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COMET_MC

active ARFF Attribution-ShareAlike (CC BY-SA) Visibility: public Uploaded 17-03-2016 by Nikita Kazeev
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Author: COMET collaboration Acknowledgements: Chen WU, Ewen Gillies Source: Unknown - Date unknown Please cite: Monte-Carlo simulation of COMET detector, COMET collaboration, http://comet.kek.jp/ ## Guess which points belong to signal track [COMET](http://comet.kek.jp/Introduction.html) is an experiment being constructed at the J-PARC proton beam laboratory in Japan. It will search for coherent neutrino-less conversion of a muon to an electron, μ- + N(A,Z) → e- + N(A,Z). This process breaks the law of lepton conservation. If detected, it will be a signal of new physics. The previous upper limit for this decay was set [5] by the SINDRUM II experiment in 2006. COMET is designed to have 10,000 times better sensitivity. Wires positions are available in a [supplementary file](https://drive.google.com/file/d/0B_gdsqrqzUJyMHcyUVFHa05FLXc/view?usp=sharing) ## Cylindrical Drift Chamber The COMET experiment is looking for muon to electron conversion, μ- + N → e- + N. COMET Phase-I will the Cylindrical Drift Chamber as the primary detector for physics measurements. Specifically, the momentum of resulting particles will be measured using the CyDet, which is a cylindrical wire array detector. The particles flying out of muon-stopping target and registered by the CyDet. Among those we are interested in tracks left by electrons with specific energy, which are produced by muon to electron conversion. The CyDet consists of 4482 sensitive wires organized in 18 layers. Each wire measures the energy deposited by a passing charged particle. Within each of the layers, the wires have same distance to the stopping target and stereometry angle. ![Scheme of COMET cylindrical detector](https://kaggle2.blob.core.windows.net/competitions/inclass/4520/media/comet_3d.gif) There is magnetic field in the detector, which causes electron moves in helical path as shown below. This electron deposits energy in the wires close to the flight path. The radius of helix is proportional to transverse momentum of the electron: R = p_t/(eB) where p_t is transverse momentum, B is strength of magnetic field, e is charge of electron. ![Trajectory of electron in margetic field](https://kaggle2.blob.core.windows.net/competitions/inclass/4520/media/COMEThelixing.png) The energy deposited on each wire is measured at the end plate of the cylindrical detector. An example of the resulting signal event can be seen below, where blue dots are background hits and red are hits from signal electrons: ![Energy depositions in COMET](https://kaggle2.blob.core.windows.net/competitions/inclass/4520/media/COMET2dprojection.png) ## More details 1. [COMET official site](http://comet.kek.jp/) 2. [COMET conceptual design report](http://comet.kek.jp/Documents_files/comet-cdr-v1.0.pdf) 3. [Раритеты микромира](https://nplus1.ru/news/2015/05/29/reareevents) - if you aren't deep into HEP, this article in russian is probably good starting point to understand what is COMET about. 4. [COMET presentation](http://www-physics.lbl.gov/seminars/old/LBNL2014KUNO.pdf) 5. [A search for μ-e conversion in muonic gold](http://www.researchgate.net/publication/226763791_A_search_for_-e_conversion_in_muonic_gold) ## Important note Datasets available for this challenge are results of preliminary Monte Carlo simulation. They don't completely represent properties of COMET's detector and thus cannot be used to estimate final properties of tracking system, but are appropriate to test different approaches to tracking. ## Acknowledgements We thank COMET collaboration (and specially Chen WU) for allowing us to use this dataset.

6 features

label (target)	numeric	3 unique values 0 missing
global_id (row identifier)	numeric	7619400 unique values 0 missing
event_id	numeric	1700 unique values 0 missing
wire_id	numeric	4482 unique values 0 missing
energy_deposit	numeric	167061 unique values 0 missing
relative_time	numeric	1171897 unique values 0 missing

Show all 6 features

107 properties

NumberOfInstances

7619400

Number of instances (rows) of the dataset.

NumberOfFeatures

Number of attributes (columns) of the dataset.

NumberOfClasses

Number of distinct values of the target attribute (if it is nominal).

NumberOfMissingValues

Number of missing values in the dataset.

NumberOfInstancesWithMissingValues

Number of instances with at least one value missing.

NumberOfNumericFeatures

Number of numeric attributes.

NumberOfSymbolicFeatures

Number of nominal attributes.

REPTreeDepth3Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpKappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxMeansOfNumericAtts

2240.5

Maximum of means among attributes of the numeric type.

MinMutualInformation

Minimal mutual information between the nominal attributes and the target attribute.

Quartile2SkewnessOfNumericAtts

2.01

Second quartile (Median) of skewness among attributes of the numeric type.

RandomTreeDepth1AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Dimensionality

Number of attributes divided by the number of instances.

MaxMutualInformation

Maximum mutual information between the nominal attributes and the target attribute.

MinNominalAttDistinctValues

The minimal number of distinct values among attributes of the nominal type.

PercentageOfBinaryFeatures

Percentage of binary attributes.

