National Centre for Particle Physics
Pusat Fizik Zarah Kebangsaan
Universiti Malaya, 50603 Kuala Lumpur, Malaysia





Perisian terbuka berkenaan fizik dan sains, terutama fizik zarah.


SMEFTsim: codes for the Standard Model Effective Field Theory (SMEFT) in FeynRules

The codes enable theoretical predictions for dimension six operator corrections to the Standard Model using numerical tools, where predictions can be made based on either the electroweak input parameter set {a^ew,m^Z,G^F} or {m^W,m^Z,G^F}. All of the baryon and lepton number conserving operators present in the SMEFT dimension six Lagrangian, defined in the Warsaw basis, are included. A flavour symmetric U(3)5 version with possible non-SM CP violating phases, a (linear) minimal flavour violating version neglecting such phases, and the fully general flavour case are each implemented. The SMEFTsim package allows global constraints to be determined on the full Wilson coefficient space of the SMEFT. As the number of parameters present is large, it is important to develop global analyses on reduced sets of parameters minimizing any UV assumptions and relying on IR kinematics of scattering events and symmetries.
Feynrules and UFO models

TikZ-network: visualizing graphs and networks in Latex

aims to provide a simple and easy tool to create, visualize and modify complex networks. The packaged is based on the PGF/TikZ languages for producing vector graphics from a geometric/algebraic description. Particular focus is made on the software usability and interoperability with other tools. Simple networks can be directly created within LaTeX, while more complex networks can be imported from external sources (e.g. igraph, networkx, QGIS, ...). Additionally, tikz-network supports visualization of multilayer networks in two and three dimensions
code source

ZhuSuan: A Library for Bayesian Deep Learning

a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks
code source