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Astronomical observatory construction plays an essential role in astronomy research, education, and tourism development worldwide. This study develops siting distribution scenarios for astronomical observatory locations in Indonesia using a suitability analysis by integrating the physical and atmospheric observatory suitability indexes, machine learning models, and long-term climate models. Subsequently, potential sites are equalized based on longitude and latitude zonal divisions considering air pollution disturbance risks. The study novelty comes from the integrated model development of physical and socio-economic factors, dynamic spatiotemporal analysis of atmospheric factors, and the consideration of equitable low air-pollution-disturbance-risk distribution in optimal country-level observatory construction scenarios. Generally, Indonesia comprises high suitability index and low multi-source air pollution risk areas, although some area has high astronomical suitability and high-medium air pollution risk. Most of Java, the east coast of Sumatra, and the west and south coasts of Kalimantan demonstrate "low astronomical suitability-high air pollution risk." A total of eighteen locations are recommended for new observatories, of which five, one, three, four, two, and three are on Sumatra, Java, Kalimantan, Nusa Tenggara, Sulawesi, and Papua, respectively. This study provides a comprehensive approach to determine the optimal observatory construction site to optimize the potential of astronomical activities.

Over the last two decades, around 300 quasars have been discovered at z ≳ 6, yet only one has been identified as being strongly gravitationally lensed. We explore a new approach—enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion—which is implemented via image-based deep learning. We first apply this approach to a systematic search for reionization-era lensed quasars, using data from the Dark Energy Survey, the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey, and the Wide-field Infrared Survey Explorer. Our search method consists of two main parts: (i) the preselection of the candidates, based on their spectral energy distributions (SEDs), using catalog-level photometry; and (ii) relative probability calculations of the candidates being a lens or some contaminant, utilizing a convolutional neural network (CNN) classification. The training data sets are constructed by painting deflected point-source lights over actual galaxy images, to generate realistic galaxy–quasar lens models, optimized to find systems with small image separations, i.e., Einstein radii of θ _E ≤ 1″. Visual inspection is then performed for sources with CNN scores of P _lens > 0.1, which leads us to obtain 36 newly selected lens candidates, which are awaiting spectroscopic confirmation. These findings show that automated SED modeling and deep learning pipelines, supported by modest human input, are a promising route for detecting strong lenses from large catalogs, which can overcome the veto limitations of primarily dropout-based SED selection approaches.

We report the results of combined analyses of X-ray and optical data of two galaxy clusters, CL 0024+1654 and RX J0152.7−1357 at redshift z = 0.395 and z = 0.830, respectively, offering a holistic physical description of the two clusters. Our X-ray analysis yielded temperature and density profiles of the gas in the intra-cluster medium (ICM). Using optical photometric and spectroscopic data, complemented with mass distribution from a gravitational lensing study, we investigated any possible correlation between the physical properties of the galaxy members, i.e. their color, morphology, and star formation rate (SFR), and their environments. We quantified the properties of the environment around each galaxy by galaxy number density, ICM temperature, and mass density. Although our results show that the two clusters exhibit a weaker correlation compared to relaxed clusters, it still confirms the significant effect of the ICM on the SFR in the galaxies. The close relation between the physical properties of galaxies and the condition of their immediate environment found in this work indicates the locality of galaxy evolution, even within a larger bound system such as a cluster. Various physical mechanisms are suggested to explain the relation between the properties of galaxies and their environment.