Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100883
A. Jana , L. Samushia
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 (), angular multipoles of the correlation function (), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining and VPF improves parameter constraints, adding the multipoles , , and to does not significantly improve the constraints.
我们研究了机器学习（ML）方法对小尺度星系聚类建模的潜力，以约束星系晕占分布（HOD）参数。我们的分析表明，虽然许多 ML 算法报告了良好的统计拟合，但它们产生的似然等值线的均值和方差与真实的模型参数相比都有很大偏差。这凸显了在星系聚类的 ML 应用中仔细处理数据和选择算法的重要性，因为如果应用不当，即使是看似稳健的方法也会导致有偏差的结果。与传统的粗暴方法相比，如果ML工具的鲁棒性得到确立，那么它就能提供一种探索HOD参数空间的有前途的方法，而且能大大降低计算成本。利用我们基于 ANN 的管道，我们成功地重现了近期文献中的一些标准结果。适当限制 HOD 参数空间、转换训练数据以及谨慎选择 ML 算法对于实现无偏且稳健的预测至关重要。在所测试的方法中，如果适当限制 HOD 先验空间，人工神经网络（ANN）在预测聚类统计数据方面的表现优于随机森林（RF）和脊回归。我们使用投影两点相关函数（wp(rp)）、相关函数的角倍率（ξℓ(r)）以及暗能量光谱仪器模拟的红色发光星系的空隙概率函数（VPF）证明了这些发现。我们的研究结果表明，将 wp(rp) 和 VPF 结合起来可以改善参数约束，但将乘数ξ0、ξ2 和ξ4 加入 wp(rp) 并不能显著改善约束。
{"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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100884
J. Mittendorf , K. Molaverdikhani , B. Ercolano , A. Giovagnoli , T. Grassi
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.
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 Venus Express and Akatsuki 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.
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 (3000 km) cloud features on Venus and can detect small-scale (25 km) wave patterns. However, at medium scales (500 km) challenges are encountered, as available resolution and distinctive features start to diminish and blended features complicate the separation of well defined clusters.
{"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 (3000<!--> <!-->km) cloud features on Venus and can detect small-scale (25<!--> <!-->km) wave patterns. However, at medium scales (<span><math><mo>∼</mo></math></span>500<!--> <!-->km) 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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100882
S. Faiz Gurmani , N. Ahmad , R. Kalsoom , S. Shahzada , M. Awais , M. Ali Shah
Solar activities play an important role in the variation of the Atmospheric Electric Field (AEF), and affect the Global Electric Circuit (GEC). The relationship between the variation of the AEF and solar activities is focused in the present study. It includes the variation in the AEF with respect to sunspot numbers, direct and indirect radiations, and solar flares during the decline phase of solar cycle 24 from 2015–2019 for Islamabad (ISL) observatory in detail, and partially for Muzaffarabad (MZF) observatory. A few of them had good relationship with the atmospheric electric field. The solar eclipse effect on the atmospheric electric field for the Muzaffarabad station is also presented in this work. A significant increase was observed during the eclipse period which led to decrease in electrical conductivity of atmospheric electric field as compared to alternate days for the same period.
{"title":"Temporal variation of atmospheric electric field in comparison with solar terrestrial activities during the 24th solar cycle","authors":"S. Faiz Gurmani , N. Ahmad , R. Kalsoom , S. Shahzada , M. Awais , M. Ali Shah","doi":"10.1016/j.ascom.2024.100882","DOIUrl":"10.1016/j.ascom.2024.100882","url":null,"abstract":"<div><div>Solar activities play an important role in the variation of the Atmospheric Electric Field (AEF), and affect the Global Electric Circuit (GEC). The relationship between the variation of the AEF and solar activities is focused in the present study. It includes the variation in the AEF with respect to sunspot numbers, direct and indirect radiations, and solar flares during the decline phase of solar cycle 24 from 2015–2019 for Islamabad (ISL) observatory in detail, and partially for Muzaffarabad (MZF) observatory. A few of them had good relationship with the atmospheric electric field. The solar eclipse effect on the atmospheric electric field for the Muzaffarabad station is also presented in this work. A significant increase was observed during the eclipse period which led to decrease in electrical conductivity of atmospheric electric field as compared to alternate days for the same period.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100882"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420006","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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100892
L.K. Sharma , S. Parekh , A.K. Yadav , N. Goyal
Our goal in this study is to build FRW cosmological models inside the 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 gravity model using a novel, straightforward parameterization of the Hubble parameter in the form . The corresponding free parameters in are limited between 1 and 2 confidence bounds using the -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 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.
