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
3,630.28
M_2
-381.86
M_3
3,757.81
mL1
3,980.20
mL3
3,647.61
mE1
2,444.86
mE3
1,800.42
mQ1
844.33
mQ3
3,954.82
mU1
3,245.40
mU3
3,064.95
mD1
2,873.34
mD3
1,344.12
At
-4,974.86
Ab
3,683.97
Atau
-232.69
mu
2,834.05
mA
3,859.09
tan(beta)
15.83
The truth!
2
M_1
275.57
M_2
810.43
M_3
213.76
mL1
1,448.44
mL3
2,457.29
mE1
3,691.35
mE3
1,376.87
mQ1
1,267.27
mQ3
2,722.50
mU1
566.68
mU3
2,855.25
mD1
3,366.87
mD3
2,457.55
At
-3,142.59
Ab
-3,981.80
Atau
3,985.09
mu
1,670.18
mA
1,932.69
tan(beta)
39.17
The truth!
3
M_1
1,600.45
M_2
3,957.56
M_3
2,993.45
mL1
3,122.17
mL3
1,959.11
mE1
1,195.59
mE3
1,501.64
mQ1
1,314.28
mQ3
3,219.42
mU1
288.82
mU3
2,023.48
mD1
866.24
mD3
3,664.67
At
4,284.74
Ab
1,574.15
Atau
688.07
mu
486.72
mA
3,968.21
tan(beta)
52.20
The truth!
4
M_1
2,323.44
M_2
3,183.19
M_3
1,177.61
mL1
1,146.60
mL3
3,930.10
mE1
2,011.18
mE3
902.28
mQ1
1,115.84
mQ3
1,328.57
mU1
2,960.82
mU3
2,204.55
mD1
2,186.93
mD3
3,378.39
At
2,554.37
Ab
-1,006.48
Atau
3,367.03
mu
-892.45
mA
1,364.63
tan(beta)
26.49
The truth!
5
M_1
3,880.34
M_2
-271.18
M_3
3,612.13
mL1
2,841.21
mL3
390.77
mE1
2,737.34
mE3
3,174.63
mQ1
2,532.57
mQ3
2,228.61
mU1
3,368.29
mU3
3,786.79
mD1
3,757.35
mD3
3,029.21
At
-3,338.00
Ab
3,494.05
Atau
1,125.66
mu
-324.04
mA
3,836.61
tan(beta)
18.97
The truth!
6
M_1
2,401.83
M_2
-1,098.13
M_3
2,782.30
mL1
854.93
mL3
3,721.99
mE1
3,074.73
mE3
253.11
mQ1
1,737.33
mQ3
831.00
mU1
1,848.36
mU3
2,876.19
mD1
2,422.25
mD3
967.16
At
-2,729.66
Ab
-3,782.48
Atau
-3,985.44
mu
244.35
mA
834.85
tan(beta)
18.41
The truth!
7
M_1
-3,261.05
M_2
557.36
M_3
3,484.83
mL1
762.06
mL3
2,921.65
mE1
2,162.48
mE3
3,676.47
mQ1
2,433.51
mQ3
3,110.58
mU1
1,683.82
mU3
2,113.16
mD1
995.66
mD3
2,148.22
At
-2,851.07
Ab
-2,039.32
Atau
1,826.68
mu
1,289.94
mA
1,701.35
tan(beta)
21.79
The truth!
8
M_1
533.53
M_2
-693.77
M_3
449.69
mL1
1,185.23
mL3
3,852.87
mE1
2,424.89
mE3
1,873.19
mQ1
2,610.67
mQ3
705.63
mU1
1,989.60
mU3
1,712.78
mD1
985.60
mD3
2,450.86
At
-2,739.53
Ab
-289.83
Atau
-1,188.80
mu
1,824.75
mA
1,215.23
tan(beta)
7.33
The truth!
9
M_1
-2,002.38
M_2
-1,534.85
M_3
3,115.82
mL1
3,292.01
mL3
2,753.31
mE1
2,045.94
mE3
3,441.20
mQ1
3,154.70
mQ3
2,635.36
mU1
1,848.63
mU3
2,736.25
mD1
2,168.90
mD3
3,208.22
At
-2,896.06
Ab
-1,384.67
Atau
264.64
mu
2,117.33
mA
1,766.55
tan(beta)
49.72
The truth!
10
M_1
-2,580.45
M_2
-1,568.21
M_3
2,493.29
mL1
3,165.24
mL3
1,406.59
mE1
2,280.18
mE3
926.53
mQ1
3,459.81
mQ3
2,362.37
mU1
2,653.21
mU3
2,631.05
mD1
3,221.43
mD3
377.29
At
-3,040.27
Ab
-3,455.96
Atau
2,764.26
mu
-141.47
mA
3,125.71
tan(beta)
32.37
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
