1. Event Filter and Trigger Menu L=1031
Sergio Grancagnolo
on behalf of the Muon Trigger software group

2. TrigMoore LVL1 LVL2 (muFast) LVL2 ID
Two different running modes:
Seeded
Reconstruction performed only in the geometrical regions provided by the RoIs of previous levels.
Full scan
Full reconstruction, ~equivalent to the offline working mode
LVL1
LVL2 (muFast)
LVL2 ID
Seeding Algorithms assume the seed is from LVL2 or a LVL1 ROI
Full functionality in barrel and end-caps
3 istances of TrigMoore called by the steering, for reconstruction in the MS, extrapolation to the IP and combination with ID tracks
TrigMoore attaches to the TE a "TrigMooreFeature" for each ROI, accessed by TrigMooreHypo for pT test
TrigMoore records in SG the TrigMooreFeature per each ROI and all reconstructed tracks in the event in a single container for conversion in Trk:Track format and subsequent output in ESD and AOD
LVL2 (muComb)
Seeding Algs
Moore Algs
Hypo Alg
TrigMoore
contains dedicated algorithms for the seeded reconstruction (finds f and RZ segments for the seeding)
drives the offline algorithms in Moore and MUID linking them to the HLT steering
Hypothesis algorithms can check the reconstructed object at each step.
TrigMoore
MuIdStandAlone
Algs
Hypo Alg
MuIdCombined
Algs
Hypo Alg
Offline ID

3. MuonEF in Rel 13.0.10 Migration to new HLT Steering (DONE)
Both FEX and Hypothesis algorithms
Migration to Configurables (DONE)
TrigMoore configurables py classes available in 3 flavours (MuonSpectrometer, Extrapolated and Combined Tracks)
New style EF muon sequences (including ID) added in TrigHypothesis/TrigMuonHypo
Migration to new EDM (DONE)
TrigMoore uses the standard reconstruction input object (PrepRawData).
The Moore offline algorithm organization has been adopted also by TrigMoore
Use of EF version of ID for combined MS-ID tracks (DONE)
MuidCombined adapted to get as input EF-ID tracks.
Replacement of the previous implementation in wich EF ID and offline ID (New Tracking or iPatRec) was used.

4. MuonEF in Rel
Implementation of REGTESTs for Monitoring and Validation (DONE)
Monitoring Histo in Hypo Algo (DONE)
Definition of jobOptions to include Muon EF in ATN test (DONE)
Bug Fix- Fix for duplicated python modules in genConf (DONE- to be collected right after )
Bug Fix- Problem in Persistification (appeared in 13.0.X nightly) Fixed in

5. Muon Slice Validation

6. Sample and releases Single mu sample centrally simulated
Private AA NTuple production by Napoli group using grid tools (GANGA)
Release , seeding by LVL2
No hypothesis algorithms, no EF cuts
Used for both LVL1 and EF studies
Single m pT (GeV) 2
2.5 3
3.5 4
4.5 5 6 7
106 events
1.2
1.4
1.3
0.78
0.5
0.67
0.55
0.21
tot ~8 x 106 events in the range 2-7 GeV
Note that only few single muon events, with pT=3÷5 GeV, pass the LVL1/LVL2 mu6 threshold and no one is reconstructed by the EF.
Even a 107 sample of low pT events is not allowing to extimate the EF efficiency with great precision below the 6 GeV threshold.

7. Efficiency curves barrel (I)
EF (MuId CB) efficiency w.r.t. LVL2 (muComb) 3 5 3 5
pT (GeV)
pT (GeV)
pT thresholds:
“4 GeV” 5 GeV

8. Efficiency curves barrel (II)
EF (MuId CB) efficiency w.r.t. LVL2 (muComb)
MuId Combined efficiency curves w.r.t. LVL2 for pT thresholds:
6 GeV 8 GeV
20 GeV 40 GeV
pT (GeV)

