Astronomy and Computing最新文献_第5页

The VSPEC Collection: A suite of utilities to model spectroscopic phase curves of 3D exoplanet atmospheres in the presence of stellar variability VSPEC 系列：在恒星变异情况下模拟三维系外行星大气光谱相位曲线的实用程序套件

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-11-06 DOI: 10.1016/j.ascom.2024.100890

Ted M. Johnson , Cameron Kelahan , Avi M. Mandell , Ashraf Dhahbi , Tobi Hammond , Thomas Barclay , Veselin B. Kostov , Geronimo L. Villanueva

{"title":"The VSPEC Collection: A suite of utilities to model spectroscopic phase curves of 3D exoplanet atmospheres in the presence of stellar variability","authors":"Ted M. Johnson , Cameron Kelahan , Avi M. Mandell , Ashraf Dhahbi , Tobi Hammond , Thomas Barclay , Veselin B. Kostov , Geronimo L. Villanueva","doi":"10.1016/j.ascom.2024.100890","DOIUrl":"10.1016/j.ascom.2024.100890","url":null,"abstract":"<div><div>We present the Variable Star PhasE Curve (<span>VSPEC</span>) Collection, a set of Python packages for simulating combined-light spectroscopic observations of 3-dimensional exoplanet atmospheres in the presence of stellar variability and inhomogeneity. <span>VSPEC</span> uses the Planetary Spectrum Generator’s Global Emission Spectra (PSG/GlobES) application along with a custom-built multi-component time-variable stellar model based on a user-defined grid of stellar photosphere models to produce spectroscopic light curves of the planet-host system. <span>VSPEC</span> can be a useful tool for modeling observations of exoplanets in transiting geometries (primary transit, secondary eclipse) as well as orbital phase curve measurements, and is built in a modular and flexible configuration for easy adaptability to new stellar and planetary model inputs. We additionally present a set of codes developed alongside the core <span>VSPEC</span> modules, including the stellar surface model generator <span>vspec-vsm</span>, the stellar spectral grid interpolation code GridPolator, and a Python interface for PSG, <span>libpypsg</span>.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"50 ","pages":"Article 100890"},"PeriodicalIF":1.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of magnetic fields on the formation of Interstellar Filaments through shock-cloud interaction 磁场通过冲击云相互作用对星际纤丝形成的影响

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100887

D. Gogoi, S.M. Borah, E. Saikia

{"title":"Effects of magnetic fields on the formation of Interstellar Filaments through shock-cloud interaction","authors":"D. Gogoi, S.M. Borah, E. Saikia","doi":"10.1016/j.ascom.2024.100887","DOIUrl":"10.1016/j.ascom.2024.100887","url":null,"abstract":"<div><div>Interstellar Filaments are ubiquitous in molecular clouds which are hotbeds for star birth. What leads to their formation has been a subject of study in recent years. In the present numerical experiment, we have looked into the role of magnetic field in formation of such structures in the context of multiple molecular cloud complexes after they were subjected to a passing shock. We found that in the absence of this field, post-shock region is turbulent, leading to higher material mixing, 17.5% in the case of the highest porous model considered which also had 42% higher area filling factor compared to models with magnetic field imposed. On the other hand in the presence of a magnetic field, processes such as ‘mass-loading’, slowing down of shock, and inhibition of instabilities are observed which we have found to facilitate the formation of less porous and hence more clumpy structures in post-shock regions. It is found that in the absence of a field, such structures are diffused and spread over a larger area. Such structures are later elongated by hydrodynamical ablation leading to filament-like structures. Morphological output images having filamentary structures are further studied using tools from Nonlinear Dynamics such as Percolation and Fractal Analysis. We find that the filaments formed without a field have higher fractal dimensions, are longer, more complex, and highly branched. Magnetic field influences the properties of the filaments, making them smaller, more confined, and less complex. Further, it is observed that the influence of <strong>B</strong> is diminished with the presence of radiative cooling, still having a subtle affect on the system’s evolution though.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100887"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radio frequency interference identification using dual cross-attention and multi-scale feature fusing 利用双交叉注意和多尺度特征融合进行射频干扰识别

