Scientific Data Mining

Principles and applications with astronomical data.

Amos Storkey

Institute for Adaptive and Neural Computation

Division of Informatics and Institute for Astronomy

University of Edinburgh

Collaborators and Thanks

Collaborative work with Nigel Hambly, Chris Williams and Bob Mann.

Thanks also to many others at the Royal Observatory, Edinburgh for their help in clarifying many of the things that an astronomical outsider might misunderstand or falsely presume!

Astro-informatics

Problems in Astronomy increasingly require use of machine learning, data mining and informatics techniques. Detection of spurious objects Record linkage Object classification and clustering Source seperation Compression Information about techniques

Galaxy spectra

James Riden, with Alan Heavens and Ben Panter.Chris Williams.

Given spectra, what can be said about the generation history and metallicity of galaxy.

Data exploration techniques: ISOMAP and LLE – find data manifold and project to low dimension.

Develop probabilistic model for galaxy generation, infer history and metallicity parameters from spectra.

Exploratory Data Analysis

Exploratory Data Analysis

Record Linkage Problem of linking records from different

datasets. There is an ambiguity in matches. Room for new techniques.

Super-resolution

Improving resolution of a single image, or combining images from different sources to provide an increased resolution.

Image cleaning and characterisation. H alpha survey. Matches in short red. Examples.

Part II – Main Problem

Locating junk objects in astronomical databases.

Makes finding non-matches across epochs or colours hard.

Supercosmos Sky Survey Data UK, ESO and Palomar Schmidt sky survey plates. Optical: 3 colours and 2 epochs, 894 fields for each

covering the Southern sky. Digitised using SuperCOSMOS to 10 micron

(0.7arcsec). 5x105 to 107 objects on the plate. Objects and features extracted from plates to form a

catalogue of stars and galaxies and characteristics (eg ellipses), but also spurious objects, eg. from satellite tracks

Average of 2 satellite tracks per plate, a few hundred to a few thousand objects per track.

Aeroplanes, diffraction spikes, halos, scratches...

Satellite track problem

Some satellite tracks tend to be recognised as a line of objects:

Optical Artefacts

Can be halos about bright stars. High density of spurious points local to the star.

(Almost) horizontal and (almost) vertical diffraction spikes are possible.

Spurious object characteristics

Spurious objects cover all the ranges of magnitude measurements, they often (but not always) have characteristics resembling those of galaxies.

In fact their characteristics are wide and various. They are not easy to detect from their characteristics alone.

Machine Learning Methods

Hough Transform and Circular Hough Transform

See http://www.anc.ed.ac.uk/~amos/hough.html

Circular Hough Transform

Hough Example: UKJ005

angle

Distance from origin0

2

dmax

Data space corresponding to bin However:

Can’t find short lines Curves are problematic Background star/galaxy density changes can

cause errors.

Renewal Strings

Hidden-Markov renewal processes. Look at all possible line segments in terms

of renewal processes. If local density is closer in signature to a

satellite track than the background stars and galaxies, then flag as a satellite track.

Benefits

Can use line widths thirty times narrower than with Hough.

Copes with curves by using local linearity rather than restricted to global linearity.

Deals with local star/galaxy density differences. Copes with partial lines, dashed lines etc.

Flexible model. Can use other data (eg ellipticity) to strengthen

classification. Bayesian.

Generative renewal string

Can generate from model.

To use

Don’t use generative model! Too hard. Look at all line segments. Transform

star/galaxy model to Poisson process on line. Run Markov chain along each line.

Simplest case: class 0 is background process. Class 1defines a renewal processes corresponding to a scratch, satellite track etc. Processing is fully Markovian.

Results

Get probabilistic results. Two possibilities: Probability of a given point being a spurious

point. Most probable classification of points.

Results

Two examples. The left example is a small scratch or track in the corner of ukj005. Right is a track on a dense plate.

Further examples

Further examples can be found at http://www.anc.ed.ac.uk/~amos/sattrackres.html

A flythrough movie of one plate can be found at http://www.anc.ed.ac.uk/~amos/demos/flythroug

hnew3c0.avi (36MB)

Conclusions

Machine Learning and Data Mining methods are, and will continue, to prove useful with astronomical databases.

Methods do not always work automatically. Some thought is needed.

Circular Hough transforms, and renewal strings have proven effective in locating a variety of spurious objects in astronomical databases.

So far have run on a quarter of one colour of SuperCOSMOS data.

Contact and URLs

http://www.anc.ed.ac.uk/~amos/

a.storkey@ed.ac.uk

http://www.roe.ac.uk/cosmos/scosmos.html