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
-313.91
M_2
-1,171.24
M_3
2,408.60
mL1
3,815.95
mL3
2,175.31
mE1
508.21
mE3
2,238.52
mQ1
1,923.87
mQ3
1,980.39
mU1
261.75
mU3
3,161.02
mD1
2,873.87
mD3
202.94
At
-6,323.16
Ab
-1,092.21
Atau
417.46
mu
2,565.08
mA
666.92
tan(beta)
6.28
The truth!
2
M_1
-3,983.83
M_2
-1,659.24
M_3
3,639.30
mL1
2,163.58
mL3
3,981.95
mE1
403.54
mE3
987.02
mQ1
2,966.68
mQ3
3,795.38
mU1
431.47
mU3
3,347.88
mD1
601.64
mD3
485.13
At
3,560.00
Ab
-1,638.35
Atau
-572.32
mu
-176.32
mA
3,029.18
tan(beta)
42.73
The truth!
3
M_1
434.97
M_2
3,891.91
M_3
1,451.41
mL1
2,748.16
mL3
3,495.59
mE1
1,732.86
mE3
3,066.78
mQ1
1,549.65
mQ3
3,384.52
mU1
2,461.94
mU3
3,733.16
mD1
1,245.41
mD3
2,047.96
At
-4,143.73
Ab
-1,561.25
Atau
1,672.18
mu
352.90
mA
1,651.71
tan(beta)
30.87
The truth!
4
M_1
-505.89
M_2
467.80
M_3
882.57
mL1
2,433.18
mL3
2,623.39
mE1
3,662.28
mE3
1,196.48
mQ1
3,915.83
mQ3
2,597.23
mU1
3,759.60
mU3
2,002.11
mD1
3,218.49
mD3
3,409.07
At
3,353.82
Ab
-652.20
Atau
-2,631.25
mu
-1,956.53
mA
3,311.64
tan(beta)
4.95
The truth!
5
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!
6
M_1
1,180.60
M_2
-2,757.96
M_3
2,928.34
mL1
3,120.82
mL3
2,716.76
mE1
879.45
mE3
3,515.45
mQ1
1,244.75
mQ3
2,279.06
mU1
3,014.79
mU3
1,984.20
mD1
3,796.51
mD3
590.59
At
-3,162.28
Ab
2,619.23
Atau
1,895.36
mu
150.59
mA
1,479.79
tan(beta)
34.46
The truth!
7
M_1
-297.13
M_2
2,953.16
M_3
1,546.90
mL1
3,858.66
mL3
1,717.43
mE1
3,895.42
mE3
1,626.24
mQ1
2,610.40
mQ3
1,981.62
mU1
468.89
mU3
3,421.59
mD1
701.96
mD3
1,906.88
At
2,737.61
Ab
-3,092.48
Atau
-1,184.48
mu
-317.41
mA
1,107.04
tan(beta)
33.21
The truth!
8
M_1
331.20
M_2
868.10
M_3
1,692.37
mL1
2,592.99
mL3
2,648.16
mE1
1,726.50
mE3
1,558.85
mQ1
3,476.61
mQ3
1,896.00
mU1
1,626.03
mU3
3,502.64
mD1
2,216.47
mD3
3,576.38
At
3,316.17
Ab
639.83
Atau
2,163.03
mu
243.74
mA
3,170.74
tan(beta)
6.89
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
621.86
M_2
2,583.91
M_3
972.65
mL1
2,717.55
mL3
3,978.84
mE1
2,814.37
mE3
1,649.86
mQ1
401.52
mQ3
3,350.16
mU1
2,902.41
mU3
3,719.37
mD1
3,831.09
mD3
1,067.48
At
-3,966.98
Ab
-852.63
Atau
-527.60
mu
2,197.87
mA
3,207.38
tan(beta)
43.06
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
