Stellar populations in the Carina region: The Galactic plane at l = 291

Previous studies of the Carina region have revealed its complexity and richness as well as a significant number of early-type stars. In many cases, these studies only concentrated on the central region or were not homogeneous. This latter aspect, in particular, is crucial because very different ages and distances for key clusters have been claimed in recent years. The aim of this work is to study in detail an area of the Galactic plane in Carina. We analyze the properties of different stellar populations and focus on a sample of open clusters and their population of YSOs and highly reddened early stars. We also studied the stellar mass distribution in these clusters and the possible scenario of their formation. Finally, we outline the Galactic spiral structure in this direction. We obtained photometric data for six young open clusters located in Carina at l = 291, and their adjacent stellar fields, which we complemented with spectroscopic observations of a few selected targets. We also culled additional information from the literature. Our results provide more reliable estimates of distances, color excesses, masses, and ages of the stellar populations in this direction. We estimate the basic parameters of the studied clusters and find that they identify two overdensities of young stellar populations. We find evidence of PMS populations inside them, with an apparent coeval stellar formation in the most conspicuous clusters. We also discuss apparent age and distance gradients in the direction NW-SE. We study the mass distributions of several clusters in the region. They consistently show a canonical IMF slope. We discover and characterise an abnormally reddened massive stellar population. Spectroscopic observations of ten stars of this latter population show that all selected targets were massive OB stars. Their location is consistent with the position of the Car-Sag spiral arm.Comment: 15 pages, 13 figure

Effect of gain and phase errors on SKA1-low imaging quality from 50-600 MHz

Simulations of SKA1-low were performed to estimate the noise level in images produced by the telescope over a frequency range 50-600 MHz, which extends the 50-350 MHz range of the current baseline design. The root-mean-square (RMS) deviation between images produced by an ideal, error-free SKA1-low and those produced by SKA1-low with varying levels of uncorrelated gain and phase errors was simulated. The residual in-field and sidelobe noise levels were assessed. It was found that the RMS deviations decreased as the frequency increased. The residual sidelobe noise decreased by a factor of ~5 from 50 to 100 MHz, and continued to decrease at higher frequencies, attributable to wider strong sidelobes and brighter sources at lower frequencies. The thermal noise limit is found to range between ~10 - 0.3 $\mu$Jy and is reached after ~100-100 000 hrs integration, depending on observation frequency, with the shortest integration time required at ~100 MHz.Comment: 23 pages, 11 figures Typo correcte

A parallel algorithm for global optimization problems in a distributed computing environment

The problem of finding a global minimum of a real function on a set S of Rn occurs in many real world problems. Since its computational complexity is exponential, its solution can be a very expensive computational task. In this paper, we introduce a parallel algorithm that exploits the latest computers in the market equipped with more than one processor, and used in clusters of computers. The algorithm belongs to the improvement of local minima algorithm family, and carries on local minimum searches iteratively but trying not to find an already found local optimizer. Numerical experiments have been carried out on two computers equipped with four and six processors; fourteen configurations of the computing resources have been investigated. To evaluate the algorithm performances the speedup and the efficiency are reported for each configuration

Bayesian evidence-driven diagnosis of instrumental systematics for sky-averaged 21-cm cosmology experiments

We demonstrate the effectiveness of a Bayesian evidence-based analysis for diagnosing and disentangling the sky-averaged 21-cm signal from instrumental systematic effects. As a case study, we consider a simulated REACH pipeline with an injected systematic. We demonstrate that very poor performance or erroneous signal recovery is achieved if the systematic remains unmodelled. These effects include sky-averaged 21-cm posterior estimates resembling a very deep or wide signal. However, when including parameterised models of the systematic, the signal recovery is dramatically improved in performance. Most importantly, a Bayesian evidence-based model comparison is capable of determining whether or not such a systematic model is needed as the true underlying generative model of an experimental dataset is in principle unknown. We, therefore, advocate a pipeline capable of testing a variety of potential systematic errors with the Bayesian evidence acting as the mechanism for detecting their presence

Bayesian evidence-driven likelihood selection for sky-averaged 21-cm signal extraction

We demonstrate that the Bayesian evidence can be used to find a good approximation of the true likelihood function of a dataset, a goal of the likelihood-free inference (LFI) paradigm. As a concrete example, we use forward modelled sky-averaged 21-cm signal antenna temperature datasets where we artificially inject noise structures of various physically motivated forms. We find that the Gaussian likelihood performs poorly when the noise distribution deviates from the Gaussian case e.g. heteroscedastic radiometric or heavy-tailed noise. For these non-Gaussian noise structures, we show that the generalised normal likelihood is on a similar Bayesian evidence scale with comparable sky-averaged 21-cm signal recovery as the true likelihood function of our injected noise. We therefore propose the generalised normal likelihood function as a good approximation of the true likelihood function if the noise structure is a priori unknown

A Bayesian approach to RFI mitigation

Interfering signals such as Radio Frequency Interference from ubiquitous satellite constellations are becoming an endemic problem in fields involving physical observations of the electromagnetic spectrum. To address this we propose a novel data cleaning methodology. Contamination is simultaneously flagged and managed at the likelihood level. It is modeled in a Bayesian fashion through a piecewise likelihood that is constrained by a Bernoulli prior distribution. The techniques described in this paper can be implemented with just a few lines of code.Comment: 6 pages, 4 figures, accepted by Physical Review D (APS