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
-277.10
M_2
-922.48
M_3
1,533.30
mL1
282.80
mL3
1,950.28
mE1
2,806.69
mE3
3,948.37
mQ1
3,908.85
mQ3
3,578.11
mU1
858.96
mU3
3,207.62
mD1
1,089.08
mD3
174.16
At
3,029.58
Ab
205.71
Atau
1,461.17
mu
200.91
mA
3,066.51
tan(beta)
22.76
The truth!
2
M_1
-303.67
M_2
1,949.12
M_3
237.01
mL1
2,893.41
mL3
3,349.01
mE1
1,266.38
mE3
3,134.58
mQ1
1,638.01
mQ3
693.20
mU1
3,871.93
mU3
1,380.86
mD1
3,593.28
mD3
1,072.23
At
2,496.60
Ab
3,897.59
Atau
2,451.57
mu
-327.13
mA
3,598.11
tan(beta)
4.46
The truth!
3
M_1
-346.77
M_2
-2,322.39
M_3
2,396.43
mL1
2,877.09
mL3
3,035.84
mE1
268.56
mE3
1,263.50
mQ1
417.12
mQ3
3,347.18
mU1
1,705.46
mU3
3,094.32
mD1
1,675.13
mD3
3,864.59
At
-5,192.75
Ab
2,142.56
Atau
3,230.45
mu
900.14
mA
2,286.05
tan(beta)
9.58
The truth!
4
M_1
-3,272.07
M_2
-608.38
M_3
1,041.64
mL1
1,937.68
mL3
2,380.99
mE1
457.05
mE3
3,927.19
mQ1
876.87
mQ3
3,265.63
mU1
3,888.87
mU3
3,079.46
mD1
3,688.45
mD3
1,813.16
At
-3,295.97
Ab
-1,336.87
Atau
-913.39
mu
-350.50
mA
2,323.75
tan(beta)
13.47
The truth!
5
M_1
-1,171.60
M_2
963.50
M_3
2,311.56
mL1
1,588.63
mL3
3,191.22
mE1
1,812.37
mE3
1,603.16
mQ1
793.06
mQ3
2,251.43
mU1
1,077.28
mU3
3,903.56
mD1
2,303.94
mD3
1,524.10
At
2,852.91
Ab
584.36
Atau
3,266.06
mu
801.18
mA
3,408.39
tan(beta)
14.13
The truth!
6
M_1
289.77
M_2
2,923.45
M_3
3,148.29
mL1
1,274.46
mL3
2,871.77
mE1
623.00
mE3
3,048.63
mQ1
414.53
mQ3
1,360.58
mU1
1,092.17
mU3
2,149.81
mD1
2,853.43
mD3
2,618.48
At
-3,515.00
Ab
-498.55
Atau
-3,190.78
mu
-314.01
mA
2,470.93
tan(beta)
49.07
The truth!
7
M_1
3,057.20
M_2
-1,509.45
M_3
1,890.45
mL1
2,992.71
mL3
794.56
mE1
474.17
mE3
3,111.38
mQ1
1,210.46
mQ3
3,701.60
mU1
2,761.72
mU3
1,550.37
mD1
2,666.03
mD3
3,842.53
At
3,529.78
Ab
-2,551.55
Atau
3,450.60
mu
456.21
mA
2,426.98
tan(beta)
10.56
The truth!
8
M_1
2,707.92
M_2
495.36
M_3
475.73
mL1
547.13
mL3
3,219.03
mE1
2,693.61
mE3
934.74
mQ1
2,293.29
mQ3
2,981.98
mU1
3,876.39
mU3
3,630.49
mD1
638.19
mD3
2,234.34
At
3,501.42
Ab
2,194.94
Atau
-2,325.56
mu
-3,059.33
mA
1,576.15
tan(beta)
13.67
The truth!
9
M_1
1,236.08
M_2
319.25
M_3
2,927.47
mL1
2,474.84
mL3
1,946.24
mE1
2,119.93
mE3
376.41
mQ1
302.50
mQ3
2,681.35
mU1
473.52
mU3
3,584.20
mD1
3,641.92
mD3
391.36
At
-4,374.58
Ab
-2,157.68
Atau
2,095.47
mu
3,238.54
mA
1,773.29
tan(beta)
29.96
The truth!
10
M_1
-740.66
M_2
3,612.59
M_3
3,982.16
mL1
2,771.25
mL3
2,718.49
mE1
2,282.71
mE3
2,986.50
mQ1
1,893.00
mQ3
3,782.87
mU1
3,926.98
mU3
2,709.69
mD1
476.66
mD3
1,460.18
At
3,607.38
Ab
-1,093.99
Atau
-1,838.38
mu
-1,380.74
mA
2,201.21
tan(beta)
56.12
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
