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
-1,398.41
M_2
-974.86
M_3
1,939.72
mL1
3,851.71
mL3
952.05
mE1
1,563.16
mE3
560.87
mQ1
204.73
mQ3
2,158.40
mU1
1,438.36
mU3
3,690.63
mD1
3,807.16
mD3
756.19
At
-2,842.64
Ab
170.85
Atau
-3,043.68
mu
439.39
mA
1,906.94
tan(beta)
13.02
The truth!
2
M_1
-1,476.95
M_2
354.74
M_3
3,109.43
mL1
1,740.89
mL3
2,600.73
mE1
908.30
mE3
2,840.25
mQ1
3,135.62
mQ3
1,901.83
mU1
3,793.76
mU3
3,791.58
mD1
3,975.82
mD3
3,983.00
At
-3,777.72
Ab
-147.98
Atau
677.65
mu
4,033.15
mA
2,035.50
tan(beta)
31.32
The truth!
3
M_1
3,607.25
M_2
-331.34
M_3
3,901.39
mL1
2,983.03
mL3
2,871.08
mE1
358.82
mE3
2,520.43
mQ1
3,969.31
mQ3
3,598.11
mU1
704.17
mU3
1,753.63
mD1
1,479.12
mD3
1,729.60
At
-2,929.67
Ab
-2,799.44
Atau
792.62
mu
1,990.01
mA
1,407.89
tan(beta)
27.85
The truth!
4
M_1
-1,487.18
M_2
-141.29
M_3
2,502.17
mL1
1,605.82
mL3
1,931.16
mE1
3,313.30
mE3
2,080.86
mQ1
738.42
mQ3
2,061.04
mU1
3,407.72
mU3
2,910.27
mD1
2,099.25
mD3
1,516.68
At
3,496.70
Ab
-1,487.63
Atau
166.41
mu
-3,978.35
mA
1,509.93
tan(beta)
8.69
The truth!
5
M_1
2,727.49
M_2
-1,476.82
M_3
2,918.83
mL1
217.45
mL3
2,578.88
mE1
2,160.20
mE3
2,280.69
mQ1
2,564.93
mQ3
1,567.98
mU1
3,936.56
mU3
3,179.59
mD1
2,560.87
mD3
1,150.66
At
-6,919.84
Ab
-1,664.73
Atau
3,305.92
mu
-104.31
mA
3,759.70
tan(beta)
31.29
The truth!
6
M_1
-3,875.83
M_2
-118.73
M_3
2,642.69
mL1
3,037.28
mL3
3,629.14
mE1
1,956.31
mE3
3,664.87
mQ1
2,171.67
mQ3
1,051.04
mU1
1,266.02
mU3
2,714.26
mD1
3,051.08
mD3
3,069.23
At
-3,050.05
Ab
1,361.44
Atau
1,333.88
mu
2,085.75
mA
1,375.36
tan(beta)
21.33
The truth!
7
M_1
-318.99
M_2
-1,651.11
M_3
1,303.87
mL1
3,860.83
mL3
748.47
mE1
701.44
mE3
2,640.49
mQ1
379.43
mQ3
2,438.14
mU1
498.27
mU3
3,581.09
mD1
3,723.09
mD3
2,297.37
At
2,951.61
Ab
-3,464.68
Atau
-1,637.18
mu
339.08
mA
2,659.83
tan(beta)
17.16
The truth!
8
M_1
2,034.31
M_2
509.20
M_3
1,590.26
mL1
2,422.09
mL3
3,535.57
mE1
3,592.56
mE3
2,794.04
mQ1
1,811.32
mQ3
1,475.83
mU1
3,403.05
mU3
1,777.87
mD1
2,756.95
mD3
3,989.85
At
2,409.27
Ab
-3,467.24
Atau
1,636.99
mu
-573.62
mA
1,681.04
tan(beta)
25.51
The truth!
9
M_1
-1,529.42
M_2
-1,253.84
M_3
3,329.01
mL1
3,130.99
mL3
2,725.40
mE1
3,587.79
mE3
1,436.32
mQ1
2,904.83
mQ3
2,891.62
mU1
743.50
mU3
2,798.08
mD1
1,966.46
mD3
726.32
At
6,681.24
Ab
-691.52
Atau
154.76
mu
147.49
mA
1,281.25
tan(beta)
3.96
The truth!
10
M_1
61.92
M_2
-3,477.53
M_3
2,881.32
mL1
803.61
mL3
2,533.52
mE1
2,672.59
mE3
1,562.39
mQ1
1,891.33
mQ3
1,319.00
mU1
1,622.85
mU3
3,704.40
mD1
1,094.53
mD3
1,081.77
At
3,856.04
Ab
552.10
Atau
3,159.35
mu
405.50
mA
3,830.66
tan(beta)
11.62
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
