ODSL - The ORIGINS "Data Science" Laboratory

ODSL provides a new pillar for novel analysis methods, algorithms and computational tools to fully exploit high-dimensional, complex data sets. ODSL will specialise on advanced techniques for pattern recognition in noisy data and for the identification and extraction of weak signals. Modern techniques in machine learning will be employed as well as augmented reality and visualisation techniques in the extraction of scientific results.

Example Projects within ODSL

Universal Imaging Using Information Field Theory

In order to reconstruct a good image of a spatially varying quantity, a field, from incomplete and noisy measurement data, it is necessary to combine the measurements with knowledge about general physical properties of the field, such as its smoothness, correlation structure, or freedom from divergence. Information field theory uses the elegant formalism of field theories to mathematically derive optimal Bayesian imaging algorithms for different measurement situations. These algorithms can be implemented efficiently and generally by means of the "Numerical Information Field Theory" (NIFTy) programming package. Algorithms using NIFTy are already used in radio and gamma-ray astronomy. NIFTy is developing into a universal tool for imaging problems in astronomy, particle physics, medicine, and other fields.

The Bayesian Analysis Toolkit

The Bayesian Analysis Toolkit, BAT, is a software package which is designed to help solve statistical problems encountered in Bayesian inference. BAT is based on Bayes' Theorem and is currently realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution and enables straightforward parameter estimation, limit setting and uncertainty propagation.  Novel sampling methods, optimization schemes and parallelization are example development areas.

The Dark Matter Data Centre

We will build a platform to host and combine overarching information on experimental studies, astronomical observations and theoretical modeling of Dark Matter to facilitate the combination and cross-correlation of existing and forthcoming data. This data centre will allow tests for Dark Matter (DM) candidates passing all existing benchmarks in cosmology, astro- and particle physics, experiments and in theory. We plan probing for tensions between different data sets and theories pointing towards new, hidden properties of DM. The data centre will make the data available to the international community for further global analysis and model benchmarking, following examples in astro- and high-energy physics.