Astronomy and Computing最新文献

In this paper, we study phase space analysis, thermodynamical geometries and stability of Phantom AdS black hole (BH). The significance of Phantom AdS BH is examined by stability conditions and divergency. The results of small and large roots and divergency are presented for different values of important parameters, graphically. Moreover, we discuss the thermodynamical geometry by using well known techniques such as Weinhold, and geothermodynamics (GTD), HPEM and Ruppeiner and analyze the structure of Phantom AdS BH. Important Physical Information is obtained by utilizing the scalar curvature and zeros of heat capacity. Furthermore, we discuss the P-V criticality to study the stability of Phantom AdS BH which present some significant and important findings.

In this work we present the improved capabilities of MATISSE (Multi-purpose Advanced Tool for Instruments for the Solar System Exploration) tool which is now able to integrate geological maps and analyze specific data based on selected parameters (target, mission, instrument, geological units and area of interest). To demonstrate the effectiveness of this approach we focused on “central pit” craters on Mercury, with particular regard to the ones exposed in the Hokusai, Victoria, and Derain quadrangles.

The use of MATISSE for this application allowed us for an analysis of these morphologies, confirming a tendency for their location on volcanic terrains. The integrated research approach adopted in this study has proven to be a significant advantage in geological analysis, accelerating the process of data collection and interpretation. In conclusion, this study shows how the continuous evolution of scientific tools devoted to data handling and management based on FAIR principles, such as MATISSE, has the potential to open new perspectives in understanding planetary-scale geological processes.

In this work, the experimental potential energy curves for ${\left(2\right)}^3\Pi$ and $X^3\Pi$ electronic states of GeC molecule have been constructed by using Rydberg-Klein-Rees (RKR) method. The radiative transition parameters viz., Franck-Condon (FC) factor, r-centroid, electronic transition moment, band strength, relative band strength, Einstein coefficients, radiative lifetime and oscillator strength for the ${\left(2\right)}^3\Pi -X^3\Pi$ system of GeC molecule have been estimated for the experimentally observed vibrational levels from Rydberg-Klein-Rees (RKR) potential and the estimated values are tabulated. The estimated effective vibrational temperature found as 5628 K for the ${\left(2\right)}^3\Pi -X^3\Pi$ system of GeC molecule. The radiative transition parameters and effective vibrational temperature are evident that the possible presence of GeC molecule in solar and sunspots atmosphere. Further, these parameters are employed in rationalizations of astrochemical and astrophysical observations.

This review summarises popular unsupervised learning methods, and gives an overview of their past, current, and future uses in astronomy. Unsupervised learning aims to organise the information content of a dataset, in such a way that knowledge can be extracted. Traditionally this has been achieved through dimensionality reduction techniques that aid the ranking of a dataset, for example through principal component analysis or by using auto-encoders, or simpler visualisation of a high dimensional space, for example through the use of a self organising map. Other desirable properties of unsupervised learning include the identification of clusters, i.e. groups of similar objects, which has traditionally been achieved by the k-means algorithm and more recently through density-based clustering such as HDBSCAN. More recently, complex frameworks have emerged, that chain together dimensionality reduction and clustering methods. However, no dataset is fully unknown. Thus, nowadays a lot of research has been directed towards self-supervised and semi-supervised methods that stand to gain from both supervised and unsupervised learning.

Searching for a suitable very long baseline (VLBI) interferometer geometry is a key task in planning observations, especially imaging sessions. VLBI image quality is characterized by $(u,v)$-coverage. With one or more radio telescopes located in space, such a task becomes more complex. This paper presents a method of recovering the optimal orbital parameters for space radio telescopes having a given desired $(u,v)$-coverage. In turn, this task can be called the inverse of the task of searching for the optimal geometry and orbital configurations of space-ground and pure space VLBI interferometers.

