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
2,664.77
M_2
2,578.67
M_3
2,356.43
mL1
435.44
mL3
2,454.19
mE1
1,274.24
mE3
2,499.36
mQ1
3,837.20
mQ3
3,988.85
mU1
1,384.70
mU3
3,819.26
mD1
1,762.14
mD3
142.87
At
-5,054.98
Ab
2,696.31
Atau
3,403.55
mu
-183.50
mA
2,334.86
tan(beta)
7.97
The truth!
2
M_1
-3,731.57
M_2
802.72
M_3
745.28
mL1
3,398.36
mL3
3,051.05
mE1
1,464.55
mE3
1,748.71
mQ1
3,465.57
mQ3
3,562.70
mU1
2,874.81
mU3
3,368.46
mD1
3,921.91
mD3
2,953.39
At
3,820.46
Ab
2,904.47
Atau
-1,384.72
mu
930.46
mA
2,979.07
tan(beta)
8.39
The truth!
3
M_1
978.87
M_2
-224.07
M_3
3,155.00
mL1
426.93
mL3
2,173.82
mE1
2,210.63
mE3
2,262.95
mQ1
3,071.19
mQ3
3,202.71
mU1
2,294.15
mU3
3,146.09
mD1
1,758.78
mD3
2,435.64
At
-6,404.81
Ab
115.91
Atau
-1,388.81
mu
2,500.46
mA
3,690.06
tan(beta)
5.33
The truth!
4
M_1
469.08
M_2
-1,619.65
M_3
3,844.23
mL1
1,035.88
mL3
1,954.91
mE1
1,135.52
mE3
1,766.02
mQ1
1,032.91
mQ3
2,220.40
mU1
1,945.66
mU3
2,266.37
mD1
3,172.00
mD3
484.93
At
-4,235.88
Ab
3,947.05
Atau
1,150.04
mu
3,027.79
mA
3,084.15
tan(beta)
20.84
The truth!
5
M_1
1,325.73
M_2
-1,735.91
M_3
1,188.64
mL1
1,312.33
mL3
1,703.19
mE1
3,834.31
mE3
1,780.36
mQ1
1,359.07
mQ3
1,358.36
mU1
3,710.92
mU3
2,021.46
mD1
1,815.17
mD3
1,739.65
At
-3,711.74
Ab
-2,866.03
Atau
-2,867.82
mu
2,358.71
mA
2,807.97
tan(beta)
7.74
The truth!
6
M_1
2,037.79
M_2
953.44
M_3
3,100.19
mL1
935.74
mL3
3,211.55
mE1
663.17
mE3
3,062.40
mQ1
2,165.97
mQ3
3,234.77
mU1
374.55
mU3
3,077.54
mD1
1,163.43
mD3
886.87
At
3,771.28
Ab
1,344.48
Atau
2,625.17
mu
153.52
mA
2,007.21
tan(beta)
31.17
The truth!
7
M_1
1,229.72
M_2
3,475.49
M_3
2,695.83
mL1
2,530.99
mL3
1,991.06
mE1
2,932.09
mE3
633.98
mQ1
1,746.57
mQ3
1,639.73
mU1
1,520.56
mU3
3,470.85
mD1
327.29
mD3
3,335.01
At
-3,348.09
Ab
-3,241.45
Atau
-2,345.13
mu
429.14
mA
2,685.00
tan(beta)
11.92
The truth!
8
M_1
3,038.54
M_2
-835.81
M_3
2,904.39
mL1
2,081.62
mL3
2,134.30
mE1
3,425.06
mE3
3,542.61
mQ1
3,777.31
mQ3
1,044.36
mU1
933.43
mU3
3,820.23
mD1
3,371.46
mD3
2,925.93
At
-7,118.10
Ab
3,133.52
Atau
-201.87
mu
976.71
mA
1,218.72
tan(beta)
7.03
The truth!
9
M_1
2,097.93
M_2
2,326.12
M_3
2,212.31
mL1
3,837.82
mL3
1,364.77
mE1
1,263.12
mE3
3,161.43
mQ1
1,762.75
mQ3
2,925.20
mU1
2,993.60
mU3
2,706.02
mD1
561.74
mD3
655.96
At
-4,337.65
Ab
1,773.55
Atau
-1,134.72
mu
-681.64
mA
2,644.60
tan(beta)
44.05
The truth!
10
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!
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
