The SUMSS CatalogueThe SUMSS Catalogue

Tara Murphy and Tom MauchTara Murphy and Tom Mauch

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Creating the catalogueCreating the catalogue

Made using the AIPS task VSADMade using the AIPS task VSADVLA Search and DestroyVLA Search and DestroyUsed for the NVSS catalogueUsed for the NVSS catalogueFits a Gaussian to each source in the input Fits a Gaussian to each source in the input

imageimageProduces a source list with fitted parametersProduces a source list with fitted parameters

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SUMSSSUMSSThe problem:

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SUMSSSUMSSThe problem:

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SUMSS artifactsSUMSS artifacts

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Pattern matchingPattern matching

Humans are exceptionally good pattern matchersBest example is face recognitionWe have high accuracy and speed

Because we can do it, doesn’t mean we can define what we are doing

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What is Machine Learning?What is Machine Learning?

Any computer system that can change its behaviour when exposed to new data so that it can perform better in the future

Useful when we can’t formulate an algorithmic solution

The classification problem:• Do experts ‘know’ what they are doing?• Wittgenstein’s ‘What is a game?’

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What is a decision tree?What is a decision tree?

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The training/testing processThe training/testing process

We trained C4.5, a decision tree, to classify the We trained C4.5, a decision tree, to classify the potential sources into 3 categories:potential sources into 3 categories:the source is ‘real’the source is an artifactthe source is in a region of low S/N

About 4500 sources were classified by handAbout 4500 sources were classified by hand These were used to train and test the decision These were used to train and test the decision

tree.tree.

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Class 1 or 2: Artefact

Class 3:Genuine

Most genuine sources selected by decision tree here!

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Classification resultsClassification results

Decision TreeDecision Tree

Hum

ansH

umans

AA NN RR

AA 2 1 -

NN - 6 -

RR 2 2 525

Southern section Northern section

We obtained a classification accuracy of ~97%

Decision TreeDecision Tree

Hum

ansH

umans

AA NN RR

AA 16 - 1

NN - 2 -

RR 1 - 333

Reference: Mauch et al., 2003, MNRAS, 342, 1117

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SUMSS artefactsSUMSS artefacts

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SUMSS artefacts - classifiedSUMSS artefacts - classified

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Uniformity of the SUMSS Uniformity of the SUMSS

cataloguecatalogue

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Data productData productCatalogue will cover southern sky with |b|>10o and <-30o.

See: www.astrop.physics.usyd.edu.au/SUMSS/

Flux limit: 6 mJy/beam at dec.<-5010 mJy/beam at dec.>-50

Completeness limit: 8 mJy/beam at dec.<-5018 mJy/beam at dec.>-50

Version 1.6 of catalogue has 200,000 sources above 6 mJy/beam.