Related papers: Machine Learning based Pointing Models for Radio/S…

The dynamic atmosphere imposes challenges to ground-based cosmic microwave background observation, especially for measurements on large angular scales. The hydrometeors in the atmosphere, mostly in the form of clouds, scatter the ambient…

The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) survey experiment that consists of three 0.5 m small-aperture telescopes (SATs) and one 6 m large-aperture telescope (LAT), sited at an elevation of 5200 m in…

The measurement of atmospheric parameters is fundamental for scientific research using stellar spectra. The Chinese Space Station Telescope (CSST), scheduled to be launched in 2024, will provide researchers with hundreds of millions of…

The Simons Observatory (SO) is a set of cosmic microwave background instruments that will be deployed in the Atacama Desert in Chile. The key science goals include setting new constraints on cosmic inflation, measuring large scale structure…

The Atacama Large Millimeter/submillimeter Array (ALMA) is an international radio telescope under construction in the Atacama Desert of northern Chile. ALMA is situated on a dry site at 5000 m elevation, allowing excellent atmospheric…

The rapid growth of large-scale radio surveys, generating over 100 petabytes of data annually, has created a pressing need for automated data analysis methods. Recent research has explored the application of machine learning techniques to…

We correlate the positions of radio galaxies in the FIRST survey with the CMB lensing convergence estimated from the Atacama Cosmology Telescope over 470 square degrees to determine the bias of these galaxies. We remove optically…

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal intricate internal structures of these objects, allowing researchers to…

A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a…

Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by…

The sensitivity of astronomical X-ray detectors is limited by the instrumental background. The background is especially important when observing low surface brightness sources that are critical for many of the science cases targeted by…

Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed…

The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in…

The Atacama Cosmology Telescope (ACT) is a 6 m telescope located in the Atacama Desert, designed to measure the cosmic microwave background (CMB) with arcminute resolution. ACT, with its third generation polarization sensitive array,…

The Cosmology Large Angular Scale Surveyor (CLASS) will measure the polarization of the Cosmic Microwave Background to search for and characterize the polarized signature of inflation. CLASS will operate from the Atacama Desert and observe…

We present a method to mitigate the atmospheric effects (residual atmospheric lines) in single-dish radio spectroscopy caused by the elevation difference between the target and reference positions. The method is developed as a script using…

Precise subtraction of foreground sources is crucial for detecting and estimating 21cm HI signals from the Epoch of Reionization (EoR). We quantify how imperfect point source subtraction due to limitations of the measurement dataset yields…

This paper proposes a method to predict received power in urban area deterministically, which can learn a prediction model from small amount of measurement data by a simulation-aided transfer learning and data augmentation. Recent…

We present the development of a data-driven, AI-based model of the Point Spread Function (PSF) that achieves higher accuracy than the current state-of-the-art approach, "PSF in the Full Field-of-View'' (PIFF). PIFF is widely used in leading…

This work presents a novel adaptive framework for simultaneously estimating spacecraft attitude and sensor misalignment. Uncorrected star tracker misalignment can introduce significant pointing errors that compromise mission objectives in…