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
1,320.05
M_2
3,138.17
M_3
1,759.92
mL1
583.29
mL3
2,525.59
mE1
3,641.97
mE3
3,956.58
mQ1
283.95
mQ3
973.75
mU1
2,036.12
mU3
2,774.86
mD1
3,509.96
mD3
3,140.05
At
2,659.51
Ab
-3,449.83
Atau
1,292.33
mu
-383.01
mA
1,154.86
tan(beta)
32.94
The truth!
2
M_1
-399.12
M_2
2,405.09
M_3
1,055.12
mL1
638.70
mL3
3,485.72
mE1
3,236.77
mE3
977.76
mQ1
1,775.79
mQ3
3,875.58
mU1
440.53
mU3
2,857.85
mD1
3,342.07
mD3
3,675.72
At
-4,282.41
Ab
3,598.76
Atau
-518.46
mu
441.38
mA
893.48
tan(beta)
8.57
The truth!
3
M_1
615.77
M_2
2,881.33
M_3
496.98
mL1
2,216.14
mL3
1,978.02
mE1
2,023.85
mE3
3,335.60
mQ1
3,485.37
mQ3
3,609.38
mU1
1,769.09
mU3
3,520.25
mD1
3,993.18
mD3
3,943.38
At
5,621.32
Ab
-3,497.16
Atau
3,999.77
mu
-2,982.81
mA
2,324.79
tan(beta)
4.04
The truth!
4
M_1
-1,331.37
M_2
-2,046.21
M_3
3,729.39
mL1
3,761.73
mL3
2,154.93
mE1
1,868.81
mE3
1,199.13
mQ1
2,654.71
mQ3
1,793.43
mU1
2,592.99
mU3
2,430.21
mD1
2,819.17
mD3
3,554.69
At
-5,790.46
Ab
-781.18
Atau
-843.16
mu
-475.50
mA
2,116.26
tan(beta)
8.71
The truth!
5
M_1
712.68
M_2
1,669.69
M_3
1,189.84
mL1
1,346.29
mL3
1,790.62
mE1
3,393.41
mE3
1,874.50
mQ1
616.34
mQ3
1,407.44
mU1
2,275.76
mU3
3,888.08
mD1
1,961.50
mD3
1,129.22
At
-3,471.82
Ab
3,419.87
Atau
-1,080.12
mu
3,147.28
mA
2,514.73
tan(beta)
32.94
The truth!
6
M_1
-953.59
M_2
-604.61
M_3
3,175.76
mL1
1,289.44
mL3
2,585.59
mE1
2,552.58
mE3
2,567.28
mQ1
2,956.81
mQ3
2,466.04
mU1
3,808.84
mU3
1,473.93
mD1
1,480.86
mD3
3,603.67
At
3,542.38
Ab
691.92
Atau
3,509.21
mu
2,121.77
mA
3,905.25
tan(beta)
10.32
The truth!
7
M_1
2,831.92
M_2
-2,960.52
M_3
229.49
mL1
3,695.62
mL3
320.03
mE1
300.83
mE3
3,513.20
mQ1
3,121.15
mQ3
1,696.52
mU1
2,693.86
mU3
3,311.40
mD1
2,063.29
mD3
554.28
At
-3,762.01
Ab
2,384.56
Atau
1,269.45
mu
-232.29
mA
2,730.07
tan(beta)
8.88
The truth!
8
M_1
1,219.76
M_2
233.46
M_3
1,605.56
mL1
2,231.86
mL3
3,011.45
mE1
1,778.50
mE3
303.06
mQ1
3,912.73
mQ3
2,614.60
mU1
3,073.40
mU3
1,743.22
mD1
2,157.08
mD3
1,297.50
At
2,340.10
Ab
2,509.26
Atau
-3,676.30
mu
-432.34
mA
608.47
tan(beta)
13.22
The truth!
9
M_1
-3,172.86
M_2
936.23
M_3
2,819.91
mL1
2,279.79
mL3
2,692.76
mE1
3,981.59
mE3
1,970.45
mQ1
3,926.64
mQ3
1,763.29
mU1
1,805.32
mU3
2,352.21
mD1
1,845.08
mD3
1,909.91
At
-3,155.22
Ab
2,425.93
Atau
1,966.99
mu
-2,757.77
mA
3,413.93
tan(beta)
14.49
The truth!
10
M_1
-2,624.64
M_2
-1,186.35
M_3
1,397.56
mL1
3,642.51
mL3
1,198.77
mE1
2,485.66
mE3
3,635.98
mQ1
3,662.14
mQ3
1,493.33
mU1
3,970.01
mU3
3,814.73
mD1
3,451.76
mD3
1,047.92
At
3,639.13
Ab
2,419.89
Atau
70.32
mu
920.16
mA
2,902.84
tan(beta)
31.91
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
