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
1,130.60
M_2
666.99
M_3
867.33
mL1
2,818.21
mL3
2,410.76
mE1
795.11
mE3
2,669.20
mQ1
803.88
mQ3
2,191.77
mU1
3,532.04
mU3
2,982.29
mD1
1,777.22
mD3
3,871.87
At
3,042.69
Ab
-3,979.68
Atau
1,488.14
mu
-2,915.18
mA
2,548.99
tan(beta)
8.52
The truth!
2
M_1
-1,785.11
M_2
950.24
M_3
834.69
mL1
3,261.94
mL3
1,741.09
mE1
1,842.61
mE3
1,020.77
mQ1
211.73
mQ3
1,347.75
mU1
656.39
mU3
1,194.04
mD1
3,832.55
mD3
923.08
At
-2,514.02
Ab
-1,336.92
Atau
-1,691.60
mu
-200.91
mA
995.61
tan(beta)
8.66
The truth!
3
M_1
252.52
M_2
1,975.84
M_3
273.77
mL1
3,041.38
mL3
3,543.16
mE1
2,534.62
mE3
3,187.93
mQ1
2,391.92
mQ3
3,889.77
mU1
2,836.87
mU3
3,720.54
mD1
201.17
mD3
2,011.16
At
-3,872.31
Ab
703.02
Atau
3,781.81
mu
2,752.86
mA
3,931.57
tan(beta)
44.28
The truth!
4
M_1
-1,888.03
M_2
-3,314.19
M_3
3,536.12
mL1
2,123.35
mL3
2,409.00
mE1
2,595.76
mE3
3,659.96
mQ1
3,775.02
mQ3
3,825.72
mU1
2,139.15
mU3
3,711.31
mD1
3,130.30
mD3
1,702.85
At
-3,113.52
Ab
-1,241.26
Atau
-904.87
mu
3,966.98
mA
1,041.07
tan(beta)
34.73
The truth!
5
M_1
2,518.19
M_2
623.56
M_3
2,258.95
mL1
653.67
mL3
781.05
mE1
1,452.28
mE3
876.66
mQ1
554.48
mQ3
2,894.62
mU1
2,981.57
mU3
1,730.65
mD1
3,831.28
mD3
2,832.60
At
2,974.84
Ab
3,258.66
Atau
222.78
mu
-3,005.12
mA
3,781.31
tan(beta)
28.72
The truth!
6
M_1
-995.38
M_2
-3,121.67
M_3
1,481.71
mL1
1,193.80
mL3
3,484.98
mE1
3,428.60
mE3
932.35
mQ1
3,494.37
mQ3
785.87
mU1
3,427.95
mU3
2,341.09
mD1
3,150.60
mD3
2,151.60
At
-3,431.78
Ab
-466.72
Atau
-2,104.73
mu
95.20
mA
3,684.69
tan(beta)
6.73
The truth!
7
M_1
-159.46
M_2
-3,449.53
M_3
662.61
mL1
549.14
mL3
2,683.64
mE1
2,604.12
mE3
2,547.79
mQ1
2,583.28
mQ3
3,191.11
mU1
986.39
mU3
3,653.14
mD1
521.02
mD3
823.55
At
-3,601.90
Ab
70.58
Atau
3,703.97
mu
163.62
mA
1,042.50
tan(beta)
59.46
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
3,407.85
M_2
203.97
M_3
3,371.70
mL1
2,740.06
mL3
1,330.97
mE1
662.89
mE3
1,406.33
mQ1
1,550.05
mQ3
2,751.27
mU1
2,144.30
mU3
1,234.15
mD1
3,437.77
mD3
598.56
At
-4,682.97
Ab
-446.09
Atau
-863.33
mu
3,773.11
mA
3,732.77
tan(beta)
20.85
The truth!
10
M_1
279.46
M_2
330.11
M_3
503.78
mL1
3,944.14
mL3
3,260.77
mE1
1,867.41
mE3
557.60
mQ1
1,716.76
mQ3
3,550.74
mU1
881.36
mU3
2,257.23
mD1
2,417.21
mD3
2,234.34
At
-2,652.87
Ab
2,688.75
Atau
949.21
mu
308.27
mA
2,297.46
tan(beta)
10.30
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
