<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Astronomy and Space Sciences | Astrostatistics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/astronomy-and-space-sciences/sections/astrostatistics</link>
        <description>RSS Feed for Astrostatistics section in the Frontiers in Astronomy and Space Sciences journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-13T11:28:21.81+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2026.1800321</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2026.1800321</link>
        <title><![CDATA[Detection of exoplanets from TESS imaging data using unsupervised machine learning techniques]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abisa Sinha Adhikary</author><author>Sourav Chakraborty</author>
        <description><![CDATA[The identification of exoplanets within habitable zones remains a central objective in modern astrophysics, particularly with the availability of large-scale photometric datasets from space-based missions such as the Transiting Exoplanet Survey Satellite (TESS). This study investigates the effectiveness of unsupervised machine learning techniques–specifically k-means and k-medians clustering–for analyzing and classifying light curves derived from galactic stellar populations. By extracting both basic and extended statistical features, dimensionality reduction methods including t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) are employed to project high-dimensional data into interpretable low-dimensional spaces. To evaluate the relevance of the identified clusters, the results are systematically compared with the TESS Objects of Interest (TOI) catalog, incorporating information on confirmed planets and candidate signals. This comparison reveals that clusters containing known TOIs often include additional unlabeled objects, suggesting the presence of potentially undiscovered exoplanet candidates. Moreover, the clustering framework effectively distinguishes between transit-like signals and noise-dominated light curves, even in sectors with few or no known TOIs. These findings highlight the capability of unsupervised learning to recover known exoplanetary signals while simultaneously identifying new candidate-rich regions within the data. The proposed framework offers a scalable and data-driven approach for prioritizing targets in large survey datasets, contributing to the advancement of automated exoplanet detection pipelines.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2026.1760522</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2026.1760522</link>
        <title><![CDATA[Towards FAIR astrophysical simulations]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Susanne Pfalzner</author><author>Stephan Hachinger</author><author>Jolanta Zjupa</author><author>Salvatore Cielo</author><author>Frank W. Wagner</author><author>Marcus Brüggen</author><author>Annika Hagemeier</author>
        <description><![CDATA[Reproducibility is a cornerstone of science. FAIR (findable, accessible, interoperable, and reusable) data is often a vital step towards testing the reproducibility of results. The implementation of FAIR principles in the astrophysical simulation community is still varied. We approach the discussion of this topic mainly from a high-performance computing (HPC) point of view. We identify the main obstacles to FAIR astrophysics simulations: First, the vast datasets created in simulations on HPC facilities complicate FAIR data management. Second, missing incentive to fully share codes, results, and diagnostic data. Third, a lack of workflows that include data publication and technical support. Therefore, particularly smaller research groups struggle due to the unavailability of dedicated personnel and time in their efforts towards FAIR and open simulations. We propose actionable steps towards achieving “FAIRer” data and open source publication standards in numerical astrophysics. Our suggestions include low-threshold methods to fulfil the basic FAIR requirements as well as basic tools for FAIR (meta-) data generation and data/code publication. This work is a high-level overview intended to initiate discussions within the community, offering initial solutions to these challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2026.1782465</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2026.1782465</link>
        <title><![CDATA[ATD-DL: a deep learning framework for faint astronomical target detection]]></title>
        <pubdate>2026-02-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Junyao He</author><author>Hao Luo</author><author>Wei Xiao</author><author>Shuwei Liu</author><author>Zhaoxiang Qi</author>
