分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) acquired tens of millions of low-resolution stellar spectra. The large amount of the spectra result in the urgency to explore automatic atmospheric parameter estimation methods. There are lots of LAMOST spectra with low signal-to-noise ratios (SNR), which result in a sharp degradation on the accuracy of their estimations. Therefore, it is necessary to explore better estimation methods for low-SNR spectra. This paper proposed a neural network-based scheme to deliver atmospheric parameters, LASSO-MLPNet. Firstly, we adopt a polynomial fitting method to obtain pseudo-continuum and remove it. Then, some parameter-sensitive features in the existence of high noises were detected using Least Absolute Shrinkage and Selection Operator (LASSO). Finally, LASSO-MLPNet used a Multilayer Perceptron network (MLPNet) to estimate atmospheric parameters $T_{\mathrm{eff}}$, log $g$ and [Fe/H]. The effectiveness of the LASSO-MLPNet was evaluated on some LAMOST stellar spectra of the common star between APOGEE (The Apache Point Observatory Galactic Evolution Experiment) and LAMOST. it is shown that the estimation accuracy is significantly improved on the stellar spectra with $10<\mathrm{SNR}\leq80$. Especially, LASSO-MLPNet reduces the mean absolute error (MAE) of the estimation of $T_{\mathrm{eff}}$, log $g$ and [Fe/H] from (144.59 K, 0.236 dex, 0.108 dex) (LASP) to (90.29 K, 0.152 dex, 0.064 dex) (LASSO-MLPNet) on the stellar spectra with $10<\mathrm{SNR}\leq20$. To facilitate reference, we release the estimates of the LASSO-MLPNet from more than 4.82 million stellar spectra with $10<\mathrm{SNR}\leq80$ and 3500 < SNR$g$ $\leq$ 6500 as a value-added output.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from LAMOST DR7 within 3 arcsec. Applying machine learning described in our previous work, we spectroscopically identified 6,190 WD objects after visual inspection, among which 1,496 targets were firstly confirmed. 32 detailed classes were adopted for them, including but not limited to DAB and DB+M. We estimated the atmospheric parameters for the DA and DB type WD using Levenberg-Marquardt least-squares algorithm (LM). Finally, a catalog of WD spectra from LAMOST was provided online.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： We present a data-driven approach to automatically detect L dwarfs from Sloan Digital Sky Survey(SDSS) images using an improved Faster R-CNN framework based on deep learning. The established L dwarf automatic detection (LDAD) model distinguishes L dwarfs from other celestial objects and backgrounds in SDSS field images by learning the features of 387 SDSS images containing L dwarfs. Applying the LDAD model to the SDSS images containing 93 labeled L dwarfs in the test set, we successfully detected 83 known L dwarfs with a recall rate of 89.25% for known L dwarfs. Several techniques are implemented in the LDAD model to improve its detection performance for L dwarfs,including the deep residual network and the feature pyramid network. As a result, the LDAD model outperforms the model of the original Faster R-CNN, whose recall rate of known L dwarfs is 80.65% for the same test set. The LDAD model was applied to detect L dwarfs from a larger validation set including 843 labeled L dwarfs, resulting in a recall rate of 94.42% for known L dwarfs. The newly identified candidates include L dwarfs, late M and T dwarfs, which were estimated from color (i-z) and spectral type relation. The contamination rates for the test candidates and validation candidates are 8.60% and 9.27%, respectively. The detection results indicate that our model is effective to search for L dwarfs from astronomical images.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
You-Fen Wang
A-Li Luo
Wen-Ping Chen
Hugh R. A. Jones
Bing Du
Yin-Bi Li
Shuo Zhang
Zhong-Rui Bai
Xiao Kong
Yan-Xin Guo
摘要： In this work, we identify 734 ultracool dwarfs with a spectral type of M6 or later, including one L0. Of this sample, 625 were studied spectroscopically for the first time. All of these ultracool dwarfs are within 360~pc, with a \textit{Gaia} G magnitude brighter than ~19.2 mag. By studying the spectra and checking their stellar parameters (Teff, logg, and [FeH] derived with the LAMOST pipeline, we found their cool red nature and their metallicity to be consistent with the nature of Galactic thin-disk objects. Furthermore, 77 of them show lithium absorption lines at 6708A, further indicating their young ages and substellar nature. Kinematics obtained through LAMOST radial velocities, along with the proper motion and parallax data from Gaia EDR3, also suggest that the majority of our targets are thin-disk objects. Kinematic ages were estimated through the relationship between the velocity dispersion and the average age for a certain population. Moreover, we identified 35 binaries, with 6 of them reported as binaries for the first time.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Siqi Liu
A-Li Luo
Huan Yang
Shi-Yin Shen
Jun-Xian Wang
Hao-Tong Zhang
Zhenya Zheng
Yi-Han Song
Xiao Kong
Jian-Ling Wang
Jian-Jun Chen
摘要： Green Pea and Blueberry galaxies are well-known for their compact size, low mass, strong emission lines and analogs to high-z Ly{\alpha} emitting galaxies. In this study, 1547 strong [OIII]{\lambda}5007 emission line compact galaxies with 1694 spectra are selected from LAMOST DR9 at the redshift range from 0.0 to 0.59. According to the redshift distribution, these samples can be separated into three groups: Blueberries, Green Peas and Purple Grapes. Optical [MgII]{\lambda}2800 line feature, BPT diagram, multi-wavelength SED fitting, MIR color, and MIR variability are deployed to identify 23 AGN candidates from these samples, which are excluded for the following SFR discussions. We perform the multi-wavelength SED fitting with GALEX UV and WISE MIR data. Color excess from Balmer decrement shows these strong [OIII]{\lambda}5007 emission line compact galaxies are not highly reddened. The stellar mass of the galaxies is obtained by fitting LAMOST calibrated spectra with the emission lines masked. We find that the SFR is increasing with the increase of redshift, while for the sources within the same redshift bin, the SFR increases with mass with a similar slope as the SFMS. These samples have a median metallicity of 12+log(O/H) of 8.10. The metallicity increases with mass, and all the sources are below the mass-metallicity relation. The direct-derived Te-based metallicity from the [OIII]{\lambda}4363 line agrees with the empirical N2-based empirical gas-phase metallicity. Moreover, these compact strong [OIII]{\lambda}5007 are mostly in a less dense environment.
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