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
-2,322.20
M_2
-250.20
M_3
3,007.28
mL1
2,698.42
mL3
1,865.42
mE1
2,557.22
mE3
877.33
mQ1
2,506.06
mQ3
3,461.29
mU1
3,710.54
mU3
3,831.23
mD1
996.81
mD3
2,346.49
At
972.94
Ab
2,994.65
Atau
-817.94
mu
-171.44
mA
2,921.50
tan(beta)
52.39
The truth!
2
M_1
-733.27
M_2
1,218.15
M_3
2,207.26
mL1
2,632.69
mL3
3,009.19
mE1
1,992.56
mE3
3,694.23
mQ1
3,644.13
mQ3
3,648.38
mU1
1,591.42
mU3
3,746.71
mD1
2,254.65
mD3
499.38
At
-2,190.53
Ab
3,347.39
Atau
46.42
mu
882.91
mA
2,099.84
tan(beta)
37.85
The truth!
3
M_1
868.31
M_2
-2,619.78
M_3
3,125.42
mL1
3,712.13
mL3
511.15
mE1
905.16
mE3
2,150.15
mQ1
3,248.85
mQ3
2,025.18
mU1
2,918.30
mU3
2,998.91
mD1
2,089.18
mD3
1,489.34
At
-3,663.12
Ab
-179.40
Atau
2,521.22
mu
-491.05
mA
3,437.25
tan(beta)
49.98
The truth!
4
M_1
644.24
M_2
1,495.11
M_3
544.69
mL1
2,896.24
mL3
3,749.37
mE1
3,942.13
mE3
1,372.89
mQ1
1,585.29
mQ3
952.54
mU1
1,636.41
mU3
3,330.34
mD1
1,898.41
mD3
3,528.61
At
-2,971.95
Ab
3,633.96
Atau
-2,880.05
mu
-2,702.29
mA
2,798.36
tan(beta)
35.67
The truth!
5
M_1
-752.80
M_2
-3,798.70
M_3
2,592.46
mL1
2,713.68
mL3
1,047.01
mE1
1,646.64
mE3
2,316.10
mQ1
2,310.10
mQ3
2,099.94
mU1
3,311.04
mU3
995.64
mD1
759.58
mD3
3,717.27
At
2,924.08
Ab
524.34
Atau
2,049.35
mu
-1,133.81
mA
983.96
tan(beta)
28.52
The truth!
6
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!
7
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!
8
M_1
2,837.39
M_2
3,881.79
M_3
879.73
mL1
1,826.19
mL3
3,921.00
mE1
272.66
mE3
3,431.79
mQ1
2,513.80
mQ3
2,765.47
mU1
3,428.19
mU3
3,101.47
mD1
552.88
mD3
1,292.72
At
-4,886.59
Ab
-3,116.73
Atau
-3,621.75
mu
-180.64
mA
1,961.60
tan(beta)
20.80
The truth!
9
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!
10
M_1
833.13
M_2
517.26
M_3
3,440.08
mL1
3,320.06
mL3
2,421.80
mE1
667.14
mE3
3,404.36
mQ1
2,935.85
mQ3
3,419.67
mU1
1,297.16
mU3
2,252.38
mD1
2,685.90
mD3
2,930.42
At
-4,327.00
Ab
-2,710.34
Atau
-445.80
mu
2,866.11
mA
2,802.00
tan(beta)
44.91
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