        <description><![CDATA[Astronomical imaging data frequently exhibit low signal-to-noise ratios (SNRs), especially for observations obtained from small-aperture, wide-field survey instruments, in which the detected signals are inherently faint and dominated by noise. Such characteristics pose a substantial technical challenge for subsequent target detection and quantitative measurement tasks. This challenge is particularly pronounced for faint astronomical targets with SNRs ranging from 1 to 10. When the SNR decreases below approximately 5, the useful signal approaches or falls beneath the detection threshold imposed by background noise, leading to a pronounced degradation in the performance of traditional threshold-based detection algorithms, such as Sextractor. Furthermore, astronomical imaging data are typically characterized by a high 16-bit dynamic range. This wide dynamic range results in the intensities of faint targets being compressed into a narrow interval of low pixel values. Standard global normalization strategies employed in deep learning models further compress this narrow intensity band, thereby suppressing and obscuring discriminative target features. To address these challenges, we propose ATD-DL, a deep learning–based framework specifically designed for faint astronomical target detection. The core of the proposed framework is an enhanced U-Net–based segmentation architecture. This architecture is integrated with a multi-stage image preprocessing pipeline, target separation, and centroid extraction modules to enable efficient and robust detection of astronomical objects. Experimental results demonstrate that the proposed method achieves excellent performance in detecting extremely faint targets with SNRs in the range 2 ≤ SNR < 5. Compared with traditional approaches, including SExtractor and DAOPHOT, the proposed framework exhibits a markedly superior detection capability under low-SNR conditions near the detection limit. In future applications, ATD-DL may be extended to space object detection tasks, where it has the potential to substantially improve the identification of extremely faint targets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2025.1674754</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2025.1674754</link>
        <title><![CDATA[Habitable exoplanet - a statistical search for life]]></title>
        <pubdate>2025-12-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Prasenjit Banerjee</author><author>Asis Kumar Chattopadhyay</author>
        <description><![CDATA[IntroductionThe identification of habitable exoplanets is an important challenge in modern space science, requiring the combination of planetary and stellar parameters to assess conditions that support life.MethodsUsing a dataset of 5867 exoplanets from the NASA Exoplanet Archive (as of April 3, 2025), we have applied Random Forest and eXtreme Gradient Boosting (XGBoost) to classify planets as habitable or non-habitable based on 32 continuous parameters, including orbital semi-major axis, planetary radius, mass, density, and stellar properties. Habitability is defined through physics-based criteria rooted in the presence of liquid water, stable climates, and Earth-like characteristics using seven key parameters: planetary radius, density, orbital eccentricity, mass, stellar effective temperature, luminosity, and orbital semi-major axis. To make the classification accurate, we deal with multicollinearity and we checked the Variance Inflation Factor (VIF). We selected parameters with VIF < 5: planetary orbital period, semi-major axis, density, eccentricity, inclination; stellar effective temperature, radius, mass, metallicity, age, density, and total proper motion. Although the defining parameters are used for labeling, only those with low VIF (orbital semi-major axis and eccentricity, planetary density, and stellar effective temperature) are retained for modeling, supplemented by additional low-VIF parameters. Class imbalance is addressed using the Random Over-Sampling Examples (ROSE) technique with both over- and under-sampling to create a balanced dataset.ResultsThe models achieve classification accuracies of 99.99% for Random Forest and 99.93% for eXtreme Gradient Boosting (XGBoost) on the test set, with high sensitivity and specificity. We analyze the data distributions of the key defining parameters, revealing skewed distributions typical of exoplanet populations. Parameter uncertainties are incorporated through Monte Carlo perturbations to assess prediction stability, showing minimal impact on overall accuracy but possible biases in borderline cases. We consider the intersection of habitable exoplanets identified by the seven defining parameters and verify with the twelve low-VIF parameters, confirming consistent classification and making habitability assessments more reliable.DiscussionOur findings highlight the potential of machine learning techniques to prioritize exoplanet targets for future observations, providing a fast and understandable approach for habitability assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2025.1659534</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2025.1659534</link>
        <title><![CDATA[Listening to stars: audio-inspired multimodal learning for star classification]]></title>
