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Using to Deduce Abundances. Mike Taylor & Angeles Diaz michael@damir.iem.csic.es. Sun. M51. Rosette. CNSFR. Abundances: “1-slide summary”. Abundances AI - A new opportunity n-D Calibrations Ideas for the future. Gas Metallicity Z  Evolutionary History of Galaxies

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  1. Using to Deduce Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es Sun M51 Rosette CNSFR

  2. Abundances: “1-slide summary” Abundances AI - A new opportunity n-D Calibrations Ideas for the future Gas Metallicity Z Evolutionary History of Galaxies Why? It’s determined by the star formation history Z =12+log(O/H) other elements (* based on Fe) Why? O+ and O++ and other ions emit prominent spectral emission lines Accurate Tgas Accurate Z (using nebular/auroral ratios xi,j) But N.B. ICFs & the Δt2-problem There are observational limits! Why? 1) General faintness of auroral lines 2) High redshift  fainter lines &line redshifting outside range (for z~0.5) 3) High Z Efficient cooling by Far-IR lines  suppression of CELs Theoretical /+ photoionisation models  Z=f(xi,j) Why?  DIAGNOSTICS for other objects with unknown Z Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  3. The IDEAL Indicator xi,j? Abundances AI - A new opportunity n-D Calibrations Ideas for the future Would be MONOTONIC with respect to metallicity Z Would be observationally detectable to high Z Would have low average dispersion (least-square error) Would span a W I D E range of Zwhen empirically-calibrated using REAL galaxy data & IF ALL THAT WENT PERFECTLY… Would be independent of chemical evolution and have physically-interpretable behaviour with Z Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  4. Indicators in “HII-like” regions Abundances AI - A new opportunity n-D Calibrations Ideas for the future Galactic HII-Regions Extra-Galactic HII-Regions HII Galaxies CNSFRs? Perez-Montero 2002 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  5. AI: An opportunity Abundances AI - A new opportunity n-D Calibrations Ideas for the future Scale-Invariant Genetic Algorithm Network(SIGAN) Architecture Weights Node Functions Dimensional Analysis Taylor & Diaz 2006 Net DNA Ecoding Multi-Layer Perceptron + Genetic Operators Mutation Cross-over + Back-Propagation + Pruning Algorithm UNIVERSAL FUNCTION APPROXIMATOR Taylor 2005 103 ERROR REDUCTION Taylor 2005 EQUATIONS Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  6. A new strategy Abundances AI - A new opportunity n-D Calibrations Ideas for the future LINE RATIOS 1 Emission line fluxes 4 5 6 2 3 GA (Genetic Algorithm) DIAGNOSTICS About the sample Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  7. How it Works: e.g. Newton’s Law Abundances AI - A new opportunity n-D Calibrations Ideas for the future IDEAL SOLUTION Neural Node Functions m h3 Identities 1 Power Laws 1 a h3 h2 F Sigmoids Harmonic Functions e h3 PRUNED NODES Gaussian Constants inputs outputs Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  8. Newton’s Law: F=ma (EXACT) Abundances AI - A new opportunity n-D Calibrations Ideas for the future G=2 G=5 G=8 G=38 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  9. Newton’s Law: F=ma (10% Errors) Abundances AI - A new opportunity n-D Calibrations Ideas for the future G=38 10% Errors G=780 G=995 G=161 Error Generation Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  10. “HII-like” Regions: HIIGs, GHIIRs & EGHIIRs Abundances AI - A new opportunity n-D Calibrations Ideas for the future log(S23) elog(S23) SIGAN h3 h2 eZ Z e h3 e-space Z (direct method) Vs S23 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  11. “HII-like” Regions: First Results Abundances AI - A new opportunity n-D Calibrations Ideas for the future SIGAN offers a simple way to do fits  overcoming the the problem of “subjectivity” Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  12. Fitting & n-D Calibrations Abundances AI - A new opportunity n-D Calibrations Ideas for the future Dimensionless Line Ratios Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  13. Ideas for the Future Abundances AI - A new opportunity n-D Calibrations Ideas for the future To test & improve 1-D calibrations of the form: Z=af(x)+b using new data from SDSS RL4 HII-like regions To find new n-D indicators & improve calibrations of the form: Z=af(x1,x2…xn)+b for HII-like regions To look for new n-D indicators & calibrators for AGNs and PNs On behalf of myself and Angeles, I would like to thank Guille Hägele and Enrique Perez-Montero for kindly making some of the observational data available and also to Monica Cardaci for helpful discussions Many thanks Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  14. Evolutionary History of Z Abundances AI - A new opportunity n-D Calibrations Ideas for the future Tremonti et al 2005 Nagao et al 2004 Redshift 0 2.5 Edmunds 2005 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  15. Δt2 & the O23 (R23) Indicator Abundances AI - A new opportunity n-D Calibrations Ideas for the future Baskin et al 2006 U=0.6 U=0.3 ORLs Transition Region CELs Bresolin 2006 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  16. Observational Limits Abundances AI - A new opportunity n-D Calibrations Ideas for the future Z=12+log(O/H)=8.66±0.05 Asplund et al 2004 Commonly used auroral-to-nebular emission line ratios… Bresolin 2006 8m telescope observing limit Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  17. O23: The “standard” diagnostic Abundances AI - A new opportunity n-D Calibrations Ideas for the future Degeneracy Need another diagnostic to break it Sensitivity on p (p=ionisation parameter) Yin et al 2006 Correcting for p-effect Pilyugin & Thuan 2005 Pilyugin & Thuan 2005 Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  18. N2 & Sensitivity to reddening Abundances AI - A new opportunity n-D Calibrations Ideas for the future Liang et al 2006 De-reddened Reddened Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  19. Going INFRA-RED… Abundances AI - A new opportunity n-D Calibrations Ideas for the future Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  20. Training & Evaluation Data Abundances AI - A new opportunity n-D Calibrations Ideas for the future Exponential Space Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

  21. “HII-like” Regions: First Results Abundances AI - A new opportunity n-D Calibrations Ideas for the future Using AI to Deduce Chemical Abundances Mike Taylor & Angeles Diaz michael@damir.iem.csic.es

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