Quartile2StdDevOfNumericAtts

236.95

Second quartile (Median) of standard deviation of attributes of the numeric type.

RandomTreeDepth1ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

EquivalentNumberOfAtts

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

MaxNominalAttDistinctValues

The maximum number of distinct values among attributes of the nominal type.

MinSkewnessOfNumericAtts

Minimum skewness among attributes of the numeric type.

PercentageOfInstancesWithMissingValues

Percentage of instances having missing values.

Quartile3AttributeEntropy

Third quartile of entropy among attributes.

RandomTreeDepth1Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

J48.00001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxSkewnessOfNumericAtts

17.12

Maximum skewness among attributes of the numeric type.

MinStdDevOfNumericAtts

Minimum standard deviation of attributes of the numeric type.

PercentageOfMissingValues

Percentage of missing values.

Quartile3KurtosisOfNumericAtts

256.81

Third quartile of kurtosis among attributes of the numeric type.

AutoCorrelation

0.72

Average class difference between consecutive instances.

RandomTreeDepth2AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxStdDevOfNumericAtts

1293.84

Maximum standard deviation of attributes of the numeric type.

MinorityClassPercentage

Percentage of instances belonging to the least frequent class.

PercentageOfNumericFeatures

100

Percentage of numeric attributes.

Quartile3MeansOfNumericAtts

1545

Third quartile of means among attributes of the numeric type.

CfsSubsetEval_DecisionStumpAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

RandomTreeDepth2ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MeanAttributeEntropy

Average entropy of the attributes.

MinorityClassSize

Number of instances belonging to the least frequent class.

PercentageOfSymbolicFeatures

Percentage of nominal attributes.

Quartile3MutualInformation

Third quartile of mutual information between the nominal attributes and the target attribute.

CfsSubsetEval_DecisionStumpErrRate

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

RandomTreeDepth2Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.0001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanKurtosisOfNumericAtts

102.67

Mean kurtosis among attributes of the numeric type.

NaiveBayesAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1AttributeEntropy

First quartile of entropy among attributes.

Quartile3SkewnessOfNumericAtts

10.07

Third quartile of skewness among attributes of the numeric type.

CfsSubsetEval_DecisionStumpKappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

RandomTreeDepth3AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMeansOfNumericAtts

634.62

Mean of means among attributes of the numeric type.

NaiveBayesErrRate

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1KurtosisOfNumericAtts

-1.2

First quartile of kurtosis among attributes of the numeric type.

Quartile3StdDevOfNumericAtts

892.29

Third quartile of standard deviation of attributes of the numeric type.

CfsSubsetEval_NaiveBayesAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

RandomTreeDepth3ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMutualInformation

Average mutual information between the nominal attributes and the target attribute.

NaiveBayesKappa

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1MeansOfNumericAtts

0.15

First quartile of means among attributes of the numeric type.

REPTreeDepth1AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesErrRate

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

RandomTreeDepth3Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNoiseToSignalRatio

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

NumberOfBinaryFeatures

Number of binary attributes.

Quartile1MutualInformation

First quartile of mutual information between the nominal attributes and the target attribute.

REPTreeDepth1ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesKappa

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

CfsSubsetEval_kNN1NAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

StdvNominalAttDistinctValues

Standard deviation of the number of distinct values among attributes of the nominal type.

J48.001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNominalAttDistinctValues

Average number of distinct values among the attributes of the nominal type.

Quartile1SkewnessOfNumericAtts

First quartile of skewness among attributes of the numeric type.

REPTreeDepth1Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_kNN1NErrRate

Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

kNN1NAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

J48.001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanSkewnessOfNumericAtts

4.43

Mean skewness among attributes of the numeric type.

Quartile1StdDevOfNumericAtts

0.35

First quartile of standard deviation of attributes of the numeric type.

REPTreeDepth2AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

CfsSubsetEval_kNN1NKappa

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W

kNN1NErrRate

Error rate achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassPercentage

Percentage of instances belonging to the most frequent class.

MeanStdDevOfNumericAtts

404.45

Mean standard deviation of attributes of the numeric type.

Quartile2AttributeEntropy

Second quartile (Median) of entropy among attributes.

REPTreeDepth2ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2

ClassEntropy

Entropy of the target attribute values.

kNN1NKappa

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassSize

Number of instances belonging to the most frequent class.

MinAttributeEntropy

Minimal entropy among attributes.

Quartile2KurtosisOfNumericAtts

2.11

Second quartile (Median) of kurtosis among attributes of the numeric type.

REPTreeDepth2Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

REPTreeDepth3AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpAUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxAttributeEntropy

Maximum entropy among attributes.

MinKurtosisOfNumericAtts

-1.2

Minimum kurtosis among attributes of the numeric type.

Quartile2MeansOfNumericAtts

82.83

Second quartile (Median) of means among attributes of the numeric type.

REPTreeDepth3ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpErrRate

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxKurtosisOfNumericAtts

505.36

Maximum kurtosis among attributes of the numeric type.

MinMeansOfNumericAtts

Minimum of means among attributes of the numeric type.

Quartile2MutualInformation

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Show all 107 properties