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
-1,723.76
M_2
-3,310.94
M_3
2,161.31
mL1
1,925.79
mL3
2,372.81
mE1
2,321.56
mE3
3,993.83
mQ1
3,163.04
mQ3
1,657.37
mU1
3,190.00
mU3
3,672.69
mD1
2,762.81
mD3
2,736.71
At
-3,508.85
Ab
-1,941.74
Atau
3,172.37
mu
2,939.87
mA
3,429.48
tan(beta)
57.21
The truth!
2
M_1
298.65
M_2
2,206.22
M_3
2,276.40
mL1
2,379.99
mL3
1,588.90
mE1
351.84
mE3
274.96
mQ1
637.40
mQ3
3,833.93
mU1
3,573.87
mU3
907.59
mD1
3,812.10
mD3
3,242.29
At
4,140.87
Ab
-181.85
Atau
-1,221.12
mu
-307.12
mA
2,197.38
tan(beta)
12.60
The truth!
3
M_1
-1,419.87
M_2
-328.18
M_3
3,842.08
mL1
3,327.61
mL3
1,507.13
mE1
2,728.35
mE3
3,836.24
mQ1
2,419.24
mQ3
3,047.55
mU1
3,147.40
mU3
2,336.12
mD1
3,640.88
mD3
389.23
At
-3,148.00
Ab
-2,396.85
Atau
1,745.36
mu
1,544.70
mA
3,212.58
tan(beta)
13.33
The truth!
4
M_1
3,088.39
M_2
-3,439.65
M_3
1,318.68
mL1
1,124.80
mL3
3,651.59
mE1
624.28
mE3
329.45
mQ1
991.26
mQ3
3,687.79
mU1
2,802.00
mU3
3,501.84
mD1
2,883.50
mD3
300.18
At
3,739.51
Ab
-3,541.69
Atau
3,221.83
mu
-263.05
mA
2,919.21
tan(beta)
29.00
The truth!
5
M_1
158.07
M_2
-2,024.54
M_3
2,918.44
mL1
941.09
mL3
902.95
mE1
3,486.50
mE3
3,966.86
mQ1
1,967.86
mQ3
1,630.29
mU1
3,754.98
mU3
2,863.66
mD1
3,775.91
mD3
1,161.30
At
-3,530.95
Ab
2,223.83
Atau
-3,331.14
mu
-163.70
mA
2,024.57
tan(beta)
39.65
The truth!
6
M_1
-3,290.71
M_2
3,365.39
M_3
1,018.69
mL1
3,115.67
mL3
1,858.69
mE1
3,350.29
mE3
1,632.68
mQ1
1,502.94
mQ3
3,881.14
mU1
621.46
mU3
2,717.58
mD1
1,633.22
mD3
2,946.24
At
4,709.69
Ab
-2,867.29
Atau
-2,602.03
mu
408.71
mA
3,321.21
tan(beta)
20.13
The truth!
7
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!
8
M_1
-494.13
M_2
1,361.78
M_3
2,219.23
mL1
1,220.45
mL3
3,673.02
mE1
1,696.28
mE3
1,768.07
mQ1
1,289.86
mQ3
1,167.81
mU1
2,486.38
mU3
624.97
mD1
1,633.49
mD3
3,339.16
At
1,477.87
Ab
2,859.85
Atau
-287.89
mu
-1,157.57
mA
2,974.26
tan(beta)
15.74
The truth!
9
M_1
-2,935.16
M_2
-2,469.07
M_3
2,071.73
mL1
1,296.90
mL3
1,103.15
mE1
1,822.28
mE3
831.47
mQ1
794.59
mQ3
3,155.19
mU1
2,022.62
mU3
3,264.28
mD1
787.83
mD3
3,312.75
At
-4,843.22
Ab
-2,975.88
Atau
1,638.24
mu
673.50
mA
1,949.49
tan(beta)
26.29
The truth!
10
M_1
-650.96
M_2
383.74
M_3
1,807.77
mL1
1,374.08
mL3
2,443.49
mE1
1,085.09
mE3
1,819.79
mQ1
2,263.92
mQ3
691.87
mU1
3,616.15
mU3
3,310.70
mD1
2,006.70
mD3
1,974.97
At
2,772.93
Ab
739.39
Atau
2,141.67
mu
1,008.91
mA
3,745.96
tan(beta)
54.76
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
