Celestial Objects Research Articles

All sky system for celestial object detection

The project involves the creation of an electronic device designed to measure various meteorological conditions and capture images of the sky from dusk till dawn. Developed entirely using open-source code, it has led to the creation of a prototype all-sky camera using a Raspberry Pi and an HQ Pi camera. This device is capable of photographing the night sky, and the captured images are then processed by a trained system to detect asteroids and bolides

Data processing pipeline for multiple-exposure photo-plate digital archives

Abstract Photo-plates are an invaluable historical legacy that have been used for over a hundred years to capture images of celestial objects. By digitizing these photo-plates and processing the images with digital image processing methods, scientists can study celestial objects that exhibit temporal variations. Multiple-exposure photo-plates are a unique type of observation data that can capture images of the same sky at different observation times in a single photo-plate. Such photo-plates can be used to discover flares or moving targets with rapid variations, but they are difficult to process automatically due to their complex observation strategies. This paper proposes a pipeline based on classical data-processing algorithms and machine-learning algorithms to detect flares or moving targets in multiple-exposure photo-plate images automatically. The pipeline was used to process several digitized multiple-exposure photo-plate images from the China Astronomical Plates Data, and preliminary results indicate that the pipeline is effective. In the future, we plan to use our method to discover more celestial objects with temporal variations from photo-plate digital archives.

A simulation framework for telescope array and its application in distributed reinforcement learning-based scheduling of telescope arrays

Time-domain astronomy necessitates continuous observation of celestial objects across the entire sky, with specific observation depth and cadence requirements. Telescope arrays, comprised of numerous wide-field optical telescopes, have emerged as a novel observational instrument for time-domain astronomy. However, the observation capabilities of ground-based optical telescope arrays are often constrained by various dynamic factors such as clouds, satellites, and sky background. To meet the observation requirements, active scheduling of telescopes within a telescope array is essential, leveraging telemetry data from the environment. However, due to the complexity and cost of telescope arrays, it is impractical to directly design or test algorithms using a physical telescope array. In this paper, we propose a framework for simulating telescope arrays that incorporates the majority of real observation effects. Building upon this framework, we further introduce a scheduler based on a distributed reinforcement learning framework to optimize the observation strategy of telescopes within the array. The scheduler is trained and evaluated using the simulator. Results demonstrate that the distributed control framework-based scheduler enhances the observation efficiency of the telescope array.

On the Most Reliable Value of the Galactic Aberration Constant

Galactic aberration (GA) is a small effect in proper motions of celestial objects with an amplitude of about 5 μas yr−1 already noticeable in highly accurate astrometric observations such as VLBI and Gaia. However accurate accounting for this effect faces difficulty caused by the uncertainty in the GA amplitude (GA constant). Its estimates derived from VLBI and Gaia data processing differ significantly, so it would be very desirable to involve another independent method to solve the problem of inconsistency between these two methods. Such a method, that I consider in this paper, is using determination of the Galactic rotation parameters by methods of stellar astronomy. The result obtained in this study showed that the GA constant estimate obtained from stellar astronomy is closer to the estimate obtained from Gaia.

A Software for RFI Analysis of Radio Environment around Radio Telescope

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities.

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations.

Titius-Bode’s Relation in Exoplanetary Systems

The Titius-Bode’s relation has been historically successful in predicting the location of Ceres in the solar system, while its physical basis remains hidden. In this study, we attempt to answer the question of whether the Titius-Bode’s relation is universally valid for exoplanetary systems with plural exoplanets. For this purpose, we statistically study the distribution of the ratio of the orbiting periods of two planets in 32 exoplanetary systems hosted by a single star. We only consider the period ratios derived from exoplanets orbiting a single star since celestial objects under investigation are kept as simple as possible and free from uncertainties such as the mass of the host star. We find that the distribution of period ratios of two exoplanets appears inconsistent with that derived from the Titius-Bode’s relation using the χ2 test. We also found that the distance distribution in exoplanetary systems unlikely follows the uniform distribution or the Poisson’s distribution. It is noted, however, that more rigorous statistical tests should be carried out to reach a more certain conclusion.

The ability of CSST to determine the orbital solutions of SB1s

The single-lined spectroscopic binaries (SB1s) are composed of two components with large difference in masses and in physical properties. The orbital and physical parameters of a large number of such binary systems are unknown due to lack of necessary observations. Compared with SB2s, SB1s can provide much more stringent constraint to stellar evolutionary models at a single age. Besides, the fainter components of SB1s are likely to be compact celestial objects, such as a black hole or a neutron star, it is therefore interesting to determine their masses and physical properties. CSST provides an excellent opportunity to study SB1s especially nearby ones, since it can detect astrometric wobble at milliarcsecond level. This implies that if the motion of the visible components can be detected, the full motion of the SB1s can be determined. In this paper, we aim to analyse the ability of CSST to improve the orbital solutions of nearby SB1s based on the available data by adding simulated CSST data of different observation times. The orbital solutions of binaries determined from non-single-star orbital models for sources compatible with combined astrometric + single lined spectroscopic orbital model of Gaia DR3 are served as the research sample to reproduce the orbital elements by fitting simulated CSST data.

