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
-313.91
M_2
-1,171.24
M_3
2,408.60
mL1
3,815.95
mL3
2,175.31
mE1
508.21
mE3
2,238.52
mQ1
1,923.87
mQ3
1,980.39
mU1
261.75
mU3
3,161.02
mD1
2,873.87
mD3
202.94
At
-6,323.16
Ab
-1,092.21
Atau
417.46
mu
2,565.08
mA
666.92
tan(beta)
6.28
The truth!
2
M_1
-2,491.11
M_2
-639.25
M_3
3,924.37
mL1
3,378.21
mL3
861.08
mE1
3,340.43
mE3
3,859.06
mQ1
526.27
mQ3
3,559.43
mU1
1,690.84
mU3
2,837.48
mD1
3,556.89
mD3
2,608.13
At
4,890.19
Ab
-865.31
Atau
247.14
mu
1,643.30
mA
2,278.74
tan(beta)
5.34
The truth!
3
M_1
-2,421.62
M_2
-849.33
M_3
3,654.36
mL1
1,683.91
mL3
2,029.02
mE1
2,609.31
mE3
1,067.63
mQ1
3,213.95
mQ3
3,038.61
mU1
1,224.47
mU3
3,479.16
mD1
2,765.80
mD3
3,129.53
At
4,255.69
Ab
1,602.39
Atau
-3,972.10
mu
-819.47
mA
884.46
tan(beta)
10.98
The truth!
4
M_1
239.59
M_2
-1,839.53
M_3
2,557.65
mL1
618.08
mL3
1,443.64
mE1
144.16
mE3
1,945.23
mQ1
906.31
mQ3
3,104.46
mU1
3,238.35
mU3
3,706.91
mD1
562.80
mD3
3,015.07
At
-4,230.37
Ab
3,997.99
Atau
-1,330.78
mu
264.88
mA
1,597.13
tan(beta)
46.45
The truth!
5
M_1
-173.23
M_2
960.76
M_3
3,825.27
mL1
2,224.57
mL3
3,014.17
mE1
1,620.34
mE3
1,130.09
mQ1
3,772.99
mQ3
3,295.38
mU1
3,953.91
mU3
2,074.92
mD1
1,478.61
mD3
2,642.82
At
-3,112.94
Ab
972.19
Atau
2,501.75
mu
168.85
mA
1,404.58
tan(beta)
40.25
The truth!
6
M_1
3,843.37
M_2
-830.68
M_3
1,256.99
mL1
1,949.14
mL3
843.70
mE1
1,831.31
mE3
935.82
mQ1
795.99
mQ3
2,827.48
mU1
1,707.98
mU3
2,760.01
mD1
2,730.23
mD3
2,657.93
At
-4,275.94
Ab
-2,530.99
Atau
-1,470.68
mu
448.40
mA
3,051.87
tan(beta)
10.67
The truth!
7
M_1
3,546.18
M_2
1,217.09
M_3
821.38
mL1
1,618.29
mL3
2,219.65
mE1
2,081.86
mE3
1,317.74
mQ1
1,520.14
mQ3
1,077.17
mU1
2,851.37
mU3
2,966.27
mD1
3,424.68
mD3
362.86
At
2,803.29
Ab
-3,060.30
Atau
-252.78
mu
190.94
mA
2,838.44
tan(beta)
20.58
The truth!
8
M_1
-137.75
M_2
2,769.03
M_3
1,385.61
mL1
474.89
mL3
1,639.81
mE1
3,584.71
mE3
135.13
mQ1
1,652.28
mQ3
3,406.45
mU1
3,036.92
mU3
3,534.23
mD1
3,358.00
mD3
1,818.82
At
-3,739.30
Ab
3,637.33
Atau
-2,122.66
mu
2,966.86
mA
3,471.20
tan(beta)
14.43
The truth!
9
M_1
-240.09
M_2
774.70
M_3
2,653.93
mL1
2,776.13
mL3
3,915.10
mE1
179.71
mE3
3,528.75
mQ1
2,873.47
mQ3
2,327.54
mU1
3,103.88
mU3
3,660.53
mD1
3,992.69
mD3
3,566.31
At
-3,604.60
Ab
-2,709.09
Atau
-3,842.65
mu
-193.95
mA
3,739.58
tan(beta)
7.17
The truth!
10
M_1
-281.28
M_2
-669.45
M_3
1,128.54
mL1
2,871.34
mL3
510.61
mE1
2,554.50
mE3
456.81
mQ1
1,961.78
mQ3
836.30
mU1
2,080.79
mU3
2,301.13
mD1
2,011.72
mD3
3,595.31
At
-2,489.49
Ab
-853.53
Atau
843.94
mu
4,001.70
mA
1,750.57
tan(beta)
17.36
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
