Astronomy and Computing最新文献

We propose the usage of an innovative method for selecting transients and variables. These sources are detected at different wavelengths across the electromagnetic spectrum spanning from radio waves to gamma-rays. We focus on radio signals and use State Space Models, which are also referred to as Dynamic Linear Models. State Space Models (and more generally parametric auto-regressive models) have been the mainstay of economic modelling for some years, but rarely they have been used in Astrophysics.

The statistics currently used to identify radio variables and transients are not sophisticated enough to distinguish different types of variability. These methods simply report the overall modulation and significance of the variability, and the ordering of the data in time is insignificant. State Space Models are much more advanced and can encode not only the amount and significance of the variability but also properties, such as slope, rise or decline for a given time $t$.

In this work, we evaluate the effectiveness of State Space Models for transient and variable detection including classification in time-series astronomy. We also propose a method for detecting a transient source hosted in a variable active galaxy, whereby the time-series of a static host galaxy and the dynamic nature of the transient in the galaxy are intertwined. Furthermore, we examine the hypothetical scenario where the target transient we want to detect is the gravitational wave source GW170817 (or similar).

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at https://pypi.org/project/distclassipy/.

A locally rotationally symmetric Bianchi type-I model has been studied with two fluids within the framework of the $f\left(T\right)$ theory of gravity. Bianchi type-I is an immediate generalization of the Friedmann–Lemaître–Robertson–Walker (FLRW) metric. We have derived the exact field equations in $f\left(T\right)$ gravity by considering the Bianchi type-I metric and applying the action for $f\left(T\right)$ theory of gravity. We have utilized the torsion scalar $T$ and the Lagrangian for matter. The field equations are obtained by taking the variation of the action with respect to the vierbein, leading to a set of equations that includes the energy–momentum tensor for two fluid sources: matter and radiation. We fit the $H\left(z\right)$ curve using 57 data points and the $R^2$-test, achieving an $R^2$ value of 0.9321, indicating a strong fit with the Observational Hubble Dataset (OHD). Cosmological parameters like energy density, pressure, and state finder diagnostics are also discussed.

This research is devoted to investigate the dynamics of test particles and the intricate topological nature of generic rotating Regular Black Holes (RBHs). By applying the Hamilton–Jacobi formalism, we have presented the paths of test particles as they move around the RBHs, graphically. The dynamics of angular momentum and energy of particles in both counter-rotation and co-rotation are studied. As these particles move around RBH, we observe the interplay of forces shaping their journey. We observe how the effective force, effective potential, and Lyapunov exponent change over time. The Lyapunov exponent, a measure of chaos in their motion, evolves, hinting at the stability of their orbits. Moreover, we study the topological properties of a generic rotating RBH and determine their topological numbers, which are sums of winding numbers around defects and probe the fabric of spacetime itself. The winding number is an integer that indicates how many times a curve encircling a defect wraps around the origin. We find that the total topological number is equal to 0 which suggest a system in balance. As the value of degree of nonlinear electrodynamics parameter ($\mu$) increases, the turning points in particle trajectories multiply and presenting the picture of more complexity. In a twist of topology, the interchange of winding numbers can cause a phase transition, reshaping the order parameter space’s topology.

AGILE is a space mission launched in 2007 to study X-ray and gamma-ray phenomena through data acquired by different payload instruments. The AGILE Team developed an application called AGILEScience that allows to visualize information about the AGILE space mission from mobile devices, such as smartphones and tablets. The AGILEScience application can be downloaded freely for iOS and Android devices.

Beside sharing information about the AGILE space mission with the public for outreach purposes, similarly to what other applications do, the AGILEScience app offers some new and unique features in gamma-ray astrophysics: (i) it gives public access in nearly real-time to the sky view of a gamma-ray satellite for the first time, (ii) it interacts with the AGILE remote gamma-ray data storage and analysis system, allowing data analysis to be sent and results to be visualized, and (iii) it allows the AGILE Team to access a password-protected section of the app to view detailed AGILE pipeline results and submit advanced analyses. The last two features are critical to allow remote and easy access to the results of the AGILE automated pipelines.

In particular, the ability to visualize results and execute manual data analysis from mobile devices is key during the follow-up of transient events and to easily monitor the satellite status via smartphone.

A flat Friedmann–Robertson–Walker (FRW) cosmological model with Wet Dark Fluid has been studied based on fractal gravity. The exact solution of the field equations is obtained using linear time-varying deceleration parameter (Akars̈u and Dereli, 2012) and assuming fractal parameter $\xi \ne 0$. The model parameters involved in the model have been constrained using the Hubble datasets $H\left(z\right)$ of 77 data points, known as cosmic chronometers (CC), recently published Pantheon, and the joint data (CC+Pantheon samples). Additionally, we compare our model with $\Lambda$CDM in standard cosmology via error bar trajectories. We study the physical and the cosmographic parameters, such as the Hubble parameter $H\left(z\right)$, deceleration parameter $q\left(z\right)$, and the higher derivatives of the deceleration parameter $q\left(z\right)$, etc. under cosmic observations. It is observed that our model transits from a decelerating state to an accelerating state at $ztr\approx 2.764$. The other astrophysical parameters such as the jerk parameter and snap parameter are also discussed. Finally, we conclude that our model is an accelerating quintessence dark energy model.

In the present work we have focused on the investigation of LRS Bianchi type –I metric within the framework of $f\left(R\right)$ theory of gravity filled with wet dark fluid as the candidate of dark energy. The solution of the metric is an accelerating universe, derived by assuming the negative constant deceleration parameter and utilizing a power law relation. Additionally, we have visually analyzed the metric's dynamical and geometrical properties through graphical representations.

In this paper, we present an innovative and powerful combination of grammatical evolution and a physics-informed neural network approach for symbolically solving the Lane–Emden type equation, which is a nonlinear ordinary differential equation. We employ a grammatical evolution algorithm based on a context-free grammar to construct a mathematical expression comprising some parameters. Subsequently, these parameters are determined using the physics-informed neural networks approach. To achieve this, the computational graph of the mathematical expression generated in each iteration of the grammatical evolution is treated as a network. To assess the proposed method, we consider the Lane–Emden type equation. The proposed method demonstrated that it is a capable method for symbolically solving nonlinear ordinary differential equations accurately.

Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.

Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy.