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
3,316.25
M_2
164.59
M_3
3,848.38
mL1
2,096.90
mL3
1,288.96
mE1
3,859.74
mE3
3,849.53
mQ1
2,056.19
mQ3
3,772.56
mU1
1,036.00
mU3
3,219.36
mD1
1,720.12
mD3
1,263.77
At
3,368.02
Ab
-1,600.31
Atau
2,293.44
mu
663.76
mA
3,114.79
tan(beta)
24.31
The truth!
2
M_1
-740.53
M_2
-1,173.12
M_3
3,331.66
mL1
1,751.02
mL3
1,294.80
mE1
3,980.74
mE3
2,913.72
mQ1
1,293.11
mQ3
1,302.53
mU1
1,627.72
mU3
3,568.21
mD1
1,810.24
mD3
2,029.38
At
5,410.64
Ab
1,803.20
Atau
-848.01
mu
-177.35
mA
938.37
tan(beta)
3.18
The truth!
3
M_1
3,680.26
M_2
102.28
M_3
1,467.52
mL1
692.85
mL3
1,613.25
mE1
1,005.81
mE3
3,928.31
mQ1
3,632.58
mQ3
3,108.12
mU1
2,081.00
mU3
3,850.66
mD1
1,607.29
mD3
1,346.24
At
201.78
Ab
-2,255.04
Atau
-1,483.18
mu
748.11
mA
934.63
tan(beta)
51.14
The truth!
4
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!
5
M_1
732.48
M_2
758.27
M_3
1,796.61
mL1
2,672.21
mL3
3,926.23
mE1
2,084.60
mE3
3,793.81
mQ1
949.64
mQ3
1,642.15
mU1
2,389.47
mU3
762.43
mD1
958.50
mD3
1,811.05
At
-3,004.42
Ab
-1,730.03
Atau
1,118.16
mu
1,841.62
mA
2,478.74
tan(beta)
34.35
The truth!
6
M_1
1,741.35
M_2
-663.36
M_3
3,804.06
mL1
1,727.38
mL3
2,042.31
mE1
3,464.58
mE3
697.30
mQ1
2,060.10
mQ3
2,800.21
mU1
2,893.80
mU3
1,973.19
mD1
1,178.55
mD3
3,841.21
At
-2,571.20
Ab
2,899.90
Atau
-2,538.77
mu
-325.68
mA
1,680.32
tan(beta)
19.44
The truth!
7
M_1
2,037.79
M_2
953.44
M_3
3,100.19
mL1
935.74
mL3
3,211.55
mE1
663.17
mE3
3,062.40
mQ1
2,165.97
mQ3
3,234.77
mU1
374.55
mU3
3,077.54
mD1
1,163.43
mD3
886.87
At
3,771.28
Ab
1,344.48
Atau
2,625.17
mu
153.52
mA
2,007.21
tan(beta)
31.17
The truth!
8
M_1
551.10
M_2
1,924.85
M_3
2,330.22
mL1
3,468.03
mL3
3,208.41
mE1
2,638.04
mE3
3,116.82
mQ1
559.95
mQ3
2,188.66
mU1
499.07
mU3
1,186.72
mD1
746.25
mD3
1,904.34
At
-4,063.66
Ab
-1,588.94
Atau
173.57
mu
809.54
mA
3,668.96
tan(beta)
16.52
The truth!
9
M_1
-3,381.98
M_2
-346.44
M_3
1,569.47
mL1
3,899.66
mL3
1,391.27
mE1
2,733.89
mE3
3,790.67
mQ1
1,646.31
mQ3
2,782.82
mU1
406.36
mU3
3,297.27
mD1
1,073.90
mD3
1,989.25
At
-4,364.95
Ab
-3,916.45
Atau
-2,821.64
mu
3,355.93
mA
3,368.31
tan(beta)
25.95
The truth!
10
M_1
2,097.93
M_2
2,326.12
M_3
2,212.31
mL1
3,837.82
mL3
1,364.77
mE1
1,263.12
mE3
3,161.43
mQ1
1,762.75
mQ3
2,925.20
mU1
2,993.60
mU3
2,706.02
mD1
561.74
mD3
655.96
At
-4,337.65
Ab
1,773.55
Atau
-1,134.72
mu
-681.64
mA
2,644.60
tan(beta)
44.05
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
