Performance of the ATLAS Detector using First Collision Data
- Aad, G. et al
- arXiv:1005.5254
| noimgLuminosity-weighted fraction of the time during stable beam operation for which the different detectors were able to take data under nominal conditions. |
| (a) Reconstructed $z$-vertex distribution calculated online by the higher level trigger for monitoring purposes. The width includes a small contribution from the experimental resolution. (b) Data-taking efficiency for periods with two circulating beams in December 2009. |
| (a) Reconstructed $z$-vertex distribution calculated online by the higher level trigger for monitoring purposes. The width includes a small contribution from the experimental resolution. (b) Data-taking efficiency for periods with two circulating beams in December 2009. |
| Time difference $\Delta t_{\mathrm MBTS}$ of hits recorded by the two MBTS scintillator wheels mounted in front of the electromagnetic end-cap wheels on both sides of the ATLAS detector; (a) time difference without any selection and (b) requiring a well-reconstructed vertex. |
| Time difference $\Delta t_{\mathrm MBTS}$ of hits recorded by the two MBTS scintillator wheels mounted in front of the electromagnetic end-cap wheels on both sides of the ATLAS detector; (a) time difference without any selection and (b) requiring a well-reconstructed vertex. |
| Instantaneous luminosity measured by the MBTS and LAr, with superimposed the LUCID and HLT vertex counting estimates normalized in such a way to give the same integrated luminosity as measured with the MBTS system. All measurements are corrected for TDAQ dead-time, except LUCID which is free from dead time effects. The short luminosity drop at 14:15 is due to inhibiting the trigger for ramping up the silicon detectors after declaration of stable LHC beams. |
| A comparison of data and simulation in the average number of hits in (a) the Pixels and (b) the SCT versus $\phi$ on selected tracks. Comparable distributions versus $\eta$ can be found in Ref.~\cite{Collaboration:2010rd}. |
| A comparison of data and simulation in the average number of hits in (a) the Pixels and (b) the SCT versus $\phi$ on selected tracks. Comparable distributions versus $\eta$ can be found in Ref.~\cite{Collaboration:2010rd}. |
| TRT hit efficiency as a function of the distance of the track from the wire in the centre of the straw in (a) the barrel and (b) the end-caps. |
| TRT hit efficiency as a function of the distance of the track from the wire in the centre of the straw in (a) the barrel and (b) the end-caps. |
| Unbiased residual distributions in the TRT barrel (a) and end-caps (b). The data points are in filled circles and the simulation in empty ones. |
| Unbiased residual distributions in the TRT barrel (a) and end-caps (b). The data points are in filled circles and the simulation in empty ones. |
| The distributions of the silicon detector unbiased residuals for (a) the pixel barrel, (b) the pixel end-cap, (c) the SCT barrel, (d) the SCT end-cap. The data are in solid circles, the simulation, which has a perfect alignment, is shown with open ones. |
| The distributions of the silicon detector unbiased residuals for (a) the pixel barrel, (b) the pixel end-cap, (c) the SCT barrel, (d) the SCT end-cap. The data are in solid circles, the simulation, which has a perfect alignment, is shown with open ones. |
| The distributions of the silicon detector unbiased residuals for (a) the pixel barrel, (b) the pixel end-cap, (c) the SCT barrel, (d) the SCT end-cap. The data are in solid circles, the simulation, which has a perfect alignment, is shown with open ones. |
| The distributions of the silicon detector unbiased residuals for (a) the pixel barrel, (b) the pixel end-cap, (c) the SCT barrel, (d) the SCT end-cap. The data are in solid circles, the simulation, which has a perfect alignment, is shown with open ones. |
| The \kshort\ candidate mass distribution using impact parameter and lifetime selections. The simulated signal and background are separately normalized to the data. |
| The fitted \kshort\ mass divided by the value found in nominal MC simulation as a function of the reconstructed decay position. The filled circles show the data, and the open symbols are for simulation samples with approximately 10\% and 20\% more silicon tracker material added. The horizontal dotted line is to guide the eye. |
