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
3,628.16
M_2
-953.33
M_3
642.25
mL1
3,261.58
mL3
473.18
mE1
3,780.22
mE3
454.02
mQ1
782.68
mQ3
2,558.35
mU1
2,922.19
mU3
3,729.38
mD1
579.50
mD3
574.03
At
3,804.28
Ab
279.89
Atau
3,717.70
mu
321.79
mA
2,190.26
tan(beta)
35.75
The truth!
2
M_1
-567.69
M_2
2,252.98
M_3
1,349.57
mL1
3,950.42
mL3
2,197.78
mE1
1,230.40
mE3
3,264.10
mQ1
1,480.92
mQ3
3,338.73
mU1
3,314.05
mU3
2,759.36
mD1
2,564.34
mD3
372.50
At
-4,443.33
Ab
-3,386.07
Atau
-2,468.56
mu
2,453.12
mA
2,510.47
tan(beta)
24.77
The truth!
3
M_1
2,087.82
M_2
-2,474.87
M_3
2,564.29
mL1
3,500.79
mL3
1,771.52
mE1
1,335.04
mE3
1,663.50
mQ1
2,416.73
mQ3
3,735.77
mU1
3,576.18
mU3
2,203.49
mD1
1,498.79
mD3
2,945.51
At
3,649.01
Ab
-2,729.32
Atau
2,080.04
mu
723.02
mA
3,181.51
tan(beta)
34.61
The truth!
4
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!
5
M_1
-2,200.56
M_2
-332.57
M_3
1,829.12
mL1
3,176.89
mL3
2,553.28
mE1
2,465.39
mE3
3,782.06
mQ1
2,264.66
mQ3
3,791.21
mU1
3,944.14
mU3
786.76
mD1
652.92
mD3
3,077.30
At
2,635.92
Ab
-3,219.40
Atau
-1,341.32
mu
-364.86
mA
3,170.31
tan(beta)
43.09
The truth!
6
M_1
868.31
M_2
-2,619.78
M_3
3,125.42
mL1
3,712.13
mL3
511.15
mE1
905.16
mE3
2,150.15
mQ1
3,248.85
mQ3
2,025.18
mU1
2,918.30
mU3
2,998.91
mD1
2,089.18
mD3
1,489.34
At
-3,663.12
Ab
-179.40
Atau
2,521.22
mu
-491.05
mA
3,437.25
tan(beta)
49.98
The truth!
7
M_1
-612.04
M_2
663.85
M_3
2,287.84
mL1
2,916.64
mL3
3,444.82
mE1
880.88
mE3
1,766.63
mQ1
531.74
mQ3
2,430.81
mU1
2,492.48
mU3
1,375.55
mD1
2,507.67
mD3
933.38
At
-3,939.74
Ab
-3,180.66
Atau
2,265.29
mu
2,848.51
mA
3,619.63
tan(beta)
49.31
The truth!
8
M_1
56.53
M_2
-3,488.08
M_3
2,285.20
mL1
1,089.53
mL3
3,842.48
mE1
2,639.78
mE3
1,124.65
mQ1
1,901.94
mQ3
1,664.39
mU1
1,623.25
mU3
2,557.87
mD1
2,238.70
mD3
2,234.34
At
-5,918.81
Ab
945.59
Atau
909.65
mu
152.24
mA
1,706.72
tan(beta)
20.34
The truth!
9
M_1
3,109.69
M_2
-112.56
M_3
2,752.52
mL1
228.21
mL3
2,953.57
mE1
1,327.25
mE3
2,823.67
mQ1
1,087.28
mQ3
1,534.41
mU1
2,788.02
mU3
2,582.24
mD1
2,560.56
mD3
2,543.42
At
3,057.94
Ab
-3,247.85
Atau
1,644.12
mu
-1,519.34
mA
3,615.08
tan(beta)
23.06
The truth!
10
M_1
3,843.37
M_2
-830.68
M_3
1,256.99
mL1
1,949.14
mL3
843.70
mE1
1,831.31
mE3
935.82
mQ1
795.99
mQ3
2,827.48
mU1
1,707.98
mU3
2,760.01
mD1
2,730.23
mD3
2,657.93
At
-4,275.94
Ab
-2,530.99
Atau
-1,470.68
mu
448.40
mA
3,051.87
tan(beta)
10.67
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
