Class Of Stars Research Articles

Classical ensembles of single-qubit quantum variational circuits for classification

AbstractThe quantum asymptotically universal multi-feature (QAUM) encoding architecture was recently introduced and showed improved expressivity and performance in classifying pulsar stars. The circuit uses generalized trainable layers of parameterized single-qubit rotation gates and single-qubit feature encoding gates. Although the improvement in classification accuracy is promising, the single-qubit nature of this architecture, combined with the circuit depth required for accuracy, limits its applications on NISQ devices due to their low coherence times. This work reports on the design, implementation, and evaluation of ensembles of single-qubit QAUM classifiers using classical bagging and boosting techniques. We demonstrate an improvement in validation accuracy for pulsar star classification. We find that this improvement is not dataset specific as we observe consistent improvements for the MNIST Digits and Wisconsin Cancer datasets. We also observe that the boosting ensemble achieves an acceptable level of accuracy with only a small amount of training, while the bagging ensemble achieves higher overall accuracy with ample training time. This shows that classical ensembles of single-qubit circuits present a new approach for certain classification problems.

Classification of Variable Star Light Curves with Convolutional Neural Network

The classification of variable stars is essential for understanding stellar evolution and dynamics. With the growing volume of light curve data from extensive surveys, there is a need for automated and accurate classification methods. Traditional methods often rely on manual feature extraction and selection, which can be time-consuming and less adaptable to large datasets. In this work, we present an approach using a convolutional neural network (CNN) to classify variable stars using only raw light curve data and their known periods, without the need for manual feature extraction or hand-selected data preprocessing. Our method utilizes phase-folding to organize the light curves and directly learns the variability patterns crucial for classification. Trained and tested on the Optical Gravitational Lensing Experiment (OGLE) dataset, our model demonstrates an average accuracy of 88% and an F1 score of 0.89 across five well-known classes of variable stars. We also compared our classification model with the Random Forest (RF) classifier and showed that our model gives better results across all of the classification metrics. By leveraging CNN, our approach does not need manual feature extraction and can handle diverse light curve shapes and sampling cadences. This automated, data-driven method offers a powerful tool for classifying variable stars, enabling efficient processing of large datasets from current and future sky surveys.

Ensemble Learning for Stellar Classification and Radius Estimation from Multimodal Data

Stellar classification and radius estimation are crucial for understanding the structure of the Universe and stellar evolution. With the advent of the era of astronomical big data, multimodal data are available and theoretically effective for stellar classification and radius estimation. A problem is how to improve the performance of this task by jointly using the multimodal data. However, existing research primarily focuses on using single-modal data. To this end, this paper proposes a model, Multi-Modal SCNet, and its ensemble model Multimodal Ensemble for Stellar Classification and Regression (MESCR) for improving stellar classification and radius estimation performance by fusing two modality data. In this problem, a typical phenomenon is that the sample numbers of some types of stars are evidently more than others. This imbalance has negative effects on model performance. Therefore, this work utilizes a weighted sampling strategy to deal with the imbalance issues in MESCR. Some evaluation experiments are conducted on a test set for MESCR and the classification accuracy is 96.1%, and the radius estimation performance Mean of Absolute Error and σ are 0.084 dex and 0.149 R ⊙, respectively. Moreover, we assessed the uncertainty of model predictions, confirming good consistency within a reasonable deviation range. Finally, we applied our model to 50,871,534 SDSS stars without spectra and published a new catalog.

LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars

With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.

