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
68.33
M_2
3,688.11
M_3
1,631.57
mL1
923.43
mL3
3,280.53
mE1
3,074.78
mE3
668.48
mQ1
1,258.40
mQ3
3,682.07
mU1
2,292.79
mU3
3,222.91
mD1
2,037.08
mD3
3,623.11
At
4,040.94
Ab
197.04
Atau
-2,695.45
mu
-104.21
mA
3,075.28
tan(beta)
34.58
The truth!
2
M_1
-3,942.36
M_2
-3,151.34
M_3
814.56
mL1
1,299.97
mL3
729.06
mE1
1,561.89
mE3
803.90
mQ1
1,951.99
mQ3
3,307.69
mU1
2,017.56
mU3
3,861.56
mD1
3,457.54
mD3
3,764.49
At
2,688.27
Ab
2,384.38
Atau
3,360.69
mu
460.59
mA
2,926.29
tan(beta)
52.56
The truth!
3
M_1
411.76
M_2
-828.11
M_3
2,724.13
mL1
654.06
mL3
488.18
mE1
1,728.47
mE3
2,676.66
mQ1
3,920.43
mQ3
1,406.78
mU1
375.47
mU3
2,210.02
mD1
2,095.70
mD3
2,287.25
At
-5,453.59
Ab
-365.98
Atau
3,390.59
mu
-1,801.82
mA
3,877.49
tan(beta)
13.34
The truth!
4
M_1
394.82
M_2
-219.23
M_3
3,462.06
mL1
3,438.79
mL3
1,233.29
mE1
1,522.51
mE3
2,442.34
mQ1
3,086.05
mQ3
3,925.49
mU1
2,777.90
mU3
3,780.05
mD1
3,170.39
mD3
2,967.60
At
-5,700.09
Ab
2,435.16
Atau
197.56
mu
4,037.93
mA
1,468.64
tan(beta)
6.96
The truth!
5
M_1
-3,725.52
M_2
-3,636.40
M_3
1,239.26
mL1
2,224.16
mL3
1,542.12
mE1
2,380.33
mE3
1,870.20
mQ1
2,831.33
mQ3
1,055.44
mU1
2,413.60
mU3
2,484.32
mD1
2,296.22
mD3
1,184.12
At
2,588.83
Ab
456.84
Atau
2,203.78
mu
-206.90
mA
2,136.16
tan(beta)
15.80
The truth!
6
M_1
-210.93
M_2
2,653.15
M_3
3,636.84
mL1
2,774.83
mL3
1,833.50
mE1
998.40
mE3
2,939.88
mQ1
1,820.23
mQ3
2,695.95
mU1
3,673.46
mU3
1,510.83
mD1
723.86
mD3
913.58
At
-3,548.09
Ab
-2,317.19
Atau
-3,649.71
mu
240.73
mA
3,252.58
tan(beta)
20.83
The truth!
7
M_1
672.47
M_2
284.10
M_3
844.14
mL1
1,121.52
mL3
405.61
mE1
2,027.38
mE3
2,735.61
mQ1
2,651.03
mQ3
3,426.82
mU1
1,108.85
mU3
3,450.17
mD1
1,348.83
mD3
939.11
At
5,139.50
Ab
-348.04
Atau
-1,631.88
mu
-1,998.08
mA
806.63
tan(beta)
6.19
The truth!
8
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!
9
M_1
-1,630.38
M_2
2,710.23
M_3
3,225.09
mL1
456.54
mL3
383.20
mE1
2,718.19
mE3
2,894.59
mQ1
337.33
mQ3
982.13
mU1
2,609.36
mU3
3,719.68
mD1
756.19
mD3
1,701.16
At
3,540.20
Ab
1,531.97
Atau
3,641.29
mu
-159.67
mA
3,253.86
tan(beta)
5.66
The truth!
10
M_1
-3,948.54
M_2
-646.38
M_3
1,724.84
mL1
1,170.32
mL3
2,624.94
mE1
2,745.01
mE3
3,786.35
mQ1
3,170.61
mQ3
2,613.78
mU1
2,226.83
mU3
2,978.36
mD1
711.05
mD3
3,397.31
At
-3,914.92
Ab
2,508.87
Atau
-3,520.08
mu
1,588.32
mA
3,598.99
tan(beta)
41.58
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
