Search Results (20)

Accurate and efficient detection of celestial objects in telescope imagery is a fundamental challenge in both professional and amateur astronomy. Traditional methods often struggle with noise, varying brightness, and object morphology. This paper introduces COSMICA, a novel, curated dataset of manually annotated astronomical images collected from amateur observations. COSMICA enables the development and evaluation of real-time object detection systems intended for practical deployment in observational pipelines. We investigate three modern YOLO architectures, YOLOv8, YOLOv9, and YOLOv11, and two additional object detection models, EfficientDet-Lite0 and MobileNetV3-FasterRCNN-FPN, to assess their performance in detecting comets, galaxies, nebulae, and globular clusters. All models are evaluated using consistent experimental conditions across multiple metrics, including mAP, precision, recall, and inference speed. YOLOv11 demonstrated the highest overall accuracy and computational efficiency, making it a promising candidate for real-world astronomical applications. These results support the feasibility of integrating deep learning-based detection systems into observational astronomy workflows and highlight the importance of domain-specific datasets for training robust AI models. Full article

Galaxy morphology classification is a crucial task in astronomy and astrophysics, providing information on galaxy formation and evolution. Traditionally, this classification has been a manual and labor-intensive process requiring significant astronomical expertise. However, advancements in artificial intelligence, particularly deep learning, offer more efficient and accurate solutions. We investigated the application of convolutional neural networks (CNNs) for galaxy morphology classification using the Galaxy10 DECals dataset. We developed and compared three models: a custom-built CNN using TensorFlow 2.18, a ResNet50 model initialized with random weights, and a pre-trained EfficientNetB5 model utilizing transfer learning, both implemented in PyTorch 2.6. Our results indicate that the custom model achieves an accuracy of 67%, while the ResNet50 and EfficientNetB5 models achieve 80 and 87% accuracy, respectively. The superior performance of the pre-trained Efficient-NetB5 model underscores the efficacy of transfer learning in astronomical image classification. These findings have significant implications for the application of deep learning techniques in astrophysical research. 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

Astronomy is entering an unprecedented era of big-data science, driven by missions like the ESA’s Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia’s vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep-learning techniques, particularly advanced neural-network architectures, in predicting photometric metallicity from time-series data. Our deep-learning models demonstrated notable predictive performance, with a low mean absolute error (MAE) of 0.0565, the root mean square error (RMSE) of 0.0765, and a high $R^2$ regression performance of 0.9401, measured by cross-validation. The weighted mean absolute error (wMAE) is 0.0563, while the weighted root mean square error (wRMSE) is 0.0763. These results showcase the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep-learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena. Full article

In astronomy, understanding the evolutionary trajectories of galaxies necessitates a robust analysis of their star formation histories (SFHs), a task complicated by our inability to observe these vast celestial entities throughout their billion-year lifespans. This study pioneers the application of the Kullback–Leibler Importance Estimation Procedure (KLIEP), an unsupervised domain adaptation technique, to address this challenge. By adeptly applying KLIEP, we harness the power of machine learning to innovatively predict SFHs, utilizing simulated galaxy models to forge a novel linkage between simulation and observation. This methodology signifies a substantial advancement beyond the traditional Bayesian approaches to Spectral Energy Distribution (SED) analysis, which are often undermined by the absence of empirical SFH benchmarks. Our empirical investigations reveal that KLIEP markedly enhances the precision and reliability of SFH inference, offering a significant leap forward compared to existing methodologies. The results underscore the potential of KLIEP in refining our comprehension of galactic evolution, paving the way for its application in analyzing actual astronomical observations. Accompanying this paper, we provide access to the supporting code and dataset on GitHub, encouraging further exploration and validation of the efficacy of the KLIEP in the field. Full article

