597

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-019-6556-3 2019

  6. [6]

    Search for single production of vector-like quarks decaying to a b quark and a Higgs boson

    CMS Collaboration, “Search for single production of vector-like quarks decaying to a b quark and a Higgs boson”,JHEP06(2018) 031,doi:10.1007/JHEP06(2018)031, arXiv:1802.01486

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep06(2018)031 2018

  7. [7]

    Search for a heavy resonance decaying into a top quark and a W boson in the lepton+jets final state at √s=13 TeV

    CMS Collaboration, “Search for a heavy resonance decaying into a top quark and a W boson in the lepton+jets final state at √s=13 TeV”,JHEP04(2022) 048, doi:10.1007/JHEP04(2022)048,arXiv:2111.10216

    work page doi:10.1007/jhep04(2022)048 2022

  8. [8]

    Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at √s=13 TeVat the CMS experiment

    CMS Collaboration, “Review of searches for vector-like quarks, vector-like leptons, and heavy neutral leptons in proton–proton collisions at √s=13 TeVat the CMS experiment”,Phys. Rept.1115(2025) 570,doi:10.1016/j.physrep.2024.09.012, arXiv:2405.17605

    work page doi:10.1016/j.physrep.2024.09.012 2025

  9. [9]

    Search for the production of single vector-like and excited quarks in the $Wt$ final state in $pp$ collisions at $\sqrt{s}$ = 8 TeV with the ATLAS detector

    ATLAS Collaboration, “Search for the production of single vector-like and excited quarks in theWtfinal state inppcollisions at √s=8 TeV with the ATLAS detector”,JHEP02 (2016) 110,doi:10.1007/JHEP02(2016)110,arXiv:1510.02664

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep02(2016)110 2016

  10. [10]

    Search for single vector-likeBquark production and decay via B→bH(b ¯b)inppcollisions at √s=13 TeV with the atlas detector

    ATLAS Collaboration, “Search for single vector-likeBquark production and decay via B→bH(b ¯b)inppcollisions at √s=13 TeV with the atlas detector”,JHEP11(2023) 168, doi:10.1007/JHEP11(2023)168,arXiv:2308.02595

    work page doi:10.1007/jhep11(2023)168 2023

  11. [11]

    HEPData record for this analysis, 2026.doi:10.17182/hepdata.168733

    work page doi:10.17182/hepdata.168733 2026

  12. [12]

    The CMS experiment at the CERN LHC

    CMS Collaboration, “The CMS experiment at the CERN LHC”,JINST3(2008) S08004, doi:10.1088/1748-0221/3/08/S08004

    work page doi:10.1088/1748-0221/3/08/s08004 2008

  13. [13]

    Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV”,JINST15(2020) P10017,doi:10.1088/1748-0221/15/10/P10017, arXiv:2006.10165

    work page doi:10.1088/1748-0221/15/10/p10017 2020

  14. [14]

    The CMS trigger system

    CMS Collaboration, “The CMS trigger system”,JINST12(2017) P01020, doi:10.1088/1748-0221/12/01/P01020,arXiv:1609.02366

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/12/01/p01020 2017

  15. [15]

    Performance of the CMS high-level trigger during LHC Run 2

    CMS Collaboration, “Performance of the CMS high-level trigger during LHC run 2”, JINST19(2024) P11021,doi:10.1088/1748-0221/19/11/P11021, arXiv:2410.17038. References 25

    work page doi:10.1088/1748-0221/19/11/p11021 2024

  16. [16]

    Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

    CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,JINST16(2021) P05014, doi:10.1088/1748-0221/16/05/P05014,arXiv:2012.06888

    work page doi:10.1088/1748-0221/16/05/p05014 2021

  17. [17]

    Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV

    CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV”,JINST13(2018) P06015, doi:10.1088/1748-0221/13/06/P06015,arXiv:1804.04528

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/13/06/p06015 2018

  18. [18]

