Related papers: Deriving star formation histories: Inverting HR di…
We present a meta-analysis of star-formation rate (SFR) indicators in the GAMA survey, producing 12 different SFR metrics and determining the SFR-M* relation for each. We compare and contrast published methods to extract the SFR from each…
Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed. Our objective is to estimate the…
Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or…
We generalize the well-known mixtures of Gaussians approach to density estimation and the accompanying Expectation--Maximization technique for finding the maximum likelihood parameters of the mixture to the case where each data point…
[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…
In this paper we investigate the impact of different star formation histories (SFHs) on the relation between stellar mass M$_{*}$ and star formation rate (SFR) using a sample of galaxies with reliable spectroscopic redshift zspec>2 drawn…
We implemented a novel technique to perform the collective spectral analysis of sets of multiple gamma-ray point sources using the data collected by the Large Area Telescope onboard the Fermi satellite. The energy spectra of the sources are…
Dynamic factor models are often estimated by point-estimation methods, disregarding parameter uncertainty. We propose a method accounting for parameter uncertainty by means of posterior approximation, using variational inference. Our…
Understanding the diversity of star formation histories (SFHs) of galaxies is key to reconstructing their evolutionary paths. Traditional models often assume parametric forms such as delayed-tau or exponentially declining models, which may…
In many Direct and Inverse Scattering problems one has to use a parameter-fitting procedure, because analytical inversion procedures are often not available. In this paper a variety of such methods is presented with a discussion of…
Indirect detection strategies of particle Dark Matter (DM) in Dwarf spheroidal satellite galaxies (dSphs) typically entail searching for annihilation signals above the astrophysical background. To robustly compare model predictions with the…
Based on the Sloan Digital Sky Survey (SDSS), we develop a new monte-carlo based method to estimate the photometric metallicity distribution function (MDF) for stars in the Milky Way. Compared with other photometric calibration methods,…
The observed prevalence of galaxies exhibiting bursty star formation histories (SFHs) at $z\gtrsim6$ has created new challenges and opportunities for understanding their formation pathways. The degenerate effects of the efficiency and…
Whilst the star formation rate (SFR) of molecular clouds and galaxies is key in understanding galaxy evolution, the physical processes which determine the SFR remain unclear. This uncertainty about the underlying physics has resulted in…
Mechanistic network models specify the mechanisms by which networks grow and change, allowing researchers to investigate complex systems using both simulation and analytical techniques. Unfortunately, it is difficult to write likelihoods…
The application of the Stromgren photometric luminosity calibrations to different types of CP stars is reexamined in the light of the new Hipparcos data. A first attempt is made to use the LM statistical parallax method (Luri et al., 1996)…
We derive a parallel sampling algorithm for computational inverse problems that present an unknown linear forcing term and a vector of nonlinear parameters to be recovered. It is assumed that the data is noisy and that the linear part of…
Physics-based covariance models provide a systematic way to construct covariance models that are consistent with the underlying physical laws in Gaussian process analysis. The unknown parameters in the covariance models can be estimated…
We present evolutionary synthesis models of starbursts on top of old stellar populations to investigate in detailed time evolution the relation between Ha luminosity and star formation rate (SFR). The models show that several effects have…
Galaxy formation, viewed as an inference problem from incomplete information, is inherently stochastic. Reducing the full simulation state to a coarse-grained set of variables integrates out unresolved degrees of freedom, motivating an…