CERN Accelerating science

ATLAS Preprints

Senast inlagda poster:
2019-01-20
05:24
Energy frontier lepton-hadron colliders, vector-like quarks and leptons, preons and so on / Sultansoy, Saleh (TOBB ETU, Ankara)
First of all, an importance of the LHC and FCC based energy frontier lepton-hadron and photon-hadron colliders is emphasised. [...]
arXiv:1901.00309.
- 6 p.
Fulltext

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2019-01-17
04:45
Top-quark mass at ATLAS and CMS / Tokar, Stanislav
The top-quark mass measurements carried out by the LHC experiments, ATLAS and CMS, are summarized. [...]
arXiv:1901.04740 ; ATL-COM-PHYS-2018-1617.
- 5 p.
Fulltext

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2019-01-14
10:31
Top-antitop charge asymmetry measurements in the dilepton channel with the ATLAS detector / Kido, Shogo (Kobe University)
We report a measurement of the charge asymmetry $A_C$ in top quark pair production with the ATLAS experiment. [...]
arXiv:1901.04242 ; ATL-PHYS-PROC-2019-009.
- 2019. - 4 p.
Original Communication (restricted to ATLAS) - Full text - Fulltext

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2019-01-11
14:45
Measurement of the $t\bar{t}Z$ and $t\bar{t}W$ cross sections in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector / ATLAS Collaboration
A measurement of the associated production of a top-quark pair ($t\bar{t}$) with a vector boson ($W$, $Z$) in proton-proton collisions at a center-of-mass energy of 13 TeV is presented, using 36.1 fb$^{-1}$ of integrated luminosity collected by the ATLAS detector at the Large Hadron Collider. [...]
arXiv:1901.03584 ; CERN-EP-2018-331.
- 2019. - 56 p.
Fulltext - Previous draft version - Fulltext