| The \dedx\ measured in data as a function of momentum. |
| The measured and simulated mass spectra of $K^+K^-$ pairs. The $\phi$ peak is fitted with a Breit-Wigner with a fixed width convoluted with a Gaussian. Both kaons must be identified through the \dedx\ measurement. |
| (a) The variance of the $d_0$ distribution as a function of $1/(p^2 \sin^3\theta )$ of the tracks for data (solid points) compared to the nominal simulation (open points). A straight line fit to the data points is also shown. (b) The lifetime-signed impact parameter significance. \vspace{-.2cm} |
| (a) The variance of the $d_0$ distribution as a function of $1/(p^2 \sin^3\theta )$ of the tracks for data (solid points) compared to the nominal simulation (open points). A straight line fit to the data points is also shown. (b) The lifetime-signed impact parameter significance. \vspace{-.2cm} |
| The vertex mass distribution for all secondary vertices with positive decay length selected in data. The expectation from simulated events, normalized to the number of jets in the data, is superimposed. |
| An event containing a secondary vertex selected by the secondary vertex algorithm. The pixel detector can be seen on the left and an expansion of the vertex region on the right. Unassociated hits, in a lighter colour, are predominantly due to unreconstructed particles such as those with transverse momenta below 0.5~\GeV. |
| The fraction of high-threshold transition radiation hits on tracks as a function of the relativistic $\gamma$ factor (see text for details). |
| The efficiency for reconstruction of a L2 track candidate as a function of the \pt\ of the matched offline track in data and Monte Carlo simulation. A fit of the threshold curve is superimposed. |
| Distribution of cluster $E_T$~(a) and $|\eta|$~(b) for all selected electron candidates. The simulation is normalized to the number of data events. |
| Distribution of cluster $E_T$~(a) and $|\eta|$~(b) for all selected electron candidates. The simulation is normalized to the number of data events. |
| Cluster $E_T$~(a) and $|\eta|$~(b) for all selected photon candidates. The simulation is normalized to the number of data events. |
| Cluster $E_T$~(a) and $|\eta|$~(b) for all selected photon candidates. The simulation is normalized to the number of data events. |
| Efficiency for the lowest threshold L1 electromagnetic trigger, a nominal 4~\GeV, as a function of the uncalibrated offline cluster transverse energy. The turn-on is shown for data (solid triangles) and non-diffractive minimum-bias simulation (open circles). |
| Fraction of energy deposited by photon candidates with $E_T >$ 2.5~\GeV\ in each layer of the electromagnetic calorimeter for data and simulation. These fractions are labelled as (a) $f_0$~for the presampler layer, (b) $f_1$~for the strip layer, (c) $f_2$~for the middle layer and $(d)~f_3$~for the back layer. Fractions can be negative due to noise fluctuations. The simulation is normalized to the number of data events. |
| Fraction of energy deposited by photon candidates with $E_T >$ 2.5~\GeV\ in each layer of the electromagnetic calorimeter for data and simulation. These fractions are labelled as (a) $f_0$~for the presampler layer, (b) $f_1$~for the strip layer, (c) $f_2$~for the middle layer and $(d)~f_3$~for the back layer. Fractions can be negative due to noise fluctuations. The simulation is normalized to the number of data events. |
| Fraction of energy deposited by photon candidates with $E_T >$ 2.5~\GeV\ in each layer of the electromagnetic calorimeter for data and simulation. These fractions are labelled as (a) $f_0$~for the presampler layer, (b) $f_1$~for the strip layer, (c) $f_2$~for the middle layer and $(d)~f_3$~for the back layer. Fractions can be negative due to noise fluctuations. The simulation is normalized to the number of data events. |
| Fraction of energy deposited by photon candidates with $E_T >$ 2.5~\GeV\ in each layer of the electromagnetic calorimeter for data and simulation. These fractions are labelled as (a) $f_0$~for the presampler layer, (b) $f_1$~for the strip layer, (c) $f_2$~for the middle layer and $(d)~f_3$~for the back layer. Fractions can be negative due to noise fluctuations. The simulation is normalized to the number of data events. |