{"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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100891
P. Sriramachandran , S.H. Nivash
<div><h3>Context</h3><div>Spectral lines of diatomic molecules are perfect tools for studying the structure of sunspots and their temperature layers and magnetic sensitive absorption features, which are typically higher than in atomic lines. The integrated intensities of a few bands in the rotational structure of the astrophysically significant <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>and <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span> systems of barium monoxide (BaO) have been measured experimentally using band spectra. An analysis of the prominent lines of (0, 0; 1, 1; 2, 2) bands of <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>transition and (0, 0; 1, 1; 2, 2) bands of <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition with those of sunspot umbral spectrum. The effective rotational temperatures of the <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition of BaO in the sunspot umbral spectrum are found to be in the range of 1600 K to 3200 K.</div></div><div><h3>Aims</h3><div>An analysis of BaO prominent rotational molecular lines of <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>and <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition with those of sunspot umbral spectral lines. To find the significant values of radiative transition parameters, vibrational temperature and the effective rotational temperature of the molecule in celestial objects.</div></div><div><h3>Methods</h3><div>Calibrated the rotational structure of molecular band heads and lines for and <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi><
{"title":"On detection of BaO molecular lines in sunspot spectrum","authors":"P. Sriramachandran , S.H. Nivash","doi":"10.1016/j.ascom.2024.100891","DOIUrl":"10.1016/j.ascom.2024.100891","url":null,"abstract":"<div><h3>Context</h3><div>Spectral lines of diatomic molecules are perfect tools for studying the structure of sunspots and their temperature layers and magnetic sensitive absorption features, which are typically higher than in atomic lines. The integrated intensities of a few bands in the rotational structure of the astrophysically significant <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>and <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span> systems of barium monoxide (BaO) have been measured experimentally using band spectra. An analysis of the prominent lines of (0, 0; 1, 1; 2, 2) bands of <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>transition and (0, 0; 1, 1; 2, 2) bands of <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition with those of sunspot umbral spectrum. The effective rotational temperatures of the <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition of BaO in the sunspot umbral spectrum are found to be in the range of 1600 K to 3200 K.</div></div><div><h3>Aims</h3><div>An analysis of BaO prominent rotational molecular lines of <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup></mrow></math></span>and <span><math><mrow><mi>A</mi><msup><mrow></mrow><mrow><mo>′</mo><mn>1</mn></mrow></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mi>X</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mo>+</mo></msup><mspace></mspace></mrow></math></span>transition with those of sunspot umbral spectral lines. To find the significant values of radiative transition parameters, vibrational temperature and the effective rotational temperature of the molecule in celestial objects.</div></div><div><h3>Methods</h3><div>Calibrated the rotational structure of molecular band heads and lines for and <span><math><mrow><msup><mi>A</mi><mn>1</mn></msup><msup><mrow><mstyle><mi>Σ</mi><","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100891"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652656","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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100885
A. Dixit , S. Gupta , A. Pradhan , S. Krishnannair
The present article deals with the isotropic cosmological model of gravity filled with bulk viscous fluid, where 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 will be located in the phantom region. The correspondence between bulk pressure and the reconstructed in f(Q) gravity has also been addressed. In the presence of holographic Ricci dark energy, the reconstructed gravity yields a transition from the quintessence era into phantom era.
{"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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100889
T. Ceulemans , F. De Ceuster , L. Decin , J. Yates
Spectral line observations are an indispensable tool to remotely probe the physical and chemical conditions throughout the universe. Modelling their behaviour is a computational challenge that requires dedicated software. In this paper, we present the first long-term stable release of Magritte, an open-source software library for line radiative transfer. First, we establish its necessity with two applications. Then, we introduce the overall design strategy and the application/programmer interface (API). Finally, we present three key improvements over previous versions: (1) an improved re-meshing algorithm to efficiently coarsen the spatial discretisation of a model; (2) a variation on Ng-acceleration, a popular acceleration-of-convergence method for non-LTE line transfer; and, (3) a semi-analytic approximation for line optical depths in the presence of large velocity gradients.
{"title":"Magritte, a modern software library for spectral line radiative transfer","authors":"T. Ceulemans , F. De Ceuster , L. Decin , J. Yates","doi":"10.1016/j.ascom.2024.100889","DOIUrl":"10.1016/j.ascom.2024.100889","url":null,"abstract":"<div><div>Spectral line observations are an indispensable tool to remotely probe the physical and chemical conditions throughout the universe. Modelling their behaviour is a computational challenge that requires dedicated software. In this paper, we present the first long-term stable release of <span>Magritte</span>, an open-source software library for line radiative transfer. First, we establish its necessity with two applications. Then, we introduce the overall design strategy and the application/programmer interface (API). Finally, we present three key improvements over previous versions: (1) an improved re-meshing algorithm to efficiently coarsen the spatial discretisation of a model; (2) a variation on Ng-acceleration, a popular acceleration-of-convergence method for non-LTE line transfer; and, (3) a semi-analytic approximation for line optical depths in the presence of large velocity gradients.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100889"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593528","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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100886
G. Teixeira , C.R. Bom , L. Santana-Silva , B.M.O. Fraga , P. Darc , R. Teixeira , J.F. Wu , P.S. Ferguson , C.E. Martínez-Vázquez , A.H. Riley , A. Drlica-Wagner , Y. Choi , B. Mutlu-Pakdil , A.B. Pace , J.D. Sakowska , G.S. Stringfellow
Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 point-source depth of 24.3, , 23.5, and 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.
