Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userConstraining the recent star formation history of galaxies : an Approximate Bayesian Computation approach
42
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
[Abridged] Although galaxies are found to follow a tight relation between their star formation rate and stellar mass, they are expected to exhibit complex star formation histories (SFH), with short-term fluctuations. The goal of this pilot study is to present a method that will identify galaxies that are undergoing a strong variation of star formation activity in the last tens to hundreds Myr. In other words, the proposed method will determine whether a variation in the last few hundreds of Myr of the SFH is needed to properly model the SED rather than a smooth normal SFH. To do so, we analyze a sample of COSMOS galaxies using high signal-to-noise ratio broad band photometry. We apply Approximate Bayesian Computation, a state-of-the-art statistical method to perform model choice, associated to machine learning algorithms to provide the probability that a flexible SFH is preferred based on the observed flux density ratios of galaxies. We present the method and test it on a sample of simulated SEDs. The input information fed to the algorithm is a set of broadband UV to NIR (rest-frame) flux ratios for each galaxy. The method has an error rate of 21% in recovering the right SFH and is sensitive to SFR variations larger than 1 dex. A more traditional SED fitting method using CIGALE is tested to achieve the same goal, based on fits comparisons through Bayesian Information Criterion but the best error rate obtained is higher, 28%. We apply our new method to the COSMOS galaxies sample. The stellar mass distribution of galaxies with a strong to decisive evidence against the smooth delayed-$tau$ SFH peaks at lower M* compared to galaxies where the smooth delayed-$tau$ SFH is preferred. We discuss the fact that this result does not come from any bias due to our training. Finally, we argue that flexible SFHs are needed to be able to cover that largest SFR-M* parameter space possible.
rate research
Read More
We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected sampling efficiency compared to standard kernels that are in common use in the ABC literature, especially as the number of parameters increases.
Approximate Bayesian computation (ABC) is computationally intensive for complex model simulators. To exploit expensive simulations, data-resampling via bootstrapping can be employed to obtain many artificial datasets at little cost. However, when using this approach within ABC, the posterior variance is inflated, thus resulting in biased posterior inference. Here we use stratified Monte Carlo to considerably reduce the bias induced by data resampling. We also show empirically that it is possible to obtain reliable inference using a larger than usual ABC threshold. Finally, we show that with stratified Monte Carlo we obtain a less variable ABC likelihood. Ultimately we show how our approach improves the computational efficiency of the ABC samplers. We construct several ABC samplers employing our methodology, such as rejection and importance ABC samplers, and ABC-MCMC samplers. We consider simulation studies for static (Gaussian, g-and-k distribution, Ising model, astronomical model) and dynamic models (Lotka-Volterra). We compare against state-of-art sequential Monte Carlo ABC samplers, synthetic likelihoods, and likelihood-free Bayesian optimization. For a computationally expensive Lotka-Volterra case study, we found that our strategy leads to a more than 10-fold computational saving, compared to a sampler that does not use our novel approach.
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the ABC approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced dimensions. While lacking the standard justifications for the Gibbs sampler, the resulting Markov chain is shown to converge in distribution under some partial independence conditions. The associated stationary distribution can further be shown to be close to the true posterior distribution and some hierarchic
We use HST observations from the Legacy Extragalactic UV Survey to reconstruct the recent star formation histories (SFHs) of three actively star-forming dwarf galaxies, NGC4449, Holmberg II and NGC1705, from their UV color-magnitude diagrams (CMDs). We apply a CMD fitting technique using two independent sets of stellar isochrones, PARSEC-COLIBRI and MIST, to assess the uncertainties related to stellar evolution modelling. Irrespective of the adopted stellar models, all the three dwarfs are found to have had almost constant star formation rates (SFRs) in the last 100-200 Myr, with modest enhancements (a factor of $sim$2) above the 100 Myr-averaged-SFR. Significant differences among the three dwarfs are found in the overall SFR, the timing of the most recent peak and the SFR$/$area. The Initial Mass Function (IMF) of NGC1705 and Holmberg II is consistent with a Salpeter slope down to $approx$ 5 M$_{odot}$, whereas it is slightly flatter, s$=-2.0$, in NGC4449. The SFHs derived with the two different sets of stellar models are consistent with each other, except for some quantitative details, attributable to their input assumptions. They also share the drawback that all synthetic diagrams predict a clear separation in color between upper main sequence and helium burning stars, which is not apparent in the data. Since differential reddening, significant in NGC4449, or unresolved binaries dont appear to be sufficient to fill the gap, we suggest this calls for a revision of both sets of stellar evolutionary tracks.
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.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