A distinction is made between data rescue (i.e., copying, digitizing, and archiving) and data recovery that implies deciphering, interpreting, and transforming early instrumental readings and their metadata to obtain high-quality datasets in modern units. This requires a multidisciplinary approach that includes: palaeography and knowledge of Latin and other languages to read the handwritten logs and additional documents; history of science to interpret the original text, data, and metadata within the cultural frame of the 17th, 18th, and early 19th centuries; physics and technology to recognize bias of early instruments or calibrations, or to correct for observational bias; and astronomy to calculate and transform the original time in canonical hours that started from twilight. The liquid-in-glass thermometer was invented in 1641 and the earliest temperature records started in 1654. Since then, different types of thermometers have been invented, based on the thermal expansion of air or selected thermometric liquids with deviation from linearity. Reference points, thermometric scales, and calibration methodologies were not comparable, and not always adequately described. Thermometers had various locations and exposures, e.g., indoor, outdoor, on windows, gardens or roofs, facing different directions. Readings were made only one or a few times a day, not necessarily respecting a precise time schedule: this bias is analysed for the most popular combinations of reading times. The time was based on sundials and local Sun, but the hours were counted starting from twilight. In 1789–1790, Italy changed system and all cities counted hours from their lower culmination (i.e., local midnight), so that every city had its local time; in 1866, all the Italian cities followed the local time of Rome; in 1893, the whole of Italy adopted the present-day system, based on the Coordinated Universal Time and the time zones. In 1873, when the International Meteorological Committee (IMC) was founded, later transformed into the World Meteorological Organization (WMO), a standardization of instruments and observational protocols was established, and all data became fully comparable. In dealing with the early instrumental period, from 1654 to 1873, the comparison, correction, and homogenization of records is quite difficult, mainly because of the scarcity or even absence of metadata. This paper deals with this confused situation, discussing the main problems, but also the methodologies to recognize missing metadata, distinguish indoor from outdoor readings, correct and transform early datasets in unknown or arbitrary units into modern units, and, finally, in which cases it is possible to reach the quality level required by the WMO. The aim is to explain the methodology needed to recover early instrumental records, i.e., the operations that should be performed to decipher, interpret, correct, and transform the original raw data into a high-quality dataset of temperature, usable for climate studies. Full article

Gravitational wave (GW) astronomy provides an independent way to estimate cosmological parameters. The detection of GWs from a coalescing binary allows a direct measurement of its luminosity distance, so these sources are referred to as “standard sirens” in analogy to standard candles. We investigate the impact of constraining cosmological models on the Einstein Telescope, a third-generation detector which will detect tens of thousands of binary neutron stars. We focus on non-flat ΛCDM cosmology and some dark energy models that may resolve the so-called Hubble tension. To evaluate the accuracy down to which ET will constrain cosmological parameters, we consider two types of mock datasets depending on whether or not a short gamma-ray burst is detected and associated with the gravitational wave event using the THESEUS satellite. Depending on the mock dataset, different statistical estimators are applied: one assumes that the redshift is known, and another marginalizes it, taking a specific prior distribution. Full article

Imaging is one of the main tools of modern astronomy—many images are collected each day, and they must be processed. Processing such a large amount of images can be complex, time-consuming, and may require advanced tools. One of the techniques that may be employed is artificial intelligence (AI)-based image detection and classification. In this paper, the research is focused on developing such a system for the problem of the Magellan dataset, which contains 134 satellite images of Venus’s surface with individual volcanoes marked with circular labels. Volcanoes are classified into four classes depending on their features. In this paper, the authors apply the You-Only-Look-Once (YOLO) algorithm, which is based on a convolutional neural network (CNN). To apply this technique, the original labels are first converted into a suitable YOLO format. Then, due to the relatively small number of images in the dataset, deterministic augmentation techniques are applied. Hyperparameters of the YOLO network are tuned to achieve the best results, which are evaluated as mean average precision (mAP@0.5) for localization accuracy and F1 score for classification accuracy. The experimental results using cross-vallidation indicate that the proposed method achieved 0.835 mAP@0.5 and 0.826 F1 scores, respectively. Full article

