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
-2,411.23
M_2
260.03
M_3
3,488.26
mL1
569.55
mL3
1,909.26
mE1
2,075.30
mE3
2,753.41
mQ1
2,985.61
mQ3
3,933.47
mU1
2,442.12
mU3
3,649.86
mD1
3,200.86
mD3
3,737.71
At
-3,961.18
Ab
2,626.75
Atau
405.80
mu
2,426.77
mA
2,350.49
tan(beta)
24.80
The truth!
2
M_1
635.84
M_2
-599.85
M_3
3,687.35
mL1
2,705.81
mL3
2,634.90
mE1
1,585.58
mE3
2,108.10
mQ1
3,503.80
mQ3
2,182.43
mU1
1,357.33
mU3
3,797.22
mD1
2,293.59
mD3
2,550.14
At
3,486.06
Ab
223.21
Atau
-642.06
mu
-2,343.57
mA
2,546.83
tan(beta)
15.63
The truth!
3
M_1
723.68
M_2
-1,710.50
M_3
1,423.21
mL1
1,626.39
mL3
3,715.46
mE1
871.13
mE3
3,155.51
mQ1
3,433.38
mQ3
3,113.03
mU1
3,345.96
mU3
3,090.29
mD1
3,813.85
mD3
879.98
At
-4,076.05
Ab
503.06
Atau
-2,024.94
mu
479.05
mA
1,500.32
tan(beta)
24.70
The truth!
4
M_1
315.13
M_2
316.17
M_3
2,677.54
mL1
2,197.19
mL3
441.16
mE1
2,915.02
mE3
2,927.92
mQ1
1,487.44
mQ3
1,744.59
mU1
621.46
mU3
2,928.77
mD1
527.53
mD3
487.60
At
-3,757.37
Ab
-378.22
Atau
-2,053.18
mu
2,226.74
mA
3,401.86
tan(beta)
34.73
The truth!
5
M_1
-2,602.68
M_2
294.45
M_3
1,613.36
mL1
2,528.34
mL3
1,395.39
mE1
2,549.94
mE3
578.86
mQ1
3,402.53
mQ3
3,941.12
mU1
1,710.49
mU3
3,523.38
mD1
3,879.91
mD3
2,145.45
At
5,938.72
Ab
-2,718.96
Atau
805.64
mu
-1,056.74
mA
2,346.28
tan(beta)
3.74
The truth!
6
M_1
-2,391.88
M_2
-286.11
M_3
3,725.28
mL1
3,741.93
mL3
572.17
mE1
3,284.49
mE3
2,412.98
mQ1
1,022.20
mQ3
3,277.58
mU1
1,805.90
mU3
3,279.81
mD1
1,987.89
mD3
160.80
At
-5,704.22
Ab
1,604.24
Atau
-1,462.28
mu
2,045.16
mA
956.11
tan(beta)
7.43
The truth!
7
M_1
-850.01
M_2
-919.45
M_3
1,451.51
mL1
1,478.83
mL3
3,266.37
mE1
2,097.39
mE3
1,032.41
mQ1
934.58
mQ3
3,199.68
mU1
1,880.73
mU3
3,949.67
mD1
3,530.86
mD3
3,532.85
At
3,000.16
Ab
2,534.35
Atau
1,681.55
mu
-685.05
mA
2,642.06
tan(beta)
30.04
The truth!
8
M_1
-1,743.93
M_2
-606.36
M_3
3,024.94
mL1
3,010.39
mL3
513.14
mE1
2,583.35
mE3
3,344.87
mQ1
1,417.92
mQ3
3,871.10
mU1
1,552.13
mU3
2,825.35
mD1
921.89
mD3
1,274.16
At
-4,614.86
Ab
-2,147.17
Atau
-2,643.89
mu
-130.85
mA
1,875.97
tan(beta)
15.31
The truth!
9
M_1
3,147.39
M_2
535.41
M_3
1,915.50
mL1
1,967.76
mL3
3,492.90
mE1
3,861.97
mE3
1,715.04
mQ1
1,146.22
mQ3
2,432.48
mU1
2,198.47
mU3
1,765.82
mD1
2,033.27
mD3
746.37
At
-3,219.06
Ab
-3,497.73
Atau
-1,997.94
mu
2,497.47
mA
3,020.92
tan(beta)
24.75
The truth!
10
M_1
237.32
M_2
1,074.89
M_3
3,777.01
mL1
1,485.07
mL3
2,402.11
mE1
643.57
mE3
912.92
mQ1
2,091.40
mQ3
3,429.53
mU1
2,125.94
mU3
2,529.29
mD1
3,389.11
mD3
2,469.32
At
3,227.86
Ab
1,520.84
Atau
521.85
mu
271.35
mA
1,682.90
tan(beta)
11.04
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
