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
3,375.42
M_2
926.15
M_3
3,920.76
mL1
3,671.22
mL3
3,041.17
mE1
3,474.24
mE3
3,510.32
mQ1
1,696.43
mQ3
3,927.28
mU1
3,466.97
mU3
3,487.42
mD1
2,551.50
mD3
1,749.10
At
-4,105.42
Ab
-2,820.53
Atau
2,619.97
mu
2,052.94
mA
1,239.98
tan(beta)
28.03
The truth!
2
M_1
589.16
M_2
-3,311.57
M_3
3,024.84
mL1
3,440.41
mL3
135.88
mE1
272.14
mE3
928.49
mQ1
2,108.03
mQ3
3,047.51
mU1
1,499.35
mU3
1,777.03
mD1
1,073.76
mD3
2,140.86
At
-3,165.89
Ab
3,366.67
Atau
480.21
mu
109.48
mA
2,178.91
tan(beta)
38.92
The truth!
3
M_1
1,656.05
M_2
-1,359.86
M_3
2,778.92
mL1
1,549.27
mL3
1,763.52
mE1
1,195.83
mE3
2,468.62
mQ1
3,993.73
mQ3
2,308.45
mU1
2,616.51
mU3
3,881.68
mD1
3,552.63
mD3
1,679.30
At
2,701.80
Ab
-1,963.01
Atau
2,163.92
mu
-1,065.92
mA
4,026.91
tan(beta)
40.71
The truth!
4
M_1
2,997.99
M_2
239.75
M_3
1,401.19
mL1
3,363.83
mL3
2,527.41
mE1
2,850.40
mE3
2,390.35
mQ1
1,134.20
mQ3
1,158.58
mU1
1,550.83
mU3
2,330.96
mD1
476.24
mD3
3,237.22
At
-2,936.80
Ab
-765.24
Atau
1,995.13
mu
-520.96
mA
3,827.87
tan(beta)
29.84
The truth!
5
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!
6
M_1
-380.92
M_2
1,351.32
M_3
308.43
mL1
3,424.59
mL3
3,411.67
mE1
1,797.50
mE3
920.39
mQ1
1,543.15
mQ3
3,439.95
mU1
3,573.58
mU3
2,302.14
mD1
2,878.70
mD3
2,422.62
At
3,490.09
Ab
-1,027.84
Atau
2,191.32
mu
-3,942.00
mA
3,634.85
tan(beta)
40.17
The truth!
7
M_1
1,351.43
M_2
-1,189.13
M_3
2,811.54
mL1
2,341.19
mL3
2,387.46
mE1
599.97
mE3
1,891.41
mQ1
2,501.07
mQ3
3,184.27
mU1
1,886.42
mU3
1,023.48
mD1
1,881.50
mD3
2,647.38
At
2,772.47
Ab
-2,825.64
Atau
-3,108.92
mu
583.03
mA
1,693.80
tan(beta)
41.81
The truth!
8
M_1
1,474.89
M_2
-632.30
M_3
2,505.22
mL1
3,842.15
mL3
889.31
mE1
2,585.48
mE3
978.13
mQ1
2,901.75
mQ3
3,283.93
mU1
3,377.64
mU3
3,821.48
mD1
768.58
mD3
2,256.21
At
3,592.02
Ab
1,520.09
Atau
-3,872.14
mu
514.63
mA
3,009.64
tan(beta)
37.96
The truth!
9
M_1
-647.58
M_2
-2,686.09
M_3
631.93
mL1
1,976.54
mL3
3,282.76
mE1
2,624.08
mE3
2,006.51
mQ1
3,242.71
mQ3
1,172.83
mU1
609.77
mU3
2,612.47
mD1
2,804.45
mD3
1,238.21
At
-2,729.41
Ab
-40.21
Atau
1,408.66
mu
-1,097.16
mA
3,879.07
tan(beta)
18.15
The truth!
10
M_1
-2,646.76
M_2
3,071.01
M_3
689.90
mL1
2,431.43
mL3
896.89
mE1
734.86
mE3
2,136.03
mQ1
1,549.02
mQ3
3,583.96
mU1
3,824.79
mU3
3,075.25
mD1
3,756.95
mD3
1,048.83
At
3,492.30
Ab
-1,643.92
Atau
-2,586.01
mu
-629.90
mA
2,339.83
tan(beta)
19.13
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
