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
1,533.09
M_2
2,832.56
M_3
2,830.26
mL1
3,475.13
mL3
724.41
mE1
3,964.45
mE3
1,880.13
mQ1
3,122.41
mQ3
597.58
mU1
1,192.31
mU3
3,506.01
mD1
2,442.08
mD3
943.32
At
4,509.40
Ab
3,121.75
Atau
1,635.28
mu
441.16
mA
1,429.09
tan(beta)
3.31
The truth!
2
M_1
3,401.44
M_2
-3,235.58
M_3
962.30
mL1
1,394.01
mL3
820.66
mE1
806.68
mE3
2,208.46
mQ1
3,739.45
mQ3
960.76
mU1
332.74
mU3
3,413.10
mD1
1,980.78
mD3
2,968.10
At
-2,701.74
Ab
-257.64
Atau
-1,577.22
mu
-416.46
mA
3,436.80
tan(beta)
56.40
The truth!
3
M_1
2,824.18
M_2
620.17
M_3
3,718.26
mL1
1,786.79
mL3
2,837.80
mE1
3,664.51
mE3
2,612.23
mQ1
709.37
mQ3
3,271.19
mU1
1,446.29
mU3
1,269.98
mD1
3,784.86
mD3
1,727.20
At
2,869.91
Ab
2,586.61
Atau
1,697.61
mu
-2,749.57
mA
3,358.80
tan(beta)
19.88
The truth!
4
M_1
-2,976.05
M_2
-624.16
M_3
1,998.92
mL1
1,127.65
mL3
2,198.70
mE1
1,802.33
mE3
2,195.28
mQ1
3,072.69
mQ3
656.26
mU1
3,064.20
mU3
2,367.68
mD1
1,139.20
mD3
2,591.45
At
-2,631.82
Ab
-410.45
Atau
2,703.63
mu
1,713.57
mA
2,419.55
tan(beta)
30.63
The truth!
5
M_1
-3,231.62
M_2
353.67
M_3
1,333.27
mL1
1,707.49
mL3
2,849.71
mE1
3,614.24
mE3
2,641.33
mQ1
2,898.11
mQ3
1,785.96
mU1
786.13
mU3
1,338.15
mD1
280.28
mD3
3,961.62
At
2,820.17
Ab
1,822.08
Atau
2,952.12
mu
-2,441.13
mA
4,028.81
tan(beta)
48.12
The truth!
6
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!
7
M_1
2,840.40
M_2
-1,587.89
M_3
3,375.23
mL1
2,872.75
mL3
1,044.47
mE1
955.13
mE3
3,451.90
mQ1
3,434.24
mQ3
2,741.75
mU1
820.79
mU3
3,925.73
mD1
3,424.87
mD3
1,485.08
At
-4,780.26
Ab
2,193.46
Atau
-2,256.54
mu
-774.71
mA
3,052.19
tan(beta)
9.44
The truth!
8
M_1
1,089.43
M_2
1,793.69
M_3
2,100.82
mL1
3,623.04
mL3
2,910.09
mE1
2,333.97
mE3
3,411.75
mQ1
956.59
mQ3
3,976.51
mU1
3,607.03
mU3
1,196.91
mD1
1,121.32
mD3
2,174.59
At
-3,768.23
Ab
623.54
Atau
-988.60
mu
2,793.11
mA
3,390.82
tan(beta)
42.41
The truth!
9
M_1
-1,453.49
M_2
3,630.46
M_3
3,190.09
mL1
1,968.32
mL3
3,769.49
mE1
1,199.64
mE3
3,632.77
mQ1
1,628.26
mQ3
3,633.90
mU1
938.50
mU3
3,899.36
mD1
388.85
mD3
3,085.48
At
4,161.09
Ab
1,045.93
Atau
3,199.49
mu
707.60
mA
3,357.62
tan(beta)
13.67
The truth!
10
M_1
561.58
M_2
774.19
M_3
1,656.05
mL1
2,932.44
mL3
2,457.75
mE1
3,562.83
mE3
1,895.92
mQ1
254.09
mQ3
3,487.24
mU1
1,053.35
mU3
3,867.72
mD1
533.60
mD3
1,326.18
At
-4,323.90
Ab
161.53
Atau
-1,972.23
mu
3,651.75
mA
3,254.51
tan(beta)
24.52
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
