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
905.14
M_2
-1,470.39
M_3
1,770.50
mL1
1,030.37
mL3
1,106.67
mE1
2,017.49
mE3
3,657.03
mQ1
990.01
mQ3
3,877.40
mU1
3,487.10
mU3
2,893.23
mD1
3,765.32
mD3
744.58
At
-4,409.84
Ab
-2,800.49
Atau
712.40
mu
1,443.64
mA
2,129.59
tan(beta)
31.37
The truth!
2
M_1
-1,728.40
M_2
3,474.84
M_3
1,648.89
mL1
3,378.87
mL3
3,979.90
mE1
3,285.58
mE3
3,480.75
mQ1
2,274.45
mQ3
3,566.33
mU1
715.56
mU3
3,616.51
mD1
2,364.56
mD3
1,043.97
At
4,529.41
Ab
-1,145.78
Atau
522.71
mu
-207.14
mA
588.95
tan(beta)
15.39
The truth!
3
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!
4
M_1
885.23
M_2
-146.93
M_3
1,015.47
mL1
624.43
mL3
925.51
mE1
1,781.39
mE3
2,910.48
mQ1
708.71
mQ3
3,987.14
mU1
2,013.19
mU3
1,798.75
mD1
3,738.43
mD3
1,341.79
At
3,340.06
Ab
-2,739.47
Atau
1,992.36
mu
-3,646.61
mA
2,536.06
tan(beta)
7.82
The truth!
5
M_1
49.79
M_2
714.89
M_3
2,935.39
mL1
1,953.83
mL3
1,365.09
mE1
2,802.61
mE3
2,920.73
mQ1
1,086.15
mQ3
3,758.90
mU1
3,999.29
mU3
2,464.49
mD1
3,094.45
mD3
2,081.24
At
-3,715.23
Ab
191.20
Atau
-1,466.40
mu
-165.09
mA
2,042.50
tan(beta)
56.58
The truth!
6
M_1
-700.49
M_2
3,526.24
M_3
1,705.02
mL1
1,397.74
mL3
2,760.06
mE1
3,175.56
mE3
2,466.51
mQ1
2,528.27
mQ3
1,924.03
mU1
1,417.14
mU3
3,736.49
mD1
3,260.35
mD3
3,879.59
At
-3,739.72
Ab
-468.20
Atau
973.04
mu
577.07
mA
1,339.65
tan(beta)
26.34
The truth!
7
M_1
-1,526.13
M_2
1,070.57
M_3
1,944.18
mL1
2,767.25
mL3
1,308.09
mE1
1,839.41
mE3
1,511.93
mQ1
3,176.73
mQ3
3,467.00
mU1
3,026.18
mU3
2,804.33
mD1
3,892.87
mD3
3,702.95
At
3,379.44
Ab
1,239.31
Atau
-1,367.97
mu
-2,499.47
mA
3,140.76
tan(beta)
41.00
The truth!
8
M_1
2,401.83
M_2
-1,098.13
M_3
2,782.30
mL1
854.93
mL3
3,721.99
mE1
3,074.73
mE3
253.11
mQ1
1,737.33
mQ3
831.00
mU1
1,848.36
mU3
2,876.19
mD1
2,422.25
mD3
967.16
At
-2,729.66
Ab
-3,782.48
Atau
-3,985.44
mu
244.35
mA
834.85
tan(beta)
18.41
The truth!
9
M_1
781.59
M_2
-1,949.53
M_3
1,064.92
mL1
3,469.36
mL3
2,267.71
mE1
1,746.78
mE3
3,436.14
mQ1
1,567.95
mQ3
727.86
mU1
2,715.08
mU3
3,142.62
mD1
2,861.56
mD3
3,832.18
At
-2,708.02
Ab
443.87
Atau
-1,185.53
mu
3,742.80
mA
2,565.58
tan(beta)
9.41
The truth!
10
M_1
291.98
M_2
-2,324.79
M_3
745.63
mL1
2,837.74
mL3
908.44
mE1
1,933.60
mE3
3,913.16
mQ1
830.85
mQ3
2,009.67
mU1
912.58
mU3
3,490.29
mD1
234.61
mD3
115.65
At
-4,046.39
Ab
-1,018.93
Atau
677.97
mu
2,331.06
mA
1,554.90
tan(beta)
6.10
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