In the era of time-domain astronomy, scientists often need to generate light curves with varying time-bin. However, an increase in time resolution typically leads to a substantial increase in data transmission. To enhance the data processing efficiency in time-domain astronomy, we propose a novel time-series data model for storing time-series observation data, and we construct the LCGCT, a tool designed to produce light curves with customisable time bins. To validate our approach, we utilise the 7-year MAXI/GSC (Gas Slit Camera of the Monitor of All-sky X-ray Image) X-ray source catalogue, incorporating its 24-h binned light curves for a comparative analysis with our approach. The results obtained confirm the accuracy and effectiveness of our proposed approach. Subsequently, we compare the storage capacity and query performance of LCGCT with a PostgreSQL-based implementation, and results show that LCGCT conserves 75% of the storage space and achieves three times the query speed. Owing to its noteworthy storage and query performance, our proposed time-series data model-based LCGCT can be used in time-domain astronomical projects with high time resolution.

We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Ly$\alpha$ optical depth fields in both real space and redshift space. Unlike previous Ly$\alpha$ codes, THALAS is fully differentiable, enabling a wide variety of potential applications for general analysis of hydrodynamical simulations and cosmological inference. To demonstrate THALAS’s capabilities, we apply it to the Ly$\alpha$ forest inversion problem: given a Ly$\alpha$ optical depth field, we reconstruct the corresponding real-space dark matter density field. Such applications are relevant to both cosmological and three-dimensional tomographic analyses of Lyman Alpha forest data.

Detailed modeling of the evolution of neutral hydrogen in the intergalactic medium during the Epoch of Reionization, $5\le z\le 20$, is critical in interpreting the cosmological signals from current and upcoming 21-cm experiments such as the Low-Frequency Array (LOFAR) and the Square Kilometre Array (SKA). Numerical radiative transfer codes provide the most physically accurate models of the reionization process. However, they are computationally expensive as they must encompass enormous cosmological volumes while accurately capturing astrophysical processes occurring at small scales ($\lesssim \mathrm{Mpc}$). Here, we present pyC ${}^2$ Ray, an updated version of the massively parallel ray-tracing and chemistry code, C ${}^2$ -Ray, which has been extensively employed in reionization simulations. The most time-consuming part of the code is calculating the hydrogen column density along the path of the ionizing photons. Here, we present the Accelerated Short-characteristics Octahedral ray-tracing (ASORA) method, a ray-tracing algorithm specifically designed to run on graphical processing units (GPUs). We include a modern Python interface, allowing easy and customized use of the code without compromising computational efficiency. We test pyC ${}^2$ Ray on a series of standard ray-tracing tests and a complete cosmological simulation with volume size ${\left(349\mathrm{Mpc}\right)}^3$, mesh size of $250^3$ and approximately $10^6$ sources. Compared to the original code, pyC ${}^2$ Ray achieves the same results with negligible fractional differences, $\sim 10^{-5}$, and a speedup factor of two orders of magnitude. Benchmark analysis shows that ASORA takes a few nanoseconds per source per voxel and scales linearly for an increasing number of sources and voxels within the ray-tracing radii.

The fusion of cutting-edge computing techniques with physical detection of gravitational waves can be a potent solution for detecting and cleaning gravitational wave data, which further helps us in the identification of potential astrophysical sources. In this review article, we discuss the role of artificial intelligence approaches in the analysis of gravitational wave data. Below, we list both ground-based interferometers (like LIGO, VIRGO, etc.) and pulse timing arrays (like Parkes pulse timing array) as the current technologies used to find gravitational waves, along with their benefits and how they can be used to find different kinds of gravitational waves. We survey all four types of gravitational waves, each requiring a unique approach to both detection and data processing. We have extensively studied the use of deep learning techniques like convolutional neural networks, autoencoders, and LSTMs in the detection and parameter estimation of gravitational waves from various possible sources, including binary neutron star mergers and neutron star-black hole mergers, in detail. The review article also includes a thorough understanding of the noise and glitches in the real-time data of gravitational waves, as well as how the effective use of machine learning and deep learning techniques can be helpful in simulating waveforms and removing noise to quantify results.

Telescope operation is exceptionally complex, generally with respect to specialized control and monitoring systems. World-class astronomical facilities usually choose tailored control solutions to meet their specific needs. However, many of these telescopes share a common control architecture composed of a three-layer architecture: a top level for services and communication between software components, an intermediate level for coordination and execution of tasks in real-time, and a low level where the end hardware devices live. The first generations of telescopes also implemented centralized and customized solutions, which later evolved to highly decentralized components based on industrial standards, middleware, and open protocols. This paper reviews control and monitoring technologies used in modern world-class terrestrial observatories.