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
-2,450.03
M_2
2,875.27
M_3
1,222.64
mL1
3,652.69
mL3
2,655.79
mE1
2,965.26
mE3
1,109.89
mQ1
744.86
mQ3
691.84
mU1
1,824.33
mU3
3,301.69
mD1
1,140.89
mD3
269.05
At
2,859.91
Ab
2,347.47
Atau
3,308.46
mu
222.31
mA
1,254.35
tan(beta)
12.13
The truth!
2
M_1
-1,088.01
M_2
1,019.94
M_3
2,503.56
mL1
1,194.87
mL3
1,597.38
mE1
3,167.17
mE3
2,577.79
mQ1
2,923.87
mQ3
3,200.84
mU1
2,460.17
mU3
3,522.86
mD1
2,748.63
mD3
2,596.07
At
2,912.01
Ab
291.40
Atau
-313.21
mu
-285.07
mA
867.97
tan(beta)
32.74
The truth!
3
M_1
3,109.47
M_2
-408.91
M_3
2,584.46
mL1
633.63
mL3
3,002.51
mE1
1,237.93
mE3
1,972.37
mQ1
2,773.15
mQ3
3,282.33
mU1
1,826.69
mU3
3,894.87
mD1
907.46
mD3
1,097.33
At
2,320.74
Ab
-360.50
Atau
3,500.25
mu
-406.38
mA
1,882.35
tan(beta)
56.46
The truth!
4
M_1
2,230.84
M_2
2,902.14
M_3
3,421.47
mL1
3,607.59
mL3
3,435.10
mE1
2,010.74
mE3
1,838.86
mQ1
3,282.74
mQ3
3,936.11
mU1
1,346.50
mU3
1,594.31
mD1
332.08
mD3
2,681.68
At
4,040.21
Ab
2,263.39
Atau
-1,404.53
mu
-290.01
mA
927.27
tan(beta)
23.78
The truth!
5
M_1
-941.10
M_2
-3,562.43
M_3
2,419.15
mL1
1,004.18
mL3
2,482.24
mE1
2,128.03
mE3
1,910.48
mQ1
629.78
mQ3
1,922.62
mU1
1,111.78
mU3
1,563.06
mD1
3,085.87
mD3
2,711.80
At
-4,882.22
Ab
-3,846.93
Atau
-2,616.41
mu
234.70
mA
1,200.77
tan(beta)
24.66
The truth!
6
M_1
714.27
M_2
3,271.00
M_3
2,968.08
mL1
731.89
mL3
3,195.25
mE1
1,024.17
mE3
1,928.70
mQ1
3,124.19
mQ3
2,651.63
mU1
2,369.85
mU3
2,500.33
mD1
2,050.06
mD3
906.33
At
-4,748.25
Ab
-1,392.11
Atau
1,972.06
mu
1,049.48
mA
1,459.46
tan(beta)
12.12
The truth!
7
M_1
-502.05
M_2
2,064.20
M_3
391.40
mL1
2,703.86
mL3
1,412.98
mE1
1,865.68
mE3
2,327.68
mQ1
1,567.77
mQ3
2,477.18
mU1
547.04
mU3
3,510.05
mD1
3,637.12
mD3
1,249.57
At
2,712.00
Ab
3,373.66
Atau
1,684.44
mu
-1,264.82
mA
3,786.91
tan(beta)
28.75
The truth!
8
M_1
-664.43
M_2
-3,024.31
M_3
3,577.68
mL1
1,599.39
mL3
759.29
mE1
3,693.98
mE3
923.42
mQ1
2,069.15
mQ3
3,150.23
mU1
454.44
mU3
2,358.80
mD1
2,870.42
mD3
1,115.30
At
4,941.16
Ab
-618.63
Atau
-1,104.95
mu
-1,769.14
mA
1,464.34
tan(beta)
3.98
The truth!
9
M_1
3,030.27
M_2
588.10
M_3
847.12
mL1
1,507.87
mL3
3,375.20
mE1
1,628.50
mE3
3,113.69
mQ1
2,796.57
mQ3
1,156.28
mU1
3,730.22
mU3
3,106.64
mD1
3,686.54
mD3
3,660.03
At
-3,508.47
Ab
-2,785.54
Atau
2,874.45
mu
3,439.88
mA
1,411.71
tan(beta)
13.01
The truth!
10
M_1
833.13
M_2
517.26
M_3
3,440.08
mL1
3,320.06
mL3
2,421.80
mE1
667.14
mE3
3,404.36
mQ1
2,935.85
mQ3
3,419.67
mU1
1,297.16
mU3
2,252.38
mD1
2,685.90
mD3
2,930.42
At
-4,327.00
Ab
-2,710.34
Atau
-445.80
mu
2,866.11
mA
2,802.00
tan(beta)
44.91
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
