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
869.50
M_2
-1,667.38
M_3
3,620.83
mL1
939.03
mL3
1,361.91
mE1
3,556.33
mE3
1,202.76
mQ1
3,505.46
mQ3
1,536.08
mU1
1,394.05
mU3
3,744.48
mD1
3,508.74
mD3
1,211.95
At
3,920.92
Ab
-1,477.78
Atau
1,305.31
mu
176.65
mA
1,148.94
tan(beta)
9.58
The truth!
2
M_1
781.14
M_2
746.06
M_3
3,325.70
mL1
833.91
mL3
1,151.32
mE1
3,498.20
mE3
2,305.71
mQ1
3,482.65
mQ3
2,883.60
mU1
2,236.28
mU3
3,006.51
mD1
1,699.67
mD3
2,322.25
At
-4,376.65
Ab
-3,404.06
Atau
1,848.51
mu
2,408.48
mA
2,480.26
tan(beta)
13.67
The truth!
3
M_1
-781.65
M_2
3,423.42
M_3
537.64
mL1
1,198.96
mL3
3,502.34
mE1
3,906.72
mE3
767.05
mQ1
1,809.88
mQ3
2,892.29
mU1
1,107.06
mU3
2,068.80
mD1
3,964.69
mD3
1,486.63
At
3,132.57
Ab
39.62
Atau
77.96
mu
-203.41
mA
2,439.94
tan(beta)
27.22
The truth!
4
M_1
1,399.06
M_2
-221.47
M_3
1,671.05
mL1
2,118.43
mL3
1,068.69
mE1
3,316.29
mE3
3,653.04
mQ1
3,570.97
mQ3
3,706.31
mU1
2,234.79
mU3
2,629.10
mD1
2,179.37
mD3
2,093.66
At
4,912.62
Ab
3,354.22
Atau
-1,205.52
mu
3,169.39
mA
3,899.47
tan(beta)
10.05
The truth!
5
M_1
-2,484.08
M_2
-1,081.29
M_3
1,809.98
mL1
911.64
mL3
3,880.67
mE1
893.68
mE3
1,192.43
mQ1
2,889.55
mQ3
3,069.99
mU1
3,412.04
mU3
2,841.35
mD1
3,966.45
mD3
997.14
At
-3,909.24
Ab
-3,298.65
Atau
2,534.38
mu
410.10
mA
1,146.96
tan(beta)
44.92
The truth!
6
M_1
-2,033.99
M_2
-1,593.64
M_3
1,464.93
mL1
2,563.93
mL3
2,212.28
mE1
3,456.25
mE3
1,597.53
mQ1
947.68
mQ3
3,045.40
mU1
2,644.00
mU3
1,420.51
mD1
3,664.94
mD3
2,299.74
At
-2,957.92
Ab
145.54
Atau
-244.55
mu
700.17
mA
1,021.26
tan(beta)
14.24
The truth!
7
M_1
-159.46
M_2
-3,449.53
M_3
662.61
mL1
549.14
mL3
2,683.64
mE1
2,604.12
mE3
2,547.79
mQ1
2,583.28
mQ3
3,191.11
mU1
986.39
mU3
3,653.14
mD1
521.02
mD3
823.55
At
-3,601.90
Ab
70.58
Atau
3,703.97
mu
163.62
mA
1,042.50
tan(beta)
59.46
The truth!
8
M_1
217.75
M_2
-200.63
M_3
2,865.15
mL1
748.76
mL3
473.87
mE1
870.07
mE3
2,385.09
mQ1
1,055.76
mQ3
3,300.54
mU1
1,901.92
mU3
969.22
mD1
1,691.92
mD3
3,427.19
At
-3,169.69
Ab
1,030.72
Atau
-1,132.13
mu
1,070.69
mA
3,578.02
tan(beta)
10.27
The truth!
9
M_1
-3,381.98
M_2
-346.44
M_3
1,569.47
mL1
3,899.66
mL3
1,391.27
mE1
2,733.89
mE3
3,790.67
mQ1
1,646.31
mQ3
2,782.82
mU1
406.36
mU3
3,297.27
mD1
1,073.90
mD3
1,989.25
At
-4,364.95
Ab
-3,916.45
Atau
-2,821.64
mu
3,355.93
mA
3,368.31
tan(beta)
25.95
The truth!
10
M_1
-647.58
M_2
-2,686.09
M_3
631.93
mL1
1,976.54
mL3
3,282.76
mE1
2,624.08
mE3
2,006.51
mQ1
3,242.71
mQ3
1,172.83
mU1
609.77
mU3
2,612.47
mD1
2,804.45
mD3
1,238.21
At
-2,729.41
Ab
-40.21
Atau
1,408.66
mu
-1,097.16
mA
3,879.07
tan(beta)
18.15
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
