how to use the seds produced by synthesis models?

Miguel Cerviño ESAC 23 March 2007 QuickTime™ and aTIFF (Uncompressed) decompressor

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How to use the SEDs produced by synthesis

models?

Miguel Cerviño (IAA-CSIC/SVO)Valentina Luridiana (IAA-CSIC/SVO)



What’s VO means in synthesis models context?

Scientific vision: VO requires to explicitly describe your data (you

must know what are you doing, without implicit assumptions)

Data models in the VO describe the possible universe of classes of data

Hence: How to describe the whole universe of synthesis

models (spectrum) results? How to include a model spectra resulting from a

combination of sources in the current spectrum data model?



Stellar populations are modeled with synthesis models

(http://ov.inaoep.mx)

+



Stellar populations are modeled with synthesis models

(http://ov.inaoep.mx)



Step 1: What is a synthesis model?:

A stellar population is the result of a combination of spectra

Real population:

Synthetic population:

Assumed representative spectrum

Assumed representative evolution and Star Formation birth rate (IMF+SFH)

Main issue: What is the accuracy and precision of this assumed representative ingredients?



Step 2: Synthesis results in VO Spectrum data model

Not precision!

Do they apply to synthesis models?



Step 3: Synthesis systematic errorsError sources in ingredients and computation:

– Atmosphere libraries• Theoretical libraries:

– Good coverage in parameter space (log g, Teff, Z, )– Real stars could be different...

– Observed libraries– They are real stars– Bad coverage in parameter space– How much are they “representative” of a class?

– Tracks/Isochrones and Synthesis models algorithms– Isochrone synthesis: (Leitherer, Bruzual...)

– Stellar evolution theory– Not all phases agree but observations, specially fast

evolutionary phases (WR,TP-AGB, 3rd parameter in HB), but they are the most luminous phases!

– Fuel Consumption Theorem: (Only for fast evo. ph.; Buzzoni, Maraston)

– Calibrated with observations (different HB morphologies)– How much are they “representative” of a class?



Step 3a: Synthesis systematic errorsCurrent status:

• There are groups evaluating errors in tracks/isochrones computations (e.j. Degl'Innocenti et al., Bressan et al....)

• There are group evaluating the error in isochrone - atmosphere model asignation (e.j. see García Vargas et al. Poster)

Summary of systematic errors

- Definition of synthesis models must include clearly - Tracks/Isocrones used - Atmosphere library used (and its coverage!) - Algorithms used in the computation of integrated

properties

- Models will include, in near future, an estimation of systematic errors --> Fitting applications should be created to allow to include (at least) observational errors in data + systematic errors in models (+ probabilistic dispersion in models: see now)



Step 4: Synthesis probability dispersion/Statistical errors

Synthesis models types- Individual Monte Carlo Simulation: (a single spectrum)

– No statistical error (since there is no statistical analysis)– But, can a single simulation be representative of a class of

objects?

- Sets of Monte Carlo simulations (a set of spectra)– Statistical error obtained from the analysis of the whole set.– A particular galaxy would be a point in the whole distribution (but

which one?)– The distributions of Monte Carlo simulations are not necessarily

gaussian ones!! (it would be needed the skewness and the kurtosis of the resulting distribution)

– “Standard” models (a single “spectra”?)– Only give one value of the spectra for given physical parameters

(age, metalicity, IMF, SFH) whatever the cluster size.

– Probabilistic models – Single mean spectra + variance + skewness + kurtosis for given

physical parameters and its variation with the observed cluster size (aperture effects...)



Standard synthesis models:

Used as deterministic tools

But they return the mean value of the observed properties - not their actual values

don’t give any further information on the distribution



Monte Carlo simulations and

Probabilistic models:

Simulations:

Barbaro, & Bertelli, 1977, A&A, 54, 243

Chiosi et al.(1988) A&A 196,84

Santos & Frogel 1997, ApJ, 479, 764

Cerviño et al. 2000 A&A 360L

Lançon & Mouhcine 2000, ASP C.S.,211, 34

Bruzual 2002, IAU Symp., 207, 616

Girardi 2002, IAU Symp., 207, 625

Cantiello et al. 2003, AJ 125, 2783

Theory (based on Poisson bins)

Buzzoni 1989, ApJS, 71, 871

Cerviño et al. 2002, A&A, 381, 51

Cerviño, & Valls-Gabaud 2003 MNRAS 338, 481

González 2004, ApJ, 611, 270

Other approaches

Cerviño & Luridiana 2004A&A...413..145

Cerviño & Luridiana 2006A&A...451..475

From Bruzual 2002, IAU Symp., 207, 616



Probabilistic Synthesis models



VO implemantation (1st approach):

Same age & Z; 90% Confidence IntervalSame age & Z; 90% Confidence IntervalDiferent age & same Z; mean valuesl



Step 4b: Synthesis probabilistic variance/statistical errorsCurrent status:

• Monte Carlo sets provide statistical errors in model results (Teramo SSP models, B&C stochastic library)

• Standard models provide only mean values (but it is not enough) • Probabilistic models provide variance (e.j. SBFs) + high order

moments that allow to estimate if the DISTRIBUTION of INTEGRATED LUMINOSITIES follows a gaussian distribution or not (e.j. Cerviño & Luridiana 2006)

Summary of probabilistic variance/statistical errors:

- It is possible to use all fields in current spectral data model (may be a bit more of effort is needed in the definition of “error” in order to allow non-gaussian distributions) and all kind of models can be described in a probabilistic unified scheme. (standard models produce a mean, probabilistic models and Monte Carlo sets produce mean, variance + other parameters of the distribution of integrated luminosity, and a single Monte Carlo model is a single “data point” in a distribution)

- They will be in the VO (e.j. PGos3 server + TSAP) at the end of summer...

- For fitting tools that use synthesis models: precision (2 fit) or accuracy (include observation errors + models systematic errors + probabilistic dispersion + over-fit test)?



Final Notes:

Best fitted result (precision) is not enough!

But the distribution of possible results are also needed

The amount of information that we can obtain from a system is limited by the system itself.

We can now, with a probabilistic approach, evaluate how much information we can obtain and avoid very precise (but inaccurate) over fitting results

how to use the seds produced by synthesis models?

Documents

synthesis models context

synthesis results

synthesis modelshttp

probabilistic models

data systematic errors

model spectra

vo spectrum data modelstep

observational errors