Data
MagicTelescope

MagicTelescope

active ARFF Publicly available Visibility: public Uploaded 05-07-2022 by Leo Grin
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Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description: Author: R. K. Bock. Major Atmospheric Gamma Imaging Cherenkov Telescope project (MAGIC) Donated by P. Savicky, Institute of Computer Science, AS of CR, Czech Republic Source: [UCI](https://archive.ics.uci.edu/ml/datasets/magic+gamma+telescope) Please cite: 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. Nucl.Instr.Meth. A, 516, pp. 511-528. The data are MC generated (see below) to simulate registration of high energy gamma particles in a ground-based atmospheric Cherenkov gamma telescope using the imaging technique. Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks through the atmosphere and gets recorded in the detector, allowing reconstruction of the shower parameters. The available information consists of pulses left by the incoming Cherenkov photons on the photomultiplier tubes, arranged in a plane, the camera. Depending on the energy of the primary gamma, a total of few hundreds to some 10000 Cherenkov photons get collected, in patterns (called the shower image), allowing to discriminate statistically those caused by primary gammas (signal) from the images of hadronic showers initiated by cosmic rays in the upper atmosphere (background). Typically, the image of a shower after some pre-processing is an elongated cluster. Its long axis is oriented towards the camera center if the shower axis is parallel to the telescope's optical axis, i.e. if the telescope axis is directed towards a point source. A principal component analysis is performed in the camera plane, which results in a correlation axis and defines an ellipse. If the depositions were distributed as a bivariate Gaussian, this would be an equidensity ellipse. The characteristic parameters of this ellipse (often called Hillas parameters) are among the image parameters that can be used for discrimination. The energy depositions are typically asymmetric along the major axis, and this asymmetry can also be used in discrimination. There are, in addition, further discriminating characteristics, like the extent of the cluster in the image plane, or the total sum of depositions. The data set was generated by a Monte Carlo program, Corsika, described in: D. Heck et al., CORSIKA, A Monte Carlo code to simulate extensive air showers, Forschungszentrum Karlsruhe FZKA 6019 (1998). The program was run with parameters allowing to observe events with energies down to below 50 GeV. Attribute Information: 1. fLength: continuous # major axis of ellipse [mm] 2. fWidth: continuous # minor axis of ellipse [mm] 3. fSize: continuous # 10-log of sum of content of all pixels [in #phot] 4. fConc: continuous # ratio of sum of two highest pixels over fSize [ratio] 5. fConc1: continuous # ratio of highest pixel over fSize [ratio] 6. fAsym: continuous # distance from highest pixel to center, projected onto major axis [mm] 7. fM3Long: continuous # 3rd root of third moment along major axis [mm] 8. fM3Trans: continuous # 3rd root of third moment along minor axis [mm] 9. fAlpha: continuous # angle of major axis with vector to origin [deg] 10. fDist: continuous # distance from origin to center of ellipse [mm] 11. class: g,h # gamma (signal), hadron (background) g = gamma (signal): 12332 h = hadron (background): 6688 For technical reasons, the number of h events is underestimated. In the real data, the h class represents the majority of the events. The simple classification accuracy is not meaningful for this data, since classifying a background event as signal is worse than classifying a signal event as background. For comparison of different classifiers an ROC curve has to be used. The relevant points on this curve are those, where the probability of accepting a background event as signal is below one of the following thresholds: 0.01, 0.02, 0.05, 0.1, 0.2 depending on the required quality of the sample of the accepted events for different experiments.

11 features

class (target)nominal2 unique values
0 missing
fLength:numeric13139 unique values
0 missing
fWidth:numeric12906 unique values
0 missing
fSize:numeric6346 unique values
0 missing
fConc:numeric5836 unique values
0 missing
fConc1:numeric4137 unique values
0 missing
fAsym:numeric13166 unique values
0 missing
fM3Long:numeric13162 unique values
0 missing
fM3Trans:numeric13016 unique values
0 missing
fAlpha:numeric12882 unique values
0 missing
fDist:numeric13065 unique values
0 missing

19 properties

13376
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
10
Number of numeric attributes.
1
Number of nominal attributes.
9.09
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
90.91
Percentage of numeric attributes.
50
Percentage of instances belonging to the most frequent class.
9.09
Percentage of nominal attributes.
6688
Number of instances belonging to the most frequent class.
50
Percentage of instances belonging to the least frequent class.
6688
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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