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
-200.53
M_2
-2,598.60
M_3
3,125.73
mL1
1,671.64
mL3
3,784.91
mE1
1,858.38
mE3
2,163.68
mQ1
785.44
mQ3
3,185.75
mU1
3,484.48
mU3
2,640.25
mD1
3,226.94
mD3
3,356.86
At
3,130.05
Ab
3,215.15
Atau
-2,850.75
mu
-216.78
mA
1,854.82
tan(beta)
28.75
The truth!
2
M_1
1,290.44
M_2
446.69
M_3
1,591.18
mL1
3,809.19
mL3
2,530.85
mE1
3,440.50
mE3
1,783.18
mQ1
3,373.30
mQ3
3,095.33
mU1
2,069.91
mU3
3,680.15
mD1
377.07
mD3
3,339.19
At
-3,929.05
Ab
-965.60
Atau
-1,724.85
mu
777.63
mA
3,625.03
tan(beta)
25.14
The truth!
3
M_1
1,244.19
M_2
-2,052.85
M_3
2,772.03
mL1
2,870.85
mL3
2,734.40
mE1
2,382.08
mE3
898.44
mQ1
2,901.58
mQ3
2,304.40
mU1
1,028.99
mU3
1,413.08
mD1
580.52
mD3
2,610.83
At
3,310.16
Ab
2,507.78
Atau
-3,171.90
mu
492.23
mA
3,170.78
tan(beta)
9.38
The truth!
4
M_1
262.21
M_2
-3,921.38
M_3
2,063.89
mL1
2,837.83
mL3
3,115.89
mE1
1,586.59
mE3
2,116.34
mQ1
471.21
mQ3
3,412.62
mU1
2,926.03
mU3
2,954.99
mD1
1,732.16
mD3
2,804.98
At
-6,047.68
Ab
1,455.32
Atau
2,159.94
mu
254.88
mA
3,305.74
tan(beta)
4.40
The truth!
5
M_1
-3,916.39
M_2
-127.70
M_3
3,185.20
mL1
3,415.38
mL3
325.45
mE1
548.57
mE3
522.01
mQ1
2,784.48
mQ3
3,038.44
mU1
304.77
mU3
1,187.34
mD1
2,425.87
mD3
3,731.94
At
3,447.23
Ab
3,248.39
Atau
2,514.29
mu
2,561.83
mA
3,152.50
tan(beta)
11.40
The truth!
6
M_1
-449.40
M_2
-2,190.85
M_3
3,194.16
mL1
1,091.81
mL3
3,322.16
mE1
2,680.03
mE3
3,697.09
mQ1
2,235.30
mQ3
909.46
mU1
498.99
mU3
3,657.04
mD1
494.78
mD3
2,541.93
At
3,439.20
Ab
3,449.54
Atau
2,671.32
mu
1,539.94
mA
3,579.76
tan(beta)
10.23
The truth!
7
M_1
3,526.52
M_2
718.16
M_3
3,426.70
mL1
3,146.19
mL3
511.34
mE1
2,829.46
mE3
2,988.67
mQ1
3,148.74
mQ3
2,397.27
mU1
1,492.89
mU3
2,117.46
mD1
3,691.05
mD3
3,394.95
At
-2,839.95
Ab
-3,624.34
Atau
514.91
mu
248.96
mA
2,003.81
tan(beta)
50.18
The truth!
8
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!
9
M_1
64.60
M_2
3,792.70
M_3
3,323.39
mL1
1,723.33
mL3
3,181.45
mE1
1,802.00
mE3
3,667.43
mQ1
984.98
mQ3
3,645.26
mU1
2,938.71
mU3
3,761.41
mD1
3,816.60
mD3
512.06
At
3,839.00
Ab
-2,998.95
Atau
-2,615.54
mu
371.60
mA
2,190.09
tan(beta)
22.07
The truth!
10
M_1
3,507.14
M_2
3,995.40
M_3
725.93
mL1
697.84
mL3
2,874.61
mE1
2,782.52
mE3
3,654.38
mQ1
554.02
mQ3
529.21
mU1
619.05
mU3
3,248.31
mD1
1,944.47
mD3
395.96
At
-3,554.75
Ab
247.76
Atau
593.32
mu
198.60
mA
2,910.71
tan(beta)
18.42
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