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

Time-domain astronomy is an active research area now, which requires frequent observations of the whole sky to capture celestial objects with temporal variations. In the optical band, several telescopes in different locations could form a distributed telescope array to capture images of celestial objects continuously. However, there are millions of celestial objects to observe each night, and only limited telescopes could be used for observation. Besides, the observation capacity of these telescopes would be affected by different effects, such as the sky background or the seeing condition. It would be necessary to develop an algorithm to optimize the observation strategy of telescope arrays according to scientific requirements. In this paper, we propose a novel framework that includes a digital simulation environment and a deep reinforcement learning algorithm to optimize observation strategy of telescope arrays. Our framework could obtain effective observation strategies given predefined observation requirements and observation environment information. To test the performance of our algorithm, we simulate a scenario that uses distributed telescope arrays to observe space debris. Results show that our algorithm could obtain better results in both discovery and tracking of space debris. The framework proposed in this paper could be used as an effective strategy optimization framework for distributed telescope arrays, such as the Sitian project or the TIDO project.

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods. This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results. In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

Study of a Minimally Deformed Anisotropic Solution for Compact Objects with Massive Scalar Field in Brans–Dicke Gravity Admitting the Karmarkar Condition

In the present paper, we focused on exploring the possibility of providing a new class of exact solutions for viable anisotropic stellar systems by means of the massive Brans–Dicke (BD) theory of gravity. In this respect, we used the decoupling of gravitational sources by minimal geometric deformation (MGD) (e−η=Ψ+βh) for compact stellar objects in the realm of embedding class-one space-time to study anisotropic solutions for matter sources through the modified Einstein field equations. For this purpose, we used the ansatz for Ψ relating to the prominent, well-known and well-behaved Finch–Skea model via Karmarkar condition, and the determination scheme for deformation function h(r) was proposed via mimic requirement on radial pressure component: θ11(r)=pr(r) and matter density: θ00(r)=ρ(r) for the anisotropic sector. Moreover, we analyzed the main physical highlights of the anisotropic celestial object by executing several physical tests for the case θ11(r)=pr(r). We have clearly shown how the parameters α, β and ωBD introduced by massive BD gravity via the MGD approach incorporating the anisotropic profile of the matter distribution have an immense effect on many physical parameters of compact bodies such as LMC X-4, LMC X-4, Her X-1, 4U 1820-30, 4U 1608-52, SAX J1808.4–658 and many others that can be fitted.

An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) has completed the observation of nearly 20 million celestial objects, including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio, these spectra often show some anomalous features that do not work well with current templates. In this paper, a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected, and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame (Spectra Analysis-Frame) is provided to explore their origins from different perspectives. The SA-Frame is composed of three parts: NAPC-Spec clustering, characterization and origin analysis. First, NAPC-Spec (Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality, resulting in 13 types. Second, characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum, where these 13 types are characterized as regular spectra with low S/Ns, splicing problems, suspected galactic emission signals, contamination from city light and un-gregarious type respectively. Third, a preliminary analysis of their origins is made from the characteristics of the observational targets, contamination from the sky, and the working status of the instruments. These results would be valuable for improving the overall data quality of large-scale spectral surveys.

An Improved Pair Method to Probe the Dust Extinction Law

Dust extinction law is crucial to recover the intrinsic energy distribution of celestial objects and infer the characteristics of interstellar dust. Based on the traditional pair method, an improved pair method is proposed to model the dust extinguished spectral energy distribution (SED) of an individual star. Instead of the mathematically parameterizing extinction curves, the extinction curves in this work are directly from the silicate-graphite dust model, so that the dust extinction law can be obtained and the dust properties can be analyzed simultaneously. The ATLAS9 stellar model atmosphere is adopted for the intrinsic SEDs in this work, while the silicate-graphite dust model with a dust size distribution of for each component is adopted for the model extinction curves. One typical extinction tracer in the dense region (V410 Anon9) and one in the diffuse region (Cyg OB2 #12) of the Milky Way are chosen to test the reliability and the practicability of the improved pair method in different stellar environments. The results are consistent with their interstellar environments and are in agreement with the previous observations and studies, which prove that the improved pair method is effective and applicable in different stellar environments. In addition to the reliable extinction results, the derived parameters in the dust model can be used to analyze the dust properties, which cannot be achieved by other methods with the mathematical extinction models. With the improved pair method, the stellar parameters can also be inferred and the extinction law beyond the wavelengths of observed data can be predicted based on the dust model as well.

Celestial objects as strongly-interacting nonannihilating dark matter detectors

Nonannihilating dark matter particles, owing to their interactions with ordinary baryonic matter, can efficiently accumulate inside celestial objects. For heavy mass, they gravitate toward the core of the celestial objects, thermalize in a small core region, and eventually form tiny black holes via core collapse, resulting destruction of the host objects. We demonstrate that the existence of a variety of celestial objects provides stringent constraints on strongly interacting heavy dark matter, a blind spot for the terrestrial dark matter detectors as well as for the cosmological probes. Celestial objects with larger sizes and lower core temperatures, such as Jupiter, are the most optimal detectors to probe the strongly interacting heavy nonannihilating dark matter.

Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning

The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data. Due to the extreme size of DES data, identifying these objects among many millions of other celestial objects is a challenging task. The problem of outlier detection is further magnified by the presence of noisy or saturated images. When the number of tested objects is extremely high, even a small rate of noise or false positives leads to a very large number of false detections, making an automatic system impractical. This study applies an automatic method for automatic detection of outlier objects in the first data release of the Dark Energy Survey. By using machine learning-based outlier detection, the algorithm is able to identify objects that are visually different from the majority of the other objects in the database. An important feature of the algorithm is that it allows to control the false-positive rate, and therefore can be used for practical outlier detection. The algorithm does not provide perfect accuracy in the detection of outlier objects, but it reduces the data substantially to allow practical outlier detection. For instance, the selection of the top 250 objects after applying the algorithm to more than 2⋅106 DES images provides a collection of uncommon galaxies. Such collection would have been extremely time-consuming to compile by using manual inspection of the data.

Impact of Heavy Ions on the Structure of Current Sheets in the Gravity Field of Exoplanets and Stars

Boundary current sheets (CSs) can be formed in collisionless space plasmas in the environment of exoplanets and cold stars. Usually they represent curved surfaces carrying the electric current analogous to the well-known planetary ionospheres, magnetopauses, or stellar coronas surrounding celestial bodies. At smaller local scales, some of them can be imagined as planar current layers of a finite scale located parallel to the surface of a celestial object and, correspondingly, perpendicular to the direction of the gravitational force. In some cases, this force crossed with magnetic field can influence the dynamics of charged particles in CSs and substantially change the structure of both the current layer and the magnetic field. We have generalized our prior model, taking into account a multi-ion plasma composition and a magnetic field configuration with a shear. It is shown that, due to the drift motion of plasma particles in the crossed gravitational and magnetic fields, the structure of CSs becomes more complex, accruing asymmetric and shifted profiles of the current and plasma densities that depend on dominating current carriers and the characteristics of the magnetic shear. We discuss possible applications of the results to the interpretation of observations of boundary layers in different space plasmas.

L-dwarf Detection from SDSS Images using Improved Faster R-CNN

We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey (SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L-dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs, including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i − z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.

Development of Teaching Materials that Enable 3D and Active Pseudo-Observation of Celestial Objects: For the 4th-Grade Science Unit “Moon and Stars”

本研究の目的は，「地球」についての基本的な概念形成の初期段階に当たる第4学年「月と星」において，児童が3次元的に，かつ能動的に月や星を疑似観察することができる教材を開発することであった。そのために，まず，実天の様子を直接3次元的に視聴でき，季節や時刻，天候に関係なく，インターネットを介して誰でも実天の観察学習を可能にする，全天球カメラシステムによって撮影された画像を用いた教材を開発した。次に，第4学年児童を対象に，開発した教材を用いて月と星の動きを調べる授業を行った。その結果，事後調査における主観評価において，肯定的に回答した児童の人数は否定的に回答したそれよりも有意に多いことが明らかとなった。以上のことから，本教材は第4学年児童の天文学習における従来の映像教材と比べて，空間的な広がりを本物に近い感覚で実感しやすいことが明らかとなった。

Anisotropic charged stellar models with modified Van der Waals EoS in f(Q) gravity

This paper is based on the study of compact stars in the context of electric fields and the nonmetricity effects of gravity. Due to this, we are motivated to build stellar models based on spherically symmetric space-time in f(Q) gravity. The space-time solution is obtained by Durgapal and Bannerji (Phys Rev D 27:328–331,1983) potential along with modified Van der Waals equation of state (EoS) p_r=eta rho ^2+ frac{beta rho }{gamma rho +1} by introducing a specific form of electric charge function q(r)=kr^3. In order to validate our charge model, we used observational data from the literature for celestial objects like Her X-1, 4U 1538-52, SAX J1808.4-3658, and SMC X-1. Furthermore, we have also retrieved the uncharged effects of gravity for the model SMC X-1 by taking k=0. Our present physical analysis shows that all the obtained features for the present solution are in excellent agreement with the viable model as far as observational data is concerned.

Impact of galactic distributions in celestial capture of dark matter

Celestial capture of dark matter provides a useful handle for constraining its particulate properties. The capture formalism is sensitive to the phase space distribution of dark matter in the vicinity of the celestial object. This article aims to systematically study the impact of uncertainties and the influence of cosmological simulations on the rate at which dark matter particles are captured inside a variety of celestial objects. Going beyond the framework of the Maxwell-Boltzmann distribution or the standard halo model, we take up pragmatic dark matter velocity distributions motivated by observations or cosmological simulations. Within the limits of the standard halo model, we report a maximum $\sim 20\%$ change in the capture rate. This number can go up to $\sim 200\%$ if dark matter particles within the galactic halo are favored to have an empirical velocity distribution profile when well-resolved and sophisticated cosmological simulations are employed to extract their parametric values.