Fragmentation and fission of giant molecular clouds occasionally results in a pair of gravitationally bound star clusters that orbit their mutual centre of mass for some time, under the influence of internal and external perturbations. We investigate the evolution of binary star clusters with different orbital configurations, with a particular focus on the Galactic tidal field. We carry out N -body simulations of evolving binary star clusters and compare our results with estimates from our semi-analytic model. The latter accounts for mass loss due to stellar evolution and two-body relaxation, and for evolution due to external tides. Using the semi-analytic model we predict the longterm evolution for a wide range of initial conditions. It accurately describes the global evolution of such systems, until the moment when a cluster merger is imminent. Nbody simulations are used to test our semi-analytic model and also to study additional features of evolving binary clusters, such as the kinematics of stars, global cluster rotation, evaporation rates, and the cluster merger process. We find that the initial orientation of a binary star cluster with respect to the Galactic field, and also the initial orbital phase, are crucial for its fate. Depending on these properties, the binaries may experience orbital reversal, spiral-in, or vertical oscillation about the Galactic plane before they actually merge at t ≈ 100 Myr, and produce rotating star clusters with slightly higher evaporation rates. The merger process of a binary cluster induces an outburst that ejects ∼ 10% of the stellar members into the Galactic field.

Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec2, while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent.

We observed night sky quality in several LAPAN stations (Agam, Bandung, Pontianak, Sumedang, Garut, Pasuruan, and Biak) which were conducted from April until July 2018 using Unihedron Sky Quality Meter LU-DL type. Observational data from all of the observational points were then sent regularly to a centralized database for further use. Although most of the measurements were done in overcast conditions, we were able to determine the representative clear sky brightness statistically. The results showed that the light pollution level of the most of the stations are moderate (the values at Biak, Agam, Sumedang, and Pontianak are 20.0, 19.5, 19.6, and 17.7 mpsas respectively) and the stations which are located near or in cities are high (Bandung and Pasuruan with 17.1 and 18.0 mpsas, respectively). In a particular station (Garut) the light pollution is low (20.6 mpsas), so it is good to make this spot to be a location of astrotourism.

The global structure of the solar corona observed in the optical window is governed by the global magnetic field with different characteristics over a solar activity cycle. The Ludendorff flattening index has become a popular measure of global structure of the solar corona as observed during an eclipse. In this study, 15 digital images of the solar corona from 1991 to 2016 were analyzed in order to construct coronal flattening profiles as a function of radius. In most cases, the profile can be modeled with a 2nd order polynomial function so that the radius with maximum flattening index (Rmax) can be determined. Along with this value, Ludendorff index (a + b) was also calculated. Both Ludendorff index and Rmax show anti-correlation with monthly sunspot number, though the Rmax values are more scattered. The variation in Rmax can be regarded as the impact of the changing coronal brightness profile over the equator.

Variable stars, or particularly eclipsing binaries, are very essential astronomical occurrence. Surveys are the backbone of astronomy, and many discoveries of variable stars are the results of surveys. All-Sky Automated Survey (ASAS) is one of the observing projects whose ultimate goal is photometric monitoring of variable stars. Since its first light in 1997, ASAS has collected 50,099 variable stars, with 11,076 eclipsing binaries among them. In the present work we focus on the period determination of the eclipsing binaries. Since the number of data points in each ASAS eclipsing binary light curve is sparse, period determination of any system is a not straightforward process. For 30 samples of such systems we compare the implementation of Lomb-Scargle algorithm which is an Fast Fourier Transform (FFT) basis and Phase Dispersion Minimization (PDM) method which is non-FFT basis to determine their period. It is demonstrated that PDM gives better performance at handling eclipsing detached (ED) systems whose variability are non-sinusoidal. More over, using semiautomatic recipes, we get better period solution and satisfactorily improve 53% of the selected object's light curves, but failed against another 7% of selected objects. In addition, we also highlight 4 interesting objects for further investigation.

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