Measurement of the photon identification efficiencies with the ATLAS detector using LHC Run-1 data

TL;DR

The paper measures ATLAS photon identification efficiencies using LHC Run-1 data (7 and 8 TeV) with three data-driven techniques to obtain robust, eta- and ET-dependent efficiencies for both converted and unconverted photons. It compares data to MC predictions, applying shower-shape corrections to reduce discrepancies and deriving data-to-MC scale factors that correct simulations in physics analyses. The study demonstrates good agreement between data and corrected MC across most of the kinematic range, with larger uncertainties at low ET and for 7 TeV data due to smaller control samples. It also examines pile-up effects and shows that SFs remain stable, enabling reliable per-event corrections in photon analyses.

Abstract

The algorithms used by the ATLAS Collaboration to reconstruct and identify prompt photons are described. Measurements of the photon identification efficiencies are reported, using 4.9 fb$^{-1}$ of pp collision data collected at the LHC at $\sqrt{s}$=7 TeV and 20.3 fb$^{-1}$ at $\sqrt{s}$=8 TeV. The efficiencies are measured separately for converted and unconverted photons, in four different pseudorapidity regions, for transverse momenta between 10 GeV and 1.5 TeV. The results from the combination of three data-driven techniques are compared to the predictions from a simulation of the detector response, after correcting the electromagnetic shower momenta in the simulation for the average differences observed with respect to data. Data-to-simulation efficiency ratios used as correction factors in physics measurements are determined to account for the small residual efficiency differences. These factors are measured with uncertainties between 0.5% and 10% in 7 TeV data and between 0.5% and 5.6% in 8 TeV data, depending on the photon transverse momentum and pseudorapidity.

Figures (19)

• Figure 1: Sketch of a barrel module (located at $\eta=0$) of the ATLAS electromagnetic calorimeter. The different longitudinal layers (one presampler, PS, and three layers in the accordion calorimeter) are depicted. The granularity in $\eta$ and $\phi$ of the cells of each layer and of the trigger towers is also shown.
• Figure 2: Distributions of the calorimetric discriminating variables (a) $F_\mathrm{side}$ and (b) $w_{s\,3}$ for converted photon candidates with $\ET > 20~\GeV$ and $|\eta| <2.37$ (excluding $1.37<|\eta|<1.52$) selected from $Z\rightarrow\ell\ell\gamma$ events obtained from the 2012 data sample (dots). The distributions for true photons from simulated $Z\rightarrow\ell\ell\gamma$ events (blue hatched and red hollow histograms) are also shown, after reweighting their two-dimensional vs $\eta$ distributions to match that of the data candidates. The blue hatched histogram corresponds to the uncorrected simulation and the red hollow one to the simulation corrected by the average shift between data and simulation distributions determined from the inclusive sample of isolated photon candidates passing the tight selection per bin of ($\eta$, ) and for converted and unconverted photons separately. The photon candidates must be isolated but no shower-shape criteria are applied. The photon purity of the data sample, i.e. the fraction of prompt photons, is estimated to be approximately 99%.
• Figure 3: Two-dimensional distribution of $m_{\ell\ell\gamma}$ and $m_{\ell\ell}$ for all reconstructed $\Zboson\rightarrow\ell\ell\gamma$ candidates after loosening the selection applied to $m_{\ell\ell\gamma}$ and $m_{\ell\ell}$. No photon identification requirements are applied. Events from initial-state ($m_{\ell\ell}\approx m_Z$) and final-state ($m_{\ell\ell\gamma}\approx m_Z$) radiation are clearly visible.
• Figure 4: Invariant mass ($m_{\mu\mu\gamma}$) distribution of events in which the unconverted photon has $10~\GeV<\ET<15$, selected in data at $\sqrt{s}= 8$ after applying all the $\Zboson\to\mu\mu\gamma$ selection criteria except that on $m_{\mu\mu\gamma}$ (black dots). No photon identification requirements are applied. The solid black line represents the result of fitting the data distribution to a sum of the signal (red dashed line) and background (blue dotted line) invariant mass distributions obtained from simulations.
• Figure 5: Diagram illustrating the process of Smirnov transformation. $R_{\phi}$ is chosen as an example discriminating variable whose distribution is particularly different between electrons and (unconverted) photons. The $R_{\phi}$ probability density function (pdf) in each sample (a) is used to calculate the respective CDF (b). From the two CDFs, a Smirnov transformation can be derived (c). Applying the transformation leads to an $R_{\phi}$ distribution of the transformed electrons which closely resembles the photon distribution (d).