evtree (version 1.0-8)

MAGICGammaTelescope: MAGIC Gamma Telescope

Description

The data was generated to simulate registration of high energy gamma particles in a Major Atmospheric Gamma-Ray Imaging Cherenkov (MAGIC) Gamma Telescope. The task is to distinguish gamma rays (signal) from hadronic showers (background).

Usage

data("MAGICGammaTelescope")

Format

A data frame containing 19,020 observations on 11 variables.

fLength

major axis of ellipse [mm].

fWidth

minor axis of ellipse [mm].

fSize

10-log of sum of content of all pixels [in #phot].

fConc

ratio of sum of two highest pixels over fSize [ratio].

fConc1

ratio of highest pixel over fSize [ratio].

fAsym

distance from highest pixel to center, projected onto major axis [mm].

fM3Long

3rd root of third moment along major axis [mm].

fM3Trans

3rd root of third moment along minor axis [mm].

fAlpha

angle of major axis with vector to origin [deg].

fDist

distance from origin to center of ellipse [mm].

class

binary variable class, with levels gamma (signal) and hadron (background).

Details

Classifying a background event as signal is worse than classifying a signal event as background. For a meaningful comparison of different classifiers the use of an ROC curve with thresholds 0.01, 0.02, 0.05, 0.1, 0.2 is suggested.

References

Bock, R.K., Chilingarian, A., Gaug, M., Hakl, F., Hengstebeck, T., Jirina, M., Klaschka, J., Kotrc, E., Savicky, P., Towers, S., Vaicilius, A., Wittek W. (2004). Methods for Multidimensional event Classification: a Case Study Using Images From a Cherenkov Gamma-Ray Telescope. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 516(1), 511--528.

P. Savicky, E. Kotrc (2004). Experimental Study of Leaf Confidences for Random Forest. In Proceedings of COMPSTAT, pp. 1767--1774. Physica Verlag, Heidelberg, Germany.

J. Dvorak, P. Savicky (2007). Softening Splits in Decision Trees Using Simulated Annealing. In Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, Part I, pp. 721--729, Springer-Verlag, New-York.

Examples

# NOT RUN {
data("MAGICGammaTelescope")
summary(MAGICGammaTelescope)
# }
# NOT RUN {
suppressWarnings(RNGversion("3.5.0"))
set.seed(1090)
mgtt <- evtree(class ~ . , data = MAGICGammaTelescope) 
mgtt
table(predict(mgtt), MAGICGammaTelescope$class)
plot(mgtt)
# }