Untangling stellar components of galaxies: Evaluation of dynamical decomposition methods in simulated galaxies with GalaxyChop

Galaxy formation is intrinsically connected to the distinct evolutionary processes of disk and spheroidal systems, which are the fundamental stellar components of galaxies. Understanding the mutual, dynamical interplay and co-evolution of these components requires a detailed dynamical analysis to allow for disentanglement of these systems. We introduce JEHistogram, a new method for the dynamical decomposition of simulated galaxies into disk and spheroidal stellar components that utilizes the angular momentum and energy of star particles. We evaluate its performance against five previously established methods using a sample of equilibrium galaxies with stellar masses in the range $10^ M_ gal /M_ $. Our assessment involves several metrics, including the completeness and purity of stellar particle classification, scale lengths, mass density profiles, velocity dispersion, and rotational velocity profiles. While all methods approximate the properties of the original components, such as mass fractions and density or velocity profiles, JEHistogram demonstrates a better accuracy, particularly in the inner regions of galaxies where component overlap complicates separation. Additionally, we apply JEHistogram to a Milky Way-like galaxy from the IllustrisTNG cosmological simulations, showcasing its capability to derive properties such as the size, mass, velocity, color, and age of dynamically defined disk and spheroidal components. All the dynamical decomposition methods we analyzed are publicly accessible through the Python package GalaxyChop

MEASNet. I. A Model for Barium Star Identification and s-process Abundance Estimation from LAMOST DR10 Low-resolution Survey

Barium stars are peculiar stars with enhanced slow neutron capture process (s-process) elements. Abundance analysis of them aids in better understanding the chemical evolution of the Milky Way. In this paper, we introduce a data-driven method named the memory-enhanced adaptive spectral network (MEASNet) to search for barium candidates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) and estimate the abundance of five s-process elements: Sr, Y, Ba, Ce, and Nd. MEASNet, trained using spectra from common stars in both LAMOST and the Galactic Archaeology with HERMES survey, showcases notable performance: for the classification task, precision = 98.22% and recall = 94.12%; in prediction, the mean absolute error for the seven elements range between 0.07 and 0.15 dex. After training, we apply the model to 4,083,003 stellar spectra from LAMOST DR10 LRS, successfully identifying 1,803,670 spectra of barium candidates ([Ba/Fe] ≥ 0.25 dex) along with their five s-process elemental abundances. The catalog enlarges the sample size, providing a wealth of data for further statistical analysis of the formation and evolution of barium stars. Meanwhile, this work highlights the potential value of MEASNet in star classification and abundance estimation, offering a strong reference for future data-driven models.

Modern Methodologies in Machine Learning–Based Classification of Variable Stars

Modern Methodologies in Machine Learning–Based Classification of Variable Stars

Supervised star, galaxy, and QSO classification with sharpened dimensionality reduction

We explored the use of broadband colors to classify stars, galaxies, and quasi-stellar objects (QSOs). Specifically, we applied sharpened dimensionality reduction (SDR)-aided classification to this problem, with the aim of enhancing cluster separation in the projections of high-dimensional data clusters to allow for better classification performance and more informative projections. The main objective of this work was to apply SDR to large sets of broadband colors derived from the CPz catalog to obtain projections with clusters of star, galaxy, and QSO data that exhibit a high degree of separation. The SDR method achieves this by combining density-based clustering with conventional dimensionality-reduction techniques. To make SDR scalable and have the ability to project samples using the earlier-computed projection, we used a deep neural network trained to reproduce the SDR projections. Subsequently classification was done by applying a $k$-nearest neighbors ($k$-NN) classifier to the sharpened projections. Based on a qualitative and quantitative analysis of the embeddings produced by SDR, we find that SDR consistently produces accurate projections with a high degree of cluster separation. A number of projection performance metrics are used to evaluate this separation, including the trustworthiness, continuity, Shepard goodness, and distribution consistency metrics. Using the $k$-NN classifier and consolidating the results of various data sets, we obtain precisions of 99.7<!PCT!>, 98.9<!PCT!>, and 98.5<!PCT!> for classifying stars, galaxies, and QSOs, respectively. Furthermore, we achieve completenesses of 97.8<!PCT!>, 99.3<!PCT!>, and 86.8<!PCT!>, respectively. In addition to classification, we explore the structure of the embeddings produced by SDR by cross-matching with data from Gaia DR3, Galaxy Zoo 1, and a catalog of specific star formation rates, stellar masses, and dust luminosities. We discover that the embeddings reveal astrophysical information, which allows one to understand the structure of the high-dimensional broadband color data in greater detail. We find that SDR-aided star, galaxy, and QSO classification performs comparably to another unsupervised learning method using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) but offers advantages in terms of scalability and interpretability. Furthermore, it outperforms traditional color selection methods in terms of QSO classification performance. Overall, we demonstrate the potential of SDR-aided classification to provide an accurate and physically insightful classification of astronomical objects based on their broadband colors.

