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
879.31
M_2
-1,405.79
M_3
767.42
mL1
2,180.58
mL3
2,913.66
mE1
2,103.29
mE3
3,263.79
mQ1
2,617.14
mQ3
3,725.08
mU1
2,178.39
mU3
2,312.35
mD1
3,294.29
mD3
3,196.96
At
-3,469.89
Ab
1,493.88
Atau
-1,841.11
mu
823.06
mA
2,806.88
tan(beta)
22.09
The truth!
2
M_1
-3,920.67
M_2
-3,369.94
M_3
2,039.42
mL1
1,560.05
mL3
1,247.18
mE1
642.64
mE3
684.48
mQ1
3,247.27
mQ3
2,628.33
mU1
1,111.18
mU3
2,338.01
mD1
1,102.78
mD3
2,640.47
At
-3,358.82
Ab
-2,392.63
Atau
503.11
mu
579.22
mA
3,704.54
tan(beta)
58.71
The truth!
3
M_1
2,826.64
M_2
-390.06
M_3
1,416.06
mL1
3,401.66
mL3
2,957.72
mE1
2,546.76
mE3
2,810.66
mQ1
2,133.33
mQ3
3,667.17
mU1
1,258.97
mU3
3,073.49
mD1
2,946.08
mD3
3,691.12
At
2,182.49
Ab
2,538.23
Atau
439.43
mu
233.50
mA
2,692.00
tan(beta)
12.31
The truth!
4
M_1
-612.04
M_2
663.85
M_3
2,287.84
mL1
2,916.64
mL3
3,444.82
mE1
880.88
mE3
1,766.63
mQ1
531.74
mQ3
2,430.81
mU1
2,492.48
mU3
1,375.55
mD1
2,507.67
mD3
933.38
At
-3,939.74
Ab
-3,180.66
Atau
2,265.29
mu
2,848.51
mA
3,619.63
tan(beta)
49.31
The truth!
5
M_1
-955.81
M_2
2,561.53
M_3
1,950.63
mL1
1,646.76
mL3
3,937.67
mE1
1,845.18
mE3
3,849.16
mQ1
1,004.48
mQ3
2,175.85
mU1
800.90
mU3
3,001.77
mD1
2,056.06
mD3
1,123.82
At
-3,692.00
Ab
-1,768.29
Atau
946.52
mu
-1,715.61
mA
3,656.98
tan(beta)
12.00
The truth!
6
M_1
-2,976.16
M_2
-496.08
M_3
3,115.83
mL1
1,396.26
mL3
3,511.19
mE1
495.41
mE3
1,951.41
mQ1
442.73
mQ3
1,228.42
mU1
632.82
mU3
3,722.60
mD1
3,473.07
mD3
3,198.23
At
-3,431.82
Ab
-2,167.77
Atau
-3,711.22
mu
1,999.18
mA
3,156.73
tan(beta)
51.72
The truth!
7
M_1
2,237.15
M_2
-2,006.43
M_3
452.40
mL1
2,446.52
mL3
2,402.05
mE1
2,221.08
mE3
642.87
mQ1
3,454.16
mQ3
3,835.06
mU1
807.95
mU3
3,153.79
mD1
477.84
mD3
2,844.98
At
3,306.78
Ab
66.66
Atau
1,907.08
mu
-474.76
mA
1,230.36
tan(beta)
48.53
The truth!
8
M_1
2,912.73
M_2
3,663.08
M_3
870.38
mL1
3,909.48
mL3
2,211.13
mE1
3,457.76
mE3
3,272.51
mQ1
613.05
mQ3
3,811.78
mU1
2,951.95
mU3
1,708.50
mD1
3,795.79
mD3
2,640.78
At
-3,279.14
Ab
-2,889.45
Atau
2,306.93
mu
239.93
mA
3,094.60
tan(beta)
32.79
The truth!
9
M_1
3,635.90
M_2
-3,024.03
M_3
1,998.55
mL1
3,055.51
mL3
3,957.04
mE1
1,516.70
mE3
1,372.18
mQ1
447.02
mQ3
2,553.90
mU1
1,493.87
mU3
3,976.99
mD1
3,612.43
mD3
2,072.44
At
-3,897.65
Ab
455.67
Atau
-512.16
mu
-288.73
mA
2,310.19
tan(beta)
29.51
The truth!
10
M_1
1,643.71
M_2
174.20
M_3
2,111.65
mL1
3,486.37
mL3
1,158.91
mE1
3,935.92
mE3
2,536.64
mQ1
1,481.51
mQ3
3,964.19
mU1
3,963.09
mU3
3,988.01
mD1
3,344.43
mD3
2,305.40
At
-1,309.48
Ab
2,852.47
Atau
2,447.43
mu
-1,654.37
mA
2,169.98
tan(beta)
49.32
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
