Search Results (54)

Astronomical spectra, which encode rich astrophysical and chemical information, are fundamental to understanding celestial objects and universal laws. The advent of large-scale spectroscopic surveys, generating tens of millions of spectra, presents significant challenges for efficient data processing and analysis. To address these challenges, we develop an AI-powered platform (named “SpecZoo”) for spectral visualization and analysis. This platform integrates modern information technology and machine learning to lower the barrier to spectral data utilization and enhance research efficiency. Its core functionalities include interactive visualization, automated spectral classification, physical parameter measurement, spectral annotation, and multi-band/multi-modal data fusion, all supported by flexible user and data management systems. It has become an essential tool for the National Astronomical Data Center, directly supporting spectral data processing and research for major projects including LAMOST, SDSS, DESI, and so on. Furthermore, the platform demonstrates strong potential for science-education integration, providing a novel resource for cultivating talent in astronomy and data science. Full article

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14,594 sources classified into extended (galaxies) and point-like (stars and quasars) objects. We assess dataset representativeness using UMAP analysis, confirming broad and consistent coverage of feature space. An XGBoost classifier, with hyperparameters tuned using automated optimization, is trained using purely photometric data (60-band J-PAS magnitudes) and combined photometric and morphological features, with performance thoroughly evaluated via ROC and purity–completeness metrics. Incorporating morphology significantly improves classification, outperforming the baseline classifications available in the catalogs. Permutation importance analysis reveals morphological parameters, particularly concentration, normalized peak surface brightness, and PSF, alongside photometric features around 4000 and 6900 Å, as crucial for accurate classifications. We release a value-added catalog with our models for star-galaxy classification, enhancing the utility of miniJPAS and J-NEP for subsequent cosmological and astrophysical analyses. Full article

The gravitational wave event GW190521 seems to be the only BH merger event possibly correlated with an electromagnetic counterpart, which appeared about 34 days after the GW event. This work aims to confirm that the electromagnetic bump towards the Active Galactic Nucleus (AGN) J1249+3449 can be explained within the framework of the gravitational microlensing phenomenon. In particular, considering the data of the Zwicky Transient Facility (ZTF), what emerges from a detailed analysis of the observed light curve using three fitting models (Point Source Point Lens, Finite Source Point Lens, Uniform Source Binary Lens) is that the optical bump can be explained as a microlensing event caused by a lens with mass $\sim$0.1 $M_{\odot }$, lying in the host galaxy of the AGN in question. Full article

The Sardinia Radio Telescope (SRT) is an Italian antenna utilized for scientific research in the field of radio astronomy across a broad frequency range from 300 MHz to 116 GHz. Among the various cryogenic receivers installed on SRT, the dual-polarized C-Low receiver operates within the frequency range of 4.2–5.6 GHz, which is the lower portion of the well-known C-band, and is installed at the Gregorian focus of the telescope. This article presents a general description of the design of the receiver, highlighting its signal acquisition chain, which conditions weak signals from the sky for transmission to the digital back-end, responsible for data processing. An analysis of the radio-frequency interference environment affecting scientific observations is also presented, together with the adopted mitigation strategies. Finally, we report the results of the characterization tests performed with the C-Low receiver at SRT, focusing on the pointing accuracy model, gain-curve calibration, focus-curve calibration, and beam-shape analysis. The results of these characterization tests demonstrate the performance and accuracy of the C-Low receiver, providing a reference for future observations and instrumentation improvements. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

This review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables researchers to analyze large, high-dimensional, and unlabeled datasets and is sometimes considered more helpful for exploratory analysis because it is not limited by present knowledge and can therefore be used to extract new knowledge. Unsupervised machine learning algorithms that have been repeatedly applied to analyze astronomical data are classified according to their usage, including dimension reduction and clustering. This review also discusses anomaly detection and symbolic regression. For each algorithm, this review discusses the algorithm’s functioning in mathematical and statistical terms, the algorithm’s characteristics (e.g., advantages and shortcomings and possible types of inputs), and the different types of astronomical data analyzed with the algorithm. Example figures are generated. The algorithms are tested on synthetic datasets. This review aims to provide an up-to-date overview of both the high-level concepts and detailed applications of various unsupervised learning methods in astronomy, highlighting their advantages and disadvantages to help researchers new to unsupervised learning. Full article

