Title: An Online Calorimeter Trigger for Removing Outsiders from Particle Beam CalibrationTests
1An 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_at_lps.
ufrj.br
2Outline
- Introduction
- Outsiders
- Results
- Conclusions
3Introduction
- 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.
4Introduction
- 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.
5Introduction
- 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.
6Introduction
- 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
7Introduction
- 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).
8Introduction
- 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
9Introduction
- 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).
10Introduction
- 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.
11Introduction
- The methodology was divided in three steps
- Muon events are acquired to form the profile
pattern for this particle. - Pion events (with outsider muons) begin to be
acquired. A network to descriminate between these
two particle is trained. - Electron events (with outsider pions and muons)
begin to be acquired. Another network is trained
to discriminate between these three particle
types.
12Online 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).
13Online Results
The correlation between NN and energy cut shows
that the technique works, although some pion
events seem to be mixed with muons.
14Online Results
15Online Results
16Online Results
17Online Results
18Online Results
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.
19Online Results
- Alternate approach
- Ein Ein
- SQRT( Et )
- Energy dependence is introduced to eliminate the
bias.
20Online Results
Is there any bias in the data now?!
21Online Results - Barrel
Comparison between the normalization by the total
energy (left) and square root (right). 20 GeV.
22Online Results - Barrel
Comparison between the normalization by the total
energy (left) and square root (right). 100 GeV.
23Online Results - Barrel
Comparison between the normalization by the total
energy (left) and square root (right). 180 GeV.
24Global Parameters StudyBarrel
Bellow is the evaluation of the parameters of the
distributions for the different methodologies.
25Cherenkov 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.
26Electron-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).
27Electron-pion separation
28Preliminary Results
Electrons x Muons analysis
29Preliminary Results
30Preliminary Results
31Preliminary Results
Electrons x Pions analysis
32Preliminary Results
33Conclusions
- 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.