Issues with cluster calibration + selection cuts for TrigEgamma note Hardeep Bansil University of Birmingham Birmingham ATLAS Weekly Meeting 12/08/2010

Sliding Window Clustering 2 Cluster finding: Towers are created by summing the cells of the presampler and the 3 EM calorimeter layers in depth. A “sliding window algorithm”, using a 5x5 window (0.125x0.125), is used to find clusters. The window slides in the towers η-φ grid to find local energy peaks. Cluster definition: Once the local energy peak is found, the algorithm clusters the energy in a fixed window size around the found peak. The window size can be a 5x5, 3x5 or 3x7 window based on the cluster position and if it is electron or photon. Cluster corrections (to offline object): When the cluster is properly defined, many corrections are made at EF to correct various biases: η, φ position corrections η, φ energy modulation corrections Intercryostat gap (TR) correction EM Layer weights correction

data10 Analysis Athena Production Updated TrigT1CaloAnalysisExamples package Using MinBias stream DESDs (DESDM_EGAMMA) Using Good Runs Lists – find r and ready and dq atlgl g and dq cp_eg_electron_barrel y+ and dq cp_eg_electron_endcap y+ and dq trele y+ Check events pass L1_MBTS_1 trigger item Get electron and photon candidates and get calibrated clusters from them (egClusterCollection) Calculate Raw Cluster E T, η, φ using energy weighting of CaloCells that make up CaloCluster (cannot get raw variables directly for these clusters) Match to RoI using Δr = √(η e/γ CL -η RoI ) 2 +(φ e/γ CL -φ RoI ) 2 Did this for run (1194 μb -1 before GRL) 3

Raw Cluster φ 4 Comparison of raw cluster to calibrated cluster φ Distributions look similar until you get to ±π Energy weighting method treated ±π as discontinuous so φ shifted towards centre Fix by asking for biggest φ difference between CaloCells CALIBRATED RAW SHIFT CELLS TO BE CONTIN- UOUS

Raw Cluster φ 5 Quick check of Δφ shows a lot of improvement However still see that Δφ is not as sharp as Δη Possibly a better way of calculating both? BEFORE AFTER ΔφΔη

Calibrated/Raw E T 6 For a cluster, this ratio = 1 ideally if everything works properly, no energy is lost elsewhere in the detector and no corrections need to be made Asked to consider the effects of removing candidate electrons and photons where Calibrated E T /Raw E T > 2 for the cluster Told that it should not be an issue for photons but is Photons Electrons

Calibrated/Raw E T 7 Low statistics at the moment but backs previous results – not really a problem in central barrel but increases the further out in η you go Look at isEm level of object failing cut – most from jets NoneNone LooseLoose MediumMedium TightTight

Good news Other people have spotted this problem and are aware of it This should not be too much of a problem in the long run as the electron and photon selections are not that clean yet (dominated by fakes) Need lots more direct photons and J/Ψ particles to look at in order to improve selection 8

Effect on Bump? Not really (for this run at least) Majority of time: one cluster matched to one RoI – Calibrated cluster has less than 3 GeV (> 2.3, 2.4) raw cluster usually has slightly smaller energy than this but this is enough to make a 3 GeV RoI Other case: 2 or more clusters matched to one RoI – One cluster from an electron and one from a photon both with energy less than 3 GeV and positions are almost identical or at least very close (one cluster may fail isEm loose) Rare case: 2 or more clusters matched to one RoI – One cluster with low energy matches to another one nearby with a much higher energy 9

Vertex cut For TrigEgamma note, I have been asked to use a cut that requires at least 1 primary vertex in an event and that the primary vertex needs at least 3 tracks – Works majority of the time What are reasons why events do not pass this cut – Occasionally only 1 or 2 tracks associated to the primary vertex – Most of the time, no tracks associated to the primary vertex – Have not seen events where there is no primary vertex yet (reassuring) What’s going on here? 10

OTX map cut Another cut I have been asked to use requires that objects which fall into some dead OTX (Optical Transmitter) region are excluded Official egamma method for offline objects A cluster is rejected for any of the following regions: – Its core (3x3 in the Middle layer) contains an isolated BAD cell – It involves a dead FEB (Front End Board) in Strips and/or Middle layer – It involves a dead FEB in Presampler – It involves a bad HV region in Strips, Middle, Presampler – It has at least one dead cell among the 8 central cells in Strips (PHOTONS ONLY) Run dependent (OTX maps made for different run ranges) Slides Code and root files SlidesCode and root files 11

OTX map cut Effect on electrons and photons : absolute number of candidates failing cuts as a function of raw η and φ (using trigger tower binning) Cut covers a greater area for photons 12 PhotonsElectrons (One dead cell among the 8 central cells in Strips?)

OTX map cut What is causing problems? A cause for concern? Presampler FEB dead Strips FEB dead Middle layer FEB dead HV Problem Others are from bad cells 13 How big an area does an FEB cover?

OTX map cut Does it kill all candidates in a given area? Make binning finer so that it gets closer to cell level Calculate efficiency of cut being able to remove candidates A few odd bins where the efficiency of cut is less than 1 but for majority of cases it will remove all objects in a given area 14

Comparing all cuts Quick look at all of the cuts that an object can fail Still get a good number of candidates to study – Hope they trigger and create an RoI (or not)! 15