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO. Full article

Cosmic demographics—the statistical study of populations of astrophysical objects—has long relied on tools from multivariate statistics for analyzing data comprising fixed-length vectors of properties of objects, as might be compiled in a tabular astronomical catalog (say, with sky coordinates, and brightness measurements in a fixed number of spectral passbands). But beginning with the emergence of automated digital sky surveys, ca. 2000, astronomers began producing large collections of data with more complex structures: light curves (brightness time series) and spectra (brightness vs. wavelength). These comprise what statisticians call functional data—measurements of populations of functions. Upcoming automated sky surveys will soon provide astronomers with a flood of functional data. New methods are needed to accurately and optimally analyze large ensembles of light curves and spectra, accumulating information both along individual measured functions and across a population of such functions. Functional data analysis (FDA) provides tools for statistical modeling of functional data. Astronomical data presents several challenges for FDA methodology, e.g., sparse, irregular, and asynchronous sampling, and heteroscedastic measurement error. Bayesian FDA uses hierarchical Bayesian models for function populations, and is well suited to addressing these challenges. We provide an overview of astronomical functional data and some key Bayesian FDA modeling approaches, including functional mixed effects models, and stochastic process models. We briefly describe a Bayesian FDA framework combining FDA and machine learning methods to build low-dimensional parametric models for galaxy spectra. Full article

Supernovae (SNe) are among the most energetic and transient events in the universe, offering crucial insights into stellar evolution, nucleosynthesis, and cosmic expansion. Optical observations have historically played a central role in the discovery, classification, and physical interpretation of SNe. In this review, we summarize recent progress in the optical study of SNe, with a focus on advancements in time-domain surveys and photometric and spectroscopic follow-up strategies. High-cadence optical monitoring is pivotal in capturing the diverse behaviors of SNe, from early-time emission to late-phase decline. Leveraging data from ARIES telescopes and national/international collaborations, we systematically investigate various SN types, including Type Iax, IIP/L, IIb, IIn/Ibn and Ib/c events. Our analysis includes light curve evolution and spectral diagnostics, providing insights into early emission signatures (e.g., shock breakout), progenitor systems, explosion mechanisms, and circumstellar medium (CSM) interactions. Through detailed case studies, we demonstrate the importance of both early-time and nebular-phase observations in constraining progenitor and CSM properties. This comprehensive approach underscores the importance of coordinated global efforts in time-domain astronomy to deepen our understanding of SN diversity. We conclude by discussing the challenges and opportunities for future optical studies in the era of wide-field observatories such as the Vera C. Rubin Observatory (hereafter Rubin), with an emphasis on detection strategies, automation, and rapid-response capabilities. Full article

Artificial intelligence (AI) applications have attracted widespread attention and have proven to be highly successful in understanding messages across various dimensions. These applications have the potential to assist astronomers in exploring the massive amounts of astronomical data. In fact, the integration of AI techniques with astronomy began some time ago, significantly advancing our understanding of the universe by aiding in exoplanet discovery, galaxy morphology classification, gravitational wave event analysis, and more. In particular, AI is now recognized as a crucial component in time-domain astronomy, particularly given the rapid evolution of targeting transients and the increasing number of candidates detected by powerful surveys. A notable success is SN 2023tyk, the first transient discovered and spectroscopically classified without human inspection, an achievement made even more remarkable given that it was identified by the Zwicky Transient Facility, which detects millions of alert sources every night. There is no doubt that AI will play a crucial role in future astronomical observations across various messenger channels, aiding in transient discovery and classification, and helping, or even replacing, observers in making real-time decisions. This review paper examines several cases where AI is transforming contemporary astronomy, especially time-domain astronomy. We discuss the AI algorithms and methodologies employed to date, highlight significant discoveries enabled by AI, and outline future research directions in this rapidly evolving field. Full article

The Hard X-ray Modulation Telescope (HXMT), China’s first X-ray astronomy satellite, has significantly contributed to the study of fast variability in black hole X-ray binaries through its broad energy coverage (1–250 keV), high timing resolution, and sensitivity to hard X-rays. This review presents a comprehensive overview of timing analysis techniques applied to black hole X-ray binaries using Insight-HXMT data. We introduce the application and comparative strengths of several time-frequency analysis methods, including traditional Fourier analysis, wavelet transform, bicoherence analysis, and Hilbert-Huang transform. These methods offer complementary insights into the non-stationary and nonlinear variability patterns observed in black hole X-ray binaries, particularly during spectral state transitions and quasi-periodic oscillations. We discuss how each technique has been employed in recent Insight-HXMT studies to characterize timing features such as low-frequency QPOs, phase lags, and power spectrum evolution across different energy bands. Moreover, we present novel phenomena revealed by Insight-HXMT observations, including the detection of high-energy QPOs, spectral parameter modulation with QPO phase, and a new classification scheme for QPO types. The integration of multiple analysis methods enables a more nuanced understanding of the accretion dynamics and the geometry of the inner accretion flow, shedding light on fundamental physical processes in relativistic environments. Full article