        <pubdate>2025-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shengwen Zhang</author><author>Yanxia Zhang</author><author>Chao Liu</author>
        <description><![CDATA[Stellar spectral classification plays a crucial role in understanding the intrinsic properties of stars, such as their temperature, composition, and luminosity. Current methods for star classification primarily rely on template fitting, color-magnitude cuts, or machine learning models that process raw 1D spectra or 2D spectral images. These approaches, however, are limited by two main factors: (i) degeneracies in spectral features that lead to confusion between adjacent spectral types, and (ii) an overreliance on flux-versus-wavelength representations, which may overlook complementary structural information. To address these limitations, we propose a novel multimodal framework for stellar spectral classification that combines 1D and 2D spectral data with audio-derived features. Motivated by the structural similarities between stellar spectra and audio signals, we introduce—for the first time—audio-inspired feature extraction techniques, including Mel spectrograms, MFCC, and LFCC, to capture frequency-domain patterns often ignored by conventional methods. Our framework employs an eight-layer CNN for processing spectral data, an EPSANet-50 for spectral images, and a three-layer CNN for audio-derived features. The outputs of these models are mapped to 256-dimensional vectors and fused via a fully connected layer, with attention mechanisms further enhancing the learning process. Experimental results demonstrate that while 1D spectral data with Coord Attention achieves an accuracy of 89.75±0.28%, the Mel spectrogram alone outperforms spectral data, reaching 90.23±0.36%. Combining 1D and 2D modalities yields 91.26±0.35%, and integrating audio features with spectra results in 89.09±0.43%. The fully multimodal approach delivers the best performance, achieving an overall accuracy of 91.79±0.11%. These findings underscore the effectiveness of incorporating audio-derived features, offering a fresh and promising approach to improving stellar spectral classification beyond existing methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2025.1587415</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2025.1587415</link>
        <title><![CDATA[A review of the search for AGB stars]]></title>
        <pubdate>2025-07-24T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Hai-Ling Lu</author><author>Yin-Bi Li</author><author>A-Li Luo</author><author>Zhi-Qiang Zou</author><author>Xiao-Ming Kong</author><author>Zhen-Ping Yi</author><author>Hugh R. A. Jones</author><author>Jun-Chao Liang</author><author>Shuo Li</author>
        <description><![CDATA[The Asymptotic Giant Branch (AGB) is the late stage of the evolution of intermediate and low-mass stars and is of great importance for understanding stellar evolution, nucleosynthesis, and the chemical evolution of galaxies. This paper systematically reviews the methods for identifying AGB stars, from both traditional approaches and machine learning techniques. By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. We also explore the possibility of using interpretable machine learning algorithms to discover new features and applying deep learning algorithms to enhance search efficiency. With the advancement of data processing technology and the widespread application of machine learning methods, future AGB star searches will be more accurate and efficient. The increased number of discoveries, enabled by more advanced search methods, will particularly enhance our ability to reveal examples of short-lived late-stage stellar evolutionary processes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2025.1473492</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2025.1473492</link>
        <title><![CDATA[Fragmentation of young massive clusters in binary components: an application of Griddy Gibbs Sampler]]></title>
        <pubdate>2025-03-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abisa Sinha Adhikary</author><author>Ankita Das</author>
        <description><![CDATA[The study of the process of hierarchical fragmentation of molecular clouds within Young Massive Clusters required modeling the Initial Mass Function by considering both binary and single-star components. Components of masses from the Gaia Early Data Release 3 (EDR3) dataset were estimated using the mass–luminosity relationship and the contribution of each mass to the total system was analyzed in the current research. Stochastic models describing the contribution of each component are developed for binary as well as single stars incorporating the escape mass theory of the assumed pair. Binary masses, fitted to suitable bi-variate distributions, were simulated using Griddy Gibbs sampler, a Markov Chain Monte Carlo (MCMC) algorithm. Stellar masses of single stars were simulated using data from suitable uni-variate distribution. The mass spectrum of the binary, as well as single star components, were then considered together to determine the initial mass function. The resulting mass function under opacity limited fragmentation scenario is further investigated at different projected distances from the cluster core to the radius where the signature of mass segregation is found.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2024.1326926</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2024.1326926</link>
