S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri and B. Stienen, The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning [arXiv:1605.02797]
SUSY-AI is a machine learning tool that is able to provide in a fraction of a second the exclusion of a pMSSM (sub)model point. This website provides a simple online interface for quick determination of exclusion of a model point using the results of ATLAS Run-I (8TeV) and ATLAS Run-II (13TeV). The papers associated with this data can be found here.
The full version of SUSY-AI is faster and can provide predicions for multiple modelpoints at the same time. It is under continuing active development and can be downloaded from the hepforge project page.
Classify alongside SUSY-AI and see how good a classifier you are
Below you'll find 10 points in the 19-dimensional pMSSM, defined by their coordinates. Based on these coordinates you could be able to classify each of them as excluded or allowed. You could... but are you able to this correctly for the 13TeV results? And are you able to beat the machine in doing so? Start classifying to find out!
P.S. Every refresh of this page gives you new points to predict!
1
M_1
541.62
M_2
1,125.13
M_3
3,759.05
mL1
1,601.27
mL3
2,533.34
mE1
3,012.43
mE3
1,182.31
mQ1
966.07
mQ3
1,205.61
mU1
2,677.29
mU3
2,033.59
mD1
803.48
mD3
2,526.49
At
-3,965.03
Ab
-1,693.69
Atau
1,587.66
mu
2,325.97
mA
3,662.75
tan(beta)
20.60
The truth!
2
M_1
-218.53
M_2
3,766.71
M_3
375.77
mL1
1,172.40
mL3
3,365.22
mE1
2,282.40
mE3
3,673.14
mQ1
1,156.06
mQ3
3,059.59
mU1
1,270.00
mU3
2,236.75
mD1
1,594.19
mD3
2,711.70
At
3,322.22
Ab
1,068.55
Atau
-341.18
mu
243.22
mA
1,409.53
tan(beta)
14.42
The truth!
3
M_1
1,275.85
M_2
-2,326.52
M_3
1,778.23
mL1
219.76
mL3
1,195.92
mE1
3,970.28
mE3
1,790.69
mQ1
1,360.39
mQ3
3,466.67
mU1
3,026.47
mU3
3,975.30
mD1
2,462.67
mD3
2,890.73
At
-2,452.48
Ab
-27.51
Atau
564.63
mu
-142.50
mA
3,568.52
tan(beta)
39.31
The truth!
4
M_1
-72.66
M_2
-469.88
M_3
2,787.73
mL1
358.08
mL3
2,177.03
mE1
3,104.27
mE3
2,859.19
mQ1
2,340.52
mQ3
3,531.24
mU1
2,323.18
mU3
2,508.02
mD1
393.08
mD3
2,408.60
At
3,031.48
Ab
-191.49
Atau
-1,225.42
mu
-117.02
mA
3,489.39
tan(beta)
17.81
The truth!
5
M_1
589.16
M_2
-3,311.57
M_3
3,024.84
mL1
3,440.41
mL3
135.88
mE1
272.14
mE3
928.49
mQ1
2,108.03
mQ3
3,047.51
mU1
1,499.35
mU3
1,777.03
mD1
1,073.76
mD3
2,140.86
At
-3,165.89
Ab
3,366.67
Atau
480.21
mu
109.48
mA
2,178.91
tan(beta)
38.92
The truth!
6
M_1
847.13
M_2
2,440.64
M_3
1,704.05
mL1
3,499.45
mL3
3,008.15
mE1
1,400.54
mE3
1,321.62
mQ1
3,498.62
mQ3
3,147.64
mU1
1,731.39
mU3
3,970.24
mD1
652.13
mD3
1,907.42
At
-5,096.84
Ab
19.66
Atau
-878.54
mu
2,403.08
mA
2,501.88
tan(beta)
20.19
The truth!
7
M_1
805.08
M_2
1,683.78
M_3
2,883.53
mL1
615.64
mL3
3,943.73
mE1
911.24
mE3
3,481.20
mQ1
1,842.07
mQ3
2,313.34
mU1
2,523.35
mU3
3,458.02
mD1
2,793.19
mD3
3,629.07
At
-4,935.12
Ab
3,201.49
Atau
1,191.55
mu
553.12
mA
2,499.24
tan(beta)
6.35
The truth!
8
M_1
-2,054.28
M_2
192.19
M_3
3,913.12
mL1
3,502.62
mL3
1,940.69
mE1
1,796.45
mE3
1,444.29
mQ1
3,918.04
mQ3
1,398.37
mU1
502.44
mU3
2,870.50
mD1
1,864.87
mD3
1,610.41
At
-3,469.97
Ab
-3,165.84
Atau
2,343.32
mu
-366.27
mA
3,314.39
tan(beta)
9.00
The truth!
9
M_1
-842.23
M_2
-3,315.76
M_3
3,031.84
mL1
3,042.17
mL3
2,082.26
mE1
3,623.74
mE3
1,226.97
mQ1
2,510.39
mQ3
3,965.99
mU1
1,795.39
mU3
683.55
mD1
1,189.87
mD3
2,835.24
At
-3,938.25
Ab
-1,678.75
Atau
-2,876.29
mu
3,310.41
mA
4,097.22
tan(beta)
16.49
The truth!
10
M_1
403.23
M_2
-1,707.19
M_3
2,907.42
mL1
1,156.99
mL3
2,170.41
mE1
3,291.67
mE3
1,160.94
mQ1
3,522.22
mQ3
1,051.65
mU1
378.03
mU3
2,441.32
mD1
3,254.62
mD3
1,951.95
At
-2,900.02
Ab
-862.99
Atau
3,927.64
mu
-240.01
mA
3,712.77
tan(beta)
35.56
The truth!
SUSY-AI and SUSY-AI Online were developed by S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri and B. Stienen. If you encounter any problems, don't hesitate to contact us! SUSY-AI and SUSY-AI Online (c) 2017
