This wiki is meant for preparation of plots and text to be approved for studies of Missing ET performance on random triggers. For internal iteration within working group only, final version will be made available in proper public place.
The ATLAS calorimeters have recorded millions of cosmic ray and random trigger. Detailed understanding and improvement of the signal reconstruction has made it possible to study the performance on these events of higher level quantities such as missing ET. Data taken with close to full detector readout in September and October 2008 has been reprocessed at the end of 2008. The performance of standard calorimeter MissingET algorithms, as planned to be used for the analysis of the collision data, on random trigger is shown here.
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 previously 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 missing ET variables are being looked at:
Since random triggers are used, no real energy is expected to be deposited in the calorimeters. So the only contribution to missing ET is electronic noise. Hence E_{X}^{miss}, E_{Y}^{miss} and E_{T} are 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 performances. The topological clustering algorithm provides a better noise suppression and therefore a better ETmiss resolution. This algorithm is close to the default ETmiss 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 an standard deviation which is equal to the respective _{noise} value derived from the CaloNoiseToolDB.
Cell and topo cluster based E_{X}^{miss} showing a good control of the energy reconstruction in the 187000 cells of the Calorimeter. The topo cluster based missing energy has better noise suppression.
eps version Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch 
<br /> 
E_{Y}^{miss} is very similar to E_{X}^{miss} as expected.
eps version Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch

<br /> 
E_{T} also has the expected Gaussian shape, with similar improvement of topocluster based missing ET w.r.t cell based. A small shift (w.r.t.) RMS of the cell based E_{T} is being scrutinised.
eps version Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch 
<br /> 
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 topo clusters based and 16 GeV for cell based) contributing to less than 0.1% of events have been understood to come from coherent noise in a specific region of LAr presampler (reference to LAr approved plots needed there when available).
eps version Contact: Tancredi.Carli@cernNOSPAMPLEASE.ch 
<br /> 
The expected E_{T}^{miss} distribution obtained by a randomisation of the cell energy with a Gaussian noise of width _{noise} is superimposed to the measured one, showing a good agreement.
Similar studies for the topocluster based missing ET requires an accurate description of the noise up to and beyond 4 , which is being worked on.
eps version

<br /> 
Use of 35 runs taken between the 1O^{th} of September and the 23^{th} of October. Thus run 91639 corresponds to day 36.
For each run, reconstruct missing E_{T} variables computed with standard topocluster algorithm, and fit with Gaussian distribution to extract mean () and standard deviation ().
Deviation of the mean of the E_{X}^{miss} distrobution () from its average value <> (0.10 ± 0.01 GeV).
eps version
 <br /> 
Deviation of the width of the E_{X}^{miss} distribution () from its average value <> (1.00 ± 0.00 GeV).
eps version
 <br /> 
Deviation of the mean of the E_{Y}^{miss} distribution () from its average value <> (0.02 ± 0.00 GeV).
eps version
 <br /> 
Deviation of the width of the E_{Y}^{miss} distribution () from its average value <> (0.93 ± 0.00 GeV).
eps version
 <br /> 
Deviation of the mean of the sumE_{T} distribution (&mu); from its average value <> (0.78 ± 0.01 GeV).
eps version
 <br /> 
Deviation of the width of the sumE_{T} distribution () from its average value <> (1.37 ± 0.01 GeV).
eps version
 <br /> 
Tancredi Carli and Jimmy Proudfoot Last reviewed by: Never reviewed
Latex rendering error!! dvi file was not created.