SFNet: Stellar Feature Network with CWT for Stellar Spectra Recognition

Stellar spectral classification is crucial in astronomical data analysis. However, existing studies are often limited by the uneven distribution of stellar samples, posing challenges in practical applications. Even when balancing stellar categories and their numbers, there is room for improvement in classification accuracy. This study introduces a Continuous Wavelet Transform using the Super Morlet wavelet to convert stellar spectra into wavelet images. A novel neural network, the Stellar Feature Network, is proposed for classifying these images. Stellar spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR9, encompassing five equal categories (B, A, F, G, K), were used. Comparative experiments validate the effectiveness of the proposed methods and network, achieving significant improvements in classification accuracy.

Photometric classification of stars around the Milky Way’s central black hole

Context. The presence of young massive stars in the Galactic Centre (GC) raises questions about how such stars could form near the massive black hole Sagittarius A* (Sgr A*). Furthermore, the shape of the initial mass function (IMF) in this region seems to differ from its standard Salpeter/Kroupa law. Due to observational challenges such as extreme extinction and crowding, our understanding of the stellar population in this region remains limited, with spectroscopic data available only for selected small and comparably bright sources. Aims. We aim to improve our knowledge about the distribution and the IMF of young, massive, stars in the vicinity of Sgr A*. Methods. We used intermediate band (IB) photometry to identify candidates for massive young stars. To ensure robust classification, we applied three different, but complementary methods: Bayesian inference, a basic neural network, and a fast gradient-boosted trees algorithm. Results. We obtain spectral energy distributions for 6590 stars, 1181 of which have been previously classified spectroscopically. We identify 351 stars that are classified as early types by all three classification methods, with 155 of them being newly identified candidates. The radial density profiles for late and early-type stars fit well with broken power laws, revealing a break radius of 9.2 ± 0.6″ for early-type stars. The late-type stars show a core-like distribution around Sgr A* while the density of the early-type stars increases steeply towards the black hole, consistent with previous work. We infer a top-heavy IMF of the young stars near Sgr A* (R < 9″), with a power-law of 1.6 ± 0.1. At greater distances from Sgr A* a standard Salpeter/Kroupa IMF can explain the data. Additionally, we demonstrate that IB photometry can also constrain the metallicities of late-type stars, estimating metallicities for over 600 late-type stars. Conclusions. The variation of the IMF with radial distance from Sgr A* suggests that different mechanisms of star formation may have been at work in this region. The top-heavy IMF in the innermost region is consistent with star formation in a disc around Sgr A*.

Machine learning based stellar classification with highly sparse photometry data.

Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Many classes of stars are identified based on their emitted spectra. In this paper, we use a combination of the multi-class multi-label Machine Learning (ML) method XGBoost and the PySSED spectral-energy-distribution fitting algorithm to classify stars into nine different classes, based on their photometric data. The classifier is trained on subsets of the SIMBAD database. Particular challenges are the very high sparsity (large fraction of missing values) of the underlying data as well as the high class imbalance. We discuss the different variables available, such as photometric measurements on the one hand, and indirect predictors such as Galactic position on the other hand. We show the difference in performance when excluding certain variables, and discuss in which contexts which of the variables should be used. Finally, we show that increasing the number of samples of a particular type of star significantly increases the performance of the model for that particular type, while having little to no impact on other types. The accuracy of the main classifier is ∼0.7 with a macro F1 score of 0.61. While the current accuracy of the classifier is not high enough to be reliably used in stellar classification, this work is an initial proof of feasibility for using ML to classify stars based on photometry.

