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
250.20
M_2
-1,592.83
M_3
3,537.05
mL1
2,085.34
mL3
818.84
mE1
2,656.11
mE3
3,616.46
mQ1
2,215.24
mQ3
3,937.90
mU1
1,983.41
mU3
933.01
mD1
3,649.58
mD3
3,681.87
At
3,218.42
Ab
1,419.34
Atau
1,766.43
mu
-252.07
mA
1,735.19
tan(beta)
58.49
The truth!
2
M_1
1,952.18
M_2
674.10
M_3
2,651.29
mL1
1,306.67
mL3
2,812.76
mE1
2,528.13
mE3
1,925.96
mQ1
2,097.62
mQ3
1,611.33
mU1
1,882.11
mU3
2,231.53
mD1
3,558.19
mD3
3,602.32
At
-3,131.44
Ab
-3,264.92
Atau
-2,846.05
mu
2,071.56
mA
4,399.10
tan(beta)
58.55
The truth!
3
M_1
-3,291.65
M_2
1,843.60
M_3
2,981.57
mL1
897.31
mL3
1,549.15
mE1
3,956.01
mE3
762.89
mQ1
2,534.79
mQ3
2,633.99
mU1
3,283.61
mU3
2,907.34
mD1
3,626.45
mD3
279.05
At
-3,172.41
Ab
-2,413.95
Atau
2,442.61
mu
527.39
mA
3,704.87
tan(beta)
31.41
The truth!
4
M_1
-3,667.58
M_2
2,106.26
M_3
1,115.17
mL1
1,489.33
mL3
3,194.42
mE1
2,292.51
mE3
3,183.37
mQ1
2,182.58
mQ3
1,581.88
mU1
1,716.81
mU3
2,999.84
mD1
2,289.18
mD3
1,034.48
At
-4,152.38
Ab
-1,199.63
Atau
2,748.40
mu
-392.91
mA
1,800.14
tan(beta)
12.53
The truth!
5
M_1
547.94
M_2
3,201.95
M_3
416.19
mL1
2,652.22
mL3
3,934.40
mE1
642.45
mE3
2,316.54
mQ1
916.62
mQ3
3,866.04
mU1
2,638.75
mU3
3,711.82
mD1
2,504.46
mD3
907.63
At
-5,895.05
Ab
-3,848.84
Atau
3,273.62
mu
-803.64
mA
2,012.27
tan(beta)
9.29
The truth!
6
M_1
1,968.05
M_2
430.55
M_3
3,545.62
mL1
1,891.44
mL3
3,809.56
mE1
3,085.24
mE3
3,253.13
mQ1
692.35
mQ3
3,106.82
mU1
1,001.47
mU3
1,685.77
mD1
3,434.16
mD3
1,485.54
At
-2,920.14
Ab
-3,212.45
Atau
-22.09
mu
2,821.81
mA
1,629.00
tan(beta)
22.60
The truth!
7
M_1
978.87
M_2
-224.07
M_3
3,155.00
mL1
426.93
mL3
2,173.82
mE1
2,210.63
mE3
2,262.95
mQ1
3,071.19
mQ3
3,202.71
mU1
2,294.15
mU3
3,146.09
mD1
1,758.78
mD3
2,435.64
At
-6,404.81
Ab
115.91
Atau
-1,388.81
mu
2,500.46
mA
3,690.06
tan(beta)
5.33
The truth!
8
M_1
-1,779.39
M_2
136.97
M_3
2,258.70
mL1
3,528.61
mL3
851.48
mE1
1,523.68
mE3
1,501.65
mQ1
2,662.31
mQ3
3,834.33
mU1
1,354.91
mU3
3,359.29
mD1
1,147.54
mD3
2,738.35
At
3,714.59
Ab
1,117.08
Atau
-263.07
mu
-673.92
mA
1,733.71
tan(beta)
32.83
The truth!
9
M_1
-269.76
M_2
-2,200.18
M_3
1,896.52
mL1
1,242.57
mL3
3,623.31
mE1
2,565.84
mE3
1,789.97
mQ1
3,830.98
mQ3
2,414.82
mU1
3,400.64
mU3
3,372.05
mD1
2,272.53
mD3
3,557.66
At
2,751.08
Ab
77.22
Atau
1,705.41
mu
-155.96
mA
1,386.93
tan(beta)
13.83
The truth!
10
M_1
-3,705.74
M_2
-535.24
M_3
2,519.42
mL1
3,871.18
mL3
3,505.97
mE1
2,207.62
mE3
3,690.37
mQ1
937.78
mQ3
3,791.77
mU1
3,371.39
mU3
3,582.26
mD1
2,460.55
mD3
182.86
At
-2,825.16
Ab
2,286.28
Atau
2,272.16
mu
1,641.06
mA
3,396.54
tan(beta)
26.15
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
