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
3,260.43
M_2
-682.23
M_3
1,750.87
mL1
1,138.98
mL3
946.50
mE1
904.36
mE3
1,032.16
mQ1
3,358.24
mQ3
1,235.45
mU1
493.25
mU3
3,739.57
mD1
3,820.22
mD3
1,309.09
At
-3,115.86
Ab
-3,175.79
Atau
2,869.95
mu
543.57
mA
3,894.06
tan(beta)
22.94
The truth!
2
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!
3
M_1
-716.06
M_2
2,475.16
M_3
1,020.65
mL1
2,337.36
mL3
1,629.81
mE1
3,226.72
mE3
2,595.54
mQ1
737.99
mQ3
2,013.76
mU1
3,010.51
mU3
3,334.70
mD1
2,182.26
mD3
3,812.15
At
4,010.01
Ab
-3,894.58
Atau
1,880.86
mu
-545.94
mA
2,400.73
tan(beta)
24.51
The truth!
4
M_1
-3,182.49
M_2
-454.30
M_3
1,051.90
mL1
294.24
mL3
2,653.40
mE1
3,533.88
mE3
118.11
mQ1
718.58
mQ3
2,630.97
mU1
2,604.80
mU3
1,957.12
mD1
1,053.12
mD3
1,467.55
At
3,860.89
Ab
-1,449.07
Atau
3,329.19
mu
112.81
mA
3,774.63
tan(beta)
7.67
The truth!
5
M_1
1,699.23
M_2
1,275.47
M_3
1,511.59
mL1
3,195.53
mL3
2,741.01
mE1
3,884.39
mE3
1,965.72
mQ1
1,926.36
mQ3
3,902.73
mU1
3,068.13
mU3
1,490.77
mD1
587.50
mD3
587.14
At
3,641.19
Ab
1,171.26
Atau
-3,526.86
mu
-185.41
mA
2,276.14
tan(beta)
29.00
The truth!
6
M_1
-427.98
M_2
-2,948.73
M_3
3,472.91
mL1
1,621.94
mL3
1,686.24
mE1
1,251.88
mE3
1,244.41
mQ1
2,288.87
mQ3
1,859.58
mU1
1,841.39
mU3
2,273.54
mD1
2,921.27
mD3
610.36
At
-3,041.52
Ab
3,695.52
Atau
-3,959.26
mu
201.59
mA
2,116.12
tan(beta)
48.40
The truth!
7
M_1
49.78
M_2
2,865.33
M_3
1,552.90
mL1
1,386.40
mL3
2,825.43
mE1
3,655.40
mE3
2,011.38
mQ1
1,299.57
mQ3
2,431.77
mU1
1,206.09
mU3
3,085.83
mD1
540.37
mD3
625.89
At
-2,845.14
Ab
929.01
Atau
3,163.63
mu
-226.95
mA
3,643.60
tan(beta)
45.13
The truth!
8
M_1
1,031.20
M_2
-307.85
M_3
666.88
mL1
3,051.04
mL3
2,921.91
mE1
1,935.75
mE3
368.10
mQ1
3,943.42
mQ3
2,626.51
mU1
1,305.36
mU3
2,831.86
mD1
2,224.98
mD3
624.39
At
2,956.69
Ab
-916.30
Atau
3,069.94
mu
2,048.68
mA
3,182.06
tan(beta)
16.85
The truth!
9
M_1
-196.96
M_2
-768.86
M_3
2,825.84
mL1
2,392.23
mL3
3,715.05
mE1
3,732.17
mE3
791.36
mQ1
1,997.35
mQ3
3,250.52
mU1
2,951.69
mU3
2,964.18
mD1
1,159.73
mD3
1,707.48
At
2,840.30
Ab
3,191.66
Atau
-2,077.47
mu
-232.81
mA
2,983.45
tan(beta)
50.25
The truth!
10
M_1
2,294.78
M_2
1,866.33
M_3
3,263.41
mL1
3,733.38
mL3
1,397.27
mE1
2,950.96
mE3
728.22
mQ1
2,354.16
mQ3
1,248.49
mU1
527.05
mU3
3,394.04
mD1
1,124.27
mD3
1,998.06
At
2,885.93
Ab
-1,604.00
Atau
-1,019.64
mu
-326.98
mA
1,297.75
tan(beta)
20.97
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