Factors of Authenticity: Exploring Santorini’s Heritage Hotels

The purpose of this article is to investigate the factors that influence the authenticity of heritage hotels in popular tourist destinations. Using the Greek island of Santorini as a case study, the following three (3) factors, which have not been systematically studied so far, are examined and appear to have a significant impact on authenticity in the conversion of a heritage architectural building into a heritage hotel: (a) its structural characteristics, (b) the influence of the star classification system and (c) the extent to which each business promotes authenticity elements and heritage identity. For the purposes of this study, a threefold methodological approach was conducted, which includes archival research in the registry of the Hellenic Chamber of Hotels for all the heritage hotels in Santorini (69 units), research in the accounting data of the island’s heritage hotels certified with the national star ranking system for their services and amenities and research analysis of the information regarding the promotion of the heritage hotels through their official websites. Additionally, a cartographic representation of the locations of these hotels was created and their positioning was compared to the boundaries of traditional or non-traditional settlements. The results validated the basic finding of the literature review, that the authenticity of a heritage hotel is a construction under constant negotiation, influenced by a multitude of factors. The conversion of a historic building into a heritage hotel is a multidimensional process that requires a delicate balance between preserving cultural value and adapting to the needs of modern tourism, as well as a continuous evaluation and review of the policies in place.

Supervised Machine Learning Algorithm for Stellar Classification

Supervised Machine Learning Algorithm for Stellar Classification

MK-like spectral classification for hot subdwarf stars with LAMOST spectra

Abstract An MK-like spectral classification has been conducted for 1224 hot subdwarf stars with LAMOST DR9 low-resolution spectra. The whole sample was divided into four categories according to the spectral line characteristics: He-normal, He-weak, He-strong C, and He-strong. Each selected spectrum was assigned a spectral class, a luminosity class, and a helium class by comparing the line depth and width with standard spectra selected in LAMOST. Relationships between atmospheric parameters and spectral classification are also presented.

FNet II: Spectral classification of quasars, galaxies, stars, and broad absorption line (BAL) quasars

FNet II: Spectral classification of quasars, galaxies, stars, and broad absorption line (BAL) quasars

TOI-1685 b Is a Hot Rocky Super-Earth: Updates to the Stellar and Planet Parameters of a Popular JWST Cycle 2 Target

We present an updated characterization of the TOI-1685 planetary system, which consists of a P b = 0.69 day ultra-short-period super-Earth planet orbiting a nearby (d = 37.6 pc) M2.5V star (TIC 28900646, 2MASS J04342248+4302148). This planet was previously featured in two contemporaneous discovery papers, but the best-fit planet mass, radius, and bulk density values were discrepant, allowing it to be interpreted either as a hot, bare rock or a 50% H2O/50% MgSiO3 water world. TOI-1685 b will be observed in three independent JWST Cycle 2 programs, two of which assume the planet is a water world, while the third assumes that it is a hot rocky planet. Here we include a refined stellar classification with a focus on addressing the host star’s metallicity, an updated planet radius measurement that includes two sectors of TESS data and multicolor photometry from a variety of ground-based facilities, and a more accurate dynamical mass measurement from a combined CARMENES, InfraRed Doppler, and MAROON-X radial velocity data set. We find that the star is very metal-rich ([Fe/H] ≃ +0.3) and that the planet is systematically smaller, lower mass, and higher density than initially reported, with new best-fit parameters of R pl = 1.468 −0.051+0.050 R ⊕ and M pl = 3.03−0.32+0.33 M ⊕. These results fall in between the previously derived values and suggest that TOI-1685 b is a hot rocky planet with an Earth-like density (ρ pl = 5.3 ± 0.8 g cm−3, or 0.96 ρ ⊕), high equilibrium temperature (T eq = 1062 ± 27 K), and negligible volatiles, rather than a water world.

