Accounting for Source Uncertainties in Analyses of Astronomical Survey Data

I discuss an issue arising in analyzing data from astronomical surveys: accounting for measurement uncertainties in the properties of individual sources detected in a survey when making inferences about the entire population of sources. Source uncertainties require the analyst to introduce unknown ``incidental'' parameters for each source. The number of parameters thus grows with the size of the sample, and standard theorems guaranteeing asymptotic convergence of maximum likelihood estimates fail in such settings. From the Bayesian point of view, the missing ingredient in such analyses is accounting for the volume in the incidental parameter space via marginalization. I use simple simulations, motivated by modeling the distribution of trans-Neptunian objects surveyed in the outer solar system, to study the effects of source uncertainties on inferences. The simulations show that current non-Bayesian methods for handling source uncertainties (ignoring them, or using an ad hoc incidental parameter integration) produce incorrect inferences, with errors that grow more severe with increasing sample size. In contrast, accounting for source uncertainty via marginalization leads to sound inferences for any sample size.Comment: 12 pages, 5 figures; to appear in Bayesian Inference And Maximum Entropy Methods In Science And Engineering: 24th International Workshop, Garching, Germany, 2004; ed. Volker Dose et al. (AIP Conference Proceedings Series

Sines, steps and droplets: Semiparametric Bayesian modeling of arrival time series

I describe ongoing work developing Bayesian methods for flexible modeling of arrival time series data without binning, aiming to improve detection and measurement of X-ray and gamma-ray pulsars, and of pulses in gamma-ray bursts. The methods use parametric and semiparametric Poisson point process models for the event rate, and by design have close connections to conventional frequentist methods currently used in time-domain astronomy.Comment: 4 pages, 1 figure; to appear in the proceedings of IAU Symposium 285, "New Horizons in Time Domain Astronomy" (proceedings eds. Elizabeth Griffin, Bob Hanisch, and Rob Seaman), Cambridge University Press; see http://www.physics.ox.ac.uk/IAUS285

Introduction to papers on astrostatistics

We are pleased to present a Special Section on Statistics and Astronomy in this issue of the The Annals of Applied Statistics. Astronomy is an observational rather than experimental science; as a result, astronomical data sets both small and large present particularly challenging problems to analysts who must make the best of whatever the sky offers their instruments. The resulting statistical problems have enormous diversity. In one problem, one may have to carefully quantify uncertainty in a hard-won, sparse data set; in another, the sheer volume of data may forbid a formally optimal analysis, requiring judicious balancing of model sophistication, approximations, and clever algorithms. Often the data bear a complex relationship to the underlying phenomenon producing them, much in the manner of inverse problems.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS234 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Covariant Symmetry Classifications for Observables of Cosmological Birefringence

Polarizations of electromagnetic waves from distant galaxies are known to be correlated with the source orientations. These quantities have been used to search for signals of cosmological birefringence. We review and classify transformation properties of the polarization and source orientation observables. The classifications give a firm foundation to certain practices which have sprung up informally in the literature. Transformations under parity play a central role, showing that parity violation in emission or in the subsequent propagation is an observable phenomenon. We also discuss statistical measures, correlations and distributions which transform properly and which can be used for systematic data analysis.Comment: 8 pages, revtex, 1 postscript figur

Wetland-based passive treatment systems for gold ore processing effluents containing residual cyanide, metals and nitrogen species

Gold extraction operations generate a variety of wastes requiring responsible disposal in compliance with current environmental regulations. During recent decades, increased emphasis has been placed on effluent control and treatment, in order to avoid the threat to the environment posed by toxic constituents. In many modern gold mining and ore processing operations, cyanide species are of most immediate concern. Given that natural degradation processes are known to reduce the toxicity of cyanide over time, trials have been made at laboratory and field scales into the feasibility of using wetland-based passive systems as low-cost and environmentally friendly methods for long-term treatment of leachates from closed gold mine tailing disposal facilities. Laboratory experiments on discrete aerobic and anaerobic treatment units supported the development of design parameters for the construction of a field-scale passive system at a gold mine site in northern Spain. An in situ pilot-scale wetland treatment system was designed, constructed and monitored over a nine-month period. Overall, the results suggest that compost-based constructed wetlands are capable of detoxifying cyanidation effluents, removing about 21.6% of dissolved cyanide and 98% of Cu, as well as nitrite and nitrate. Wetland-based passive systems can therefore be considered as a viable technology for removal of residual concentrations of cyanide from leachates emanating from closed gold mine tailing disposal facilities

Bayesian Adaptive Exploration

I describe a framework for adaptive scientific exploration based on iterating an Observation--Inference--Design cycle that allows adjustment of hypotheses and observing protocols in response to the results of observation on-the-fly, as data are gathered. The framework uses a unified Bayesian methodology for the inference and design stages: Bayesian inference to quantify what we have learned from the available data and predict future data, and Bayesian decision theory to identify which new observations would teach us the most. When the goal of the experiment is simply to make inferences, the framework identifies a computationally efficient iterative ``maximum entropy sampling'' strategy as the optimal strategy in settings where the noise statistics are independent of signal properties. Results of applying the method to two ``toy'' problems with simulated data--measuring the orbit of an extrasolar planet, and locating a hidden one-dimensional object--show the approach can significantly improve observational efficiency in settings that have well-defined nonlinear models. I conclude with a list of open issues that must be addressed to make Bayesian adaptive exploration a practical and reliable tool for optimizing scientific exploration.Comment: 17 pages, 5 figure

Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations

We describe work in progress by a collaboration of astronomers and statisticians developing a suite of Bayesian data analysis tools for extrasolar planet (exoplanet) detection, planetary orbit estimation, and adaptive scheduling of observations. Our work addresses analysis of stellar reflex motion data, where a planet is detected by observing the "wobble" of its host star as it responds to the gravitational tug of the orbiting planet. Newtonian mechanics specifies an analytical model for the resulting time series, but it is strongly nonlinear, yielding complex, multimodal likelihood functions; it is even more complex when multiple planets are present. The parameter spaces range in size from few-dimensional to dozens of dimensions, depending on the number of planets in the system, and the type of motion measured (line-of-sight velocity, or position on the sky). Since orbits are periodic, Bayesian generalizations of periodogram methods facilitate the analysis. This relies on the model being linearly separable, enabling partial analytical marginalization, reducing the dimension of the parameter space. Subsequent analysis uses adaptive Markov chain Monte Carlo methods and adaptive importance sampling to perform the integrals required for both inference (planet detection and orbit measurement), and information-maximizing sequential design (for adaptive scheduling of observations). We present an overview of our current techniques and highlight directions being explored by ongoing research.Comment: 29 pages, 11 figures. An abridged version is accepted for publication in Statistical Methodology for a special issue on astrostatistics, with selected (refereed) papers presented at the Astronomical Data Analysis Conference (ADA VI) held in Monastir, Tunisia, in May 2010. Update corrects equation (3