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We study here a novel application of Kim and Lee charged wormholes assuming them to be dark halo objects playing the role of lenses in the Galactic microlensing with source stars belonging to the Galactic Bulge and the Large Magellanic Cloud. First, we observe that both the backreacted scalar ( α ) and electrically ( Q ) charged wormholes have the same zero ADM mass as has the background Ellis–Bronnikov wormhole having a special equation of state parameter γ = - 1 . In particular, we argue that, for α ≠ 0 , the solution formally resembles, but can at best be sourcewise different from, that of the background wormhole. The charge ( Q ≠ 0 ) thus provides an extra degree of freedom that introduces a non-trivial redshift function Φ to the background, alters its throat radius to r th , yet keeps the wormhole massless. Second, we focus on this electrically charged case and calculate the light deflection angle up to 4th PPN order, analyze the effect of Q on the lensing observables such as the image positions, magnification, centroid and time delay of images of the source stars. Third, we analyze the probabilistic features such as optical depth and event rate estimated on the basis of the hypothesis that the wormhole lens could be bound or unbound to our Galaxy. Finally, we report an intriguing qualitative prediction that, compared to the Schwarzschild black hole, the Paczyński light curves of the electrically charged wormhole are much dimmer that also show characteristic gutters at the times the source enters and exits the Einstein ring. The gutters gradually come together as Q approaches the extreme limit r th 2 , at which the Einstein radius R E vanishes so that the source crosses it instantly. It is speculated that re-analyzing past data on Galactic microlensing may betray the presence of charged wormholes.

Salinity is among the main drivers that impacts physiological and biochemical parameters of photosynthetic organisms, usually affecting the pattern of growth and biomass composition. Since managing salinity is key for optimal microalgae outdoor mass production, effects of low salinities (from 35 to 10‰) on biomass production, pigment concentration, and photosynthetic performance of Isochrysis galbana are here described. Lower salinities than 35 ‰ affected neither cell growth estimated by cell density nor the final density of cells at the final stage of growth, but were associated with a decrease in biovolume and final yield of dry biomass. In comparison with cells grown at 35 ‰ salinity, cultivation at 10 ‰ salinity resulted in a 45% reduction of biovolume and a 62% decrease in final biomass. Growth in downwards 35‰ salinities did not alter the cellular content regarding chlorophylls and total carotenoids at the late exponential growth phase, but the content of these pigments increased in the stationary growth phase irrespective of salinity. At the end of growth, the cellular quota for total lipids was essentially not modified by decreasing salinity. Moreover, there was no difference between the maximum and the effective quantum yield of Photosystem II of I. galbana during exponential growth at the tested salinity treatments. These yields were reduced to a larger extent when cells were in the stationary growth phase. Rapid light curves showed that the maximum electron transport rate (rETRm) and the photosynthetic efficiency of cells were also more affected by lower salinities at the stationary growth phase.

This paper presents a novel multiplicative particle swarm optimization algorithm (MPSO) for optimization on the space of quaternions. It uses multiplicative quaternion kinematics to propagate quaternion particles, rather than the generic additive kinematics used in the traditional particle swarm optimization algorithm. The performance of the MPSO is demonstrated on the problem of attitude determination from light curves, which presents many challenges to traditional filtering-based attitude estimation approaches. The MPSO is shown to be able to successfully determine the attitude and angular velocity of an object from a set of multi-observer light-curve measurements. The MPSO is compared to other optimization approaches for this problem, and equal or superior performance is demonstrated on a variety of test cases. It is shown that the MPSO can be used to initialize conventional filters in order to successfully estimate attitude in situations where pure filtering would fail.

The TESS light curve of the silicon Ap star MX TrA was modeled using the observational surface distribution of silicon, iron, helium, and chromium obtained previously with the Doppler Imaging technique. The theoretical light curve was calculated using a grid of synthetic fluxes from line-by-line stellar atmosphere models with individual chemical abundances. The observational TESS light curve was fitted by a synthetic one with an accuracy better than 0.001 mag. The influence of Si and Fe abundance stratification on the amplitude of variability was estimated. Also, the wavelength dependence of the photometric amplitude and phase of the maximum light was modeled showing the typical Ap Si star behavior with increased amplitude and anti-phase variability in far ultraviolet caused by the flux redistribution.

Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although various approaches have been proposed to address this issue, their implementation is often challenging due to complexity or uncertain performance. In this study, we introduce the BSN application, whose name is taken from the BSN project. The application is designed for analyzing contact binary system light curves, supporting photometric data, and employing an MCMC algorithm for efficient parameter estimation. The BSN application generates synthetic light curves more than 40 times faster than PHOEBE during the MCMC fitting process. The BSN application enhances light curve analysis with an expanded feature set and a more intuitive interface while maintaining compliance with established scientific standards. In addition, we present the first light curve analyses of four contact binary systems based on the TESS data, utilizing the BSN application version 1.0. We also conducted a light curve analysis using the PHOEBE Python code and compared the resulting outputs. Two of the target systems exhibited asymmetries in the maxima of their light curves, which were appropriately modeled by introducing a cold starspot on one of the components. The estimated mass ratios of these total-eclipse systems place them within the category of low mass ratio contact binary stars. The estimation of the absolute parameters for the selected systems was carried out using the P−a empirical relationship. Based on the effective temperatures and masses of the components, three of the target systems were classified as A-subtype, while TIC 434222993 was identified as a W-subtype system.

In this paper, we analyze and interpret light curves obtained by observing with the HST telescope the transit of exoplanet HD 189733b across the disk of the star. Observations are carried out in a wide wavelength range of 5500–10 500 Å, which makes it possible to identify the relation between the wavelength and the data obtained during the interpretation of the radius of the planet. It is also shown that this dependence can be explained by the presence of a Rayleigh atmosphere on the planet while the possible parameters of this atmosphere are also approximately estimated.

We propose a simple method for estimating the fill-out factor of overcontact binary systems using the derivatives of light curves. We synthesized 74,431 sample light curves, covering the typical parameter space of overcontact binaries. On the basis of a recent study that proposed a new classification scheme using light curve derivatives up to the fourth order, the sample light curves were classified. Among the classified types, for systems exhibiting high mass ratios and high inclinations (i.e., SPf type), we found that the fill-out factor has a strong correlation with the time interval between two local extrema in the third derivatives of their light curves. An empirical formula to estimate the fill-out factor was derived using regression analysis for the identified correlation. Application to real overcontact binary data demonstrated that the proposed method is practical for obtaining reliable estimates of the fill-out factor and its associated uncertainties.

Using a high-precision algorithm for interpreting transit light curves in a model of a classical eclipsing binary star-exoplanet system, we studied the possibility of determining the system parameters in the absence of a priori knowledge of the orbital eccentricity. It was shown that it is impossible to determine the exact value of the eccentricity and periastron longitude based on the main minimum of the transit light curve alone. Also, at an observational accuracy of ~1% of the eclipse depth, the uncertainty in the eccentricity and periastron longitude together causes a significant uncertainty in the values of the component radii (a two-threefold error relative to the true values) and the orbital inclination angle. However, the ratios of the system component radii and the limb darkening coefficients are determined with good accuracy. At an increase in the observational accuracy to 0.1% of the eclipse depth, it becomes possible to determine the component radii and the orbital inclination angle when interpreting the light curve with allowance for the eccentricity.

Current methods of performing Initial Orbit Determination (IOD) in near-earth orbital regions cannot be directly extended to cislunar space due to changes in gravitational models that must be utilized. For the case of cislunar orbits, the Moon’s gravitational influence necessitates that orbital motions be described by three-body dynamics. Three-body dynamics produce orbits that are generally not elliptical, fixed to an orbital plane, or geometrically simple. This change in assumptions that can be made complicates the task of performing IOD and has led to the investigation of alternative methods and features to classify cislunar orbit parameters. In this paper, we explore the potential of utilizing Machine Learning (ML) algorithms to constrain potential IOD search spaces to specific cislunar families. We accomplish this by training the ML classifier to predict cislunar family from a light curve. To generate training data for this ML effort, we introduce a novel simulation pipeline that produces radiometrically validated light curves using the Digital Imaging and Remote Sensing Image Generation (DIRSIG™) engine. This pipeline ingests various parameters, including: initial Circular Restricted Three-Body Problem (CR3BP) state vectors, satellite material properties, telescope optical properties, and satellite 3D models. The pipeline produces light curves capturing the influence of these factors on the observed visual magnitude signature as a function of elapsed time and phase angle. This pipeline was utilized to produce approximately 3500 light curves of various cislunar orbital families as captured from the perspective of two observing locations: the spaced-based Earth-Moon L1 point, and a Lunar surface based point. These light curves were used as training datasets for neural network models to perform classification of cislunar family via input time-series vectors consisting of two features: visual magnitude and phase angle. Our machine learning technique first uses a warping technique to construct constant sized observational input of the light curves into a multi-layer perceptron, the architecture of which was chosen by a search over a large landscape of possibilities optimizing for validation prediction accuracy. The selected model was then trained on various scenarios of input data, again doing a search over several available hyper-parameters. It was found that the trained models were able to achieve test data-set accuracy of 93.4% for light curves captured from the face of the moon, and 87.5% when combined with light curves captured from the space-based L1 point.

We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback–Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, which is an astronomical measurement of variability in light curves; at the same time, we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output.