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
298.65
M_2
2,206.22
M_3
2,276.40
mL1
2,379.99
mL3
1,588.90
mE1
351.84
mE3
274.96
mQ1
637.40
mQ3
3,833.93
mU1
3,573.87
mU3
907.59
mD1
3,812.10
mD3
3,242.29
At
4,140.87
Ab
-181.85
Atau
-1,221.12
mu
-307.12
mA
2,197.38
tan(beta)
12.60
The truth!
2
M_1
1,877.73
M_2
-3,078.52
M_3
2,131.99
mL1
2,697.62
mL3
2,754.45
mE1
2,951.63
mE3
3,395.69
mQ1
2,091.18
mQ3
3,570.81
mU1
2,779.08
mU3
3,935.12
mD1
3,645.88
mD3
2,869.04
At
3,985.82
Ab
-688.28
Atau
-274.19
mu
-1,952.80
mA
3,955.00
tan(beta)
10.45
The truth!
3
M_1
1,275.85
M_2
-2,326.52
M_3
1,778.23
mL1
219.76
mL3
1,195.92
mE1
3,970.28
mE3
1,790.69
mQ1
1,360.39
mQ3
3,466.67
mU1
3,026.47
mU3
3,975.30
mD1
2,462.67
mD3
2,890.73
At
-2,452.48
Ab
-27.51
Atau
564.63
mu
-142.50
mA
3,568.52
tan(beta)
39.31
The truth!
4
M_1
-3,551.82
M_2
-1,762.83
M_3
2,173.23
mL1
1,535.60
mL3
2,129.42
mE1
2,911.44
mE3
3,181.92
mQ1
2,942.56
mQ3
3,038.72
mU1
1,314.47
mU3
2,854.96
mD1
919.71
mD3
2,210.05
At
3,697.56
Ab
1,153.40
Atau
-3,371.44
mu
220.20
mA
675.67
tan(beta)
7.17
The truth!
5
M_1
453.91
M_2
-409.11
M_3
3,867.96
mL1
1,124.98
mL3
3,079.33
mE1
1,243.70
mE3
3,024.47
mQ1
1,496.58
mQ3
3,369.47
mU1
1,958.92
mU3
1,998.89
mD1
2,755.13
mD3
3,519.65
At
-2,691.71
Ab
3,324.41
Atau
814.23
mu
-328.38
mA
1,489.86
tan(beta)
12.42
The truth!
6
M_1
3,435.37
M_2
955.25
M_3
3,416.39
mL1
3,112.13
mL3
3,034.10
mE1
1,911.90
mE3
1,102.72
mQ1
2,260.99
mQ3
3,119.34
mU1
3,764.02
mU3
2,995.76
mD1
3,464.58
mD3
3,511.19
At
4,296.71
Ab
-423.42
Atau
-2,017.62
mu
-181.70
mA
1,877.29
tan(beta)
23.19
The truth!
7
M_1
708.60
M_2
1,778.11
M_3
1,517.70
mL1
1,132.59
mL3
999.63
mE1
949.92
mE3
1,920.87
mQ1
862.97
mQ3
3,035.11
mU1
1,527.01
mU3
3,978.45
mD1
2,609.07
mD3
3,430.06
At
5,102.15
Ab
3,387.65
Atau
3,586.30
mu
-2,199.40
mA
1,297.68
tan(beta)
9.58
The truth!
8
M_1
1,272.41
M_2
-265.91
M_3
2,998.13
mL1
971.05
mL3
3,795.92
mE1
1,062.86
mE3
891.84
mQ1
804.08
mQ3
1,831.35
mU1
451.05
mU3
2,244.65
mD1
968.85
mD3
349.82
At
-3,534.79
Ab
1,200.17
Atau
-610.20
mu
3,000.77
mA
1,612.07
tan(beta)
23.67
The truth!
9
M_1
561.58
M_2
774.19
M_3
1,656.05
mL1
2,932.44
mL3
2,457.75
mE1
3,562.83
mE3
1,895.92
mQ1
254.09
mQ3
3,487.24
mU1
1,053.35
mU3
3,867.72
mD1
533.60
mD3
1,326.18
At
-4,323.90
Ab
161.53
Atau
-1,972.23
mu
3,651.75
mA
3,254.51
tan(beta)
24.52
The truth!
10
M_1
-676.39
M_2
-2,279.99
M_3
1,841.46
mL1
2,682.53
mL3
1,053.64
mE1
1,816.30
mE3
3,484.77
mQ1
2,222.36
mQ3
3,372.11
mU1
432.21
mU3
3,096.15
mD1
488.92
mD3
699.55
At
5,218.91
Ab
3,347.23
Atau
3,059.27
mu
-3,917.44
mA
2,883.77
tan(beta)
3.84
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
