The ATLAS calorimeters have recorded millions of random triggered events, i.e. events with no real energy deposit in the calorimeters, but noise. Detailed understanding and improvement of the signal reconstruction has made it possible to use these events to study the performance of higher level quantities such as the missing transverse energy (E_{T}^{miss}). Data are taken with full calorimeter readout. The performance of standard calorimeter missing E_{T} algorithms, as planned to be used for the analysis of collision data, is shown here for these events.

The missing vectorial and scalar transverse energies in the calorimeters are reconstructed using two methods:

- The cell-based method uses all cells above a noise threshold of two standard deviations (|E|>2*). This method is a simple one that characterises the basic detector performance.

- The topocluster-based method uses topological clusters measured in the calorimeter. Clusters are derived from calorimeter cells by adding the energy in neighbouring cells with a dynamical topological cluster algorithm. All the cells in the neighbourhood of the cluster are included, if they have an energy larger than a predefined threshold (neighbour threshold). The procedure is repeated until no cells in the neighbourhood of the cluster are found. As a last step all cells surrounding the cluster are merged to the cluster. The default configuration uses a seed threshold of 4 standard deviations and a neighbour threshold of 2 standard deviations.

The width of the energy distribution in each cell, _{noise}, has been estimated on a cell by cell basis for both LAr and Tile calorimeters as the RMS of the energy distribution in one early calibration run, and recorded in the database used at reconstruction level Cells with very high noise are masked early in calorimeter reconstruction. A "Gaussian noise model" parametrises the cell energy distribution, based on values derived from a simple Gaussian distribution. For each cell, energy values are picked by this Gaussian distribution which is centered at 0 and has a standard deviation which is equal to the respective _{noise} value.

E_{T}^{miss} is defined as:

- E
_{X}^{miss}= - E sincos - E
_{Y}^{miss}= - E sinsin - E
_{T}^{miss}= √((E_{X}^{miss})^{2}+(E_{Y}^{miss})^{2})

Detailed analysis is performed with 64689 random events from run 150541, taken the 23^{th} of November 2009.
The cell based algorithm is a simple one that is used to assess the basic calorimeter performance. A fair agreement is obtained with the Gaussian noise model. The topological clustering algorithm provides a better noise suppression and therefore a better missing E_{T} resolution. This algorithm is close to the default missing E_{T} reconstruction algorithm that will be used for the analysis of collision data since it provides more refined results. However it requires a more accurate description of the noise in the calorimeter. Data and Monte-Carlo mismatch is mainly explained by the non-Gaussian behavior of TIle calorimeter cells.

Contact E. Petit, P. Pralavorio

-- PascalPRALAVORIO - 30 Apr 2009

Topic revision: r3 - 2009-12-07 - PascalPRALAVORIO

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