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
-273.15
M_2
1,288.93
M_3
3,960.83
mL1
3,572.01
mL3
736.13
mE1
2,986.81
mE3
2,968.47
mQ1
3,041.76
mQ3
2,714.63
mU1
2,579.07
mU3
1,256.29
mD1
3,397.67
mD3
3,724.38
At
-3,388.22
Ab
-1,473.98
Atau
-3,139.41
mu
273.63
mA
1,952.92
tan(beta)
20.41
The truth!
2
M_1
370.57
M_2
2,549.35
M_3
341.47
mL1
3,067.90
mL3
702.85
mE1
3,944.66
mE3
3,211.03
mQ1
2,591.50
mQ3
295.97
mU1
1,977.40
mU3
2,825.50
mD1
1,699.73
mD3
555.58
At
-2,578.07
Ab
-3,125.17
Atau
-353.12
mu
919.79
mA
1,887.97
tan(beta)
18.69
The truth!
3
M_1
-3,983.83
M_2
-1,659.24
M_3
3,639.30
mL1
2,163.58
mL3
3,981.95
mE1
403.54
mE3
987.02
mQ1
2,966.68
mQ3
3,795.38
mU1
431.47
mU3
3,347.88
mD1
601.64
mD3
485.13
At
3,560.00
Ab
-1,638.35
Atau
-572.32
mu
-176.32
mA
3,029.18
tan(beta)
42.73
The truth!
4
M_1
241.03
M_2
1,388.64
M_3
2,246.67
mL1
2,712.32
mL3
1,931.41
mE1
2,943.09
mE3
1,058.70
mQ1
2,705.50
mQ3
1,473.36
mU1
1,392.97
mU3
1,756.11
mD1
959.43
mD3
2,632.54
At
2,761.55
Ab
-3,570.53
Atau
-1,224.11
mu
-240.49
mA
1,573.43
tan(beta)
25.01
The truth!
5
M_1
620.34
M_2
2,329.92
M_3
936.89
mL1
2,776.53
mL3
3,767.30
mE1
3,504.53
mE3
1,784.47
mQ1
632.37
mQ3
1,733.33
mU1
2,514.45
mU3
3,977.21
mD1
627.08
mD3
2,123.63
At
-2,769.25
Ab
3,288.33
Atau
761.55
mu
-301.74
mA
2,628.07
tan(beta)
38.70
The truth!
6
M_1
-150.83
M_2
-3,683.99
M_3
3,034.99
mL1
2,665.78
mL3
2,159.17
mE1
3,860.04
mE3
3,918.70
mQ1
1,042.01
mQ3
2,208.48
mU1
563.06
mU3
2,618.89
mD1
3,678.29
mD3
2,016.04
At
4,104.26
Ab
220.41
Atau
-3,943.25
mu
-164.97
mA
2,505.91
tan(beta)
10.45
The truth!
7
M_1
2,029.26
M_2
695.66
M_3
3,476.76
mL1
3,113.83
mL3
2,250.44
mE1
3,304.10
mE3
3,950.50
mQ1
3,648.03
mQ3
1,691.04
mU1
596.13
mU3
3,676.71
mD1
3,311.40
mD3
2,325.35
At
2,322.84
Ab
3,553.88
Atau
-3,598.85
mu
-707.13
mA
3,678.71
tan(beta)
37.14
The truth!
8
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!
9
M_1
-1,394.37
M_2
574.05
M_3
3,089.46
mL1
3,242.00
mL3
2,408.88
mE1
679.64
mE3
2,910.14
mQ1
1,693.00
mQ3
3,820.26
mU1
3,349.63
mU3
3,300.87
mD1
1,477.54
mD3
1,292.24
At
-2,288.94
Ab
464.31
Atau
-2,963.94
mu
1,889.43
mA
1,937.84
tan(beta)
26.14
The truth!
10
M_1
-2,646.76
M_2
3,071.01
M_3
689.90
mL1
2,431.43
mL3
896.89
mE1
734.86
mE3
2,136.03
mQ1
1,549.02
mQ3
3,583.96
mU1
3,824.79
mU3
3,075.25
mD1
3,756.95
mD3
1,048.83
At
3,492.30
Ab
-1,643.92
Atau
-2,586.01
mu
-629.90
mA
2,339.83
tan(beta)
19.13
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
