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
-3,104.35
M_2
-3,315.08
M_3
3,375.28
mL1
3,525.21
mL3
3,630.05
mE1
3,768.42
mE3
438.38
mQ1
2,190.06
mQ3
3,098.87
mU1
1,205.66
mU3
1,652.33
mD1
3,360.29
mD3
3,190.24
At
-3,486.60
Ab
-184.76
Atau
3,294.48
mu
262.61
mA
730.96
tan(beta)
26.63
The truth!
2
M_1
2,359.45
M_2
-1,026.23
M_3
298.51
mL1
2,110.93
mL3
2,969.88
mE1
1,839.52
mE3
1,805.68
mQ1
2,496.70
mQ3
2,375.52
mU1
2,658.34
mU3
3,170.21
mD1
1,333.18
mD3
2,569.18
At
-4,131.53
Ab
3,075.15
Atau
1,927.00
mu
194.40
mA
3,379.11
tan(beta)
7.10
The truth!
3
M_1
-2,023.34
M_2
382.83
M_3
3,847.16
mL1
1,383.19
mL3
3,277.53
mE1
905.15
mE3
3,848.06
mQ1
3,149.37
mQ3
3,701.19
mU1
2,131.50
mU3
3,909.10
mD1
1,195.62
mD3
980.99
At
-4,309.47
Ab
2,164.82
Atau
1,327.05
mu
1,717.52
mA
3,550.74
tan(beta)
47.74
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
2,707.92
M_2
495.36
M_3
475.73
mL1
547.13
mL3
3,219.03
mE1
2,693.61
mE3
934.74
mQ1
2,293.29
mQ3
2,981.98
mU1
3,876.39
mU3
3,630.49
mD1
638.19
mD3
2,234.34
At
3,501.42
Ab
2,194.94
Atau
-2,325.56
mu
-3,059.33
mA
1,576.15
tan(beta)
13.67
The truth!
6
M_1
2,904.69
M_2
-1,684.94
M_3
591.85
mL1
1,134.32
mL3
1,057.12
mE1
2,832.25
mE3
3,173.38
mQ1
2,686.68
mQ3
1,602.12
mU1
2,592.02
mU3
3,419.90
mD1
1,679.38
mD3
2,972.38
At
3,185.45
Ab
-1,185.43
Atau
-3,070.70
mu
-376.28
mA
2,779.18
tan(beta)
29.50
The truth!
7
M_1
-3,452.51
M_2
2,520.93
M_3
2,224.55
mL1
2,260.92
mL3
1,272.98
mE1
1,400.34
mE3
2,847.66
mQ1
3,198.36
mQ3
3,238.18
mU1
3,483.46
mU3
1,189.23
mD1
1,795.89
mD3
806.12
At
-3,585.80
Ab
2,384.03
Atau
3,972.10
mu
232.00
mA
1,267.70
tan(beta)
53.22
The truth!
8
M_1
-278.86
M_2
190.86
M_3
405.16
mL1
1,145.05
mL3
1,304.86
mE1
712.02
mE3
1,783.27
mQ1
1,382.43
mQ3
1,555.86
mU1
1,684.33
mU3
1,362.87
mD1
3,327.31
mD3
1,002.76
At
-2,130.52
Ab
-2,767.96
Atau
2,049.92
mu
654.53
mA
1,805.93
tan(beta)
35.77
The truth!
9
M_1
47.44
M_2
-1,238.94
M_3
3,939.08
mL1
2,181.93
mL3
611.47
mE1
3,400.04
mE3
2,432.59
mQ1
819.28
mQ3
3,278.67
mU1
2,488.46
mU3
2,282.91
mD1
2,120.48
mD3
1,053.83
At
-4,302.09
Ab
-1,062.89
Atau
-3,701.15
mu
-154.96
mA
3,435.09
tan(beta)
10.60
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
