OPTIMIZER Home Page
What is
the SNR that I need to achieve to obtain reliable galaxy properties?
What is
the optimal set of observables to derive these properties for my galaxy
sample?
What are
the uncertainties and degeneracies expected between these properties for
the SNR and observables chosen?
Introduction
These are some of the most common questions that arise
while designing a new project or preparing an observing proposal on galaxy
evolution. Unfortunately, very few studies have analyzed in detail and
in a systematic way the effects of the observational errors and the set
of observables on the uncertainties and degeneracies between the different
parameters affecting the Spectral Energy Distributions of galaxies (a noteworthly
exception is the optimization of the Photometric Redshifts Technique; see
e.g. Kodama, Bell & Bower 1999; Bolzonella, Miralles & Pello 2000;
Wolf, Meisenheimer & Roser 2001). OPTIMIZER is a powerful tool
designed to optimize the observing time in those studies on galaxy evolution
based in the comparison of photometric data with the predictions of evolutionary
synthesis models. It allows to derive the Signal-to-Noise ratios and set
of observables needed to determine precise age, formation timescale, dust
extinction, and metallicity values for a given galaxy.
Method
In order to address these questions we have artificially
generated a large sample of galaxies (~9000) with different star formation
histories, ages, metallicities, dust content, etc. We have considered exponential
star formation histories parameterized by the timescale of formation. The
actual UV-optical-NIR mass-to-light ratios (M/L) and colors for these galaxies
have been derived using the predictions of evolutionary synthesis models
(Bruzual & Charlot 2001) including the contribution of the nebular
continuum (see Table 1 for the range in galaxy
properties of the sample generated). Then, these M/L and the corresponding
colors have been perturbed adopting different observational errors and
compared back with the evolutionary synthesis models grouped in different
sets (see Table 2 for the sets of observables
considered). The comparison has been performed using a combination of Monte
Carlo simulations, a Maximum Likelihood Estimator, and Principal Component
Analysis (see Gil de Paz et al. 2000c). The ranges in galaxy properties
used for this comparison (see Table 1) are slightly
wider than those used for generating the sample. Using this procedure
we have been able to compute the uncertainties and degeneracies between
the galaxy physical properties as function of (1) the set of observables
available, (2) the observational errors, and (3) the galaxy properties,
including star formation history, age, extinction, metallicity, and redshift
(z = 0.0, 0.7, & 1.4).
Results
Once these quantities were derived
we computed the mean differences between the output and input values along
with the mean 1-sigma spread, at fixed interval in the input properties.
In Figure 1 we show the results obtained before
and after computing the mean differences and 1-sigma errors for a subsample
of 500 nearby galaxies. Due to the relevance of the K-band luminosities
in order to derive stellar masses in nearby (Aragón-Salamanca et
al. 1993; Gil de Paz et al. 2000c) and intermediate-redshift galaxies (see
Brinchmann & Ellis 2000 and references therein), the mean differences
between the derived and input
K-band M/L were also computed. The
values for the mean uncertainties in the derived properties are given in
Table 3. For a detailed
description of the results shown in the graphs given below see Gil de Paz
& Madore (2001).
-
Nearby galaxies (z=0): (formation
timescale, extinction, age, metallicity) and M/L
-
Intermediate-redshift galaxies (z=0.7):
(formation timescale, extinction, age, metallicity)
and M/L
-
High-redshift galaxies (z=1.4):
(formation
timescale, extinction, age, metallicity) and M/L
-
PEGASE vs. GISSEL (z=0):
(formation
timescale, extinction, age, metallicity) and M/L
Bibliography
Aragón-Salamanca et al. 1993, MNRAS 262, 764.
Bolzonella, Miralles & Pelló 2000, A&A
363, 476.
Brinchmann & Ellis 2000, MNRAS 536, 77.
Bruzual & Charlot 2001, in preparation (GISSEL99).
Gil de Paz & Madore 2001, AJ, accepted (astro-ph/0201013)
Gil de Paz et al. 2000a, A&AS 145, 377.
Gil de Paz, Zamorano & Gallego 2000b, A&A 361,
465.
Gil de Paz et al. 2000c, MNRAS 316, 357.
Kodama, Bell & Bower 1999, MNRAS 302, 152.
Wolf, Meisenheimer & Röser 2001, A&A 365,
660.
If you are interested in OPTIMIZING
your observations send me an e-mail to: agpaz(at)astrax.fis.ucm.es
Last modified on: September 11th 2006