        <title><![CDATA[Bayesian inference: more than Bayes’s theorem]]></title>
        <pubdate>2024-10-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Thomas J. Loredo</author><author>Robert L. Wolpert</author>
        <description><![CDATA[Bayesian inference gets its name from Bayes’s theorem, expressing posterior probabilities for hypotheses about a data generating process as the (normalized) product of prior probabilities and a likelihood function. But Bayesian inference uses all of probability theory, not just Bayes’s theorem. Many hypotheses of scientific interest are composite hypotheses, with the strength of evidence for the hypothesis dependent on knowledge about auxiliary factors, such as the values of nuisance parameters (e.g., uncertain background rates or calibration factors). Many important capabilities of Bayesian methods arise from use of the law of total probability, which instructs analysts to compute probabilities for composite hypotheses by marginalization over auxiliary factors. This tutorial targets relative newcomers to Bayesian inference, aiming to complement tutorials that focus on Bayes’s theorem and how priors modulate likelihoods. The emphasis here is on marginalization over parameter spaces—both how it is the foundation for important capabilities, and how it may motivate caution when parameter spaces are large. Topics covered include the difference between likelihood and probability, understanding the impact of priors beyond merely shifting the maximum likelihood estimate, and the role of marginalization in accounting for uncertainty in nuisance parameters, systematic error, and model misspecification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2024.1415323</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2024.1415323</link>
        <title><![CDATA[Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models]]></title>
        <pubdate>2024-09-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. Kiruthiga</author><author>S. Mythili</author>
        <description><![CDATA[Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast ionospheric TEC changes linked to seismic events in Indonesia. The study focuses on two significant earthquakes: the December 2004 Sumatra earthquake and the August 2012 Sulawesi earthquake. GPS TEC data from a BAKO station near Indonesia and solar and geomagnetic data were utilized to assess the causes of TEC variations. The December 2004 Sumatra earthquake, registering a magnitude of 9.1–9.3, exhibited notable TEC variations 5 days before the event. Analysis revealed that the TEC variations were weakly linked to solar and geomagnetic activities. Both ARMA and CoK models were employed to predict TEC variations during the Earthquakes. The ARMA model demonstrated a maximum TEC prediction of 50.92 TECU and a Root Mean Square Error (RMSE) value of 6.15, while the CoK model predicted a maximum TEC of 50.68 TECU with an RMSE value of 6.14. The August 2012 Sulawesi earthquake having a magnitude of 6.6, revealed TEC anomalies 6 days before the event. For both the Sumatra and Sulawesi earthquakes, the GPS TEC variations showed weak associations with solar and geomagnetic activities but stronger correlations with the earthquake-induced electric field for the considered two stations. The ARMA model predicted a maximum TEC of 54.43 TECU with an RMSE of 3.05, while the CoK model predicted a maximum TEC of 52.90 TECU with an RMSE of 7.35. Evaluation metrics including RMSE, Mean Absolute Deviation (MAD), Relative Error, and Normalized RMSE (NRMSE) were employed to assess the accuracy and reliability of the prediction models. The results indicated that while both models captured the general trend in TEC variations, nuances emerged in their responses to seismic events. The ARMA model demonstrated heightened sensitivity to seismic disturbances, particularly evident on the day of the earthquake, whereas the CoK model exhibited more consistent performance across pre- and post-earthquake periods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2024.1229092</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2024.1229092</link>
        <title><![CDATA[Uncovering the heterogeneity of a solar flare mechanism with mixture models]]></title>
        <pubdate>2024-03-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bach Viet Do</author><author>Yang Chen</author><author>XuanLong Nguyen</author><author>Ward Manchester</author>
        <description><![CDATA[The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly being adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, similar patterns should be shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1158213</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1158213</link>