9. Low pT thresholds single muon rates
Luminosity: L = 1031cm-2s-1
“4 GeV” 1031cm-2s-1
Barrel
(Hz)
Endcap
p/K
133 80
beauty 19 21
charm 11 12
top
6∙10-4
8∙10-4 W
0.03
0.04
TOTAL
163
113
5 GeV 1031cm-2s-1
Barrel
(Hz)
Endcap
p/K 44 30
beauty 11 13
charm 6 7
top
5∙10-4
7∙10-4 W
0.03
0.04
TOTAL 61 49

10. Other EF Trigger rates 6 GeV 1033cm-2s-1 Barrel (Hz) Endcap beauty 641
845
charm
327
426
top
0.05
0.07 W
2.9
4.0
p/K
1918
1462
TOTAL
2889
2737
8 GeV 1033cm-2s-1
Barrel
(Hz)
Endcap
beauty
172
291
charm 77
134
top
0.06
0.05 W
2.8
3.9
p/K
281
313
TOTAL
533
742
20 GeV 1034cm-2s-1
Barrel
(Hz)
Endcap
beauty 73
118
charm 28 46
top
0.27
0.32 W
22.3
32.6
p/K 50 48
TOTAL
173
244
40 GeV 1034cm-2s-1
Barrel
(Hz)
Endcap
beauty
2.5
4.5
charm
0.87
1.6
top
0.07 W
3.9
7.1
p/K
0.2
0.3
TOTAL
7.5
13.6

11. Muon HLT Data Quality

12. Muon Slice Data Quality (I)
Preliminary considerations on DQ monitoring and assessment for the muon slice presented at TDAQ Data Quality Wshop in Zeuthen (February 28-March ), focusing on LVL2 and EF, in online/offline
e.g. for EF online monitoring:
hit #/track per tecnology;
geometrical track parameters;
pt spectrum;
track quality (2);
residual distributions;
matching with ID;
combined tracks/moore tracks;
ratio of positive/negative tracks;
matched hits/total hits in the seed region;
Monitoring activities are going to be organized for LVL1/2/3 in the appropriate environments, to use general tools and DQ Monitoring framework, to move the first steps towards muon trigger slice DQ
Nothing exists for the moment for Muon Slice DQA but what implemented for monitoring during 2004 test beam (A. Di Mattia for LVL2) and the test of the trigger slices on the pre-series machines at Point 1 in december 2006 (D. Scannicchio for EF) can be a starting point for Data Quality Monitoring

13. Muon Slice Data Quality (II)
e.g. TrigMoore histos from the last technical run
Trigger/TrigAlgorithms/TrigMoore/src/TrigMooreHisto.cxx
(here obtained running the jobOptions prepared for the on-line with a bytestream file containing 50 top events as input: muons are selected by the LVL2 and the EF muon algorithms)
completed in release 13 for TrigMoore Hypothesis algos

14. Proposal for L = 1031 cm-2s-1 L1 trig. item Rate LVL2 EF MU4 ~1 kHz
μComb
TrigMoore
TrigDiMuon
DiMuon
PS/PT
1 Hz PT
MU6
227 Hz
80.8 Hz
56.3 Hz
InDet only
μFast only
99.7 Hz
MU10
112 Hz
~10 Hz

15. Proposal for L = 1031 cm-2s-1 L1 trig. item Rate LVL2 EF MU15 19 Hz
μComb
TrigMoore
~2 Hz
InDet only PT
μFast only
PS/PT
1 Hz
MU20
14 Hz
MU40
8 Hz
2MU4
~9 Hz
???
2MU6
4 Hz
2MU10
~1 Hz
2MU20
< 1 Hz
2MU40
Express streams

16. Trigger efficiency from Z → μ+μ-
Double Object with orthogonal Signature (DOS) method
Double Object (DO) method
Backgrounds from BBμμX, Wμv, Zττ
Different reconstruction modes
1-2 % statistical uncertainty with few pb-1 data
Differential (η,φ) trigger efficiency determination
CSC AOD analysis is going to be finalized