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100881

Y. Dao , B. Liang , L. Hao , S. Feng , S. Wei , W. Dai , F. Gu

{"title":"Radio frequency interference identification using dual cross-attention and multi-scale feature fusing","authors":"Y. Dao , B. Liang , L. Hao , S. Feng , S. Wei , W. Dai , F. Gu","doi":"10.1016/j.ascom.2024.100881","DOIUrl":"10.1016/j.ascom.2024.100881","url":null,"abstract":"<div><div>Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of <span><math><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span>, <span><math><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span>, and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn><mo>−</mo><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></math></span>, respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in <span><math><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn><mo>−</mo><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></math></span>, effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100881"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Constraining Galaxy-Halo connection using machine learning 利用机器学习限制银河与光环的联系

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100883

A. Jana , L. Samushia

{"title":"Constraining Galaxy-Halo connection using machine learning","authors":"A. Jana , L. Samushia","doi":"10.1016/j.ascom.2024.100883","DOIUrl":"10.1016/j.ascom.2024.100883","url":null,"abstract":"<div><div>We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (<span><math><mrow><msub><mrow><mi>w</mi></mrow><mrow><mi>p</mi></mrow></msub><mrow><mo>(</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span>), angular multipoles of the correlation function (<span><math><mrow><msub><mrow><mi>ξ</mi></mrow><mrow><mi>ℓ</mi></mrow></msub><mrow><mo>(</mo><mi>r</mi><mo>)</mo></mrow></mrow></math></span>), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining <span><math><mrow><msub><mrow><mi>w</mi></mrow><mrow><mi>p</mi></mrow></msub><mrow><mo>(</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> and VPF improves parameter constraints, adding the multipoles <span><math><msub><mrow><mi>ξ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>ξ</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, and <span><math><msub><mrow><mi>ξ</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span> to <span><math><mrow><msub><mrow><mi>w</mi></mrow><mrow><mi>p</mi></mrow></msub><mrow><mo>(</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> does not significantly improve the constraints.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100883"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying the clouds of Venus using unsupervised machine learning 利用无监督机器学习对金星云层进行分类

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100884

J. Mittendorf , K. Molaverdikhani , B. Ercolano , A. Giovagnoli , T. Grassi

{"title":"Classifying the clouds of Venus using unsupervised machine learning","authors":"J. Mittendorf , K. Molaverdikhani , B. Ercolano , A. Giovagnoli , T. Grassi","doi":"10.1016/j.ascom.2024.100884","DOIUrl":"10.1016/j.ascom.2024.100884","url":null,"abstract":"<div><div>Because Venus is completely shrouded by clouds, they play an important role in the planet’s atmospheric dynamics. Studying the various morphological features observed on satellite imagery of the Venusian clouds is crucial to understanding not only the dynamic atmospheric processes, but also interactions between the planet’s surface structures and atmosphere. While attempts at manually categorizing and classifying these features have been made many times throughout Venus’ observational history, they have been limited in scope and prone to subjective bias. We therefore present and investigate an automated, objective, and scalable approach for their classification using unsupervised machine learning that can leverage full datasets of past, ongoing, and future missions.</div><div>To achieve this, we introduce a novel framework to generate nadir observation patches of Venus’ clouds at fixed consistent scales from satellite imagery data of the <em>Venus Express</em> and <em>Akatsuki</em> missions. Such patches are then divided into classes using an unsupervised machine learning approach that consists of encoding the patch images into feature vectors via a convolutional neural network trained on the patch datasets and subsequently clustering the obtained embeddings using hierarchical agglomerative clustering.</div><div>We find that our approach demonstrates considerable accuracy when tested against a curated benchmark dataset of Earth cloud categories, is able to identify meaningful classes for global-scale (3000km) cloud features on Venus and can detect small-scale (25km) wave patterns. However, at medium scales (<span><math><mo>∼</mo></math></span>500km) challenges are encountered, as available resolution and distinctive features start to diminish and blended features complicate the separation of well defined clusters.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100884"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal variation of atmospheric electric field in comparison with solar terrestrial activities during the 24th solar cycle 第 24 个太阳周期期间大气电场的时间变化与太阳地面活动的比较

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100882

S. Faiz Gurmani , N. Ahmad , R. Kalsoom , S. Shahzada , M. Awais , M. Ali Shah

引用次数: 0

A comprehensive analysis of observational cosmology in f(Q) gravity with deep learning and MCMC method 用深度学习和 MCMC 方法全面分析 f(Q) 引力下的观测宇宙学

