CBM Progress Report 2006 - GSI

Simulations CBM Progress Report 2006
Ring recognition in the RICH detector of CBM
S. Lebedev 1 , A. Ayriyan 1 , C. Höhne 2 , and G. Ososkov 1
1 LIT JINR, Dubna Russia; 2 Gesellschaft für Schwerionenforschung mbH, Darmstadt, Germany
Two algorithms for ring finding in RICH were developed:
the first is based on information obtained by propagating
tracks from the STS detector, and the second which
is a standalone approach is based on the Hough Transform
(HT) [1] combined with preliminary area clustering to decrease
combinatorics.
Both ring-finders are working with good efficiency, however,
there is a considerable number of fake rings present
after ring finding. Routines have to be developed to reject
them. A typical fake ring is formed by ”stealing” hits
from neighboring rings which seriously disturbs ring parameters.
Our strategy was to develop cuts for a set of parameters
which could be used to reject fake rings without a
drop of the ring finding efficiency. Two approaches of fake
ring rejection were developed: 2D cuts and a method based
on an artificial neural network (ANN). 7 ring parameters
were used for the fake rejection which were found to be essential:
the number of hits in a narrow corridor around the
ring; the number of hits in the ring; the distance between
closest track projection and ring center; the biggest angle
between neighboring hits; the χ 2 - criterion, and the radial
position of the ring on the photodetector plane.
Figure 1: a) Ring finding efficiency for electrons in dependence
on pt and rapidity (HT ring finder and ANN fake
rejection), b) Distances between ring center and the closest
track.
A table summarizing the efficiencies is presented below.
HT HT+ HT+
2D cuts ANN cut
Electrons, % 95.36 91.39 91.34
Fakes/event 15.41 2.59 0.91
Clones/event 7.07 2.23 1.24
Table 1: Efficiency of ring finding for electrons, number of
fakes and clone rings per event.
10
HT+ANN for fake rejection showed very good results
in terms of ring finding efficiency and fake and clone ring
rates. As next step towards particle identification, robust
algorithms for ring fitting of measured points were studied
and optimized. We compared the currently used Crawford
method [5] with methods known as COP (Chernov-
Ososkov-Pratt) [2] and TAU (Taubin) [3]. To satisfy the
robustness requirement we used both, the optimal and the
Tukey’s weight functions [4]. Testing algorithms on large
statistics showed that the best performance was reached
with the TAU method (see Fig.2).
Figure 2: The chi-squared criterion χ 2 vs radial position on
the photodetector plane. a) simple method b) TAU method
All results presented above were extracted for central
Au+Au collisions at 25 AGeV with additionally added 5e +
and 5e − at the main vertex in order to enhance electron
statistics over the full phase space. The described methods
were used in the event reconstruction chain for electron
identification in RICH and proved a very good performance
[6]. Next their optimization is planned as well as a further
extension of ring fitting to ellipse fitting algorithms.
References
[1] Hough P.V. C. Method and Means for Recognizing Complex
Patterns, U.S. Patent 3,069,654 1962.
[2] N. I. Chernov and G. A. Ososkov, Effective algorithms for
circle fitting, Comp. Phys. Comm. 33 (1984) 329-333.
[3] G. Taubin, Estimation Of Planar Curves, Surfaces And Nonplanar
Space Curves Defined By Implicit Equations, With
Applications to Edge And Range Image Segmentation, IEEE
Transactions on Pattern Analysis and Machine Intelligence
13, 1991, 1115-1138.
[4] G. Ososkov, I. Puzynin, A. Polyansky, Modern methods of experimental
data processing in high energy physics, PEPAN,
v.33, p. 3 (2002) 676-745.
[5] J. F. Crawford, A non-iterative method for fitting circular arcs
to measured points, Nucl. Instr. and Meth. 211 (1983) 223-
225.
[6] C. Höhne et al., Electron identification with RICH and TRD,
this report.