1. W-DHCAL Analysis Overview José Repond Argonne National Laboratory

2. Data Quality I -Dead ASICs/FEBs/RPCs Check noise runs Make table of dead regions as function of run-ranges This information needs to be implemented into the MC simulation -Hot regions Mostly hits close to ground connectors Regions need to be excluded (also in MC simulation) - Square events and ASICs Need simple tool to identify and Reject events or Reject corresponding hits (Burak Bilki already has an algorithm)

3. Data Quality II -Select time window Only accept hits with Δ TS = -18, -19 (values might be different for muon runs!) Cut removes tail (6%) → minor effect on resolution (at most 3%) What to do about simulation? -Eliminate double hits Hits with identical x,y,z but different TS need to be eliminated ← important! Time →

4. Energy reconstruction in the DHCAL Data Consists of hit patterns of pads with signal above 1 threshold and their time-stamps with 100 ns resolution Incident particle energy reconstruction To first order E ∝ N N = ∑ layer N i … total number of hits Correction for contribution from noise E ∝ N - N noise N noise … accidental hits Correction for variation in chamber inefficiency E ∝ ∑ layer N i ·(ε 0 /ε i ) – N noise ε 0 … average DHCAL efficiency ε i … efficiency of layer i Correction for variation in pad multiplicity E ∝ ∑ layer N i ·(ε 0 /ε i ) ·(μ 0 /μ i ) – N noise μ 0 … average pad multiplicity ` μ i … average pad multiplicity of layer i Second order corrections Compensate for e/h ≠ 1 Saturation (more than 1 particle/pad) …

5. Clustering of Hits Nearest neighbor clustering Require 1 common side between hits Performed in each layer individually (no cross-correlation between layers) Determine un-weighted average of all hits in a given cluster (x cluster,y cluster ) Other clustering algorithms Conceivable, but not explored (is it necessary?) 1 cluster2 clusters

6. Tracks Loop over layers for layer irequest that all other layers have N j cluster ≤ 1 request that number of hits in tracking clusters N j hit ≤ 4 request at least 10/38(52) layers with tracking clusters fit straight line to (x cluster,z) and (y cluster,z) of all tracking clusters j calculate χ 2 of track request that χ 2 /N track < 1.0 inter/extrapolate track to layer i search for matching clusters in layer i within record number of hits in matching cluster

7. Track Segments Define tracking layers for each layer i e.g. 2 previous plus 2 following layers Find track segment Loop over all clusters in first tracking layer Look for a cluster within a small angle in the second tracking layer Calculate straight line through both clusters and extrapolate to 3 rd and 4 th tracking layer Look for a cluster close to the extrapolated positions in 3 rd and 4 th tracking layers Fit a straight line through the 4 tracking clusters request that χ 2 /N track < something inter/extrapolate track to layer i search for matching clusters in layer i within record number of hits in matching cluster

8. Noise Studies -Guang Yang (IIT) working on steel data Aim: publish paper this year -Identify dead regions See above - Measure noise rate As function of x,y,z Produce average over detector (don’t include hot hits, as discussed above) Analyze noise runs and correlate to T and p -Analyze time bins -20 and -21 (cross check) 0.02% of hits Correlate to noise runs taken close in (real) time Correlate this noise rate to the average number of hits/event in a given run -Create noise files To overlay with MC events (for systematic studies)

9. Muon analysis -Analyze muon runs Trigger counters 30 x 30 cm 2 Trigger counters moved to 9 individual positions ~5 Mevents Check timing-bin cuts -Analyze electron/pion data Trigger counters 10 x 10 cm 2 Decent muon peak in almost all runs -Use both tracks and track segments - Align layers in x and y -Measure Efficiency, average pad multiplicity Versus x,y,z and t Study muon response as function of muon momentum -Cross correlate Muon peaks with noise rate in electron/pion data and with T/p