    Description and performance of track and primary-vertex reconstruction with the CMS tracker

    CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,JINST9(2014) P10009, doi:10.1088/1748-0221/9/10/P10009,arXiv:1405.6569

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/9/10/p10009 2014

  19. [19]

    Development of the CMS detector for the CERN LHC Run 3

    CMS Collaboration, “Development of the CMS detector for the CERN LHC Run 3”, JINST19(2024) P05064,doi:10.1088/1748-0221/19/05/P05064, arXiv:2309.05466

    work page doi:10.1088/1748-0221/19/05/p05064 2024

  20. [20]

    Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid

    CMS Collaboration, “Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid”, CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015

    2015

  21. [21]

    Particle-flow reconstruction and global event description with the CMS detector

    CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,JINST12(2017) P10003,doi:10.1088/1748-0221/12/10/P10003, arXiv:1706.04965

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/12/10/p10003 2017

  22. [22]

    The anti-k_t jet clustering algorithm

    M. Cacciari, G. P . Salam, and G. Soyez, “The anti-kT jet clustering algorithm”,JHEP04 (2008) 063,doi:10.1088/1126-6708/2008/04/063,arXiv:0802.1189

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1126-6708/2008/04/063 2008

  23. [23]

    FastJet user manual

    M. Cacciari, G. P . Salam, and G. Soyez, “FastJet user manual”,Eur. Phys. J. C72(2012) 1896,doi:10.1140/epjc/s10052-012-1896-2,arXiv:1111.6097

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-012-1896-2 2012

  24. [24]

    Pileup mitigation at CMS in 13 TeV data

    CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”,JINST15(2020) P09018, doi:10.1088/1748-0221/15/09/p09018,arXiv:2003.00503

    work page doi:10.1088/1748-0221/15/09/p09018 2020

  25. [25]

    Pileup Per Particle Identification

    D. Bertolini, P . Harris, M. Low, and N. Tran, “Pileup per particle identification”,JHEP10 (2014) 059,doi:10.1007/JHEP10(2014)059,arXiv:1407.6013

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep10(2014)059 2014

  26. [26]

    Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV

    CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,JINST12(2017) P02014, doi:10.1088/1748-0221/12/02/P02014,arXiv:1607.03663

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/12/02/p02014 2017

  27. [27]

    Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS

    CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS”,Eur. Phys. J. C81(2021) 800, doi:10.1140/epjc/s10052-021-09538-2,arXiv:2104.01927

    work page doi:10.1140/epjc/s10052-021-09538-2 2015

  28. [28]

    CMS luminosity measurement for the 2017 data-taking period at√s=13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at√s=13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018

    2017

  29. [29]

    CMS luminosity measurement for the 2018 data-taking period at√s=13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at√s=13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019

    2018

  30. [30]

    Precision luminosity measurement in proton-proton collisions at√s=13 TeV with the CMS detector

    CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at√s=13 TeV with the CMS detector”, CMS Physics Analysis Summary CMS-PAS-LUM-20-001, 2025

    2025

  31. [31]

    The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

    J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations”,JHEP07 (2014) 079,doi:10.1007/JHEP07(2014)079,arXiv:1405.0301. 26

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep07(2014)079 2014

  32. [32]

    QCD next-to-leading-order predictions matched to parton showers for vector-like quark models

    B. Fuks and H.-S. Shao, “QCD next-to-leading-order predictions matched to parton showers for vector-like quark models”,Eur. Phys. J. C77(2017) 135, doi:10.1140/epjc/s10052-017-4686-z,arXiv:1610.04622

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-017-4686-z 2017

  33. [33]

    Single production of vector-like quarks: the effects of large width, interference and NLO corrections

    A. Deandrea et al., “Single production of vector-like quarks: the effects of large width, interference and NLO corrections”,JHEP08(2021) 107, doi:10.1007/JHEP08(2021)107,arXiv:2105.08745. [Erratum: doi:10.1007/JHEP11(2022)028]

    work page doi:10.1007/jhep08(2021)107 2021

  34. [34]

    Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations

    P . Artoisenet, R. Frederix, O. Mattelaer, and R. Rietkerk, “Automatic spin-entangled decays of heavy resonances in Monte Carlo simulations”,JHEP03(2013) 015, doi:10.1007/JHEP03(2013)015,arXiv:1212.3460

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep03(2013)015 2013

  35. [35]

    Single production of vector-like quarks with large width at the Large Hadron Collider

    A. Carvalho et al., “Single production of vectorlike quarks with large width at the Large Hadron Collider”,Phys. Rev. D98(2018), no. 1, 015029, doi:10.1103/PhysRevD.98.015029,arXiv:1805.06402

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1103/physrevd.98.015029 2018

  36. [36]

    Matching Parton Showers and Matrix Elements

    S. Hoeche et al., “Matching parton showers and matrix elements”, inHERA and the LHC: A Workshop on the Implications of HERA for LHC Physics, p. 288. 2005. arXiv:hep-ph/0602031.doi:10.5170/CERN-2005-014.288

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5170/cern-2005-014.288 2005

  37. [37]

    Merging meets matching in MC@NLO

    R. Frederix and S. Frixione, “Merging meets matching inMC@NLO”,JHEP12(2012) 061, doi:10.1007/JHEP12(2012)061,arXiv:1209.6215

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep12(2012)061 2012

  38. [38]

    A Positive-Weight Next-to-Leading-Order Monte Carlo for Heavy Flavour Hadroproduction

    S. Frixione, G. Ridolfi, and P . Nason, “A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction”,JHEP09(2007) 126, doi:10.1088/1126-6708/2007/09/126,arXiv:0707.3088

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1126-6708/2007/09/126 2007

  39. [39]

    NLO single-top production matched with shower in POWHEG: s- and t-channel contributions

    S. Alioli, P . Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower inPOWHEG:s- andt-channel contributions”,JHEP09(2009) 111, doi:10.1088/1126-6708/2009/09/111,arXiv:0907.4076. [Erratum: doi:10.1007/JHEP02(2010)011]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1126-6708/2009/09/111 2009

  40. [40]

    Single-top Wt-channel production matched with parton showers using the POWHEG method

    E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEGmethod”,Eur. Phys. J. C71(2011) 1547, doi:10.1140/epjc/s10052-011-1547-z,arXiv:1009.2450

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-011-1547-z 2011

  41. [41]

    An Introduction to PYTHIA 8.2

    T. Sj ¨ostrand et al., “An introduction to PYTHIA 8.2”,Comput. Phys. Commun.191(2015) 159,doi:10.1016/j.cpc.2015.01.024,arXiv:1410.3012

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1016/j.cpc.2015.01.024 2015

  42. [42]

    Parton distributions from high-precision collider data

    NNPDF Collaboration, “Parton distributions from high-precision collider data”,Eur. Phys. J. C77(2017) 663,doi:10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-017-5199-5 2017

  43. [43]

    Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements

    CMS Collaboration, “Extraction and validation of a new set of CMSPYTHIA8 tunes from underlying-event measurements”,Eur. Phys. J. C80(2020) 4, doi:10.1140/epjc/s10052-019-7499-4,arXiv:1903.12179

    work page doi:10.1140/epjc/s10052-019-7499-4 2020

  44. [44]

    GEANT4—a simulation toolkit

    GEANT4 Collaboration, “GEANT4—a simulation toolkit”,Nucl. Instrum. Meth. A506 (2003) 250,doi:10.1016/S0168-9002(03)01368-8

    work page doi:10.1016/s0168-9002(03)01368-8 2003

  45. [45]

    Search for supersymmetry in pp collisions at sqrt(s) = 13 TeV in the single-lepton final state using the sum of masses of large-radius jets

