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.
Download SUSY-AIIf you use SUSY-AI in your scientific work, don't forget to cite us.
About SUSY-AI Online
Physics and machine learning
This page gives a short summary of the science behind (this online interface of) SUSY-AI. It aims at quickly creating a feeling for the physics of the program and the output of the program. This approach may leave some open questions. For a full description of SUSY-AI and the physics described by it, the reader is refered to our research paper:
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 Online contains a machine learning algorithm. By learning the patterns of model exclusion in the 19-dimensional pMSSM parameter space, this algorithm is able to predict the exclusion of any model point in this model space as if it were calculated by a full exclusion analysis by the ATLAS collaboration. However, since training is inherently done on a finite set of model points and the basis of machine learning is formed by statistical methods, the output of the algorithm is subject to statistical fluctionations. In our paper (see link above) we have however shown that the trained algorithm is able to make predictions with 93.2% accuracy. This accuracy can be enhanced by requiring a minimum confidence level: model points with a minimum confidence level of 0.95 for example have an accuracy on their prediction of over 99%.
The input for SUSY-AI Online is a set of 19 input parameters related to the soft breaking of the supersymmetric spectrum. The variables in this set are labeled as they would be in .slha files. For completeness a list of these variables (each a short explanation and their default location in .slha files) can be found in the table below.
| Variable | About | .slha BLOCK | .slha SWITCH |
|---|---|---|---|
| M1 | Bino mass parameter | MSOFT | 1 |
| M2 | Wino mass parameter | MSOFT | 2 |
| M3 | Gluino mass parameter | MSOFT | 3 |
| mL1 | First and second generation left-handed slepton breaking mass | MSOFT | 31 |
| mL3 | Third generation left-handed slepton breaking mass | MSOFT | 33 |
| mE1 | First and second generation right-handed slepton breaking mass | MSOFT | 34 |
| mE3 | Third generation right-handed slepton breaking mass | MSOFT | 36 |
| mQ1 | First and second generation left-handed squark breaking mass | MSOFT | 41 |
| mQ3 | Third generation left-handed squark breaking mass | MSOFT | 43 |
| mU1 | First and second generation right-handed up-type squark breaking mass | MSOFT | 44 |
| mU3 | Third generation right-handed up-type squark breaking mass | MSOFT | 46 |
| mD1 | First and second generation right-handed down-type squark breaking mass | MSOFT | 47 |
| mD3 | Third generation right-handed down-type squark breaking mass | MSOFT | 1 |
| At | Trilinear stop Yukawa coupling | AU or TU | 3,3 |
| Ab | Trilinear sbottom Yukawa coupling | AD or TD | 3,3 |
| Atau | Trilinear stau Yukawa coupling | AE or TE | 3,3 |
| mu | Higgsino mass parameter | HMIX | 1 |
| MA^2 | Pseudoscalar Higgs mass (squared) | HMIX | 4 |
| tan(beta) | Ratio of vacuum expectation values of H^0_u and H^0_d | HMIX | 2 |
Talks
The SUSY-AI (Online) collaboration has given various talks on the program and (online) interface. A list of all talks and slides used in them are (if applicable) to be found here.
Research
SUSY-AI is a webinterface to an instance of SUSY-AI: a Python software package mimicking ATLAS exclusion limits. These exclusion limits are generated with machine learning: having learned the exclusion of over 300,000 model points in the pMSSM, it is able to predict the exclusion of unseen model points with a minimum accuracy of 93.2%. Papers describing the data that was used in this training can be found here. For a full description of the use of machine learning in the generalisation of LHC exclusion limits, see the original paper:
Webapplication
SUSY-AI Online is a webinterface to an instance of SUSY-AI: a Python package that contains all functionalities of SUSY-AI Online. The advantage of using the package locally however is that it can then also perform batch predictions, allowing for exclusion determination of 10,000 per second.
The software running on the server is a version of SUSY-AI that is optimized for running in webapplication-like environments. As a consequence, the SUSY-AI Online is considerably slower than the original SUSY-AI package. If the user requires faster predictions or predictions on large batches of spectrum files/coordinates, downloading SUSY-AI and running it locally is recommended.
More information on SUSY-AI (i.e. on how to install and use it) can be found on the project website: http://susyai.hepforge.org/
Changelog
All notable changes to this project will be documented here. The format is based on Keep a Changelog (http://keepachangelog.com/). Please note that the versioning of SUSY-AI Online is independent of the versioning of SUSY-AI.
To Do
- Currently there are no planned updates in the near future.
Suggestions? Contact us!
Contact
Although both SUSY-AI and SUSY-AI Online have been build with great attention to detail, some errors may still persist in their current releases. If you encounter any of these errors, or if you have questions about the software (or maybe suggestions on how it could be made even better?), don't hesitate to contact us on: b.stienen@hef.ru.nl.If you encounter any problems, don't hesitate to contact us!
SUSY-AI and SUSY-AI Online (c) 2017