        <title><![CDATA[Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies]]></title>
        <pubdate>2023-10-09T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Tobías I. Liaudat</author><author>Jean-Luc Starck</author><author>Martin Kilbinger</author>
        <description><![CDATA[The accurate modelling of the point spread function (PSF) is of paramount importance in astronomical observations, as it allows for the correction of distortions and blurring caused by the telescope and atmosphere. PSF modelling is crucial for accurately measuring celestial objects’ properties. The last decades have brought us a steady increase in the power and complexity of astronomical telescopes and instruments. Upcoming galaxy surveys like Euclid and Legacy Survey of Space and Time (LSST) will observe an unprecedented amount and quality of data. Modelling the PSF for these new facilities and surveys requires novel modelling techniques that can cope with the ever-tightening error requirements. The purpose of this review is threefold. Firstly, we introduce the optical background required for a more physically motivated PSF modelling and propose an observational model that can be reused for future developments. Secondly, we provide an overview of the different physical contributors of the PSF, which includes the optic- and detector-level contributors and atmosphere. We expect that the overview will help better understand the modelled effects. Thirdly, we discuss the different methods for PSF modelling from the parametric and non-parametric families for ground- and space-based telescopes, with their advantages and limitations. Validation methods for PSF models are then addressed, with several metrics related to weak-lensing studies discussed in detail. Finally, we explore current challenges and future directions in PSF modelling for astronomical telescopes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1197358</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1197358</link>
        <title><![CDATA[Efficient galaxy classification through pretraining]]></title>
        <pubdate>2023-08-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jesse Schneider</author><author>David C. Stenning</author><author>Lloyd T. Elliott</author>
        <description><![CDATA[Deep learning has increasingly been applied to supervised learning tasks in astronomy, such as classifying images of galaxies based on their apparent shape (i.e., galaxy morphology classification) to gain insight regarding the evolution of galaxies. In this work, we examine the effect of pretraining on the performance of the classical AlexNet convolutional neural network (CNN) in classifying images of 14,034 galaxies from the Sloan Digital Sky Survey Data Release 4. Pretraining involves designing and training CNNs on large labeled image datasets unrelated to astronomy, which takes advantage of the vast amounts of such data available compared to the relatively small amount of labeled galaxy images. We show a statistically significant benefit of using pretraining, both in terms of improved overall classification success and reduced computational cost to achieve such performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1228508</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1228508</link>
        <title><![CDATA[iid2022: a workshop on statistical methods for event data in astronomy]]></title>
        <pubdate>2023-07-24T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Eric D. Feigelson</author><author>Massimiliano Bonamente</author>
        <description><![CDATA[We review the iid2022 workshop on statistical methods for X-ray and γ-ray astronomy and high–energy astrophysics event data in astronomy, held in Guntersville, AL, on Nov. 15–18 2022. New methods for faint source detection, spatial point processes, variability and spectral analysis, and machine learning are discussed. Ideas for future developments of advanced methodology are shared.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1124317</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1124317</link>
        <title><![CDATA[Measurement methods for gamma-ray bursts redshifts]]></title>
        <pubdate>2023-07-05T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Mengci Li</author><author>Zhe Kang</author><author>Chao Wu</author><author>Chengzhi Liu</author><author>Jirong Mao</author><author>Zhenwei Li</author><author>Shiyu Deng</author><author>Bingli Niu</author><author>Ping Jiang</author>