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2018-12-30
17:48
Search for top-quark decays $t \rightarrow Hq$ with 36 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}=13$ TeV with the ATLAS detector / ATLAS Collaboration
A search for flavour-changing neutral current decays of a top quark into an up-type quark ($q=u, c$) and the Standard Model Higgs boson, $t\rightarrow Hq$, is presented [...]
arXiv:1812.11568 ; CERN-EP-2018-295.
- 2018. - 66 p.
00017 : $\Hbb$ search: Comparison of the distributions of the LH discriminant after preselection of the $\Hc$ (red dashed) and $\Hu$ (blue dotted) signals, and the $t\bar{t}\to WbWb$ background (black solid) in different regions considered in the analysis: (a) (4j, 2b), (b) (4j, 3b), (c) (4j, 4b), (d) (5j, 2b), (e) (5j, 3b), (f) (5j, $\geq$4b), (g) ($\geq$6j, 2b), (h) ($\geq$6j, 3b), and (i) ($\geq$6j, $\geq$4b). In the regions with $\geq$4 $b$-tagged jets, the signal acceptance is small, which translates into a small number of events for the simulated samples. Therefore, only two bins are used for these distributions. - 00017 : $\Hbb$ search: Comparison of the distributions of the LH discriminant after preselection of the $\Hc$ (red dashed) and $\Hu$ (blue dotted) signals, and the $t\bar{t}\to WbWb$ background (black solid) in different regions considered in the analysis: (a) (4j, 2b), (b) (4j, 3b), (c) (4j, 4b), (d) (5j, 2b), (e) (5j, 3b), (f) (5j, $\geq$4b), (g) ($\geq$6j, 2b), (h) ($\geq$6j, 3b), and (i) ($\geq$6j, $\geq$4b). In the regions with $\geq$4 $b$-tagged jets, the signal acceptance is small, which translates into a small number of events for the simulated samples. Therefore, only two bins are used for these distributions. - 00037 : - 00037 : - 00036 \small {Summary of the best-fit $\BR(t\to Hu)$ for the individual searches as well as their combination, assuming $\BR(t\to Hc)=0$. } - 00036 \small {Summary of the best-fit $\BR(t\to Hu)$ for the individual searches as well as their combination, assuming $\BR(t\to Hc)=0$. } - 00014 \small {Summary of the best-fit $\BR(t\to Hc)$ for the individual searches as well as their combination, assuming $\BR(t\to Hu)=0$. } - 00014 \small {Summary of the best-fit $\BR(t\to Hc)$ for the individual searches as well as their combination, assuming $\BR(t\to Hu)=0$. } - 00018 : - 00018 : - 00005 : - 00005 : - 00035 : Caption not extracted - 00035 : Caption not extracted - 00039 : Caption not extracted - 00039 : Caption not extracted - 00006 : - 00006 : - 00024 : - 00024 : - 00012 : Caption not extracted - 00012 : Caption not extracted - 00023 : - 00023 : - Fulltext - Fulltext - 00033 : - 00033 : - 00004 : Caption not extracted - 00004 : Caption not extracted - 00034 : Caption not extracted - 00034 : Caption not extracted - 00007 $\Hbb$ search: Comparison between the data and predicted background for the event yields in each of the analysis regions considered before the fit to data (``Pre-Fit''). All events satisfy the preselection requirements, whereas those with exactly two $b$-tagged jets are in addition required to have a value of the likelihood discriminant above 0.6 (see Section~\ref{sec:likelihood_discriminant}). Backgrounds are normalised to their nominal cross sections. The small contributions from $W/Z$+jets, single-top-quark, diboson and multijet backgrounds are combined into a single background source referred to as ``Non-$\ttbar$''. The expected $\Hc$ and $\Hu$ signals (dashed histograms) are shown separately normalised to $\BR(t\to Hq)=1\%$. The bottom panel displays the ratio of data to the SM background (``Bkg'') prediction. The hashed area represents the total uncertainty of the background, excluding the normalisation uncertainty of the $\ttbin$ background, which is determined via a likelihood fit to data. - 00007 $\Hbb$ search: Comparison between the data and predicted background for the event yields in each of the analysis regions considered before the fit to data (``Pre-Fit''). All events satisfy the preselection requirements, whereas those with exactly two $b$-tagged jets are in addition required to have a value of the likelihood discriminant above 0.6 (see Section~\ref{sec:likelihood_discriminant}). Backgrounds are normalised to their nominal cross sections. The small contributions from $W/Z$+jets, single-top-quark, diboson and multijet backgrounds are combined into a single background source referred to as ``Non-$\ttbar$''. The expected $\Hc$ and $\Hu$ signals (dashed histograms) are shown separately normalised to $\BR(t\to Hq)=1\%$. The bottom panel displays the ratio of data to the SM background (``Bkg'') prediction. The hashed area represents the total uncertainty of the background, excluding the normalisation uncertainty of the $\ttbin$ background, which is determined via a likelihood fit to data. - 00002 : - 00002 : - 00027 : Caption not extracted - 00027 : Caption not extracted - 00020 : - 00020 : - 00003 : - 00003 : - 00015 : - 00015 : - 00010 : Caption not extracted - 00010 : Caption not extracted - 00009 : - 00009 : - 00031 : Caption not extracted - 00031 : Caption not extracted - 00021 : - 00021 : - 00022 : - 00022 : - 00008 : - 00008 : - 00019 : Caption not extracted - 00019 : Caption not extracted - 00030 : - 00030 : - 00001 : Caption not extracted - 00001 : Caption not extracted - 00029 : - 00029 : - 00038 : - 00038 : - 00025 : - 00025 : - 00026 : - 00026 : - 00028 : - 00028 : - 00032 : - 00032 : - 00000 : Caption not extracted - 00000 : Caption not extracted - 00016 : Caption not extracted - 00016 : Caption not extracted - 00013 : - 00013 : - 00011 : Caption not extracted - 00011 : Caption not extracted - Fulltext - Fulltext

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2018-12-23
23:02
Observation of electroweak $W^{\pm}Z$ boson pair production in association with two jets in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector / ATLAS Collaboration
An observation of electroweak $W^{\pm}Z$ production in association with two jets in proton-proton collisions is presented. [...]
arXiv:1812.09740 ; CERN-EP-2018-286.
- 2018. - 41 p.
Fulltext - Previous draft version - Fulltext

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2018-12-23
22:52
Search for large missing transverse momentum in association with one top-quark in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS detector / ATLAS Collaboration
This paper describes a search for events with one top-quark and large missing transverse momentum in the final state. [...]
arXiv:1812.09743 ; CERN-EP-2018-301.
- 2018. - 51 p.
Fulltext - Previous draft version - Fulltext