    CMS Collaboration, “Search for supersymmetry in pp collisions at √s=13 TeV in the single-lepton final state using the sum of masses of large-radius jets”,JHEP08(2016) 122,doi:10.1007/JHEP08(2016)122,arXiv:1605.04608. References 27

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep08(2016)122 2016

  46. [46]

    The Catchment Area of Jets

    M. Cacciari, G. P . Salam, and G. Soyez, “The catchment area of jets”,JHEP04(2008) 005, doi:10.1088/1126-6708/2008/04/005,arXiv:0802.1188

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1126-6708/2008/04/005 2008

  47. [47]

    Jet flavour classification using DeepJet

    E. Bols et al., “Jet flavour classification using DeepJet”,JINST15(2020) P12012, doi:10.1088/1748-0221/15/12/P12012,arXiv:2008.10519

    work page doi:10.1088/1748-0221/15/12/p12012 2020

  48. [48]

    Performance of the DeepJet b tagging algorithm using 41.9/fb of data from proton-proton collisions at 13 TeV with Phase 1 CMS detector

    CMS Collaboration, “Performance of the DeepJet b tagging algorithm using 41.9/fb of data from proton-proton collisions at 13 TeV with Phase 1 CMS detector”, CMS Detector Performance Summary CMS-DP-2018-058, 2018

    2018

  49. [49]

    Jet tagging via particle clouds , volume=

    H. Qu and L. Gouskos, “ParticleNet: Jet tagging via particle clouds”,Phys. Rev. D101 (2020) 056019,doi:10.1103/PhysRevD.101.056019,arXiv:1902.08570

    work page doi:10.1103/physrevd.101.056019 2020

  50. [50]

    Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s} =$ 13 TeV using the CMS detector

    CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at √s=13 TeV using the CMS detector”,JINST14(2019) P07004,doi:10.1088/1748-0221/14/07/P07004,arXiv:1903.06078

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/14/07/p07004 2019

  51. [51]

    Data-driven estimation of background distribution through neural autoregressive flows

    S. Choi, J. Lim, and H. Oh, “Data-driven estimation of background distribution through neural autoregressive flows”, 8, 2020.arXiv:2008.03636

    arXiv 2020

  52. [52]

    Evidence for four-top quark production in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Evidence for four-top quark production in proton-proton collisions at √s=13 TeV”,Phys. Lett. B844(2023) 138076, doi:10.1016/j.physletb.2023.138076,arXiv:2303.03864

    work page doi:10.1016/j.physletb.2023.138076 2023

  53. [53]

    Improved extrapolation methods of data-driven background estimations in high energy physics

    S. Choi and H. Oh, “Improved extrapolation methods of data-driven background estimations in high energy physics”,Eur. Phys. J. C81(2021) 643, doi:10.1140/epjc/s10052-021-09404-1,arXiv:1906.10831

    work page doi:10.1140/epjc/s10052-021-09404-1 2021

  54. [54]

    Neural autoregressive flows

    C.-W. Huang, D. Krueger, A. Lacoste, and A. C. Courville, “Neural autoregressive flows”, inInternational Conference on Machine Learning. 2018

    2018

  55. [55]

    Rectified linear units improve restricted boltzmann machines

    V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines”, inProceedings of the 27th International Conference on Machine Learning. 2010

    2010

  56. [56]

    Deep Learning

    I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”. MIT Press, 2016

    2016

  57. [57]

    A kernel two-sample test

    A. Gretton et al., “A kernel two-sample test”,J. Mach. Learn. Res.13(2012) 723

    2012

  58. [58]

    Robust locally weighted regression and smoothing scatterplots

    W. S. Cleveland, “Robust locally weighted regression and smoothing scatterplots”,J. Am. Stat. Assoc.74(1979) 829,doi:10.1080/01621459.1979.10481038

    work page doi:10.1080/01621459.1979.10481038 1979

  59. [59]