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100892

L.K. Sharma , S. Parekh , A.K. Yadav , N. Goyal

{"title":"A comprehensive analysis of observational cosmology in f(Q) gravity with deep learning and MCMC method","authors":"L.K. Sharma , S. Parekh , A.K. Yadav , N. Goyal","doi":"10.1016/j.ascom.2024.100892","DOIUrl":"10.1016/j.ascom.2024.100892","url":null,"abstract":"<div><div>Our goal in this study is to build FRW cosmological models inside the <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> theory of gravity framework by using Bayesian statistics and deep learning method. We investigate the universe’s accelerating behaviour for a specific version of the <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> gravity model using a novel, straightforward parameterization of the Hubble parameter in the form <span><math><mrow><mi>H</mi><mo>=</mo><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub><msup><mrow><mrow><mo>(</mo><mn>1</mn><mo>+</mo><mi>z</mi><mo>)</mo></mrow></mrow><mrow><mn>1</mn><mo>+</mo><msub><mrow><mi>q</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>−</mo><msub><mrow><mi>q</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></msup><mi>e</mi><mi>x</mi><mi>p</mi><mrow><mo>(</mo><msub><mrow><mi>q</mi></mrow><mrow><mn>1</mn></mrow></msub><mi>z</mi><mo>)</mo></mrow></mrow></math></span>. The corresponding free parameters in <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> are limited between 1<span><math><mi>σ</mi></math></span> and 2<span><math><mi>σ</mi></math></span> confidence bounds using the <span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-minimization procedure. The results show that all the numbers we got are in the ballpark of what cosmological observations would predict. In our model, we examined the physical behaviour of the cosmos using characteristics such as energy density, pressure, and equation of state. We analysed kinematic factors including Hubble parameter, acceleration parameter, and universe age in our model. In our concept, the deceleration parameter <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> represents the universe’s transition from deceleration to acceleration. We employ a novel approach for parameter estimation by utilizing a mixed neural network (MNN) that combines artificial neural networks (ANN) and mixture density networks (MDN). This new methodology leverages the strengths of ANN, MDN, and MNN to enhance the accuracy of parameter estimation.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100892"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On detection of BaO molecular lines in sunspot spectrum 关于探测太阳黑子光谱中的氧化钡分子线

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100891

P. Sriramachandran , S.H. Nivash

引用次数: 0

Computation of bulk viscous pressure with observational constraints via scalar field in the General relativity and f(Q) gravity 通过广义相对论和 f(Q) 引力中的标量场计算具有观测约束条件的体粘性压力

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100885

A. Dixit , S. Gupta , A. Pradhan , S. Krishnannair

{"title":"Computation of bulk viscous pressure with observational constraints via scalar field in the General relativity and f(Q) gravity","authors":"A. Dixit , S. Gupta , A. Pradhan , S. Krishnannair","doi":"10.1016/j.ascom.2024.100885","DOIUrl":"10.1016/j.ascom.2024.100885","url":null,"abstract":"<div><div>The present article deals with the isotropic cosmological model of <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> gravity filled with bulk viscous fluid, where <span><math><mi>Q</mi></math></span> is the non-metricity term and it is responsible for the gravitational interaction. Aside from the tachyon and quintessence scalar fields, the modified Einstein’s field equations have been resolved through the application of the power law form of the expansion. In this model, the Markov chain Monte Carlo (MCMC) analysis method has been utilized to obtained the best-fit value of the model parameter and it confirms that the model satisfies the recent observational data. We have also examined the EoS parameter for bulk viscosity in these cosmological contexts and it has been determined that <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub></math></span> will be located in the phantom region. The correspondence between bulk pressure and the reconstructed <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mo>,</mo><mi>Q</mi></mrow></msub></math></span> in f(Q) gravity has also been addressed. In the presence of holographic Ricci dark energy, the reconstructed <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> gravity yields a transition from the quintessence era into phantom era.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100885"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Magritte, a modern software library for spectral line radiative transfer 马格里特，光谱线辐射传递现代软件库

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-10-01 DOI: 10.1016/j.ascom.2024.100889

T. Ceulemans , F. De Ceuster , L. Decin , J. Yates

引用次数: 0