测光宽视场巡天正在以前所未有的细节对天空进行成像。这些巡天在高效估算星系光度红移的同时准确量化相关的不确定性方面面临着巨大的挑战。在这项工作中，我们通过探索在 17,000 平方度的广阔区域内估算星系光度红移的概率密度函数（PDF）来应对这一挑战，该区域涵盖了中位 5σ 点源深度为 g = 24.3、r=23.9、i = 23.5 和 z = 22.8 等的天体。我们的方法使用了深度学习，特别是将循环神经网络架构与混合密度网络相整合，利用星等和颜色作为输入特征，在整个DECam局部体积探测（DELVE）巡天足迹中构建光度红移PDF。随后，我们对估算方法的可靠性和稳健性进行了严格评估，将其性能与其他成熟的机器学习方法进行对比，以确保红移估算的质量。我们的最佳结果约束了测光红移，偏差为-0.0013，散度为0.0293，离群分数为5.1%。这些点估计值都附有校准良好的 PDF，并使用概率积分变换和 Odds 分布等诊断工具进行了评估。我们还解决了 PDF 在磁盘空间存储方面的可访问性问题，以及生成其相应参数所需的时间要求。我们提出了一种新颖的自动编码器模型，可将 PDF 参数数组的大小减少到原来的六分之一，从而将生成 PDF 所需的时间大幅减少到直接从幅值生成 PDF 所需的八分之一。
{"title":"Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2","authors":"G. Teixeira , C.R. Bom , L. Santana-Silva , B.M.O. Fraga , P. Darc , R. Teixeira , J.F. Wu , P.S. Ferguson , C.E. Martínez-Vázquez , A.H. Riley , A. Drlica-Wagner , Y. Choi , B. Mutlu-Pakdil , A.B. Pace , J.D. Sakowska , G.S. Stringfellow","doi":"10.1016/j.ascom.2024.100886","DOIUrl":"10.1016/j.ascom.2024.100886","url":null,"abstract":"<div><div>Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5<span><math><mi>σ</mi></math></span> point-source depth of <span><math><mi>g</mi></math></span> <span><math><mo>=</mo></math></span> 24.3, <span><math><mrow><mi>r</mi><mo>=</mo><mn>23</mn><mo>.</mo><mn>9</mn></mrow></math></span>, <span><math><mi>i</mi></math></span> <span><math><mo>=</mo></math></span> 23.5, and <span><math><mi>z</mi></math></span> <span><math><mo>=</mo></math></span> 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>0013</mn></mrow></math></span>, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100886"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420004","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}
Pub Date : 2024-10-01DOI: 10.1016/j.ascom.2024.100888
A. Beesham , R.K. Tiwari , B.K. Shukla , D. Sofuoğlu , A. Tiwari
This paper investigates the dynamics of cosmic expansion within the framework of gravity, focusing on the late-time behavior of the universe modeled as a flat Friedmann–Lemaître–Robertson–Walker spacetime. We derive an analytical solution for the field equations and employ advanced statistical techniques, including the Markov Chain Monte Carlo (MCMC) method, to determine best-fit values for the key cosmological parameters, such as the Hubble parameter and the deceleration parameter. Our findings reveal a transition from a decelerating to an accelerating phase of cosmic expansion, aligning closely with observational data as in the CDM model. The analysis of energy conditions indicates that the strong energy condition is violated, in keeping with the current accelerated expansion of the universe and the nature of dark energy. By elucidating the quintessence behavior of our model through statefinder and Om diagnostics, this study contributes to a deeper understanding of cosmic evolution and the fundamental forces at play. The insights gained pave the way for future research into alternative cosmological models, inviting further exploration of the mysteries surrounding dark energy and the ultimate fate of the universe.