We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, Mathematics, and Medicine. Our analysis is defined over a large-scale corpus comprising 60K abstracts structured as four scientific entities process, method, material, and data. Thus, our study presents, for the first time, an analysis of a large-scale multidisciplinary corpus under the construct of four named entity labels that are specifically defined and selected to be domain-independent as opposed to domain-specific. The work is then inadvertently a feasibility test of characterizing multidisciplinary science with domain-independent concepts. Further, to summarize the distinct facets of scientific knowledge per concept per discipline, a set of word cloud visualizations are offered. The STEM-NER-60k corpus, created in this work, comprises over 1 M extracted entities from 60k STEM articles obtained from a major publishing platform and is publicly released. Full article

Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station. Full article

Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BiGR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings. Full article

The phenomenon of big data has occurred in many fields of knowledge, one of which is astronomy. One example of a large dataset in astronomy is that of numerically integrated time series asteroid orbital elements from a time span of millions to billions of years. For example, the mean motion resonance (MMR) data of an asteroid are used to find out the duration that the asteroid was in a resonance state with a particular planet. For this reason, this research designs a computational model to obtain the mean motion resonance quickly and effectively by modifying and implementing the Symbolic Aggregate Approximation (SAX) algorithm and the motif discovery random projection algorithm on big data platforms (i.e., Apache Hadoop and Apache Spark). There are five following steps on the model: (i) saving data into the Hadoop Distributed File System (HDFS); (ii) importing files to the Resilient Distributed Datasets (RDD); (iii) preprocessing the data; (iv) calculating the motif discovery by executing the User-Defined Function (UDF) program; and (v) gathering the results from the UDF to the HDFS and the .csv file. The results indicated a very significant reduction in computational time between the use of the standalone method and the use of the big data platform. The proposed computational model obtained an average accuracy of 83%, compared with the SwiftVis software. Full article

The unprecedented quality of the astrometric measurements obtained with the ESA Gaia spacecraft have initiated a revolution in Milky Way astronomy. Studies of star clusters in particular have been transformed by the precise proper motions and parallaxes measured by Gaia over the entire sky as well as Gaia’s deep all-sky photometry. This paper presents an overview of the many topics of cluster science that have been impacted by the Gaia DR1, DR2, and EDR3 catalogues from their release to the end of the year 2021. These topics include the identification of known clusters and the discovery of new objects, the formation of young clusters and associations, and the long-term evolution of clusters and their stellar content. In addition to the abundance of scientific results, Gaia is changing the way astronomers work with high-volume and high-dimensionality datasets and is teaching us precious lessons to deal with its upcoming data releases and with the large-scale astronomical surveys of the future. Full article

The “leaky pipeline” in STEM remains an open issue. The integration of multiple STEM subjects, especially technology, is a promising approach, and pre-collegiate STEM teachers are particularly underprepared in this content area. In this case study, the authors explore and characterize the discussions of pre-collegiate STEM teachers among themselves when working with a large astronomy dataset using a web-based spreadsheet tool. The authors used a feminist social constructivism theoretical framework and obtained observational field notes on five, in-service, STEM primary and secondary teachers (purposefully selected from 15 potential groups). The participants were audio and video recorded as they worked on the activity for two hours. Discourse analysis was used as qualitative analysis. Results show that the participants positioned group members with higher social status (based on gender, degrees, experience, etc.) as peer mentors. The peer mentors controlled the computer and guided the others to develop pedagogical content knowledge. The computer was also used as a technological bridge between science and math concepts. Participants showed evidence of not only integrating STEM concepts in their discussion, but also made connections to the science-adjacent topics of geography and technical writing. Suggestions are made for teachers and professional development workshop organizers to help ameliorate inequity in this setting. Full article

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAE${}_t$) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAE${}_t$). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAE${}_t$ embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the $F_1$ score in the task of classifying exoplanets. Moreover, the S-VRAE${}_t$ model increases all the advantages of VRAE${}_t$, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves. Full article