        <description><![CDATA[In the era of multi-messenger astronomy, gamma-ray bursts (GRBs) with known redshifts, especially high-redshift GRBs, are a powerful tool for studying the structure and evolution of the early Universe. We review the background, the history, and the application of measurement methods of GRB redshifts in astronomy. Based on different observation targets, two measurement methods are mainly introduced. One is on GRB afterglow, the other is on GRB host galaxy. There are various processing methods belonging to measurement methods based on afterglow, including spectral measurement method of afterglow and afterglow spectral energy distribution fitting method with improved methods. There are also numerous measurement methods based on host galaxy, such as spectral measurement method of host galaxy, template matching method of host galaxy, some automatic spectroscopic redshift measurement methods, and machine learning methods. We subsequently introduce the principles, effects, and performance of these methods. We enumerate several detection and measurement instruments, which have been used in observation. The characteristics, advantages, disadvantages, and applicability of the GRB redshift measurement methods are summarized and analyzed. Furthermore, we provide a data set of 611 GRBs with measured redshift. The data set has been collected since 1997. Analysis and statistics are presented based on this data set. We summarize the characteristics of GRBs such as location, time, and accuracy. Finally, we introduce Space-based multi-band astronomical Variable Objects Monitor (SVOM) mission dedicated to searching high redshift GRBs. We also introduce the application prospect of various redshift measurement methods in SVOM mission.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1139120</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1139120</link>
        <title><![CDATA[Constraining cosmological parameters from N-body simulations with variational Bayesian neural networks]]></title>
        <pubdate>2023-06-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Héctor J. Hortúa</author><author>Luz Ángela García</author><author>Leonardo Castañeda C.</author>
        <description><![CDATA[Introduction: Methods based on deep learning have recently been applied to recover astrophysical parameters, thanks to the ability of these techniques to capture information from complex data. One of these schemes is the approximate Bayesian neural network (BNN), which has demonstrated to yield a posterior distribution into the parameter space that is extremely helpful for uncertainty quantification. However, modern neural networks tend to produce overly confident uncertainty estimates and introduce bias when applying BNNs to data.Method: In this work, we implement multiplicative normalizing flows (MNFs), a family of approximate posteriors for the parameters of BNNs with the purpose of enhancing the flexibility of the variational posterior distribution, to extract Ωm, h, and σ8 from the QUIJOTE simulations. We compared the latter method with the standard BNNs and the Flipout estimator.Results: We have found that the use of MNFs consistently outperforms the standard BNNs with a percent difference in the mean squared error of 21%, in addition to high-accuracy extraction of σ8 (r2 = 0.99), with precise and consistent uncertainty estimates.Discussions: These findings imply that MNFs provide a more realistic predictive distribution closer to the true posterior, mitigating the bias introduced by the variational approximation and allowing us to work with well-calibrated networks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1161939</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1161939</link>
        <title><![CDATA[Phase coherence of solar wind turbulence from the Sun to Earth]]></title>
        <pubdate>2023-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Masaru Nakanotani</author><author>Lingling Zhao</author><author>Gary P. Zank</author>
        <description><![CDATA[The transport of energetic particles in response to solar wind turbulence is important for space weather. To understand charged particle transport, it is usually assumed that the phase of the turbulence is randomly distributed (the random phase approximation) in quasi-linear theory and simulations. In this paper, we calculate the coherence index, Cϕ, of solar wind turbulence observed by the Helios 2 and Parker Solar Probe spacecraft using the surrogate data technique to check if the assumption is valid. Here, values of Cϕ = 0 and 1 indicate that the phase coherence is random and correlated, respectively. We estimate that the coherence index at the resonant scale of energetic ions (10 MeV protons) is 0.1 at 0.87 and 0.65 au, 0.18 at 0.29 au, and 0.3 (0.35) at 0.09 au for super (sub)-Alfvénic intervals, respectively. Since the random phase approximation corresponds to Cϕ = 0, this may indicate that the random phase approximation is not valid for the transport of energetic particles in the inner heliosphere, especially very close to the Sun (∼0.09 au).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1098345</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1098345</link>
        <title><![CDATA[ARMA model development and analysis for global temperature uncertainty]]></title>