本文在 f(R,Lm) 引力框架内研究了宇宙膨胀的动力学，重点是以平坦的弗里德曼-勒梅特尔-罗伯逊-沃克时空为模型的宇宙晚期行为。我们推导出了场方程的解析解，并采用了先进的统计技术，包括马尔可夫链蒙特卡洛（MCMC）方法，以确定哈勃参数和减速参数等关键宇宙学参数的最佳拟合值。我们的发现揭示了宇宙膨胀从减速阶段向加速阶段的过渡，与 ΛCDM 模型中的观测数据非常吻合。对能量条件的分析表明，强能量条件被违反了，这与当前宇宙的加速膨胀和暗能量的性质是一致的。这项研究通过状态探测器和 Om 诊断阐明了我们模型的本质行为，有助于加深对宇宙演化和基本作用力的理解。所获得的洞察力为未来研究其他宇宙学模型铺平了道路，并将进一步探索围绕暗能量和宇宙最终命运的奥秘。
{"title":"Accelerating universe in f(R,Lm) gravity","authors":"A. Beesham , R.K. Tiwari , B.K. Shukla , D. Sofuoğlu , A. Tiwari","doi":"10.1016/j.ascom.2024.100888","DOIUrl":"10.1016/j.ascom.2024.100888","url":null,"abstract":"<div><div>This paper investigates the dynamics of cosmic expansion within the framework of <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>R</mi><mo>,</mo><msub><mrow><mi>L</mi></mrow><mrow><mi>m</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> gravity, focusing on the late-time behavior of the universe modeled as a flat Friedmann–Lemaître–Robertson–Walker spacetime. We derive an analytical solution for the field equations and employ advanced statistical techniques, including the Markov Chain Monte Carlo (MCMC) method, to determine best-fit values for the key cosmological parameters, such as the Hubble parameter and the deceleration parameter. Our findings reveal a transition from a decelerating to an accelerating phase of cosmic expansion, aligning closely with observational data as in the <span><math><mi>Λ</mi></math></span>CDM model. The analysis of energy conditions indicates that the strong energy condition is violated, in keeping with the current accelerated expansion of the universe and the nature of dark energy. By elucidating the quintessence behavior of our model through statefinder and Om diagnostics, this study contributes to a deeper understanding of cosmic evolution and the fundamental forces at play. The insights gained pave the way for future research into alternative cosmological models, inviting further exploration of the mysteries surrounding dark energy and the ultimate fate of the universe.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100888"},"PeriodicalIF":1.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571258","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}
Pub Date : 2024-09-19DOI: 10.1016/j.ascom.2024.100880
I. Hristov, R. Hristova
A new efficient approach for searching three-body periodic equal-mass collisionless orbits passing through Eulerian configuration is presented. The approach is based on a symmetry property of the solutions at the half period. Depending on two previously established symmetry types on the shape sphere, each solution is presented by one or two distinct initial conditions (one or two points in the search domain). A numerical search based on Newton’s method on a relatively coarse search grid for solutions with relatively small scale-invariant periods is conducted. The linear systems at each Newton’s iteration are computed by high order high precision Taylor series method. The search produced 12,431 initial conditions (i.c.s) corresponding to 6333 distinct solutions. In addition to passing through the Eulerian configuration, 35 of the solutions are also free-fall ones. Although most of the found solutions are new, all linearly stable solutions among them (only 7) are old ones. Particular attention is paid to the details of the high precision computations and the analysis of accuracy. All i.c.s are given with 100 correct digits.
{"title":"An efficient approach for searching three-body periodic orbits passing through Eulerian configuration","authors":"I. Hristov, R. Hristova","doi":"10.1016/j.ascom.2024.100880","DOIUrl":"10.1016/j.ascom.2024.100880","url":null,"abstract":"<div><div>A new efficient approach for searching three-body periodic equal-mass collisionless orbits passing through Eulerian configuration is presented. The approach is based on a symmetry property of the solutions at the half period. Depending on two previously established symmetry types on the shape sphere, each solution is presented by one or two distinct initial conditions (one or two points in the search domain). A numerical search based on Newton’s method on a relatively coarse search grid for solutions with relatively small scale-invariant periods <span><math><mrow><msup><mrow><mi>T</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo><</mo><mn>70</mn></mrow></math></span> is conducted. The linear systems at each Newton’s iteration are computed by high order high precision Taylor series method. The search produced 12,431 initial conditions (i.c.s) corresponding to 6333 distinct solutions. In addition to passing through the Eulerian configuration, 35 of the solutions are also free-fall ones. Although most of the found solutions are new, all linearly stable solutions among them (only 7) are old ones. Particular attention is paid to the details of the high precision computations and the analysis of accuracy. All i.c.s are given with 100 correct digits.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100880"},"PeriodicalIF":1.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000957/pdfft?md5=e0e9ef0e698ea1e1adc33a7e5eff7275&pid=1-s2.0-S2213133724000957-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315442","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}
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