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2018-12-21
21:07
Properties of $g\rightarrow b\bar{b}$ at small opening angles in $pp$ collisions with the ATLAS detector at $\sqrt{s}=13$ TeV / ATLAS Collaboration
The fragmentation of high-energy gluons at small opening angles is largely unconstrained by present measurements. [...]
arXiv:1812.09283 ; CERN-EP-2018-323.
- 2018. - 39 p.
00006 Schematic diagrams illustrating the $\Delta R(b,b)$ and $\Delta\mathrm{\theta}_\text{ppg,gbb}$ observables. In this example, the gluon is emitted at $\eta=0$. - 00006 Schematic diagrams illustrating the $\Delta R(b,b)$ and $\Delta\mathrm{\theta}_\text{ppg,gbb}$ observables. In this example, the gluon is emitted at $\eta=0$. - 00006 Schematic diagrams illustrating the $\Delta R(b,b)$ and $\Delta\mathrm{\theta}_\text{ppg,gbb}$ observables. In this example, the gluon is emitted at $\eta=0$. - 00003 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00003 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00003 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00001 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00001 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00001 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00004 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00004 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00004 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00008 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00008 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00008 The pre-fit (MC) and post-fit (data) flavor fractions for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right) are indicated with open and solid markers respectively. The error bars include only statistical uncertainties from the flavor-fraction fit. The fit's systematic uncertainties are comparable in magnitude, but correlated across the bins. The impact of both the flavor-fraction fit's statistical and systematic uncertainties on the final results is presented in Table~\ref{tab:syst}. - 00010 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00010 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00010 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00007 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00007 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00007 The unfolded distribution of $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). Error bands represent the sum in quadrature of statistical and systematic uncertainties (see Section~\ref{sec:systs}). These data are compared with predictions from the \PYTHIA and \SHERPA MC simulations. The bands for the \PYTHIA prediction represented by a square indicate the Var2$\pm$ variations (dominated by a $\pm 10\%$ variation in the final state shower $\alpha_s$). The additional set of \PYTHIA markers use $m_{bb}^2/4$ for the renormalization scale. - 00002 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - 00002 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - 00002 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - 00009 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00009 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00009 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00005 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00005 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00005 The detector response is represented as the conditional probability of the detector-level quantity given the particle-level quantity, written as $\Pr(\text{detector-level}|\text{particle-level})$, in simulation for $\Delta R(b,b)$ (top left), $\Delta\mathrm{\theta}_\text{ppg,gbb}$ (top right), $z(p_\text{T})$ (bottom left), and $\log(m_{bb}/p_\text{T})$ (bottom right). The small anti-diagonal component for $\Delta\mathrm{\theta}_\text{ppg,gbb}$ is due to cases where the leading and subleading track-jets are swapped between detector level and particle level so $\Delta\mathrm{\theta}_\text{ppg,gbb}\mapsto \pi-\Delta\mathrm{\theta}_\text{ppg,gbb}$. - 00000 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - 00000 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - 00000 The distribution of $\subsdzero$ in data and in simulation, post-fit, for the higher-$p_\text{T}$ track-jet (left) and for the lower-$p_\text{T}$ track-jet (right) in the bin $0.25<\Delta R(b,b)<0.3$. The three components are the signal double-$b$ (`BB'), the background single $b$ (`B'), and the background non-$b$ components (`L+C'). Percentages reported in the legend indicate the pre- and post-fit fraction of each component. Only data and MC statistical uncertainties are shown. The lower panel shows the ratio between data and the post-fit simulation. - Fulltext - Fulltext - Fulltext - Fulltext - Fulltext

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2018-12-21
17:17
Search for chargino and neutralino production in final states with a Higgs boson and missing transverse momentum at $\sqrt{s} = 13$ TeV with the ATLAS detector / ATLAS Collaboration
A search is conducted for the electroweak pair production of a chargino and a neutralino $pp \rightarrow \tilde\chi^\pm_1 \tilde\chi^0_2$, where the chargino decays into the lightest neutralino and a $W$ boson, $\tilde\chi^\pm_1 \rightarrow \tilde\chi^0_1 W^{\pm}$, while the neutralino decays into the lightest neutralino and a Standard Model-like 125 GeV Higgs boson, $\tilde\chi^0_2 \rightarrow \tilde\chi^0_1 h$. [...]
arXiv:1812.09432 ; CERN-EP-2018-306.
- 2018. - 58 p.
Fulltext - Previous draft version - Fulltext

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2018-12-18
14:09
Search for single production of vector-like quarks decaying into $Wb$ in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector / ATLAS Collaboration
A search for singly produced vector-like quarks $Q$, where $Q$ can be either a $T$ quark with charge $+2/3$ or a $Y$ quark with charge $-4/3$, is performed in proton-proton collision data at a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of $36.1 \text{fb}^{-1}$, recorded with the ATLAS detector at the LHC in 2015 and 2016. [...]
arXiv:1812.07343 ; CERN-EP-2018-226.
- 2018. - 55 p.
Fulltext - Previous draft version - Fulltext

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