The life of the Sun

Understanding the development and evolution of the Sun is critical for understanding the origins of our solar system and the circumstances for life on Earth. We can learn about our suns dynamics, energy output, and magnetic activity by analysing its evolution, and we can even forecast solar events that affect our world. Extensive study has indicated that the Sun is now in its main sequence stage, but research on its future development, such as becoming a red giant or crashing into a white dwarf, is still underway. Solar evolution research also sheds light on the inner workings of other stars and fundamental processes in the cosmos. The Hertzsprung-Russell diagram aids in the classification and understanding of stars by providing essential information about their life cycle and attributes. The pre-main sequence, main sequence, and post-main sequence stages of the Suns origin and development are discussed in this article. It examines the origin of the Sun from interstellar materials as well as the probable impact of core-collapse supernovae. It also delves into the main sequence period, where stars masses and ages play important roles in shaping their properties. The main sequences existence is linked to hydrostatic equilibrium and the balance of energy production and gas weight. The scattering of stars that depart from the main sequence, such as red giants and white dwarfs, is also discussed in the article. Finally, it dives into post-main sequence development, in which the Sun switches from hydrogen fusion to helium fusion, causing the star to expand and cool. Finally, the Suns ultimate phase of development involves the burning of hydrogen and helium, which results in the expulsion of outer layers and the creation of a planetary nebula. The remaining core cools and transforms into a black dwarf, bereft of nuclear and gravitational energy sources.

Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine

Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90–95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

Review on Photometric Study of Variable Stars

This literature review focuses on variable stars, which exhibit changes in brightness over time. This topic is vital in astronomical research, particularly due to the abundance of telescope data available in archives. The photometric study of variable stars is significant for understanding stellar evolution, classifying stars, and contributing to cosmological research. It enables precise distance measurements, examines exoplanetary systems, and identifies rare astronomical phenomena, thereby expanding our understanding of the universe. The study begins by outlining the fundamentals of variable stars and three methods for analyzing them: photometry, spectroscopy, and spectrophotometry. However, the focus of this study is solely on photometric studies. This work presents an overview of the photometric study of variable stars. Addressing research gaps in photometric studies of variable stars is crucial for improving our understanding of stellar behavior and evolution, with broader implications for astrophysics and cosmology. Future directions include conducting photometric studies on variable stars using machine learning to develop an automated variable star classification system utilizing online telescopic data archives.

Stellar Classification with Vision Transformer and SDSS Photometric Images

With the development of large-scale sky surveys, an increasing number of stellar photometric images have been obtained. However, most stars lack spectroscopic data, which hinders stellar classification. Vision Transformer (ViT) has shown superior performance in image classification tasks compared to most convolutional neural networks (CNNs). In this study, we propose an stellar classification network based on the Transformer architecture, named stellar-ViT, aiming to efficiently and accurately classify the spectral class for stars when provided with photometric images. By utilizing RGB images synthesized from photometric data provided by the Sloan Digital Sky Survey (SDSS), our model can distinguish the seven main stellar categories: O, B, A, F, G, K, and M. Particularly, our stellar-ViT-gri model, which reaches an accuracy of 0.839, outperforms traditional CNNs and the current state-of-the-art stellar classification network SCNet when processing RGB images synthesized from the gri bands. Furthermore, with the introduction of urz band data, the overall accuracy of the stellar-ViT model reaches 0.863, further demonstrating the importance of additional band information in improving classification performance. Our approach showcases the effectiveness and feasibility of using photometric images and Transformers for stellar classification through simple data augmentation strategies and robustness analysis of training dataset sizes. The stellar-ViT model maintains good performance even in small sample scenarios, and the inclusion of urz band data reduces the likelihood of misclassifying samples as lower-temperature subtypes.