10. Simulation Strategy GEANT4 Experimental set-up Beam (E,particle,x,y,x’,y’) Points (E depositions in gas gap: x,y,z) RPC response simulation Measured signal Q distribution Hits DATA Hits Comparison Parameters Exponential slope a 1, a 2 Ratio between exponentials R Threshold T Distance cut d cut Charge adjustment Q 0 With muons – tune a, T, (d cut ), and Q 0 With positrons – tune d cut Pions – no additional tuning

11. RPCSIM Parameters Distance d cut Distance under which there can be only one avalanche (one point of a pair of points randomly discarded if closer than d cut ) Charge Q 0 Shift applied to charge distribution to accommodate possible differences in the operating point of RPCs Slope a 1 Slope of exponential decrease of charge induced in the readout plane Slope a 2 Slope of 2 nd exponential, needed to describe tail towards larger number of hits Ratio R Relative contribution of the 2 exponentials Threshold T Threshold applied to the charge on a given pad to register a hit

12. After tuning in ‘clean regions’… 1 exponential function 2 exponential function Different definition of ‘clean’ regions RPC_sim_4RPC_sim_3

13. Response over entire plane I Response at edge of chamber reproduced by attenuating charge

14. Adequate Interesting Response over entire plane II Higher multiplicity in top chamber → Temperature? → Gas poisoning ? → Increased p in bottom RPC (NO)

15. Tuning of Simulation CERN data Different operating conditions than a Fermilab → Tuning exercise needs to be repeated Due to Erik’s tent → very stable T conditions (not so at Fermilab) Begin with Defining ‘average’ operating condition in muon runs Tune simulation (5 parameters) in ‘clean’ regions Tune simulation at edges

16. -Burak Bilki working on this with Steel data -Factorization in time and location Assume that response in x,y,z correlated in time: R(x,y,z,t) = R(x,y,z) x R(t) -Correct individual runs for changes as function of time 1 constant as function of time Constant determined from: bins (-20,-21), noise runs, muon peak ← to be studied -Factorization transversely and longitudinally Assume R(x,y,z) = R(x,y within 1 RPC) x R(RPC index) R(RPC index) = ( ε 0 /ε i )(μ 0 /μ i ) …1 constant per RPC No correction for x,y non-uniformity of individual RPCs -Correct for spill time Determine correction from change in the position of the response peak(s) Also, study correction using response to muon tracks as function of spill time and x/y -Estimate systematic uncertainties Turn on/off corrections Calibration

17. Electron data Data samples at following energy settings 1,2,3,4,5,6,7,8,12,20,30,40,(60) GeV [Can we collect higher energies in August? Requires Wolfgang’s asymmetric analyzing magnet setting (to correct for SR)] Simulation GEANT4 has no significant uncertainties (compared to the precision of our data) Response sensitive to d cut – parameter (last parameter to be tuned) Measure Response (linearity, resolution) Measure shower shapes Study software compensation Longitudinal calibration Being studied by Jacob Smith for Steel Technique applied by ATLAS Improve resolution?

18. Pion data Data samples at following energy settings 2,3,4,5,6,7,8,9,10,15,20,30,40,50,60,80,100,120,150,180 GeV Will collect higher energies in August 180 – 300 GeV (preferentially negative) Simulation GEANT4 has significant uncertainties with the simulation of hadronic showers RPC_sim has no more parameters to tune (absolute prediction) Measure Interaction layer (preliminary algorithm exists) Response (linearity, resolution) Measure shower shapes Study software compensation Longitudinal calibration Being studied by Jacob Smith for Steel Improve resolution?

19. Plan for future data taking August 2012 7 days at SPS Higher energies 180 – 300 GeV pions (negative) High energy (50,60,70) electrons (if possible) November 2012 7 days at SPS High statistics points at 20, 180 GeV (negative) High-rate operating conditions at 180 GeV (negative) → Reduced HV together with high gain amplification Tile-cal technical prototype (requires dedicated 0-2 days)