Scientific Data Mining

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20 Νοε 2013 (πριν από 4 χρόνια και 7 μήνες)

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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!


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


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

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

There is an ambiguity in matches.

Room for new techniques.


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.


Part II

Main Problem

Locating junk objects in astronomical

Makes finding non
matches across
epochs or colours

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). 5x10

to 10

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

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


Circular Hough Transform

Hough Example: UKJ005


Distance from origin




Data space corresponding to bin


Can’t find short lines

Curves are problematic

Background star/galaxy density changes can
cause errors.

Renewal Strings

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.


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


Generative renewal string


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.


Get probabilistic results. Two

Probability of a given point being a spurious

Most probable classification of points.


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

A flythrough movie of one plate can be
found at



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