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
382.39
M_2
-2,177.05
M_3
891.44
mL1
1,965.76
mL3
2,369.65
mE1
2,093.48
mE3
3,824.57
mQ1
838.94
mQ3
1,752.53
mU1
259.93
mU3
3,976.18
mD1
713.21
mD3
319.99
At
-4,098.87
Ab
-3,254.21
Atau
3,433.30
mu
650.84
mA
2,417.23
tan(beta)
12.91
The truth!
2
M_1
2,994.05
M_2
-1,819.16
M_3
2,749.95
mL1
264.70
mL3
1,898.53
mE1
895.40
mE3
3,988.73
mQ1
2,335.11
mQ3
1,552.09
mU1
2,057.86
mU3
3,939.43
mD1
1,289.38
mD3
2,198.22
At
2,748.85
Ab
2,273.49
Atau
-3,093.05
mu
181.25
mA
3,817.74
tan(beta)
40.85
The truth!
3
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!
4
M_1
-498.00
M_2
899.29
M_3
3,527.16
mL1
1,179.87
mL3
2,422.04
mE1
3,213.11
mE3
3,334.33
mQ1
1,568.52
mQ3
1,250.01
mU1
3,603.78
mU3
2,000.03
mD1
3,657.62
mD3
1,501.76
At
2,903.00
Ab
268.40
Atau
3,154.31
mu
-1,496.69
mA
975.39
tan(beta)
5.51
The truth!
5
M_1
-220.06
M_2
2,230.44
M_3
1,620.15
mL1
3,890.55
mL3
408.32
mE1
3,448.26
mE3
2,624.40
mQ1
304.34
mQ3
267.22
mU1
3,333.93
mU3
3,265.43
mD1
3,957.69
mD3
1,427.11
At
-3,069.65
Ab
-550.18
Atau
2,034.36
mu
1,876.92
mA
1,473.78
tan(beta)
8.37
The truth!
6
M_1
-2,733.09
M_2
-3,032.87
M_3
3,509.11
mL1
3,041.06
mL3
1,380.87
mE1
1,283.05
mE3
1,540.74
mQ1
1,243.77
mQ3
3,077.47
mU1
3,295.74
mU3
2,349.49
mD1
1,940.91
mD3
3,169.90
At
4,125.13
Ab
-2,752.17
Atau
-1,464.51
mu
-436.94
mA
1,414.22
tan(beta)
26.57
The truth!
7
M_1
1,572.12
M_2
-197.46
M_3
2,029.42
mL1
1,292.98
mL3
1,923.02
mE1
2,643.52
mE3
3,672.51
mQ1
1,123.47
mQ3
3,243.95
mU1
3,227.24
mU3
1,971.96
mD1
1,559.58
mD3
579.23
At
3,159.71
Ab
2,588.69
Atau
-1,948.60
mu
-3,748.06
mA
2,769.00
tan(beta)
14.13
The truth!
8
M_1
-2,684.72
M_2
-259.95
M_3
2,970.20
mL1
3,620.34
mL3
600.38
mE1
3,155.46
mE3
3,233.54
mQ1
1,541.62
mQ3
885.84
mU1
3,014.28
mU3
1,958.88
mD1
687.62
mD3
3,106.60
At
2,891.68
Ab
-3,879.20
Atau
-827.50
mu
3,261.02
mA
4,000.72
tan(beta)
51.57
The truth!
9
M_1
-448.46
M_2
976.00
M_3
3,977.84
mL1
1,568.25
mL3
458.26
mE1
652.37
mE3
480.77
mQ1
1,679.68
mQ3
3,883.20
mU1
3,134.64
mU3
3,004.99
mD1
3,104.71
mD3
1,231.09
At
-3,207.87
Ab
102.32
Atau
3,156.91
mu
431.52
mA
2,747.59
tan(beta)
23.40
The truth!
10
M_1
-1,060.66
M_2
-2,831.47
M_3
3,062.10
mL1
1,453.25
mL3
3,022.51
mE1
1,841.56
mE3
1,280.47
mQ1
1,922.48
mQ3
2,551.69
mU1
3,413.90
mU3
3,620.31
mD1
860.39
mD3
3,897.88
At
-3,910.11
Ab
-2,900.73
Atau
160.16
mu
2,838.92
mA
3,861.18
tan(beta)
6.41
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