| Distributions of calorimeter variables compared between data and simulation for all photon candidates with \pt\ above 2.5~\GeV. Shown are the shower width in the middle layer of the EM~calorimeter, $w_2$~(a), and the variables $E_{\rm ratio}$~(b) and $w_{s3}$~(c), which characterize the shower shape in the first (strips) EM~layer. The simulation is normalized to the number of data events. |
| Distributions of calorimeter variables compared between data and simulation for all photon candidates with \pt\ above 2.5~\GeV. Shown are the shower width in the middle layer of the EM~calorimeter, $w_2$~(a), and the variables $E_{\rm ratio}$~(b) and $w_{s3}$~(c), which characterize the shower shape in the first (strips) EM~layer. The simulation is normalized to the number of data events. |
| Distributions of calorimeter variables compared between data and simulation for all photon candidates with \pt\ above 2.5~\GeV. Shown are the shower width in the middle layer of the EM~calorimeter, $w_2$~(a), and the variables $E_{\rm ratio}$~(b) and $w_{s3}$~(c), which characterize the shower shape in the first (strips) EM~layer. The simulation is normalized to the number of data events. |
| Distributions of track-calorimeter matching variables for all electron candidates compared between data and simulation. (a) shows the difference in $\eta$ in the first calorimeter layer (see Section~\ref{sec:matching}) and (b) shows the match in charge-signed-$\phi$ in the second. The simulation is normalized to the number of data events. |
| Distributions of track-calorimeter matching variables for all electron candidates compared between data and simulation. (a) shows the difference in $\eta$ in the first calorimeter layer (see Section~\ref{sec:matching}) and (b) shows the match in charge-signed-$\phi$ in the second. The simulation is normalized to the number of data events. |
| Distributions of tracking variables for all electron candidates compared between data and simulation. The number of Pixel~(a) and SCT~(b) hits on the electron tracks are shown, the fraction of high-threshold TRT~hits for candidates with~$|\eta|<2.0$ and with a total number of TRT~hits larger than ten~(c), and the transverse impact parameter,~$d_0$, with respect to the reconstructed primary vertex~(d). The simulation is normalized to the number of data events. |
| Distributions of tracking variables for all electron candidates compared between data and simulation. The number of Pixel~(a) and SCT~(b) hits on the electron tracks are shown, the fraction of high-threshold TRT~hits for candidates with~$|\eta|<2.0$ and with a total number of TRT~hits larger than ten~(c), and the transverse impact parameter,~$d_0$, with respect to the reconstructed primary vertex~(d). The simulation is normalized to the number of data events. |
| Distributions of tracking variables for all electron candidates compared between data and simulation. The number of Pixel~(a) and SCT~(b) hits on the electron tracks are shown, the fraction of high-threshold TRT~hits for candidates with~$|\eta|<2.0$ and with a total number of TRT~hits larger than ten~(c), and the transverse impact parameter,~$d_0$, with respect to the reconstructed primary vertex~(d). The simulation is normalized to the number of data events. |
| Distributions of tracking variables for all electron candidates compared between data and simulation. The number of Pixel~(a) and SCT~(b) hits on the electron tracks are shown, the fraction of high-threshold TRT~hits for candidates with~$|\eta|<2.0$ and with a total number of TRT~hits larger than ten~(c), and the transverse impact parameter,~$d_0$, with respect to the reconstructed primary vertex~(d). The simulation is normalized to the number of data events. |
| Ratio, $E/p$, between cluster energy and particle track momentum (a) for electron candidates and (b) for electrons from converted photons. In each case candidates with \pt\ above 2.5~\GeV\ in the calorimeter are shown. Sub-figure (a) is dominated by real electrons. The simulation is normalized to the number of data events. |
