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
978.07
M_2
-2,389.02
M_3
1,028.09
mL1
947.19
mL3
1,351.79
mE1
795.31
mE3
3,174.48
mQ1
2,501.44
mQ3
2,445.22
mU1
289.57
mU3
3,350.34
mD1
2,794.94
mD3
2,042.13
At
3,132.76
Ab
3,517.63
Atau
-3,035.29
mu
295.46
mA
1,914.82
tan(beta)
29.71
The truth!
2
M_1
1,909.05
M_2
176.57
M_3
651.63
mL1
1,425.79
mL3
2,816.08
mE1
3,966.78
mE3
2,096.24
mQ1
2,604.72
mQ3
1,394.13
mU1
2,390.43
mU3
3,035.42
mD1
3,852.27
mD3
3,303.06
At
2,410.59
Ab
-355.76
Atau
-2,841.34
mu
-685.48
mA
3,240.92
tan(beta)
53.04
The truth!
3
M_1
-2,421.62
M_2
-849.33
M_3
3,654.36
mL1
1,683.91
mL3
2,029.02
mE1
2,609.31
mE3
1,067.63
mQ1
3,213.95
mQ3
3,038.61
mU1
1,224.47
mU3
3,479.16
mD1
2,765.80
mD3
3,129.53
At
4,255.69
Ab
1,602.39
Atau
-3,972.10
mu
-819.47
mA
884.46
tan(beta)
10.98
The truth!
4
M_1
3,590.60
M_2
3,884.58
M_3
1,921.42
mL1
3,437.79
mL3
1,904.92
mE1
3,776.89
mE3
833.59
mQ1
521.15
mQ3
2,925.74
mU1
2,685.76
mU3
2,334.24
mD1
1,698.72
mD3
266.90
At
-3,588.99
Ab
-3,302.83
Atau
3,447.94
mu
393.08
mA
1,209.84
tan(beta)
34.61
The truth!
5
M_1
1,699.23
M_2
1,275.47
M_3
1,511.59
mL1
3,195.53
mL3
2,741.01
mE1
3,884.39
mE3
1,965.72
mQ1
1,926.36
mQ3
3,902.73
mU1
3,068.13
mU3
1,490.77
mD1
587.50
mD3
587.14
At
3,641.19
Ab
1,171.26
Atau
-3,526.86
mu
-185.41
mA
2,276.14
tan(beta)
29.00
The truth!
6
M_1
-2,888.39
M_2
-315.23
M_3
2,048.55
mL1
3,322.36
mL3
1,438.68
mE1
2,789.64
mE3
3,367.10
mQ1
3,335.09
mQ3
2,709.01
mU1
867.61
mU3
2,428.69
mD1
3,978.08
mD3
2,250.79
At
-3,130.20
Ab
3,138.90
Atau
-1,819.09
mu
-171.60
mA
2,337.34
tan(beta)
21.77
The truth!
7
M_1
-502.05
M_2
2,064.20
M_3
391.40
mL1
2,703.86
mL3
1,412.98
mE1
1,865.68
mE3
2,327.68
mQ1
1,567.77
mQ3
2,477.18
mU1
547.04
mU3
3,510.05
mD1
3,637.12
mD3
1,249.57
At
2,712.00
Ab
3,373.66
Atau
1,684.44
mu
-1,264.82
mA
3,786.91
tan(beta)
28.75
The truth!
8
M_1
2,102.37
M_2
-2,085.40
M_3
1,536.41
mL1
2,749.88
mL3
1,408.69
mE1
175.66
mE3
1,669.71
mQ1
3,093.29
mQ3
3,380.69
mU1
424.19
mU3
2,127.83
mD1
2,149.00
mD3
1,165.42
At
-3,744.50
Ab
3,695.71
Atau
1,234.45
mu
137.25
mA
2,336.53
tan(beta)
39.90
The truth!
9
M_1
-611.25
M_2
-902.58
M_3
1,941.25
mL1
2,216.88
mL3
640.49
mE1
2,904.00
mE3
2,676.87
mQ1
1,210.42
mQ3
3,340.36
mU1
3,761.30
mU3
3,756.29
mD1
1,952.46
mD3
3,729.66
At
-3,073.76
Ab
-2,065.12
Atau
2,238.93
mu
612.30
mA
1,551.50
tan(beta)
35.13
The truth!
10
M_1
1,955.20
M_2
1,339.60
M_3
1,256.03
mL1
3,840.58
mL3
3,854.88
mE1
1,028.55
mE3
1,141.16
mQ1
256.91
mQ3
3,206.46
mU1
3,960.10
mU3
2,713.57
mD1
2,249.72
mD3
480.78
At
4,613.53
Ab
3,066.55
Atau
-2,568.03
mu
412.43
mA
2,644.73
tan(beta)
4.98
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
