Tutorial, Bayes net for forensic DNA analysis, 
Satellite, Dice, DNA Helix, Distance Matrix of pairs of amino acids, 
Bristol Balloon festival
SuSTaIn Events Astrostatistics Overview Invited speakers Programme Posters Registration Financial Assistance Venue Enquiries

send an email

SuSTaIn EdgeCutter One Day Workshop on


Royal Statistical Society, London, UK
17th December 2014


Sarah Bridle

Measuring Galaxy Shapes from Noisy, Blurry Images.

I will describe the great potential and possible limitations of using the bending of light by gravity (gravitational lensing) to constrain the mysterious dark energy which seems to dominate the contents of our Universe. In particular we have to remove the blurring effects of our telescopes and the atmosphere to extreme precision, to measure the shapes of galaxies to extreme accuracy. I will discuss the recent GREAT image analysis Challenges we have set to astronomers and beyond and review some recent progress in tackling the problem. In particular I will focus on the upcoming Dark Energy Survey which will measure approximate distances and shapes of 300 million galaxies over one eighth of the sky.

Alan Heavens

Measuring the Length of the Cosmological Ruler

The Baryon Acoustic Oscillation feature is a key observable in cosmology, corresponding to the maximum distance sound waves can travel in the Universe. It leaves a measurable imprint in the clustering of galaxies at late times. Normally cosmological models are fitted to BAO data along with everything else, but it turns out to be possible to measure it in a way that is completely independent of the cosmological model. With very weak assumptions of symmetry and a smooth expansion history (not even assuming General Relativity), the length of the BAO scale is measured using MCMC techniques to be 103.9+/-2.3 Mpc/h, a length that any theoretical model must account for to be viable. Since the length is set early on in the Universe's history, the conclusions are independent of late-time physics such as Dark Energy properties or late modifications to gravity. Even without GR, we find that the Universe looks very much like the standard LCDM model, for which the scale is 99.3+/-2.1 Mpc/h.

Jason McEwen

Sparsity in Astrophysics: Astrostatistics meets Astroinformatics

Astrostatistics has become a well-established sub-field, where powerful statistical methods are developed and applied to extract scientific information from astrophysical observations. Astroinformatics, on the other hand, is an evolving but less mature sub-field, where informatics techniques provide a powerful alternative approach for extracting scientific information from observational data. Informatics techniques have close links with information theory, signal processing and computational harmonic analysis, and have been demonstrated to be very effective. Wavelet methods, for example, allow one to probe both spatial- and scale-dependent signal characteristics simultaneously. Such techniques are very effective in studying physical phenomena since many physical processes are manifest on particular physical scales, while also spatially localised. Recent developments in this domain have led to the theory of compressive sensing, a revolutionary breakthrough in the field of sampling theory, which exploits the sparsity of natural signals. I will introduce compressive sensing from both the synthesis and analysis perspectives, highlighting statistical connections, and discuss the application of such techniques to radio interferometric imaging.

Xiao-Li Meng

Principled Corner Cutting in Astrostatistics: Valid Statistical Comparisons Without Valid MCMC Output

Modern statistical data analysis is complex. The competing demands of massive data streams and sophisticated science-driven models require us to make compromises—sometimes unconsciously—between methodological rigor and practical constraints like time, energy, funding, expertise, access to information, etc. Astrostatistics is certainly not immune to such corner cutting. Having to prematurely terminate an MCMC (Markov chain Monte Carlo) sampler is a familiar example, all the more so when a suite of samplers are deployed to simulate the null distribution of a test statistic. This does not necessarily imply, however, that the resulting statistical inference is invalid. In the context of hypothesis testing and model selection, it is entirely possible to use the output of premature MCMC samplers to form p-values that are valid in that they exhibit the desired Type-I error. The cost of this faster computation is the potential loss of statistical power. Using the example of detecting X-ray jets associated with quasars, we demonstrate that this trade-off may be worthwhile; a 65% increase in computational power, for example, may be traded for a 15% reduction in statistical power. We provide a theoretical framework for investigating such Monte Carlo tests by formulating them as a new class of randomized tests. This is a new use of randomization tests which were proposed to achieve theoretically exact Type-I errors, not to navigate the practical trade-offs between statistical and computational efficiency. (This is joint work with Nathan Stein and David van Dyk.)

Daniel Mortlock

Bayesian Model Comparison in Astronomy and Cosmology.

Bayesian inference provides a self-consistent method of model comparison, provided that i) there are at least two models under consideration and ii) all the models in question have fully-specified and proper parameter priors. Unfortunately, these requirements are not always satisfied in astronomy and cosmology: despite the existence of exquisitely-characterised measurements and quantitative physical models (i.e., sufficient to compute a believable likelihood), these models generally have parameters without well-motivated priors, making completely rigorous model comparison a formal impossibility. Still, huge advances have been made in cosmology, in particular, in the last few decades, implying that model comparison (and testing) is possible in practice even without fully specified priors. I will discuss the above principles and then illustrate some test cases of varying rigour, outlining some schemes for formalising heuristic approaches to model testing within a Bayesian framework.

Jean-Luc Starck

Inverse Problems in Astrophysics

We present the concept of sparse representations, and we show how sparsity helps us to regularize several inverse problems that occurs in different astronomical projects such as Corot, PLANCK or Euclid. Several applications will be addressed: the case of missing data, the component separation problem from one image or from multichannel data, and finally the 3D weak lensing tomographic density map reconstruction.

Licia Verde

Beyond Precision Cosmology

The avalanche of data over the past 10-20 years has propelled cosmology into the “precision era”. The next challenge cosmology has to meet is to enter the era of accuracy. Because of the intrinsic nature of studying the Cosmos and the sheer amount of data available and coming, the only way to meet these challenges is by developing suitable and specific statistical techniques. The road from precision Cosmology to accurate Cosmology goes through statistical Cosmology. I will outline some open challenges and discuss some specific examples.

Ian Vernon

Bayesian Emulation and History Matching for the Uncertainty Analysis of a Galaxy Formation Simulation

Cosmologists test their knowledge of the evolution of structure formation by developing models of galaxy formation. Many models are now so complex that analysing their behaviour, comparing them to observed data and performing a full uncertainty analysis all represent extremely challenging tasks. Major difficulties include the significant run time required for one model evaluation and dealing with the large numbers of input parameters that must be specified for the model to run.

This project deals with a complex model of the Universe known as Galform, developed by the ICC group, at Durham University. This model simulates the creation and evolution of millions of galaxies from the beginning of the Universe until the current day, a process which is very sensitive to current theories of cosmology and structure formation. The outputs of Galform can be compared to available observational data, and the general goal of the project is to identify which input parameter specifications will give rise to acceptable matches between model output and observed data, given the many types of uncertainty present in such a situation. As the model takes significant time to run, and the input parameter space is large, this is a very difficult task.

We have solved this problem using general techniques related to the Bayesian treatment of uncertainty for computer models. These techniques are centred around the use of emulators: stochastic approximations to the full Galform model that mimic the behaviour of Galform with differing and controllable levels of accuracy over the input space, and which are several orders of magnitude faster to evaluate. Emulators allow detailed analysis of the structure of the model, global parameter space searching techniques (known as history matching), and efficient visualisation of results. They fit naturally within a comprehensive Bayesian uncertainty analysis, and are applicable to an extremely wide class of complex models, across a range of scientific disciplines.

Back to Programme page

Back to main Astrostatistics Workshop page