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
3,630.28
M_2
-381.86
M_3
3,757.81
mL1
3,980.20
mL3
3,647.61
mE1
2,444.86
mE3
1,800.42
mQ1
844.33
mQ3
3,954.82
mU1
3,245.40
mU3
3,064.95
mD1
2,873.34
mD3
1,344.12
At
-4,974.86
Ab
3,683.97
Atau
-232.69
mu
2,834.05
mA
3,859.09
tan(beta)
15.83
The truth!
2
M_1
42.66
M_2
-533.61
M_3
1,995.79
mL1
485.17
mL3
2,505.16
mE1
963.03
mE3
2,260.26
mQ1
666.26
mQ3
1,910.73
mU1
1,289.53
mU3
3,621.01
mD1
1,534.85
mD3
2,577.04
At
3,967.67
Ab
2,588.85
Atau
-3,816.80
mu
-148.63
mA
813.88
tan(beta)
4.88
The truth!
3
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!
4
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!
5
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!
6
M_1
853.27
M_2
-817.02
M_3
3,266.59
mL1
2,847.28
mL3
2,524.18
mE1
3,859.60
mE3
3,063.43
mQ1
3,643.46
mQ3
2,716.63
mU1
1,858.35
mU3
1,454.94
mD1
1,239.27
mD3
2,740.79
At
-3,903.78
Ab
3,917.23
Atau
649.76
mu
2,690.89
mA
3,450.15
tan(beta)
34.33
The truth!
7
M_1
-621.20
M_2
-1,175.16
M_3
3,851.73
mL1
3,491.95
mL3
3,942.65
mE1
3,370.65
mE3
2,328.25
mQ1
1,796.20
mQ3
2,740.23
mU1
2,566.23
mU3
3,829.76
mD1
1,011.04
mD3
626.85
At
2,674.69
Ab
-2,635.56
Atau
2,865.49
mu
-655.96
mA
2,176.93
tan(beta)
47.49
The truth!
8
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!
9
M_1
-1,038.19
M_2
-1,662.68
M_3
3,277.52
mL1
568.67
mL3
1,898.78
mE1
1,007.80
mE3
2,417.17
mQ1
3,349.00
mQ3
2,594.42
mU1
3,761.85
mU3
3,876.84
mD1
1,298.71
mD3
3,753.28
At
3,802.26
Ab
-2,451.06
Atau
1,329.27
mu
481.80
mA
3,907.25
tan(beta)
43.20
The truth!
10
M_1
-2,147.08
M_2
-944.21
M_3
3,321.61
mL1
1,182.43
mL3
3,846.15
mE1
3,806.62
mE3
2,663.20
mQ1
1,825.98
mQ3
3,443.22
mU1
3,590.30
mU3
1,342.02
mD1
3,283.09
mD3
3,883.07
At
3,094.82
Ab
3,144.56
Atau
2,347.99
mu
2,092.67
mA
3,535.57
tan(beta)
22.81
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