    J., & Beauchamp, J

    W. S. Cleveland and S. J. Devlin, “Locally weighted regression: An approach to regression analysis by local fitting”,J. Am. Stat. Assoc.83(1988) 596, doi:10.1080/01621459.1988.10478639

    work page doi:10.1080/01621459.1988.10478639 1988

  60. [60]

    Measurement of the inelastic proton-proton cross section at $\sqrt{s}=$ 13 TeV

    CMS Collaboration, “Measurement of the inelastic proton-proton cross section at√s=13 TeV”,JHEP07(2018) 161,doi:10.1007/JHEP07(2018)161, arXiv:1802.02613

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1007/jhep07(2018)161 2018

  61. [61]

    Charmonium Spectroscopy from Radiative Decays of theJ/ψandψ′

    J. E. Gaiser, “Charmonium Spectroscopy from Radiative Decays of theJ/ψandψ′”. PhD thesis, 1982

    1982

  62. [62]

    The CMS statistical analysis and combination tool: Combine

    CMS Collaboration, “The CMS statistical analysis and combination tool: COMBINE”, Comput. Softw. Big Sci.8(2024) 19,doi:10.1007/s41781-024-00121-4, arXiv:2404.06614. 28

    work page doi:10.1007/s41781-024-00121-4 2024

  63. [63]

    The ROOFITtoolkit for data modeling

    W. Verkerke and D. Kirkby, “The ROOFITtoolkit for data modeling”, inProc. 13th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2003): La Jolla CA, United States, March 24–28. 2003.arXiv:physics/0306116

    Pith/arXiv arXiv 2003

  64. [64]

    The RooStats Project

    L. Moneta et al., “The ROOSTATSproject”, inProc. 13th International Workshop on Advanced Computing and Analysis T echniques in Physics Research (ACAT 2010): Jaipur, India, February 22–27. 2010.arXiv:1009.1003.doi:10.22323/1.093.0057

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.22323/1.093.0057 2010

  65. [65]

    Fitting using finite Monte Carlo samples

    R. Barlow and C. Beeston, “Fitting using finite Monte Carlo samples”,Comput. Phys. Commun.77(1993) 219,doi:10.1016/0010-4655(93)90005-W

    work page doi:10.1016/0010-4655(93)90005-w 1993

  66. [66]

    Incorporating Nuisance Parameters in Likelihoods for Multisource Spectra

    J. S. Conway, “Incorporating nuisance parameters in likelihoods for multisource spectra”, inPHYSTAT 2011, p. 115. 2011.arXiv:1103.0354. doi:10.5170/CERN-2011-006.115

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5170/cern-2011-006.115 2011

  67. [67]

    Asymptotic formulae for likelihood-based tests of new physics

    G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics”,Eur. Phys. J. C71(2011) 1554, doi:10.1140/epjc/s10052-011-1554-0,arXiv:1007.1727. [Erratum: doi:10.1140/epjc/s10052-013-2501-z]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-011-1554-0 2011

  68. [68]

    Confidence Level Computation for Combining Searches with Small Statistics

    T. Junk, “Confidence level computation for combining searches with small statistics”, Nucl. Instrum. Meth. A434(1999) 435,doi:10.1016/S0168-9002(99)00498-2, arXiv:hep-ex/9902006

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1016/s0168-9002(99)00498-2 1999

  69. [69]

    Presentation of search results: The CL s technique

    A. L. Read, “Presentation of search results: The CL s technique”,J. Phys. G28(2002) 2693, doi:10.1088/0954-3899/28/10/313. 29 A The CMS Collaboration University of Tirana and Polytechnic University of Tirana, TIRANA, Albania K. Tauqeer Yerevan Physics Institute, Yerevan, Armenia A. Gevorgyan , A. Hayrapetyan, V . Makarenko , A. Tumasyan1 Marietta Blau Ins...

    work page doi:10.1088/0954-3899/28/10/313 2002