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Bringing order to chaos New data-mining techniques for new surveys

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Bringing order to chaos New data-mining techniques for new surveys

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RUHR-UNIVERSITÄT BOCHUM Bringing order to chaos New data-mining techniques for new surveys Peter-Christian Zinn Astronomical Institute of Ruhr-University, Bochum, Germany CSIRO Astronomy & Space Science, Sydney, Australia

  2. ∑~70 million Norris et al. (2011) Why new data handling techniques? • New radio surveys will produce lots of data! • ASKAP/EMU ~ 70 million objects • LOFAR/Tier 1 ~ 7 million objects • WSRT/WODAN ~ 10 million objects • New optical/NIR surveys will produce even more data! • Pan-STARRS/PS1-3π~ 5-30 billion objects • LSST/Galaxy “gold sample“~ 10 billion objects • Astronomers go wild! • Tera-, exa-, petabyte scale Mostly no spectra available LSST Science Book

  3. Implications for survey science • There are no spectroscopic redshifts • Redshift information must be accessed on other ways → photometric (better: statistical) redshifts • There are no spectral classifications • Classification of an object must be inferred on other ways → Flux ratios or SED-fitting (better: kNN classification) becomes more important • There are no spectroscopically derived parameters • Classic parameters such as metallicity must be derived on other ways → scaling relations (better: kNN regression) must be utilized

  4. Fan et al. 2001 Common approaches • Define plain color criteria • Model SEDs • Look for morphology, scaling relations, ... • PROs: • Physically motivated • Easy to reproduce in 2d diagrams • High completeness • CONs: • Global model • Does not work for high dimensions • Many false positive candidates

  5. Our approach: k nearest neighbors 0 0 0 4 • use k-Nearest Neighbours • local model • works fine in high dimensions • does not require physical assumptions • good reference samples available ? mean=1 median=0

  6. Example 1: statistical redshifts • stat-z for ATLAS • ATLAS has spec-z for ~30% of allobjects • Training with 12-band data (ugriz,IRAC,MIPS24,13cm,20cm) Comparison: Cardamone et al. (2010) 14-band photo-z: 0.026 PCZ, Polsterer & Gieseke (subm.) • Advantages of statistical redshifts • No assumptions must be made (no template SEDs, luminosity range, dust reddening, flux homogenization, ...) • Computation much faster than for class. photo-z (tstat-z~ n*log2(n) | tphoto-z~ nα , α>2) PCZ, Polsterer & Gieseke (subm.)

  7. Redshift estimation for SDSS quasars • kNN regression model + selected reference set • 77,000 references reduced to 1,100 objects • optimized for z > 4.8 • 4 colors used Laurino et al. (2011) Polsterer, PCZ & Gieseke (2012)

  8. kNN-based classification of ATLAS test-sample yields combined false classification rate of 9% Smolcic et al. (2008) achieve contamination rates between 15% - 20% using a highly sophisticated photometric method Example 2: object classification • SF / AGN separation • Classical tool: BPT-diagram (requires spectroscopy) • Alternative: MIR color-color selection (not very reliable) • SED fitting (work-intensive) PCZ et al. (in prep)

  9. Example 3: metallicity • Metallicity from L-Z relation • Spectroscopic input: SDSS metallicities as derived by Brinchman et al. (2004) • Lr-Z relation calibrated by the 2dF survey (Lamareille et al. 2004) applied to Galactic extinction-corrected fluxes • No other assumptions made • Metallicity from kNN regression • Spectroscopic input: SDSS metallici-ties derived by Brinchman+ (2004) • kNN regression with respect to the 90 nearest neighbors • No other assumptions made PCZ, Polsterer & Gieseke (subm.) PCZ, Polsterer & Gieseke (subm.)

  10. Summary • We presented the first results of utilizing advanced machine-learning techniques to classify/analyze large data sets. • Dealing with large data sets will become increasingly important due to the enormous amounts of data forthcoming (radio) surveys will produce. • A k nearest neighbor-based approach was tested on available data from ATLAS, COSMOS and the SDSS. • Results for redshifts, object classifications and the regressional computation of astrophysical quantities (e.g. metallicity) all yield promising results. • Data-mining will already play an important role in currently upcoming projects, e.g. ASKAP/EMU.

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