17. Muons from p/K

18. Strategy
extract from minimum bias and dijets of various energy a global pT vs h distribution for pions and kaons, re-weighting to a common value of the integrated luminosity
Use the particle-generator to generate single pions according to te previous distribution
simulate the decay of pions with G4 saving only events were the pion decay before the muon spectrometer
study a procedure to identify and reject this kind of muons

19. Event Sample Particle selection requiring: p PDG code |h|<2.7
File Name
Events
Cross Section (mb)
minimum bias
csc pythia_minbias.evgen.EVNT.v _xxxxx
124200
92E00 J1
csc J1_pythia_jetjet.evgen.EVNT.v _xxxxx
242287
1.376E00 J2
csc J2_pythia_jetjet.evgen.EVNT.v _xxxxx
257508
9.327E-02 J3
csc J3_pythia_jetjet.evgen.EVNT.v _xxxxx
30299
5.884E-03 J4
csc J4_pythia_jetjet.evgen.EVNT.v _xxxxx
40400
3.084E-04 J5
csc J5_pythia_jetjet.evgen.EVNT.v _xxxxx
5050
1.247E-05 J6
csc J6_pythia_jetjet.evgen.EVNT.v _xxxxx
50000
3.604E-07 J7
csc J7_pythia_jetjet.evgen.EVNT.v _xxxxx
5.707E-09 J8
csc J8_pythia_jetjet.evgen.EVNT.v _xxxxx
10100
2.444E-11
Particle selection requiring:
p PDG code
|h|<2.7
pT>500MeV
generation at the interaction point (0.,0.,0.)
and filling a two-dimensional histogram of pT vs h, one entry for each pion.

20. pT vs h distributions
Some existing correlation between pT and h requires a generation according to the 2-D distribution
The solution that I adopted is to slice the 2-D histograms in pT bins and simulate using the 1-D projections in pT and h
Minimum bias
Supposing an efficiency e=10-4 between 2.5 GeV and 7.5 GeV, and a granularity of 500 MeV, to extimate an error of s(e)10%
 106 events needed in each bin
Dijets J8
If only ~1% of the pions decays in the volume before the calorimeter
at least 109 events should be generated
A tool was developed to force all the generated pions to decay: the PionDecayer

21. Produced sample TOTAL: 25.800 files 1.065.000 events ~1.7Tb
no available tools to randomly extract from a 2D distribution
solution: slice the 2D distribution in pt bins (of ~same content) and generate according to the 1D distributions of pT and Eta
misal1_mc singlePion_pTSlice_0_of_30.digit.v
misal1_mc singlePion_pTSlice_1_of_30.digit.v
misal1_mc singlePion_pTSlice_2_of_30.digit.v …
misal1_mc singlePion_pTSlice_29_of_30.digit.v
misal1_mc singlePion_pTSlice_30_of_30.digit.v
TOTAL: files events ~1.7Tb
Since, for a given pion direction, the decay is forced into a definite path length L, the single event probability is

22. use of grid infrastructures absolutely necessary !
Since full statistics has become available all efforts have been directed to apply the muon slice and the standard ntuple analysis to the single pi sample
with some addition specific to the pi->mu case
keep track of the muon mother and its decay vertex
keep track of the links between a combined reconstructed muon and its ID and MS seeds, etc...).
first attempt:
copy from USA grid to castor, submit jobs to lxplus queues for running the specific reconstruction + analysis code turned out to be unaffordable mostly due to castor access too slow and unstable
use of grid infrastructures absolutely necessary !
- the solution: use LCG-grid o come si chiama, after moving the files to
Napoli tier-2 - affordable but still very time consuming;
- copy from castor to NA tier-2 took 4/5 ?? days
- reconstruction jobs submitted (just half?? sample processed yet)...

23. Problems Many problems encountered to complete the analysis chain:
Last simulation job finished ~ half april
Copy output to castor from US grid ~1 week
Many attempts to run the muon trigger slice
Run athena jobs directly on castor  failed
Copy from castor to local nfslocal disk failed
Replicate from castor to european gridongoing

24. Backup

25. LVL1/2 Mu6 30
500
3.6k
352k
EF mu6
88%