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
3,088.39
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!
2
M_1
631.10
M_2
1,631.31
M_3
2,475.40
mL1
1,149.63
mL3
2,593.54
mE1
3,369.28
mE3
2,809.60
mQ1
337.63
mQ3
2,158.37
mU1
1,820.62
mU3
3,266.39
mD1
674.99
mD3
2,349.24
At
-3,688.62
Ab
1,459.21
Atau
514.14
mu
2,361.22
mA
3,945.62
tan(beta)
26.68
The truth!
3
M_1
2,010.15
M_2
-211.13
M_3
2,687.36
mL1
1,011.45
mL3
2,333.37
mE1
2,097.00
mE3
787.55
mQ1
2,055.80
mQ3
1,318.22
mU1
1,144.84
mU3
2,216.75
mD1
475.64
mD3
1,962.34
At
-4,420.93
Ab
1,811.99
Atau
1,054.78
mu
1,507.29
mA
2,154.37
tan(beta)
8.92
The truth!
4
M_1
645.39
M_2
-3,520.54
M_3
2,997.82
mL1
1,765.08
mL3
1,939.26
mE1
717.73
mE3
1,894.44
mQ1
2,434.22
mQ3
1,805.40
mU1
3,556.24
mU3
2,030.84
mD1
1,932.02
mD3
3,330.44
At
3,475.98
Ab
13.15
Atau
-718.02
mu
-853.31
mA
1,281.19
tan(beta)
7.26
The truth!
5
M_1
-266.05
M_2
3,942.36
M_3
1,641.19
mL1
2,676.33
mL3
1,836.00
mE1
3,846.84
mE3
3,068.64
mQ1
828.79
mQ3
1,148.30
mU1
1,382.82
mU3
3,418.30
mD1
3,283.42
mD3
208.16
At
-2,937.67
Ab
1,868.35
Atau
1,193.35
mu
274.29
mA
1,692.92
tan(beta)
44.80
The truth!
6
M_1
-812.31
M_2
1,958.00
M_3
647.16
mL1
917.45
mL3
2,954.04
mE1
2,215.66
mE3
1,360.02
mQ1
3,325.70
mQ3
3,861.74
mU1
2,558.72
mU3
3,066.14
mD1
2,041.46
mD3
3,540.90
At
4,251.19
Ab
839.39
Atau
-1,552.75
mu
-1,573.53
mA
1,267.19
tan(beta)
7.53
The truth!
7
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!
8
M_1
52.32
M_2
-3,462.73
M_3
2,807.53
mL1
323.03
mL3
667.13
mE1
2,384.72
mE3
1,026.45
mQ1
608.33
mQ3
3,445.50
mU1
1,504.27
mU3
3,640.21
mD1
1,556.16
mD3
1,855.28
At
-6,098.03
Ab
3,059.54
Atau
-3,466.15
mu
211.02
mA
2,693.72
tan(beta)
21.19
The truth!
9
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!
10
M_1
311.69
M_2
244.00
M_3
2,533.46
mL1
1,360.60
mL3
2,602.96
mE1
2,618.86
mE3
2,922.82
mQ1
2,751.66
mQ3
3,062.14
mU1
340.36
mU3
2,244.51
mD1
2,881.56
mD3
3,432.38
At
-2,517.63
Ab
1,243.42
Atau
1,453.28
mu
-415.22
mA
1,266.64
tan(beta)
11.19
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