Integrated teaching encourages students to think across disciplines and view key human issues from various perspectives. Although mathematics and geography are taught as separate subjects in schools, they frequently intersect in real-world issues, with scientific problems often analyzed using mathematical methods. The purpose of this article is to systematically review the understanding of study characteristics, teaching content, and forms of integration between geography and mathematics. A systematic review of 26 studies was conducted in accordance with PRISMA guidelines, involving searches of four databases from 2000 to 2023. Screening and selection were performed independently by two researchers. Data were analyzed via structured qualitative content analysis. This systematic review demonstrates that integrated teaching can improve knowledge and skills of students compared to segregated teaching. The findings reveal that contents such as Education for Sustainable Development, cartography, and astronomy and space travel are the main topics covered in subject-integrated mathematics and geography lessons. The study also highlights gaps, especially in long-term effects and teacher involvement in quantitative research. Full article

To computer-simulate solar-concentrating facilities, an accurate knowledge of the Sun’s position as a function of latitude, longitude, time and date is required. In this work, it is reported first a simplified description of a general algorithm, developed by the astronomy community to accomplish that. Our implementation of this algorithm (included in our Light Analysis Modelling package) has been successfully validated against well trusted astronomy data. The software was then used to produce a wide range of results for 2024, for two well-known research facilities, the most northern (Jülich, Germany) and the most southern (Protaras, Cyprus) heliostat fields listed in the official SFERA-III EU project. This includes altitude and azimuth data, sunrise and sunset data, analemma curves, angular speed data and geocentric Sun trajectories around the observer’s position. Other ray-tracing techniques are also reported to help simulate the Sun vectors reaching the solar devices. The truly inspiring results obtained show how important this type of software is, from the scientific and industrial point of view, to better understand our relationship with our neighbor star, the Sun. Full article

For any radio frequency (RF) sensor (receiver) to function optimally, the ambient noise field strength, converted to electrical power by the transducer (antenna), must be lower than the in-ternal noise of that sensor. Therefore, knowledge of the expected ambient noise level is essential for the design of sensors for earth observation, atmospheric research, radio astronomy or navigation. The International Telecommunication Union (ITU) provides a model that predicts ambient man-made noise levels, differentiated by frequency, origin and environment. This is entirely empirical model is based on data from the 1960′s and 1970′s. In recent years, 90,205 noise measurements have been collected to update the model. The analysis of that data set presented here is essential as it shows a pitfall to avoid: despite to size of the data set it is sparce over the parameter space, and unacceptable biases occur when a purely empirical model is based on them. The paper proposes another approach: to create a mathematical model based on physics that can be fine-tuned and validated using these collected measurements, without producing the biases. A revolutionary side effect of such a model would be the linking of two currently isolated domains, that of spectrum management and electromagnetic compatibility. Full article

Recent advancements have shown tensions between observations and our current understanding of the Universe. Such observations may include the $H_0$ tension and massive galaxies at high redshift that are older than traditional galaxy formation models have predict. Since these observations are based on redshift as the primary distance indicator, a bias in the redshift may explain these tensions. While redshift follows an established model, when applied to astronomy it is based on the assumption that the rotational velocity of the Milky Way galaxy relative to the observed galaxies has a negligible effect on redshift. But given the mysterious nature of the physics of galaxy rotation, that assumption needed to be tested. The test was done by comparing the redshift of galaxies rotating in the same direction relative to the Milky Way to the redshift of galaxies rotating in the opposite direction relative to the Milky Way. The results show that the mean redshift of galaxies that rotate in the same direction relative to the Milky Way is higher than the mean redshift of galaxies that rotate in the opposite direction. Additionally, the redshift difference becomes larger as the redshift gets higher. The consistency of the analysis was verified by comparing data collected by three different telescopes, annotated using four different methods, released by three different research teams, and covering both the northern and southern ends of the galactic pole. All the datasets are in excellent agreement with each other, showing consistency in the observed redshift bias. Given the “reproducibility crisis” in science, all the datasets used in this study are publicly available, and the results can be easily reproduced. This observation could be the first direct empirical reproducible observation for the Zwicky’s “tired-light” model. Full article