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
3,375.42
M_2
926.15
M_3
3,920.76
mL1
3,671.22
mL3
3,041.17
mE1
3,474.24
mE3
3,510.32
mQ1
1,696.43
mQ3
3,927.28
mU1
3,466.97
mU3
3,487.42
mD1
2,551.50
mD3
1,749.10
At
-4,105.42
Ab
-2,820.53
Atau
2,619.97
mu
2,052.94
mA
1,239.98
tan(beta)
28.03
The truth!
2
M_1
-72.66
M_2
-469.88
M_3
2,787.73
mL1
358.08
mL3
2,177.03
mE1
3,104.27
mE3
2,859.19
mQ1
2,340.52
mQ3
3,531.24
mU1
2,323.18
mU3
2,508.02
mD1
393.08
mD3
2,408.60
At
3,031.48
Ab
-191.49
Atau
-1,225.42
mu
-117.02
mA
3,489.39
tan(beta)
17.81
The truth!
3
M_1
3,025.06
M_2
-3,124.89
M_3
1,519.35
mL1
1,244.75
mL3
1,322.27
mE1
646.30
mE3
3,918.51
mQ1
1,699.88
mQ3
2,968.96
mU1
3,617.54
mU3
2,597.90
mD1
3,177.06
mD3
1,760.40
At
3,069.65
Ab
3,516.89
Atau
-1,337.49
mu
-182.01
mA
3,455.80
tan(beta)
58.95
The truth!
4
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!
5
M_1
329.40
M_2
-317.14
M_3
1,816.32
mL1
2,192.20
mL3
1,521.28
mE1
3,194.58
mE3
2,809.50
mQ1
3,439.54
mQ3
2,882.80
mU1
771.66
mU3
3,327.90
mD1
526.84
mD3
1,754.96
At
-4,478.90
Ab
-1,744.72
Atau
1,989.48
mu
3,948.56
mA
3,439.16
tan(beta)
57.90
The truth!
6
M_1
461.76
M_2
1,280.25
M_3
1,007.57
mL1
1,421.76
mL3
3,065.96
mE1
688.50
mE3
1,292.59
mQ1
3,257.64
mQ3
345.14
mU1
2,860.75
mU3
3,070.21
mD1
467.63
mD3
2,213.62
At
2,661.67
Ab
736.53
Atau
1,166.17
mu
-3,440.38
mA
3,753.90
tan(beta)
12.43
The truth!
7
M_1
-3,415.07
M_2
3,634.67
M_3
1,461.11
mL1
2,854.29
mL3
1,786.52
mE1
1,636.65
mE3
786.44
mQ1
390.75
mQ3
1,997.05
mU1
1,791.82
mU3
2,047.81
mD1
502.92
mD3
2,675.31
At
-3,630.90
Ab
-670.17
Atau
-2,582.92
mu
185.66
mA
2,168.15
tan(beta)
32.20
The truth!
8
M_1
-507.24
M_2
2,628.57
M_3
3,167.45
mL1
3,010.74
mL3
3,382.74
mE1
769.44
mE3
828.79
mQ1
682.22
mQ3
1,372.71
mU1
2,439.19
mU3
1,515.99
mD1
3,015.49
mD3
1,217.59
At
-3,178.74
Ab
-1,862.18
Atau
-2,293.70
mu
2,625.51
mA
2,764.42
tan(beta)
13.63
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
