Related papers: Deriving star formation histories: Inverting HR di…
We propose a limit-cycle scenario of star formation history for any morphological type of spiral galaxies. It is known observationally that the early-type spiral sample has a wider range of the present star formation rate (SFR) than the…
Pairwise Markov Random Fields (MRFs) or undirected graphical models are parsimonious representations of joint probability distributions. Variables correspond to nodes of a graph, with edges between nodes corresponding to conditional…
In many practical situations we would like to estimate the covariance matrix of a set of variables from an insufficient amount of data. More specifically, if we have a set of $N$ independent, identically distributed measurements of an $M$…
Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an…
Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples…
Statistical inverse learning aims at recovering an unknown function $f$ from randomly scattered and possibly noisy point evaluations of another function $g$, connected to $f$ via an ill-posed mathematical model. In this paper we blend…
Iterative Proportional Fitting (IPF), combined with EM, is commonly used as an algorithm for likelihood maximization in undirected graphical models. In this paper, we present two iterative algorithms that generalize upon IPF. The first one…
Most statistical software packages implement numerical strategies for computation of maximum likelihood estimates in random effects models. Little is known, however, about the algebraic complexity of this problem. For the one-way layout…
The colour-magnitude diagrams of resolved stellar populations are the best tool to study the star formation histories of the host galactic regions. In this review the method to derive star formation histories by means of synthetic…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
The need to unravel modulations hidden in noisy time series of experimental data is a well known problem, traditionally attacked through a variety of methods, among which a popular tool is the so called Lomb-Scargle periodogram. Recently,…
We construct three extreme different scenarios of the star formation histories applicable to a sample of dwarf galaxies, based either on their present metallicity or their luminosity. The three possible scenarios imply different mechanical…
In this paper, we address the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multi-stage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards…
We present a randomized maximum a posteriori (rMAP) method for generating approximate samples of posteriors in high dimensional Bayesian inverse problems governed by large-scale forward problems. We derive the rMAP approach by: 1) casting…
In the field of gamma-ray astronomy, irregular and noisy datasets make difficult the characterization of light-curve features in terms of statistical significance while properly accounting for trial factors associated with the search for…
Star formation (SF) in the interstellar medium (ISM) is fundamental to understanding galaxy evolution and planet formation. However, efforts to develop closed-form analytic expressions that link SF with key influencing physical variables,…
The total extragalactic background radiation can be an important test of the global star formation history (SFH). Using direct observational estimates of the SFH, along with standard assumptions about the initial mass function (IMF), we…
We develop a non-parametric inverse method to investigate the star formation rate, the metallicity evolution and the reddening properties of galaxies based on their spectral energy distributions (SEDs). This approach allows us to clarify…
Orbital motions from binary stars can broaden the observed line-of-sight velocity distribution of a stellar system, artificially inflating the measured line-of-sight velocity dispersion, which can in turn lead to erroneous conclusions about…
We present a model-fit pipeline to determine the stellar parameters of M-type dwarfs, which is an improvement upon our previous work described in Hejazi et al. 2020. We apply this pipeline to analyze the low-resolution (R~2000) spectra of…