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 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:
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.
Cell and topoclusterbased E_{X}^{miss} and E_{Y}^{miss} distributions, showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topoclusterbased distributions show better noise suppression than when using the cellbased method.  
E_{T} also has the expected Gaussian shape, with a similar improvement of the topoclusterbased evaluation compared to the cellbased one. A small shift (compared to the RMS of the distribution) of the cellbased E_{T} is visible, and is being studied further.  
Inclusive distributions of E_{T}^{miss} for both methods are shown, showing again the better noise suppression of the topocluster method. Tails in the distribution (beyond 8 GeV for topoclusterbased, and 16 GeV for cellbased, variables), contributing less than 0.1% of events, have been understood to come from coherent noise in a specific region of LAr presampler.  
The expected E_{T}^{miss} distribution obtained by a randomisation of the cell energy with a Gaussian noise of width _{noise}, superimposed on the measured cellbased E_{T}^{miss} distribution. A good description of the observed distribution is seen. Similar studies for the topoclusterbased missing E_{T} require an accurate description of the noise up to and beyond 4 : this work is in progress. 
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 ().
Deviation of the mean of the E_{X}^{miss} distribution () from its average value <> (0.103 ± 0.005 GeV). Good stability is seen over the month and a half period.


Deviation of the width of the E_{X}^{miss} distribution () from its average value <> (1.000 ± 0.005 GeV). Good stability is seen over the month and a half period, with no significant change with time. 

Deviation of the mean of the E_{Y}^{miss} distribution () from its average value <> (0.023 ± 0.004 GeV). Good stability is seen over the month and a half period, with no significant change with time.


Deviation of the width of the E_{Y}^{miss} distribution () from its average value <> (0.932 ± 0.003 GeV). Good stability is seen over the month and a half period, with no significant change with time. 

Deviation of the mean of the E_{T} distribution (); from its average value <> (0.780 ± 0.014 GeV). Good stability is seen over the month and a half period: variations are observed, but are small compared to the width of the distribution (~1.4 GeV). 

Deviation of the width of the E_{T} distribution () from its average value <> (1.372 ± 0.006 GeV). Good stability is seen over the month and a half period, with no significant change with time.

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