PyParadise Developed by: Bernd Husemann (MPIA), Omar Choudhury (AIP)! C. Jakob Walcher Leibniz Institut für Astrophysik Potsdam (AIP)
Physical properties Physics 101: A physical property is described by a number a unit an errorbar Only well-defined quantities can be measured. The Star Formation History is not a well-defined quantity. The contribution of stars aged between 1*10 9 and 5*10 9 yrs to the total luminosity in the V-band IS a well-defined quantity. 2
Fitting Spectral Energy Distributions Minimizing χ2 is a maximum likelihood estimation of the fitted parameters if the measurement errors are independent and normally distributed. Good fit if χ 2 is a measure of probability: P (D M) e 2 /2 χ 2 compares a model and a dataset A model is a prior! There is no fitting without prior! Press+, Numerical Recipes 3
Fitting optical galaxy spectra: decompose into three Χ 2 Kinematics of stars Stellar populations 2 = nx i=0 A amplitude, v velocity, σ kin velocity dispersion! 2 nx Fi S i G(A, v, kin) F i P M k=1 a ks i [t k,z k,t k ] 2 = i=0 i a k weights, t k age, Z k Metallicity, T k Extinction i 2 Emission lines 2 = nx F i i=0 i P L k=1 A kg i [ k,v, kin]! 2 A k amplitude, λ k line ID, v velocity, σ kin velocity dispersion Fi, Si, sigmai, n Number of pixels 4
Stellar populations considerations Linear, but ill-posed problem Regularization assumes something Bootstrap your way through the problem Transmission - Extinction issue Normalize spectra (but restrict wavelength range) Stellar populations accuracy continuum subtraction precision = kinematics determination First case stelpop models Second case stars better! 5
Pyparadise three main modules Non-linear (MCMC) fit of kinematics v, sigma, (h3, h4) Linear (NNLS) inversion for stellar populations <age>, <feh>, etc. (also per age bins) Non-linear (MCMC) fit of emission lines F, v, sigma 6
Pyparadise fiducial run Initial stelpop Kinematics fit Stelpop fit Kinematics fit Stelpop fit Emission line fit 7
Pyparadise bootstrap run Disturb spectrum, subsample template basis Do N times Kinematics fit Stelpop fit (Optional) Emission fit (Optional) 8
Pyparadise bootstrap run Disturb spectrum, subsample template basis Do N times Kinematics fit Stelpop fit (Optional) Emission fit (Optional) Mean, variance 8
Overall results Stellar populations Mean light weighted ages, Z, etc. for all templates Mean light weighted ages, Z, etc. in bins of ages Bootstrap-based errorbars Weights of each SSP Kinematics Light weighted v, sigma Bootstrap-based errorbars (incl. stelpop degeneracies) Emission lines Fluxes, v, sigma Bootstrap-based errorbars (incl. stelpop and kin degeneracies) 9
Fit quality examples 10
Fit quality examples 11
Things to do when you fit spectra (and that you can do with PyParadise - but actually with any good software!)
Verify convergence! parameter value MCMC iteration number 13
Our result Test your model! Literature value Globular Cluster Spectrum Walcher+2009 Spectrum of NGC6553: Schiavon+05 See also Conroy and Vazdekis models 14
Test your method on the relevant parameter! Age [α/fe] Recovered [Fe/H] Z Input 15
Test applicability of Χ 2 statistics! theory data Walcher et al., 2015 16
Test applicability of Χ 2 statistics! theory data Walcher et al., 2008 17
Test applicability of Χ 2 statistics! theory data NO! Walcher et al., 2008 17
Look at your residuals! Rest-frame Observed- frame Walcher et al., 2015 18
Look at your residuals! 19
Look at your residuals! Template mismatch dominates! CALIFA Pipeline vs1.4 / DR2 19
Look at your residuals! Some molecular feature Template mismatch dominates! CALIFA Pipeline vs1.4 / DR2 19
There is science in template mismatch! 20
Do not trust good fits! observed spec fit around 4000A fit around 5000A 21
Do not trust good fits! observed spec fit around 4000A fit around 5000A 21
e] with velocity dispersion, stellar mass and mean light-weighted age for our sample galaxies. It is clear ge. Note also the change of slope in the relation between age and [ /Fe] 9 Gyr. Average error bars are anel and spearman rank correlation coefficients are also noted. Do not over-interpret your results Do not trust details in your star history! Fig. 14. Average star formation history of all galaxies information the observed sample in terms of the present day contribution of each stellar population to the total luminosity of thea galaxy. All galaxies contribute equally Peaks are consequence of to this average, i.e. the total luminosity for each galaxy has been normalized to one before averaging. Regularization is a possible solution, but imposes smooth may success not forbethe true identified in SFHs, Sect. 4.3 andwhich the lower recovery s. [ /Fe] for the luminosity in our sample. The thick solid y (Z = 0.017). The two dashed ocus, i.e. for [Z] = 0.15 and smaller24.11.2016 contribution to the SELGIFS school, Walcher intermediate component with respect to the old one we identi- 22
e] with velocity dispersion, stellar mass and mean light-weighted age for our sample galaxies. It is clear ge. Note also the change of slope in the relation between age and [ /Fe] 9 Gyr. Average error bars are anel and spearman rank correlation coefficients are also noted. Do not over-interpret your results This MUST be wrong! Do not trust details in your star history! Fig. 14. Average star formation history of all galaxies information the observed sample in terms of the present day contribution of each stellar population to the total luminosity of thea galaxy. All galaxies contribute equally Peaks are consequence of to this average, i.e. the total luminosity for each galaxy has been normalized to one before averaging. Regularization is a possible solution, but imposes smooth may success not forbethe true identified in SFHs, Sect. 4.3 andwhich the lower recovery s. [ /Fe] for the luminosity in our sample. The thick solid y (Z = 0.017). The two dashed ocus, i.e. for [Z] = 0.15 and smaller24.11.2016 contribution to the SELGIFS school, Walcher intermediate component with respect to the old one we identi- 22
Installing and running PyParadise
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