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
1,432.76
M_2
-2,767.52
M_3
2,701.84
mL1
3,971.16
mL3
1,897.80
mE1
1,720.59
mE3
3,041.26
mQ1
2,650.21
mQ3
1,888.78
mU1
623.71
mU3
3,130.95
mD1
216.13
mD3
3,787.70
At
3,634.69
Ab
2,872.26
Atau
-768.93
mu
-126.04
mA
3,384.50
tan(beta)
47.00
The truth!
2
M_1
822.10
M_2
1,005.24
M_3
1,249.57
mL1
913.14
mL3
2,068.02
mE1
3,859.20
mE3
3,479.88
mQ1
3,262.05
mQ3
3,350.25
mU1
1,875.97
mU3
1,487.00
mD1
2,152.25
mD3
2,182.15
At
-2,785.33
Ab
3,380.30
Atau
-2,927.85
mu
1,390.97
mA
1,806.55
tan(beta)
30.92
The truth!
3
M_1
-3,872.69
M_2
-1,204.01
M_3
2,899.00
mL1
1,605.62
mL3
2,831.44
mE1
3,087.74
mE3
1,298.75
mQ1
3,609.48
mQ3
1,611.97
mU1
3,803.77
mU3
3,888.26
mD1
3,618.91
mD3
2,277.13
At
-4,179.77
Ab
3,955.54
Atau
3,013.14
mu
3,336.08
mA
2,114.10
tan(beta)
27.43
The truth!
4
M_1
-2,491.11
M_2
-639.25
M_3
3,924.37
mL1
3,378.21
mL3
861.08
mE1
3,340.43
mE3
3,859.06
mQ1
526.27
mQ3
3,559.43
mU1
1,690.84
mU3
2,837.48
mD1
3,556.89
mD3
2,608.13
At
4,890.19
Ab
-865.31
Atau
247.14
mu
1,643.30
mA
2,278.74
tan(beta)
5.34
The truth!
5
M_1
-2,976.05
M_2
-624.16
M_3
1,998.92
mL1
1,127.65
mL3
2,198.70
mE1
1,802.33
mE3
2,195.28
mQ1
3,072.69
mQ3
656.26
mU1
3,064.20
mU3
2,367.68
mD1
1,139.20
mD3
2,591.45
At
-2,631.82
Ab
-410.45
Atau
2,703.63
mu
1,713.57
mA
2,419.55
tan(beta)
30.63
The truth!
6
M_1
-2,272.07
M_2
1,491.37
M_3
2,878.65
mL1
2,678.89
mL3
3,902.54
mE1
1,966.74
mE3
3,126.01
mQ1
2,426.81
mQ3
3,275.50
mU1
2,287.69
mU3
3,332.62
mD1
380.45
mD3
2,640.90
At
-3,706.55
Ab
3,866.22
Atau
590.57
mu
-540.91
mA
2,408.46
tan(beta)
49.26
The truth!
7
M_1
3,124.62
M_2
-232.12
M_3
618.84
mL1
504.14
mL3
3,144.56
mE1
1,702.40
mE3
1,187.66
mQ1
3,100.54
mQ3
1,618.72
mU1
3,653.08
mU3
2,172.71
mD1
2,100.34
mD3
904.09
At
-2,459.74
Ab
-1,689.53
Atau
1,503.88
mu
2,038.21
mA
3,678.80
tan(beta)
46.48
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
-1,336.42
M_2
3,470.69
M_3
1,899.55
mL1
2,456.02
mL3
2,323.42
mE1
2,828.38
mE3
1,172.83
mQ1
2,302.33
mQ3
378.99
mU1
3,392.98
mU3
3,615.77
mD1
1,707.19
mD3
974.31
At
3,303.72
Ab
-3,409.32
Atau
-2,683.52
mu
-350.87
mA
2,690.96
tan(beta)
30.11
The truth!
10
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!
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
