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
1,172.53
M_2
3,988.22
M_3
1,041.38
mL1
776.67
mL3
2,176.12
mE1
881.16
mE3
2,277.97
mQ1
2,970.21
mQ3
2,499.29
mU1
3,922.40
mU3
3,326.34
mD1
2,008.50
mD3
3,265.18
At
3,792.85
Ab
-3,446.80
Atau
-2,384.15
mu
-103.84
mA
3,298.58
tan(beta)
34.40
The truth!
2
M_1
-1,405.73
M_2
-491.11
M_3
421.18
mL1
1,262.89
mL3
669.58
mE1
582.67
mE3
1,396.05
mQ1
3,976.62
mQ3
3,748.17
mU1
3,801.87
mU3
3,798.87
mD1
3,068.08
mD3
3,255.81
At
5,684.13
Ab
3,632.09
Atau
1,809.41
mu
-1,974.16
mA
3,264.38
tan(beta)
8.59
The truth!
3
M_1
-580.97
M_2
553.03
M_3
1,822.09
mL1
3,893.58
mL3
674.34
mE1
1,437.11
mE3
2,296.76
mQ1
1,726.02
mQ3
3,961.41
mU1
932.29
mU3
3,013.50
mD1
928.16
mD3
3,229.00
At
3,930.58
Ab
274.91
Atau
1,950.69
mu
-1,239.58
mA
1,822.11
tan(beta)
8.06
The truth!
4
M_1
909.02
M_2
867.12
M_3
2,177.77
mL1
2,130.51
mL3
2,680.04
mE1
2,360.95
mE3
1,085.96
mQ1
2,567.26
mQ3
3,918.27
mU1
852.55
mU3
3,088.43
mD1
1,369.61
mD3
918.88
At
-3,911.18
Ab
-1,513.69
Atau
-2,134.46
mu
1,438.80
mA
855.99
tan(beta)
17.16
The truth!
5
M_1
-453.25
M_2
1,477.75
M_3
1,234.11
mL1
2,657.12
mL3
1,290.30
mE1
2,188.02
mE3
2,237.84
mQ1
1,761.29
mQ3
3,559.39
mU1
1,133.25
mU3
3,019.06
mD1
235.81
mD3
1,379.93
At
4,223.44
Ab
2,366.82
Atau
-688.35
mu
-3,387.25
mA
1,128.41
tan(beta)
8.43
The truth!
6
M_1
1,500.31
M_2
-3,434.11
M_3
728.58
mL1
3,057.19
mL3
368.61
mE1
3,711.62
mE3
3,425.41
mQ1
1,140.07
mQ3
1,167.28
mU1
1,586.38
mU3
3,877.74
mD1
3,541.26
mD3
980.04
At
3,630.09
Ab
2,907.41
Atau
3,594.97
mu
-148.19
mA
2,532.13
tan(beta)
17.89
The truth!
7
M_1
1,522.76
M_2
1,755.76
M_3
1,089.79
mL1
1,018.71
mL3
3,935.94
mE1
1,435.78
mE3
1,093.27
mQ1
2,986.13
mQ3
1,382.06
mU1
3,862.83
mU3
3,640.55
mD1
2,893.70
mD3
2,562.51
At
-3,620.21
Ab
-1,962.20
Atau
-1,170.69
mu
878.99
mA
3,701.25
tan(beta)
16.37
The truth!
8
M_1
331.20
M_2
868.10
M_3
1,692.37
mL1
2,592.99
mL3
2,648.16
mE1
1,726.50
mE3
1,558.85
mQ1
3,476.61
mQ3
1,896.00
mU1
1,626.03
mU3
3,502.64
mD1
2,216.47
mD3
3,576.38
At
3,316.17
Ab
639.83
Atau
2,163.03
mu
243.74
mA
3,170.74
tan(beta)
6.89
The truth!
9
M_1
2,507.41
M_2
1,884.95
M_3
2,821.25
mL1
972.05
mL3
542.27
mE1
3,098.04
mE3
212.18
mQ1
1,682.53
mQ3
2,114.21
mU1
1,718.17
mU3
3,299.45
mD1
1,142.02
mD3
3,623.82
At
2,773.58
Ab
-390.51
Atau
-1,852.48
mu
-221.12
mA
2,636.01
tan(beta)
30.72
The truth!
10
M_1
3,651.44
M_2
-2,572.65
M_3
1,754.69
mL1
2,429.03
mL3
2,131.52
mE1
2,970.03
mE3
3,575.87
mQ1
1,483.41
mQ3
3,842.32
mU1
2,305.76
mU3
2,491.40
mD1
846.89
mD3
3,793.81
At
-3,834.62
Ab
476.99
Atau
-3,374.89
mu
-686.73
mA
3,669.43
tan(beta)
37.25
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
