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
-2,908.36
M_2
240.13
M_3
933.15
mL1
3,304.04
mL3
1,584.41
mE1
2,939.52
mE3
1,304.87
mQ1
1,124.80
mQ3
3,998.20
mU1
357.47
mU3
3,627.36
mD1
3,651.92
mD3
3,201.24
At
6,201.89
Ab
1,706.02
Atau
623.31
mu
1,964.44
mA
2,806.83
tan(beta)
4.91
The truth!
2
M_1
-1,366.64
M_2
-273.54
M_3
3,460.13
mL1
2,063.72
mL3
2,370.73
mE1
3,043.29
mE3
2,409.62
mQ1
2,727.45
mQ3
2,726.14
mU1
2,038.25
mU3
2,607.99
mD1
3,838.63
mD3
2,694.30
At
-2,777.85
Ab
-3,093.94
Atau
-3,717.63
mu
1,464.85
mA
2,157.04
tan(beta)
15.89
The truth!
3
M_1
-2,316.14
M_2
-270.12
M_3
1,329.72
mL1
549.57
mL3
2,615.57
mE1
953.24
mE3
1,203.93
mQ1
2,520.80
mQ3
3,563.96
mU1
980.22
mU3
3,022.98
mD1
2,811.39
mD3
2,826.87
At
5,087.83
Ab
842.69
Atau
-1,327.66
mu
-1,937.05
mA
1,232.51
tan(beta)
4.97
The truth!
4
M_1
-846.44
M_2
-3,422.21
M_3
1,719.67
mL1
3,310.86
mL3
2,617.96
mE1
2,977.59
mE3
3,587.07
mQ1
3,861.46
mQ3
3,832.33
mU1
675.67
mU3
2,876.61
mD1
2,111.07
mD3
3,041.61
At
3,698.70
Ab
2,719.27
Atau
-1,367.93
mu
-1,378.88
mA
2,210.67
tan(beta)
19.35
The truth!
5
M_1
-266.05
M_2
3,942.36
M_3
1,641.19
mL1
2,676.33
mL3
1,836.00
mE1
3,846.84
mE3
3,068.64
mQ1
828.79
mQ3
1,148.30
mU1
1,382.82
mU3
3,418.30
mD1
3,283.42
mD3
208.16
At
-2,937.67
Ab
1,868.35
Atau
1,193.35
mu
274.29
mA
1,692.92
tan(beta)
44.80
The truth!
6
M_1
2,672.50
M_2
1,256.89
M_3
1,925.63
mL1
3,876.93
mL3
2,918.44
mE1
3,325.95
mE3
3,424.93
mQ1
3,536.53
mQ3
1,405.96
mU1
2,868.66
mU3
2,137.93
mD1
1,371.10
mD3
1,272.68
At
-3,465.99
Ab
554.03
Atau
436.22
mu
2,997.32
mA
2,661.18
tan(beta)
14.19
The truth!
7
M_1
711.16
M_2
-1,112.29
M_3
1,919.01
mL1
2,777.50
mL3
1,999.32
mE1
752.02
mE3
1,992.70
mQ1
2,927.16
mQ3
3,620.72
mU1
3,710.97
mU3
2,918.17
mD1
717.99
mD3
475.78
At
-3,426.70
Ab
-3,910.25
Atau
-933.84
mu
3,242.17
mA
2,617.73
tan(beta)
26.61
The truth!
8
M_1
-2,989.64
M_2
318.55
M_3
1,651.64
mL1
389.47
mL3
3,629.90
mE1
3,918.34
mE3
3,681.96
mQ1
2,743.15
mQ3
2,664.01
mU1
598.79
mU3
1,161.02
mD1
3,311.43
mD3
3,272.41
At
-2,673.25
Ab
-2,885.54
Atau
3,813.85
mu
-131.10
mA
2,627.74
tan(beta)
11.06
The truth!
9
M_1
3,641.05
M_2
-224.24
M_3
3,443.83
mL1
3,419.40
mL3
2,465.11
mE1
2,811.70
mE3
617.27
mQ1
3,809.25
mQ3
2,858.98
mU1
2,556.54
mU3
3,028.11
mD1
3,382.59
mD3
3,056.52
At
4,697.19
Ab
3,570.05
Atau
-3,982.77
mu
-941.61
mA
960.92
tan(beta)
7.25
The truth!
10
M_1
46.81
M_2
1,153.99
M_3
1,013.61
mL1
3,106.56
mL3
3,225.84
mE1
133.44
mE3
1,531.45
mQ1
1,306.29
mQ3
1,847.45
mU1
1,626.61
mU3
3,447.45
mD1
1,310.43
mD3
1,761.78
At
3,128.00
Ab
2,444.70
Atau
884.81
mu
-237.73
mA
745.25
tan(beta)
11.24
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
