An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

Yang, Haifeng; Yin, Xiaolin; Cai, Xingjuan; Yang, Yuqing; Luo, A-Li; Bai, Zhong-Rui; Zhou, Lichan; Zhao, Xuehua; Xun, Yaling

doi:10.1088/1674-4527/acc507

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…Clustering methods offer a promising solution to tackle the complexities of big data. Densitybased clustering methods, in particular, are widely used due to their ability to handle large databases and effectively handle noisy data (Ester et al 1996;Hai-Feng et al 2023). One such algorithm is DBSCAN, which has been extended to include variants such as OPTICS, ST-DBSCAN, and MR-DBSCAN (Ankerst et al 1999;Birant & Kut 2007;He et al 2011).…”

Section: B Related Workmentioning

confidence: 99%

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

ELFarra,

Salaha,

Ashour

2024

Data Science Journal

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Clustering is a fundamental technique in data mining and machine learning, aiming to group data elements into related clusters. However, traditional clustering algorithms, such as K-means, suffer from limitations such as the need for user-defined parameters and sensitivity to initial conditions. This paper introduces a novel clustering algorithm called Black Hole Clustering (BHC), which leverages the concept of gravity to identify clusters. Inspired by the behavior of masses in the physical world, gravity-based clustering treats data points as mass points that attract each other based on distance. This approach enables the detection of high-density clusters of arbitrary shapes and sizes without the need for predefined parameters. We extensively evaluate BHC on synthetic and real-world datasets, demonstrating its effectiveness in handling complex data structures and varying point densities. Notably, BHC excels in accurate prediction of the number of clusters and achieves competitive clustering accuracy rates. Moreover, its parameter-free nature enhances clustering accuracy, robustness, and scalability. These findings represent a significant contribution to advanced clustering techniques and pave the way for further research and application of gravity-based clustering in diverse fields. BHC offers a promising approach to addressing clustering challenges in complex datasets, opening up new possibilities for improved data analysis and pattern discovery.

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Section: B Related Workmentioning

confidence: 99%

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

ELFarra,

Salaha,

Ashour

2024

Data Science Journal

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“…With reference to previous studies (Qin et al 2019;Shang et al 2022;Cai et al 2022;Yang et al 2023), all raw spectra are preprocessed in the following steps. The results of the data processing are displayed in Figure 7, and the specific steps are as follows:…”

Section: Preprocessingmentioning

confidence: 99%

A Sample of Am and Ap Candidates from LAMOST DR10 (v1.0) Based on the Ensemble Regression Model

Yang 杨,

Wang 王,

Cai 蔡

et al. 2024

ApJS

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Large samples of Am and Ap stars are helpful in studying the interplay between phenomena like atomic diffusion, magnetic fields, and stellar rotation in stellar astrophysics. Existing samples of Am and Ap stars, mostly obtained from spectral data with a signal-to-noise ratio in the g band (S/Ng) greater than 50, can benefit from expansion by exploring spectra with lower S/Ng. Therefore, this paper proposes an ensemble regression model applicable to spectra with a minimum S/Ng of 30. Using the model, we identify 21,361 Am candidates, of which 11,614 are new, and 6182 Ap candidates, of which 4978 are new, from LAMOST DR10. The Am sample size has increased by 60% and the Ap sample size has increased by 180% compared to the previous sample. In terms of effective temperature (T eff), the Am candidates range mainly from 6000 to 8500 K, while the Ap candidates range from 6000 to 11,700 K. The surface gravity ( log g ) distributions for Am and Ap candidates differ in the range of 3.25–4.75 dex. The number of Am candidates increases stepwise, in contrast to the relatively uniform distribution of Ap candidates across the entire surface gravity range. Regarding metallicity ([Fe/H]), Am candidates typically range from −0.75 to 0.38 dex, peaking near 0 dex, while Ap candidates are distributed from −1.38 to 0.38 dex, with a peak near −0.5 dex.

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“…In recent years, deep-learning methods have achieved notable success in many tasks (Devlin et al 2019;Yang et al 2022Yang et al , 2023aYang et al , 2023bHanda et al 2023;Kirillov et al 2023;Cai et al 2024aCai et al , 2024b. Detecting galaxy pairs in SDSS with deep-learning methods has its advantages and challenges.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Galaxy Pairs in the SDSS Photometric Images based on Faster-RCNN

Shi 史,

Yang 杨,

Cai 蔡

et al. 2024

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Galaxy pairs hold significant importance in understanding the evolution of galaxies, and the extensive search for a large sample of galaxy pairs is meaningful. In this article, we develop a deep learning-based approach for the search of galaxy pairs and conduct a comprehensive search on Sloan Digital Sky Survey (SDSS) images. In nine million photometric images, 17,965 physical galaxy pairs with spectral or photometric redshifts are detected. Four sets of results are provided, including physical pairs determined by two spectral redshifts, two photometric redshifts, one spectral redshift, and one photometric redshift, and visual irregular pairs that have no precise redshift information but can be inferred as physical galaxy pairs based on the morphological changes. Then their morphological and physical characteristics are explored, the redshifts of most targets are around 0.1, and as the redshift difference between two galaxies increases, the number of galaxy pairs gradually reduces. The distributions of star formation rate (SFR) are not the same for different morphologies of galaxy pairs, irregular pairs have higher SFR than the other three types, and statistics indicate that the SFR of galaxies depends on both nearby galaxies and internal properties. Color and stellar mass are also key properties of galaxies which can reflect the status of galaxy pairs. Compared to other surveys, a greater number of galaxy pair targets are detected, and this is also the first extensive detection of galaxy pairs in SDSS images using photometric redshifts. These galaxy pair samples can greatly aid in the study of galaxy evolution.

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

Cited by 7 publications

References 32 publications

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

A Sample of Am and Ap Candidates from LAMOST DR10 (v1.0) Based on the Ensemble Regression Model

A Survey of Galaxy Pairs in the SDSS Photometric Images based on Faster-RCNN

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