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
522.75
M_2
1,534.61
M_3
1,225.44
mL1
1,714.38
mL3
3,677.76
mE1
2,501.58
mE3
1,934.54
mQ1
1,084.29
mQ3
3,376.46
mU1
476.20
mU3
3,401.06
mD1
1,995.65
mD3
463.69
At
3,631.67
Ab
395.40
Atau
-136.25
mu
-507.58
mA
2,368.99
tan(beta)
24.49
The truth!
2
M_1
63.21
M_2
950.01
M_3
2,345.06
mL1
2,075.89
mL3
339.44
mE1
1,563.29
mE3
600.30
mQ1
1,396.29
mQ3
2,968.79
mU1
1,125.86
mU3
1,162.77
mD1
3,670.70
mD3
1,876.15
At
-3,453.80
Ab
-3,822.51
Atau
2,643.89
mu
-184.54
mA
2,431.36
tan(beta)
41.12
The truth!
3
M_1
2,914.03
M_2
-509.06
M_3
3,940.37
mL1
1,239.04
mL3
2,932.30
mE1
2,610.01
mE3
2,047.21
mQ1
2,236.36
mQ3
2,920.00
mU1
1,614.68
mU3
3,064.24
mD1
476.07
mD3
1,051.37
At
-3,782.73
Ab
-420.19
Atau
2,902.25
mu
774.14
mA
1,409.97
tan(beta)
36.19
The truth!
4
M_1
-1,550.25
M_2
314.93
M_3
687.14
mL1
1,309.55
mL3
1,077.66
mE1
1,816.64
mE3
1,850.74
mQ1
3,556.86
mQ3
3,552.22
mU1
2,989.67
mU3
3,993.89
mD1
647.91
mD3
2,895.67
At
-1,567.74
Ab
2,812.42
Atau
-3,369.74
mu
1,971.20
mA
3,716.80
tan(beta)
27.37
The truth!
5
M_1
1,963.82
M_2
304.41
M_3
1,312.61
mL1
2,711.48
mL3
997.19
mE1
3,884.97
mE3
1,726.69
mQ1
3,983.33
mQ3
3,954.72
mU1
875.56
mU3
1,956.43
mD1
3,588.85
mD3
2,408.43
At
-4,005.35
Ab
693.81
Atau
3,831.34
mu
3,877.36
mA
3,450.22
tan(beta)
20.75
The truth!
6
M_1
-428.61
M_2
-246.98
M_3
487.24
mL1
1,787.59
mL3
1,875.65
mE1
3,084.32
mE3
2,703.38
mQ1
3,993.94
mQ3
1,760.28
mU1
3,291.54
mU3
2,604.64
mD1
3,823.04
mD3
2,158.53
At
-3,605.56
Ab
-1,253.15
Atau
557.62
mu
3,969.34
mA
3,319.68
tan(beta)
42.03
The truth!
7
M_1
2,518.19
M_2
623.56
M_3
2,258.95
mL1
653.67
mL3
781.05
mE1
1,452.28
mE3
876.66
mQ1
554.48
mQ3
2,894.62
mU1
2,981.57
mU3
1,730.65
mD1
3,831.28
mD3
2,832.60
At
2,974.84
Ab
3,258.66
Atau
222.78
mu
-3,005.12
mA
3,781.31
tan(beta)
28.72
The truth!
8
M_1
362.17
M_2
2,366.60
M_3
2,497.30
mL1
3,165.97
mL3
2,498.86
mE1
3,190.58
mE3
2,647.74
mQ1
1,513.56
mQ3
3,606.62
mU1
1,441.23
mU3
1,156.94
mD1
3,615.00
mD3
1,604.46
At
-4,300.64
Ab
-2,818.96
Atau
-3,491.56
mu
-335.25
mA
2,120.38
tan(beta)
13.00
The truth!
9
M_1
3,880.34
M_2
-271.18
M_3
3,612.13
mL1
2,841.21
mL3
390.77
mE1
2,737.34
mE3
3,174.63
mQ1
2,532.57
mQ3
2,228.61
mU1
3,368.29
mU3
3,786.79
mD1
3,757.35
mD3
3,029.21
At
-3,338.00
Ab
3,494.05
Atau
1,125.66
mu
-324.04
mA
3,836.61
tan(beta)
18.97
The truth!
10
M_1
-1,649.90
M_2
-584.25
M_3
1,631.90
mL1
810.94
mL3
942.89
mE1
3,755.12
mE3
3,636.73
mQ1
3,780.67
mQ3
1,852.02
mU1
711.68
mU3
1,736.79
mD1
3,080.38
mD3
1,494.78
At
-2,365.82
Ab
3,580.43
Atau
2,751.51
mu
2,088.66
mA
1,689.85
tan(beta)
16.83
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
