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Register a new userA transdimensional Bayesian method to infer the star formation history of resolved stellar populations
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Joseph Walmswell Mr
Publication date
2013
fields
Physics
and research's language is
English
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We propose a new method to infer the star formation histories of resolved stellar populations. With photometry one may plot observed stars on a colour-magnitude diagram (CMD) and then compare with synthetic CMDs representing different star formation histories. This has been accomplished hitherto by parametrising the model star formation history as a histogram, usually with the bin widths set by fixed increases in the logarithm of time. A best fit is then found with maximum likelihood methods and we consider the different means by which a likelihood can be calculated. We then apply Bayesian methods by parametrising the star formation history as an unknown number of Gaussian bursts with unknown parameters. This parametrisation automatically provides a smooth function of time. A Reversal Jump Markov Chain Monte Carlo method is then used to find both the most appropriate number of Gaussians, thus avoiding avoid overfitting, and the posterior probability distribution of the star formation rate. We apply our method to artificial populations and to observed data. We discuss the other advantages of the method: direct comparison of different parametrisations and the ability to calculate the probability that a given star is from a given Gaussian. This allows the investigation of possible sub-populations.
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We present a new method to determine the star formation and metal enrichment histories of any resolved stellar system. This method is based on the fact that any observed star in a colour-magnitude diagram will have a certain probability of being associated with an isochrone characterised by an age t and metallicity [Fe/H] (i.e. to have formed at the time and with the metallicity of that isochrone). We formulate this as a maximum likelihood problem that is then solved with a genetic algorithm. We test the method with synthetic simple and complex stellar populations. We also present tests using real data for open and globular clusters. We are able to determine parameters for the clusters (t, [Fe/H]) that agree well with results found in the literature. Our tests on complex stellar populations show that we can recover the star formation history and age-metallicity relation very accurately. Finally, we look at the history of the Carina dwarf galaxy using deep BVI data. Our results compare well with what we know about the history of Carina.
220 -
Eric E. Martinez-Garcia
, Gustavo Bruzual
, Gladis Magris C. andn Rosa A. Gonzalez-Lopezlira
2017
We have obtained the time and space-resolved star formation history (SFH) of M51a (NGC 5194) by fitting GALEX, SDSS, and near infrared pixel-by-pixel photometry to a comprehensive library of stellar population synthesis models drawn from the Synthetic Spectral Atlas of Galaxies (SSAG). We fit for each space-resolved element (pixel) an independent model where the SFH is averaged in 137 age bins, each one 100 Myr wide. We used the Bayesian Successive Priors (BSP) algorithm to mitigate the bias in the present-day spatial mass distribution. We test BSP with different prior probability distribution functions (PDFs); this exercise suggests that the best prior PDF is the one concordant with the spatial distribution of the stellar mass as inferred from the near infrared images. We also demonstrate that varying the implicit prior PDF of the SFH in SSAG does not affects the results. By summing the contributions to the global star formation rate of each pixel, at each age bin, we have assembled the resolved star formation history of the whole galaxy. According to these results, the star formation rate of M51a was exponentially increasing for the first 10 Gyr after the Big Bang, and then turned into an exponentially decreasing function until the present day. Superimposed, we find a main burst of star formation at t 11.9 Gyr after the Big Bang.
We present a detailed analysis of the star formation history (SFH) of three fields in M33 located ~ 4 - 6 visual scale lengths from its nucleus. These fields were imaged with the Advanced Camera for Surveys on the Hubble Space Telescope and reach ~ 2.5 magnitudes below the red clump of core helium burning stars. The observed color-magnitude diagrams are modeled as linear combinations of individual synthetic populations with different ages and metallicities. To gain a better understanding of the systematic errors we have conducted the analysis with two different sets of stellar evolutionary tracks which we designate as Padova (Girardi et al. 2000) and Teramo (Pietrinferni et al. 2004). The precise details of the results depend on which tracks are used but we can make several conclusions that are fairly robust despite the differences. Both sets of tracks predict the mean age to increase and the mean metallicity to decrease with radius. Allowing age and metallicity to be free parameters and assuming star formation began ~ 14 Gyr ago, we find that the mean age of all stars and stellar remnants increases from ~ 6 Gyr to ~ 8 Gyr and the mean global metallicity decreases from ~ -0.7 to ~ -0.9. The fraction of stars formed by 4.5 Gyr ago increases from ~ 65% to ~ 80%. The mean star formation rate 80 - 800 Myr ago decreases from ~ 30% of the lifetime average to just ~ 5%. The random errors on these estimates are ~ 10%, 1.0 Gyr, and 0.1 dex. By comparing the results of the two sets of stellar tracks for the real data and for test populations with known SFH we have estimated the systematic errors to be 15%, 1.0 Gyr, and 0.2 dex. These do not include uncertainties in the bolometric corrections or variations in alpha-element abundance which deserve future study.
We present the star formation history of the extremely metal-poor dwarf galaxy DDO 68, based on our photometry with the Advanced Camera for Surveys. With a metallicity of only $12+log(O/H)=7.15$ and a very isolated location, DDO 68 is one of the most metal-poor galaxies known. It has been argued that DDO 68 is a young system that started forming stars only $sim 0.15$ Gyr ago. Our data provide a deep and uncontaminated optical color-magnitude diagram that allows us to disprove this hypothesis, since we find a population of at least $sim 1$ Gyr old stars. The star formation activity has been fairly continuous over all the look-back time. The current rate is quite low, and the highest activity occurred between 10 and 100 Myr ago. The average star formation rate over the whole Hubble time is $simeq 0.01$ M$_{odot}$ yr$^{-1}$, corresponding to a total astrated mass of $simeq 1.3 times 10^8$ M$_{odot}$. Our photometry allows us to infer the distance from the tip of the red giant branch, $D = 12.08 pm 0.67$ Mpc; however, to let our synthetic color-magnitude diagram reproduce the observed ones we need a slightly higher distance, $D=12.65$ Mpc, or $(m-M)_0 = 30.51$, still inside the errors of the previous determination, and we adopt the latter. DDO 68 shows a very interesting and complex history, with its quite disturbed shape and a long Tail probably due to tidal interactions. The star formation history of the Tail differs from that of the main body mainly for an enhanced activity at recent epochs, likely triggered by the interaction.
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we approach the Large Synoptic Survey Telescope (LSST) era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (Teff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/MMT. In sum, the training set includes ~9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts Teff, log g, and [Fe/H] from photometric time-domain observations. Our final, optimized model produces a cross-validated root-mean-square error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for Teff, log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a ~12-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for ~54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.
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