        <pubdate>2023-04-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mahmud Hasan</author><author>Gauree Wathodkar</author><author>Mathias Muia</author>
        <description><![CDATA[Temperature uncertainty models for land and sea surfaces can be developed based on statistical methods. In this paper, we developed a novel time-series temperature uncertainty model, which is the autoregressive moving average (ARMA) (1,1) model. The model was developed for an observed annual mean temperature anomaly X(t), which is a combination of a true (latent) global anomaly Y(t) for a year (t) and normal variable w(t). The uncertainty is taken as the variance of w(t), which was divided into land surface temperature (LST) uncertainty, sea surface temperature (SST) uncertainty, and the corresponding source of uncertainty. The ARMA model was analyzed and compared with autoregressive (AR) and autoregressive integrated moving average (ARIMA) for the data obtained from the NASA Goddard Institute for Space Studies Surface Temperature (GISTEMP) Analysis. The statistical analysis of the autocorrelation function (ACF), partial autocorrelation function (PACF), normal quantile–quantile (normal Q-Q) plot, density of the residuals, and variance of normal variable w(t) shows that ARMA (1,1) fits better than AR (1) and ARIMA (1, d, 1) for d = 1, 2.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2023.1163530</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2023.1163530</link>
        <title><![CDATA[Editorial: Applications of statistical methods and machine learning in the space sciences]]></title>
        <pubdate>2023-03-16T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Bala Poduval</author><author>Karly M. Pitman</author><author>Olga Verkhoglyadova</author><author>Peter Wintoft</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2022.1115995</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2022.1115995</link>
        <title><![CDATA[Editorial: Multi-scale magnetic field measurements in the multi-phase interstellar medium]]></title>
        <pubdate>2023-01-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Siyao Xu</author><author>Alex Lazarian</author><author>Martin Houde</author><author>Thiem Hoang</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fspas.2022.980759</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fspas.2022.980759</link>
        <title><![CDATA[Towards an AI-based understanding of the solar wind: A critical data analysis of ACE data]]></title>
        <pubdate>2022-11-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>S. Bouriat</author><author>P. Vandame</author><author>M. Barthélémy</author><author>J. Chanussot</author>
        <description><![CDATA[All artificial intelligence models today require preprocessed and cleaned data to work properly. This crucial step depends on the quality of the data analysis being done. The Space Weather community increased its use of AI in the past few years, but a thorough data analysis addressing all the potential issues is not always performed beforehand. Here is an analysis of a largely used dataset: Level-2 Advanced Composition Explorer’s SWEPAM and MAG measurements from 1998 to 2021 by the ACE Science Center. This work contains guidelines and highlights issues in the ACE data that are likely to be found in other space weather datasets: missing values, inconsistency in distributions, hidden information in statistics, etc. Amongst all specificities of this data, the following can seriously impact the use of algorithms: Histograms are not uniform distributions at all, but sometime Gaussian or Laplacian. Algorithms will be inconsistent in the learning samples as some rare cases will be underrepresented. Gaussian distributions could be overly brought by Gaussian noise from measurements and the signal-to-noise ratio is difficult to estimate. Models will not be reproducible from year to year due to high changes in histograms over time. This high dependence on the solar cycle suggests that one should have at least 11 consecutive years of data to train the algorithm. Rounding of ion temperatures values to different orders of magnitude throughout the data, (probably due to a fixed number of bits on which measurements are coded) will bias the model by wrongly over-representing or under-representing some values. There is an extensive number of missing values (e.g., 41.59% for ion density) that cannot be implemented without pre-processing. Each possible pre-processing is different and subjective depending on one’s underlying objectives A linear model will not be able to accurately model the data. Our linear analysis (e.g., PCA), struggles to explain the data and their relationships. However, non-linear relationships between data seem to exist. Data seem cyclic: we witness the apparition of the solar cycle and the synodic rotation period of the Sun when looking at autocorrelations.Some suggestions are given to address the issues described to enable usage of the dataset despite these challenges.]]></description>
      </item>
      </channel>
    </rss>