A method of searching for supernova candidates from massive galaxy spectra

Tu, Liangping; Luo, A-Li; Wu, Fuchao; Zhao, Yi

doi:10.1007/s11433-010-4126-7

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…SNe 0305-51613-575 and 0472-51955-247 were previously reported by Madgwick et al (2003). SNe 1266-52709-024 and 1304-52993-552 were reported by Tu et al (2010) …”

Section: Appendix A: Notes On Individual Supernovaementioning

confidence: 69%

A unified explanation for the supernova rate-galaxy mass dependence based on supernovae detected in Sloan galaxy spectra

Graur¹,

Bianco²,

Modjaz

2015

Monthly Notices of the Royal Astronomical Society

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Using a method to discover and classify supernovae (SNe) in galaxy spectra, we detect 91 Type Ia SNe (SNe Ia) and 16 Type II SNe (SNe II) among ∼ 740 000 galaxies of all types and ∼ 215 000 star-forming galaxies without active galactic nuclei, respectively, in Data Release 9 of the Sloan Digital Sky Survey. Of these SNe, 15 SNe Ia and 8 SNe II are new discoveries reported here for the first time. We use our SN samples to measure SN rates per unit mass as a function of galaxy stellar mass, star-formation rate (SFR), and specific SFR (sSFR), as derived by the MPA-JHU Galspec pipeline. We show that correlations between SN Ia and SN II rates per unit mass and galaxy stellar mass, SFR, and sSFR can be explained by a combination of the respective SN delay-time distributions (the distributions of times that elapse between the formation of a stellar population and all ensuing SNe), the ages of the surveyed galaxies, the redshifts at which they are observed, and their star-formation histories. This model was first suggested by Kistler et al. for the SN Ia rate-mass correlation, but is expanded here to SNe II and to correlations with galaxy SFR and sSFR. Finally, we measure a volumetric SN II rate at redshift 0.075 of R II,V = 0.621

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“…SNe 0305-51613-575 and 0472-51955-247 were previously reported by Madgwick et al (2003). SNe 1266-52709-024 and 1304-52993-552 were reported by Tu et al (2010) …”

Section: Appendix A: Notes On Individual Supernovaementioning

confidence: 69%

A unified explanation for the supernova rate-galaxy mass dependence based on supernovae detected in Sloan galaxy spectra

Graur¹,

Bianco²,

Modjaz

2015

Monthly Notices of the Royal Astronomical Society

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“…supernovae detection is then carried out on the residual between the galaxy spectrum and its best eigenspectra fit, using various methods of template fitting: Madgwick et al (2003) order to obtain a residual which is noiseless, and then crosscorrelated it with a set of type Ia spectral templates. Tu et al (2010); Krughoff et al (2011);Graur & Maoz (2013);Graur, Bianco & Modjaz (2015) used PCA or singular value decomposition (SVD) of supernovae spectroscopic templates to fit the residual. In all of these cases, it was necessary to use both galaxy and supernovae templates in order to detect the supernovae.…”

Section: Galaxies Hosting Supernovaementioning

confidence: 99%

The weirdest SDSS galaxies: results from an outlier detection algorithm

Baron¹,

Poznanski²

2016

Mon. Not. R. Astron. Soc.

132

107

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How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and close galaxy pairs. We find galaxies with high ionisation lines, galaxies which host supernovae, and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them peculiar. It can be executed on imaging, time-series, and other spectroscopic data, operates well with thousands of features, is not sensitive to missing values, and is easily parallelisable.

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“…Using LOF method, researchers have found some spectra with unusual continuum, some spectra having distinct characteristics like that of binary stars, emission stars, carbon stars and even some interesting spectra of unknown type. For example, Tu et al(2009) and Tu et al(2010) applied a LOF based outlier-detection algorithm to find out supernovae from the SDSS galaxy spectra ( [21]; [22]). Wei et al(2013) proposed a novel outlier-mining method, the Monte Carlo Local Outlier Factor (MCLOF), which was used to select outlier spectra from SDSS DR8, with a result of a total of 37,033 outlier spectra [23].…”

Section: Basic Methods 2: Lofmentioning

confidence: 99%

Study on Outliers in the Big Stellar Spectral Dataset of the Fifth Data Release (DR5) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

Lu,

Luo,

Wang

et al. 2021

Preprint

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To study the quality of stellar spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and the correctness of the corresponding stellar parameters derived by the LASP (LAMOST Stellar Parameter Pipeline), the outlier analysis method is applied to the archived AFGK stars in the fifth data release (DR5) of LAMOST. The outlier factor is defined in order to sort more than 3 million stellar spectra selected from the DR5 Stellar Parameter catalog. We propose an improved Local Outlier Factor (LOF) method based on Principal Component Analysis and Monte Carlo to enable the computation of the LOF rankings for randomly picked sub-samples that are computed in parallel by multiple computers, and finally to obtain the outlier ranking of each spectrum in the entire dataset. Totally 3,627 most outlier ranked spectra, around one-thousandth of all spectra, are selected and clustered into 10 groups, and the parameter density distribution of them conforms to the parameter distribution of LAMOST DR5, which suggests that in the whole parameter space the probability of bad spectra is uniformly distributed. By cross-matching the 3,627 spectra with APOGEE, we obtain 122 common ones. The published parameters calculated from LASP agree with APOGEE for the 122 spectra although there are bad pixels or bad flux calibrations in them. On the other hand, some outlier spectra show strong nebular contamination warning the corresponding parameters should be carefully used. A catalog and a spectral atlas of all the 3,627 outliers can be found at the link http://paperdata.china-vo.org/LY_ paper/dr5Outlier/dr5Outlier_resource.zip.

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A method of searching for supernova candidates from massive galaxy spectra

Cited by 10 publications

References 22 publications

A unified explanation for the supernova rate-galaxy mass dependence based on supernovae detected in Sloan galaxy spectra

A unified explanation for the supernova rate-galaxy mass dependence based on supernovae detected in Sloan galaxy spectra

The weirdest SDSS galaxies: results from an outlier detection algorithm

Study on Outliers in the Big Stellar Spectral Dataset of the Fifth Data Release (DR5) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

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