The ATLAS calorimeters have recorded millions of cosmic ray and random triggered events. 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 missing E_{T}. Data taken with close to full detector readout in September and October 2008 were reprocessed at the end of 2008. The performance of standard calorimeter missing E_{T} algorithms, as planned to be used for the analysis of collision data, on is shown here on a sample of events collected with the random trigger.

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 (known in ATLAS as CaloNoiseToolDB). Cells with very high noise are masked early in calorimeter reconstruction.

Different E_{T}^{miss}-related variables are studied here:

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

Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to the missing E_{T} is electronic noise.
Hence E_{X}^{miss}, E_{Y}^{miss} and E_{T} may be expected to follow Gaussian distributions
centered on 0.

Detailed analysis has been made with 50292 random events from run 91639, taken the 14^{th} of October 2008.

The cell based algorithm is a simple one that is used to assess the basic calorimeter performance.
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.

The 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 derived from the CaloNoiseToolDB.

The following plots use data taken in 35 runs between 10^{th} September and 23^{rd} October 2008. Run 91639 was taken on the following day (day 36).

For each run, missing E_{T} variables were computed with the standard topocluster algorithm, and fit with Gaussian distributions to extract the mean () and standard deviation ().

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Topic revision: r22 - 2010-12-06 - ElmarRitsch

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