Cataclysmic variables based on the stellar spectral survey LAMOST DR3

Han, Xianming L.; Zhang, Liyun; Shi, Jianrong; Pi, Qingfeng; Lu, Hongpeng; Zhao, L. B.; Terheide, Rachel; Jiang, Lixue

doi:10.1088/1674-4527/18/6/68

Cited by 12 publications

(7 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They identified a sample of 10 CVs, two of which are new discoveries. Han et al (2018) collected the spectra of 48 known CVs by cross-matching the published catalogs with LAMOST DR3, and found three new CVs using the method proposed by Jiang et al (2013). They investigated not only spectroscopic properties of all these CVs, but also light curves of five CVs through follow-up photometric observations.…”

mentioning

confidence: 99%

Spectroscopically Identified Cataclysmic Variables from the LAMOST Survey. I. The Sample

Hou

Luo

et al. 2020

View full text Add to dashboard Cite

A sample of Cataclysmic Variables (CVs) is presented including spectroscopically identified 380 spectra of 245 objects, of which 58 CV candidates are new discoveries. The BaggingTopPush and the Random Forest algorithms are applied to the Fifth Data Release (DR5) of LAMOST to retrieve CVs with strong emission lines and with broad absorption lines respectively. Based on spectroscopic classification, 134 dwarf novae, 41 nova-like variables and 19 magnetic CVs are identified from the sample. In addition, 89 high-inclination systems and 33 CVs showing companion stars are recognized and discussed for their distinct spectral characteristics. Comparisons between CVs from LAMOST and from published catalogs are made in spatial and magnitude distribution, and the difference of their locus in Gaia color-absolute magnitude diagram (CaMD) are also investigated. More interestingly, for two dwarf novae observed through LAMOST and SDSS in different epoch, their spectra both in quiescence phase and during outburst are exhibited.

show abstract

mentioning

confidence: 99%

Spectroscopically Identified Cataclysmic Variables from the LAMOST Survey. I. The Sample

Hou

Luo

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We first introduce a machine learning algorithm called UMAP for screening out the spectra with prominent Hα emission features, which leads to a total of 169,509 spectra. The sample data used for supervised machine learning comes from 10 published CV or X-ray binary catalogues which are: Downes & Shara (1993); Downes et al (1997); Downes et al (2001); Szkody et al (2011); Jiang et al (2013); Breedt et al (2014); Coppejans et al (2016); Han et al (2018); Ritter & Kolb (2003); Hou et al (2020). The PyHammer v2.0 program is introduced to fit the LAMOST low-resolution spectra to help the manual inspection.…”

Section: Methodsmentioning

confidence: 99%

“…Coppejans et al (2016) published an outburst catalogue which contains a wide variety of observational properties for 722 dwarf nova-type CVs and 309 CVs of other types from the CRTS. By cross-matching the published catalogues with LAMOST DR3, Han et al (2018) collected 48 known CVs, and they found three new CVs using the method adopted by Jiang et al (2013). Szkody et al (2020) published 329 objects as known or candidate CVs during the first year of testing and operation of the Zwicky Transient Facility.…”

Section: Introductionmentioning

confidence: 99%

A catalogue of 323 cataclysmic variables from LAMOST DR6

Sun,

Cheng,

et al. 2021

Preprint

View full text Add to dashboard Cite

In this work, we present a catalogue of cataclysmic variables (CVs) identified from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 Hα-emission spectra, and obtain a classification accuracy of the algorithm of over 99.6% from the cross-validation set. We then apply the template matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which help us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further discuss the new CV candidates in subtypes based on their spectral features, including five DN subtype during outbursts, five NL subtype and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry, and confirm their previous classification by the LAMOST spectra.

show abstract

“…The binary system consists of a white dwarf star [2] and a late main-sequence companion star [3]. The companion star transfers material to the main star through the accretion disk [4][5][6]. According to their amplitudes and timescale of variability and magnetism, CVs can be divided into five subtypes, namely, Novae-Like variables (NLs), Classical Novae (CNs), Dwarf Novae (DNs), Recurrent Novae (RNs), and Magnetic Cataclysmic Variables (MCVs) [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional ways to search for CVs are spectroscopic and photometric observations [6]. The light curves of followup observations can help to further divide the CVs into subtypes.…”

Section: Introductionmentioning

confidence: 99%

Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7

Chen

Bin

et al. 2021

Universe

View full text Add to dashboard Cite

The search for special and rare celestial objects has always played an important role in astronomy. Cataclysmic Variables (CVs) are special and rare binary systems with accretion disks. Most CVs are in the quiescent period, and their spectra have the emission lines of Balmer series, HeI, and HeII. A few CVs in the outburst period have the absorption lines of Balmer series. Owing to the scarcity of numbers, expanding the spectral data of CVs is of positive significance for studying the formation of accretion disks and the evolution of binary star system models. At present, the research for astronomical spectra has entered the era of Big Data. The Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) has produced more than tens of millions of spectral data. the latest released LAMOST-DR7 includes 10.6 million low-resolution spectral data in 4926 sky regions, providing ideal data support for searching CV candidates. To process and analyze the massive amounts of spectral data, this study employed the Light Gradient Boosting Machine (LightGBM) algorithm, which is based on the ensemble tree model to automatically conduct the search in LAMOST-DR7. Finally, 225 CV candidates were found and four new CV candidates were verified by SIMBAD and published catalogs. This study also built the Gradient Boosting Decision Tree (GBDT), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) models and used Accuracy, Precision, Recall, the F1-score, and the ROC curve to compare the four models comprehensively. Experimental results showed that LightGBM is more efficient. The search for CVs based on LightGBM not only enriches the existing CV spectral library, but also provides a reference for the data mining of other rare celestial objects in massive spectral data.

show abstract

Cataclysmic variables based on the stellar spectral survey LAMOST DR3

Cited by 12 publications

References 94 publications

Spectroscopically Identified Cataclysmic Variables from the LAMOST Survey. I. The Sample

Spectroscopically Identified Cataclysmic Variables from the LAMOST Survey. I. The Sample

A catalogue of 323 cataclysmic variables from LAMOST DR6

Automatic Search of Cataclysmic Variables Based on LightGBM in LAMOST-DR7

Contact Info

Product

Resources

About