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
2,986.95
M_2
-1,914.36
M_3
1,143.97
mL1
2,870.89
mL3
1,910.65
mE1
1,263.50
mE3
3,247.51
mQ1
2,492.60
mQ3
3,035.11
mU1
3,936.52
mU3
3,075.40
mD1
2,877.13
mD3
2,828.01
At
3,136.79
Ab
1,181.24
Atau
1,839.11
mu
1,041.48
mA
3,504.78
tan(beta)
13.06
The truth!
2
M_1
-640.29
M_2
-2,197.07
M_3
1,286.13
mL1
3,097.85
mL3
1,807.35
mE1
1,620.26
mE3
1,132.70
mQ1
1,032.65
mQ3
3,081.81
mU1
2,423.11
mU3
1,847.94
mD1
517.08
mD3
1,144.65
At
3,599.02
Ab
-1,131.06
Atau
-246.96
mu
-801.17
mA
3,752.22
tan(beta)
36.12
The truth!
3
M_1
1,828.30
M_2
951.83
M_3
433.93
mL1
3,296.21
mL3
796.02
mE1
2,811.07
mE3
2,042.74
mQ1
1,843.91
mQ3
1,989.95
mU1
3,848.65
mU3
2,347.52
mD1
3,609.79
mD3
801.45
At
2,589.76
Ab
1,228.86
Atau
2,161.01
mu
-396.87
mA
734.29
tan(beta)
44.19
The truth!
4
M_1
285.88
M_2
1,845.64
M_3
3,566.07
mL1
3,523.40
mL3
914.39
mE1
2,600.69
mE3
365.05
mQ1
3,609.65
mQ3
2,159.17
mU1
2,052.93
mU3
3,667.54
mD1
3,946.67
mD3
3,752.22
At
-3,611.03
Ab
-306.93
Atau
-492.57
mu
2,005.04
mA
784.16
tan(beta)
8.78
The truth!
5
M_1
237.32
M_2
1,074.89
M_3
3,777.01
mL1
1,485.07
mL3
2,402.11
mE1
643.57
mE3
912.92
mQ1
2,091.40
mQ3
3,429.53
mU1
2,125.94
mU3
2,529.29
mD1
3,389.11
mD3
2,469.32
At
3,227.86
Ab
1,520.84
Atau
521.85
mu
271.35
mA
1,682.90
tan(beta)
11.04
The truth!
6
M_1
2,912.73
M_2
3,663.08
M_3
870.38
mL1
3,909.48
mL3
2,211.13
mE1
3,457.76
mE3
3,272.51
mQ1
613.05
mQ3
3,811.78
mU1
2,951.95
mU3
1,708.50
mD1
3,795.79
mD3
2,640.78
At
-3,279.14
Ab
-2,889.45
Atau
2,306.93
mu
239.93
mA
3,094.60
tan(beta)
32.79
The truth!
7
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!
8
M_1
-1,187.96
M_2
-3,990.88
M_3
1,836.65
mL1
3,494.37
mL3
2,337.93
mE1
2,094.05
mE3
2,259.31
mQ1
1,831.62
mQ3
2,808.54
mU1
1,893.25
mU3
3,815.14
mD1
1,629.52
mD3
2,731.82
At
3,733.97
Ab
1,562.99
Atau
-1,768.27
mu
243.04
mA
3,433.44
tan(beta)
43.50
The truth!
9
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!
10
M_1
-1,633.94
M_2
-2,475.25
M_3
2,090.94
mL1
2,785.97
mL3
794.80
mE1
1,348.20
mE3
1,098.43
mQ1
1,523.50
mQ3
2,908.04
mU1
3,176.49
mU3
2,823.19
mD1
3,698.83
mD3
2,226.03
At
-3,971.11
Ab
2,513.44
Atau
-3,031.76
mu
770.25
mA
2,509.11
tan(beta)
6.93
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
