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
-1,679.62
M_2
-473.87
M_3
927.58
mL1
3,114.39
mL3
3,065.70
mE1
2,316.96
mE3
399.11
mQ1
3,412.24
mQ3
2,062.29
mU1
2,109.99
mU3
3,696.27
mD1
2,322.04
mD3
2,583.54
At
-2,765.87
Ab
-2,207.76
Atau
-3,158.63
mu
-235.31
mA
3,746.19
tan(beta)
12.60
The truth!
2
M_1
-3,720.15
M_2
860.07
M_3
3,050.40
mL1
2,456.67
mL3
3,760.36
mE1
2,361.23
mE3
2,326.61
mQ1
884.71
mQ3
2,147.18
mU1
3,410.18
mU3
2,515.78
mD1
2,650.40
mD3
2,688.76
At
-5,653.72
Ab
3,611.82
Atau
566.22
mu
-3,250.43
mA
3,341.73
tan(beta)
6.35
The truth!
3
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!
4
M_1
-2,869.10
M_2
-2,888.89
M_3
2,996.24
mL1
2,719.69
mL3
2,707.03
mE1
2,008.37
mE3
3,112.58
mQ1
3,931.63
mQ3
1,791.65
mU1
2,942.97
mU3
3,126.94
mD1
931.50
mD3
3,489.48
At
-6,857.47
Ab
748.58
Atau
3,232.83
mu
218.70
mA
2,654.85
tan(beta)
27.54
The truth!
5
M_1
573.24
M_2
-2,164.76
M_3
1,754.22
mL1
2,695.57
mL3
3,663.27
mE1
873.54
mE3
2,570.26
mQ1
474.06
mQ3
1,221.28
mU1
2,537.83
mU3
2,401.02
mD1
1,584.31
mD3
3,989.33
At
-4,220.27
Ab
-1,244.12
Atau
-2,264.13
mu
3,223.13
mA
2,281.73
tan(beta)
28.17
The truth!
6
M_1
-1,729.10
M_2
-370.41
M_3
1,212.54
mL1
1,698.71
mL3
2,136.05
mE1
3,240.00
mE3
1,672.32
mQ1
3,348.07
mQ3
821.64
mU1
2,721.02
mU3
1,380.41
mD1
2,846.62
mD3
1,870.83
At
-2,644.59
Ab
-1,864.08
Atau
1,007.94
mu
1,690.05
mA
885.21
tan(beta)
17.36
The truth!
7
M_1
714.27
M_2
3,271.00
M_3
2,968.08
mL1
731.89
mL3
3,195.25
mE1
1,024.17
mE3
1,928.70
mQ1
3,124.19
mQ3
2,651.63
mU1
2,369.85
mU3
2,500.33
mD1
2,050.06
mD3
906.33
At
-4,748.25
Ab
-1,392.11
Atau
1,972.06
mu
1,049.48
mA
1,459.46
tan(beta)
12.12
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
929.70
M_2
2,301.11
M_3
2,235.91
mL1
3,974.48
mL3
3,372.09
mE1
2,607.85
mE3
1,781.68
mQ1
899.90
mQ3
1,836.22
mU1
2,090.29
mU3
1,355.25
mD1
2,342.51
mD3
1,362.10
At
-4,300.57
Ab
1,960.52
Atau
-2,231.63
mu
-1,421.04
mA
2,799.08
tan(beta)
19.37
The truth!
10
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!
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
