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ATLAS Preprints

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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, 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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2018-12-16
10:56
Top quarks and exotics at ATLAS and CMS / Serkin, Leonid (INFN Gruppo Collegato di Udine and ICTP, Trieste)
An overview of recent searches with top quarks in the final state using up to 36 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS and CMS experiments at the LHC is presented. [...]
arXiv:1901.01765 ; ATL-PHYS-PROC-2018-195.
- 2018. - 6 p.
Original Communication (restricted to ATLAS) - Full text - Fulltext

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2018-12-14
09:37
Exotics at the LHC / Del Re, Daniele (INFN, Rome ; Rome U.) /ATLAS, CMS and LHCB Collaborations
LHC has worked beautifully and provided more than 100 fb$^{-1}$ at 13 TeV. Thanks to this enormous statistics of p-p collisions, LHC experiments have been able to explore several different new physics scenarios. [...]
CMS-CR-2018-392.- Geneva : CERN, 2018 - 11 p. Fulltext: PDF;
In : XXXIX International Conference on High Energy Physics, Seoul, Kor, 4 - 11 Jul 2018

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2018-12-14
09:37
Top pair production measurements, inclusive and differential / Fernandez Menendez, Javier (Oviedo U.) /ATLAS, CMS and LHCB Collaborations
Recent results and state-of-the-art on top-quark pair production cross sections in both inclusive and differential measurements are presented, obtained using data collected with the ATLAS, CMS and LHCb experiments during the LHC Run1 and Run2 periods up to the year 2016 at centre-of-mass energies ranging from 5.02 to 13TeV. Results are confronted against Standard Model (SM) next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) predictions, several Monte Carlo (MC) generators and tunes, and parton density function variations, with a special focus on the latest Run2 results..
CMS-CR-2018-126.- Geneva : CERN, 2018 - 9 p. Fulltext: PDF;
In : Sixth Annual Conference on Large Hadron Collider Physics, Bologna, Italy, 4 - 9 Jun 2018

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