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
-439.10
M_2
3,599.65
M_3
2,309.93
mL1
1,066.95
mL3
2,508.30
mE1
2,536.79
mE3
3,516.95
mQ1
1,079.71
mQ3
934.69
mU1
263.00
mU3
3,995.15
mD1
2,281.58
mD3
3,383.10
At
-3,232.12
Ab
-3,478.62
Atau
712.63
mu
2,779.95
mA
4,121.83
tan(beta)
57.94
The truth!
2
M_1
-1,734.00
M_2
2,061.73
M_3
1,585.27
mL1
1,504.32
mL3
1,623.43
mE1
3,645.83
mE3
1,146.68
mQ1
768.25
mQ3
2,725.13
mU1
378.75
mU3
3,865.35
mD1
599.86
mD3
2,516.20
At
4,414.24
Ab
3,106.45
Atau
3,846.04
mu
-118.04
mA
3,516.36
tan(beta)
42.52
The truth!
3
M_1
-1,105.27
M_2
522.58
M_3
1,172.75
mL1
3,143.71
mL3
931.52
mE1
2,531.04
mE3
1,144.62
mQ1
1,348.01
mQ3
3,024.22
mU1
1,363.72
mU3
3,857.46
mD1
2,099.73
mD3
1,364.52
At
-4,459.23
Ab
-1,660.61
Atau
-1,484.57
mu
3,717.93
mA
1,533.22
tan(beta)
38.58
The truth!
4
M_1
-1,576.46
M_2
383.87
M_3
2,370.92
mL1
1,151.98
mL3
3,310.90
mE1
1,861.74
mE3
686.46
mQ1
3,732.60
mQ3
1,718.36
mU1
3,653.76
mU3
1,910.72
mD1
2,341.22
mD3
863.49
At
-2,673.05
Ab
-2,783.54
Atau
-3,965.07
mu
-149.05
mA
2,629.91
tan(beta)
12.25
The truth!
5
M_1
3,813.86
M_2
1,185.42
M_3
3,094.03
mL1
298.71
mL3
1,978.40
mE1
2,460.82
mE3
3,131.33
mQ1
610.74
mQ3
2,749.08
mU1
2,679.01
mU3
2,961.42
mD1
1,674.20
mD3
480.19
At
-2,988.24
Ab
-3,516.64
Atau
2,331.36
mu
103.53
mA
2,528.94
tan(beta)
24.92
The truth!
6
M_1
1,304.79
M_2
-165.71
M_3
3,499.61
mL1
3,278.68
mL3
1,710.47
mE1
2,621.14
mE3
3,925.68
mQ1
2,168.12
mQ3
3,903.45
mU1
1,553.54
mU3
3,964.59
mD1
2,850.43
mD3
3,028.74
At
-3,778.68
Ab
-481.77
Atau
980.07
mu
-353.51
mA
2,837.89
tan(beta)
39.80
The truth!
7
M_1
-449.40
M_2
-2,190.85
M_3
3,194.16
mL1
1,091.81
mL3
3,322.16
mE1
2,680.03
mE3
3,697.09
mQ1
2,235.30
mQ3
909.46
mU1
498.99
mU3
3,657.04
mD1
494.78
mD3
2,541.93
At
3,439.20
Ab
3,449.54
Atau
2,671.32
mu
1,539.94
mA
3,579.76
tan(beta)
10.23
The truth!
8
M_1
-2,054.28
M_2
192.19
M_3
3,913.12
mL1
3,502.62
mL3
1,940.69
mE1
1,796.45
mE3
1,444.29
mQ1
3,918.04
mQ3
1,398.37
mU1
502.44
mU3
2,870.50
mD1
1,864.87
mD3
1,610.41
At
-3,469.97
Ab
-3,165.84
Atau
2,343.32
mu
-366.27
mA
3,314.39
tan(beta)
9.00
The truth!
9
M_1
-601.50
M_2
-3,868.88
M_3
1,014.31
mL1
1,567.29
mL3
1,712.45
mE1
2,820.85
mE3
2,574.28
mQ1
529.53
mQ3
2,310.59
mU1
1,345.26
mU3
2,215.01
mD1
736.43
mD3
1,556.99
At
-3,109.23
Ab
1,531.83
Atau
3,757.69
mu
2,566.75
mA
2,801.51
tan(beta)
13.55
The truth!
10
M_1
1,337.22
M_2
2,341.47
M_3
3,340.65
mL1
2,934.12
mL3
798.84
mE1
2,797.52
mE3
2,721.38
mQ1
2,416.85
mQ3
2,503.31
mU1
3,140.30
mU3
3,458.73
mD1
1,098.18
mD3
1,633.81
At
-3,280.91
Ab
989.95
Atau
1,223.41
mu
665.37
mA
2,782.94
tan(beta)
44.88
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
