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
-395.61
M_2
643.44
M_3
3,404.59
mL1
3,287.92
mL3
1,837.75
mE1
3,847.70
mE3
3,216.99
mQ1
1,612.13
mQ3
1,351.44
mU1
900.24
mU3
2,744.92
mD1
578.17
mD3
3,072.48
At
3,144.82
Ab
-1,261.71
Atau
1,793.59
mu
-1,837.81
mA
787.91
tan(beta)
14.91
The truth!
2
M_1
636.67
M_2
-1,338.95
M_3
1,131.03
mL1
952.16
mL3
2,259.32
mE1
2,582.26
mE3
3,683.93
mQ1
747.56
mQ3
3,686.18
mU1
1,029.63
mU3
2,741.83
mD1
3,982.27
mD3
2,578.70
At
3,668.92
Ab
3,536.60
Atau
651.89
mu
-273.17
mA
3,056.02
tan(beta)
21.37
The truth!
3
M_1
-591.37
M_2
2,540.85
M_3
2,018.12
mL1
1,632.97
mL3
1,743.11
mE1
3,542.33
mE3
2,962.61
mQ1
1,157.05
mQ3
493.05
mU1
961.00
mU3
3,231.03
mD1
3,264.17
mD3
1,317.72
At
2,928.36
Ab
-2,151.51
Atau
2,178.98
mu
-3,087.36
mA
4,230.86
tan(beta)
9.94
The truth!
4
M_1
-2,402.20
M_2
-218.26
M_3
2,029.23
mL1
489.05
mL3
419.92
mE1
2,008.66
mE3
784.87
mQ1
1,750.69
mQ3
3,543.22
mU1
2,337.41
mU3
3,686.85
mD1
533.32
mD3
1,635.73
At
2,707.00
Ab
3,255.97
Atau
-2,499.12
mu
1,744.41
mA
3,040.12
tan(beta)
16.13
The truth!
5
M_1
-3,730.04
M_2
1,138.23
M_3
1,906.33
mL1
3,202.98
mL3
2,439.71
mE1
1,483.33
mE3
2,985.70
mQ1
3,254.51
mQ3
3,469.32
mU1
3,479.98
mU3
2,840.52
mD1
3,300.08
mD3
394.43
At
-2,851.32
Ab
1,859.10
Atau
3,918.27
mu
140.85
mA
1,380.10
tan(beta)
21.08
The truth!
6
M_1
-1,734.62
M_2
-1,986.37
M_3
2,801.09
mL1
542.37
mL3
817.80
mE1
491.20
mE3
1,238.87
mQ1
2,707.64
mQ3
3,381.58
mU1
2,435.26
mU3
3,907.03
mD1
2,142.62
mD3
745.44
At
-4,944.11
Ab
907.04
Atau
-1,234.53
mu
450.87
mA
3,439.46
tan(beta)
48.00
The truth!
7
M_1
1,572.96
M_2
-3,906.64
M_3
2,686.05
mL1
3,037.44
mL3
3,710.95
mE1
948.21
mE3
3,315.67
mQ1
1,889.71
mQ3
3,800.50
mU1
2,123.63
mU3
1,526.71
mD1
1,309.45
mD3
3,167.22
At
3,635.45
Ab
1,727.47
Atau
2,142.02
mu
709.91
mA
3,472.25
tan(beta)
33.27
The truth!
8
M_1
2,961.80
M_2
-517.11
M_3
3,186.70
mL1
1,953.20
mL3
3,649.45
mE1
3,064.79
mE3
3,263.66
mQ1
3,345.65
mQ3
1,901.98
mU1
822.34
mU3
1,143.60
mD1
1,941.14
mD3
3,157.69
At
-2,788.69
Ab
-1,581.52
Atau
2,537.20
mu
1,815.67
mA
3,414.67
tan(beta)
30.43
The truth!
9
M_1
-772.48
M_2
-749.42
M_3
3,436.52
mL1
995.89
mL3
3,721.33
mE1
2,202.36
mE3
1,220.43
mQ1
1,730.93
mQ3
1,410.79
mU1
2,239.50
mU3
3,227.69
mD1
831.87
mD3
1,834.53
At
2,819.32
Ab
3,104.17
Atau
2,453.78
mu
-2,834.37
mA
2,322.97
tan(beta)
9.22
The truth!
10
M_1
779.47
M_2
1,498.80
M_3
624.23
mL1
2,133.46
mL3
2,444.67
mE1
3,200.72
mE3
2,964.42
mQ1
888.59
mQ3
3,952.70
mU1
3,699.39
mU3
3,837.23
mD1
2,207.15
mD3
1,830.43
At
-4,819.46
Ab
673.79
Atau
-2,553.51
mu
2,023.27
mA
3,706.66
tan(beta)
41.24
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
