1. An Online Calorimeter Trigger for Removing Outsiders from Particle Beam CalibrationTests Denis O. Damazio José Manoel de Seixas Signal Processing Lab – LPS COPPE-EE damazio,seixas@lps.ufrj.br

2. Outline Introduction Outsiders Results Conclusions

3. Introduction Attempting to search deeper in the matter, CERN is now preparing a new proton to proton collider, the LHC. The LHC will be colliding bunches of particles at 14 TeV. For operating at LHC conditions, the ATLAS detector is presently being built.

4. Introduction The ATLAS detector relies very much on the calorimeter system, which comprises hadronic (Tilecal) and e.m. (Liquid Argon) sections. The Tilecal prototyping is finished and the detector modules are being constructed.

5. Introduction Tilecal is split into a central section (Barrel) and two lateral sections (Extended Barrels). Tilecal is made of iron (absorber) and scintillating tile (active). Detector segmentation comprises 3 sampling layers, which produce 92(Barrel)/56(EB) signals.

6. Introduction A fraction of the modules is calibrated using particle beams. Despite beam quality, contamination is unavoidable.  pions and muons for electron beam selection.  muons in pion beam selection

7. Introduction Classically, contamination (outsiders) is removed offline, using both calorimeter and auxiliary detectors information. In terms of beam period efficiency, it would be attractive to remove outsiders online (shorter acquisition time periods).

8. Introduction Neural networks may use the detailed energy deposition profiles furnished by Tilecal to accomplish this online task. Online training Neural networks  Efficient for pattern recognition problems.  Easy to implement digitally.  high-speed processing

9. Introduction The online neural system used in the September/ 2001 testbeam was running in the Read-Out Driver Crate. Data are fetched from the Rod, normalized (by the total energy) and feed the NN. The NN response is added to the event data structure (as Status Word).

10. Introduction The neural network was a feed-forward fully conected network and was trained with the supervised backpropagation algorithm. Data coming from the beam line, was kept in a circular buffer to be used for training. New events substitute older ones. Using multithread processing, one thread trained the network, while the other just answered to incoming events. This assures fast response and fast training.

11. Introduction The methodology was divided in three steps : 1) Muon events are acquired to form the profile pattern for this particle. 2) Pion events (with outsider muons) begin to be acquired. A network to descriminate between these two particle is trained. 3) Electron events (with outsider pions and muons) begin to be acquired. Another network is trained to discriminate between these three particle types.

12. Online Results " The online test was perfomed in the second phase (pion/muon) of the methodology, with 180 GeV pions. " Most of the identified pions were close to their target (1). " Outsiders are in the muon target (-1).

13. Online Results The correlation between NN and energy cut shows that the technique works, although some pion events seem to be mixed with muons.

14. Online Results

18. The pions classified as muons have a muon like profile. Only an energy reference may allow discrimination. Thus, being independent in energy (normalizing by the total energy) produces this bias (pions penetrating deeply in the calorimeter may not be detected.

19. Online Results " Alternate approach: Ein = Ein SQRT(| Et |) " Energy dependence is introduced to eliminate the bias.

20. Online Results Is there any bias in the data now?!

21. Online Results - Barrel Comparison between the normalization by the total energy (left) and square root (right). 20 GeV.

22. Online Results - Barrel Comparison between the normalization by the total energy (left) and square root (right). 100 GeV.

23. Online Results - Barrel Comparison between the normalization by the total energy (left) and square root (right). 180 GeV.

24. Global Parameters Study Barrel Bellow is the evaluation of the parameters of the distributions for the different methodologies.

25. Cherenkov Counter The Cherenkov counter can be used to help discriminating between electrons and pion This helps to validate the NN system when electron-pion-muon separation is of concern.

26. Electron-pion-muon separation - 20 GeV Agreement with both energy cut and Cherenkov counter. NN trained with data normalized by the sum of energy (left) and square root (right).

27. Electron-pion separation

28. Preliminary Results Electrons x Muons analysis

29. Preliminary Results

31. Electrons x Pions analysis

32. Preliminary Results

33. Conclusions An online neural network trigger was tested during Tilecal testbeam calibration period. The system was running the pion-muon discrimination. Due to the normalization applied (energy independent), the system was introducing some bias in the pion data. Analysis suggested the usage of SQRT(|Et|) as normalization factor to eliminate such bias. This introduces energy dependency. Global calorimeter performance was insensitive to NN cut. Preliminary results for electron-pion and electron-muon discrimination showed alternative ways for online trigger. The event rate was around 2200 events/spill, which meets the speed requirements. A peak on 5502 was registered.