| Ratio, $E/p$, between cluster energy and particle track momentum (a) for electron candidates and (b) for electrons from converted photons. In each case candidates with \pt\ above 2.5~\GeV\ in the calorimeter are shown. Sub-figure (a) is dominated by real electrons. The simulation is normalized to the number of data events. |
| Distribution of the energy fraction in the strip layer of the EM~calorimeter as extracted from data compared to the truth from simulation. The results are shown for both components of the electron candidates: electrons from conversions~(a) and hadrons~(b). The simulation is normalized to the number of data events. |
| Distribution of the energy fraction in the strip layer of the EM~calorimeter as extracted from data compared to the truth from simulation. The results are shown for both components of the electron candidates: electrons from conversions~(a) and hadrons~(b). The simulation is normalized to the number of data events. |
| Distribution of the $E/p$ as extracted from data compared to the truth from simulation. The results are shown for both components of the electron candidates: electrons from conversions~(a) and hadrons~(b). The simulation is normalized to the number of data events. \vspace{-0.2cm} |
| Distribution of the $E/p$ as extracted from data compared to the truth from simulation. The results are shown for both components of the electron candidates: electrons from conversions~(a) and hadrons~(b). The simulation is normalized to the number of data events. \vspace{-0.2cm} |
| Comparison between converted photon candidates, for which both tracks have silicon hits, in data and non-diffractive minimum-bias Monte Carlo simulation. (a) Opening angle in the $rz$ plane between the two tracks ($\Delta(1/\mathrm{tan}\theta)$); (b) 3D distance of closest approach between the two tracks, dr. The distributions are normalized to the same number of conversion candidates in data and Monte Carlo simulation. |
| Comparison between converted photon candidates, for which both tracks have silicon hits, in data and non-diffractive minimum-bias Monte Carlo simulation. (a) Opening angle in the $rz$ plane between the two tracks ($\Delta(1/\mathrm{tan}\theta)$); (b) 3D distance of closest approach between the two tracks, dr. The distributions are normalized to the same number of conversion candidates in data and Monte Carlo simulation. |
| Distribution of conversion candidate radius, (a), and $\eta$, (b). The points show the distribution for data; the open histograms, the total from the Monte Carlo simulation and the filled component shows the expected contribution of true photon conversions. The contribution from the Dalitz decays of neutral mesons is shown in sub-figure (a). The Monte Carlo simulation is normalized to number of conversion candidates in the data, although in subsequent analysis normalization is to the number in the beam pipe. |
| Distribution of conversion candidate radius, (a), and $\eta$, (b). The points show the distribution for data; the open histograms, the total from the Monte Carlo simulation and the filled component shows the expected contribution of true photon conversions. The contribution from the Dalitz decays of neutral mesons is shown in sub-figure (a). The Monte Carlo simulation is normalized to number of conversion candidates in the data, although in subsequent analysis normalization is to the number in the beam pipe. |
| (a) Diphoton invariant mass distribution for the \pizero\ selection for data and Monte Carlo. The Monte Carlo is normalized to the same number of entries as the data. (b) Invariant mass distribution from one converted and one unconverted photon. The data are represented by points and the Monte Carlo simulations are shown as histograms. |
| (a) Diphoton invariant mass distribution for the \pizero\ selection for data and Monte Carlo. The Monte Carlo is normalized to the same number of entries as the data. (b) Invariant mass distribution from one converted and one unconverted photon. The data are represented by points and the Monte Carlo simulations are shown as histograms. |
| Diphoton invariant mass spectrum with tighter selection criteria to extract the $\eta$ peak with the fit superimposed to the data. The Monte Carlo simulation sample is normalized to the number of entries in the distribution for data. |
| Distributions of (a) the number of clusters per jet and (b) the fraction of energy per cluster for jets reconstructed with topological clusters using the \antikt~algorithm with R=0.6. |
| Distributions of (a) the number of clusters per jet and (b) the fraction of energy per cluster for jets reconstructed with topological clusters using the \antikt~algorithm with R=0.6. |
| Distributions of (a) \pt\ for all jets and (b) $\Delta \phi$ for events with two or more jets. These are shown for jets reconstructed with topological clusters using the \antikt~algorithm with R=0.6. |
| Distributions of (a) \pt\ for all jets and (b) $\Delta \phi$ for events with two or more jets. These are shown for jets reconstructed with topological clusters using the \antikt~algorithm with R=0.6. |
| L1 jet trigger efficiency for the triggers with 50\% efficiency at around 15~\GeV\ and 20~\GeV\ for data (solid) and simulation (open) together with a fit to the Monte Carlo using a standard threshold function (see text). |
| L1 jet trigger efficiency for the triggers with 50\% efficiency at around 15~\GeV\ and 20~\GeV\ for data (solid) and simulation (open) together with a fit to the Monte Carlo using a standard threshold function (see text). |
| (a) The electromagnetic radius\, $R_{\mathrm EM}$ (see text) of the inclusive reconstructed tau candidates. (b) The same variable with a tightened selection requiring di-jet events. The Monte Carlo is normalized to the same number of candidates as in the data. |
| (a) The electromagnetic radius\, $R_{\mathrm EM}$ (see text) of the inclusive reconstructed tau candidates. (b) The same variable with a tightened selection requiring di-jet events. The Monte Carlo is normalized to the same number of candidates as in the data. |
| Distribution of \MET~as measured in data from randomly triggered events. Only cells belonging to topological clusters are included in the calculation; their energies are calibrated at the EM scale. |
| Distribution of \py~(a,b)~and \MET~(c,d) as measured in data from minimum-bias events (dots) at 0.9~\TeV\ (a,c) and 2.36~\TeV\ (b,d) centre-of-mass energies. In the calculation only topological cluster cells are used, with energies calibrated at the EM scale. The expectations from Monte Carlo simulation are superimposed (histograms) and normalized to the number of events in data. \vspace{-0.5cm} |
| Distribution of \py~(a,b)~and \MET~(c,d) as measured in data from minimum-bias events (dots) at 0.9~\TeV\ (a,c) and 2.36~\TeV\ (b,d) centre-of-mass energies. In the calculation only topological cluster cells are used, with energies calibrated at the EM scale. The expectations from Monte Carlo simulation are superimposed (histograms) and normalized to the number of events in data. \vspace{-0.5cm} |
| Distribution of \py~(a,b)~and \MET~(c,d) as measured in data from minimum-bias events (dots) at 0.9~\TeV\ (a,c) and 2.36~\TeV\ (b,d) centre-of-mass energies. In the calculation only topological cluster cells are used, with energies calibrated at the EM scale. The expectations from Monte Carlo simulation are superimposed (histograms) and normalized to the number of events in data. \vspace{-0.5cm} |
| Distribution of \py~(a,b)~and \MET~(c,d) as measured in data from minimum-bias events (dots) at 0.9~\TeV\ (a,c) and 2.36~\TeV\ (b,d) centre-of-mass energies. In the calculation only topological cluster cells are used, with energies calibrated at the EM scale. The expectations from Monte Carlo simulation are superimposed (histograms) and normalized to the number of events in data. \vspace{-0.5cm} |
| \MET~resolution as a function of the total transverse energy (\sumet) for minimum-bias events. The line shows a fit to the resolution expected from the Monte Carlo simulation and the full dots (open squares) represents the results with data at 0.9 (2.36)~\TeV. \px, \py, \sumet~are computed with topological cluster cells at EM scale. |
| The distribution of $\eta$ and $p$ of reconstructed muons in the $0.9$~\TeV\ data. The simulation distributions are normalized to the number of entries in data. \vspace{-0.5cm} |
| The distribution of $\eta$ and $p$ of reconstructed muons in the $0.9$~\TeV\ data. The simulation distributions are normalized to the number of entries in data. \vspace{-0.5cm} |