اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدJ-PLUS: Identification of low-metallicity stars with artificial neural networks using SPHINX
84
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present a new methodology for the estimation of stellar atmospheric parameters from narrow- and intermediate-band photometry of the Javalambre Photometric Local Universe Survey (J-PLUS), and propose a method for target pre-selection of low-metallicity stars for follow-up spectroscopic studies. Photometric metallicity estimates for stars in the globular cluster M15 are determined using this method. By development of a neural-network-based photometry pipeline, we aim to produce estimates of effective temperature, $T_{rm eff}$, and metallicity, [Fe/H], for a large subset of stars in the J-PLUS footprint. The Stellar Photometric Index Network Explorer, SPHINX, is developed to produce estimates of $T_{rm eff}$ and [Fe/H], after training on a combination of J-PLUS photometric inputs and synthetic magnitudes computed for medium-resolution (R ~ 2000) spectra of the Sloan Digital Sky Survey. This methodology is applied to J-PLUS photometry of the globular cluster M15. Effective temperature estimates made with J-PLUS Early Data Release photometry exhibit low scatter, sigma($T_{rm eff}$) = 91 K, over the temperature range 4500 < $T_{rm eff}$ (K) < 8500. For stars from the J-PLUS First Data Release with 4500 < $T_{rm eff}$ (K) < 6200, 85 $pm$ 3% of stars known to have [Fe/H] <-2.0 are recovered by SPHINX. A mean metallicity of [Fe/H]=-2.32 $pm$ 0.01, with a residual spread of 0.3 dex, is determined for M15 using J-PLUS photometry of 664 likely cluster members. We confirm the performance of SPHINX within the ranges specified, and verify its utility as a stand-alone tool for photometric estimation of effective temperature and metallicity, and for pre-selection of metal-poor spectroscopic targets.
قيم البحث
اقرأ أيضاً
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated spectroscopi
c observations. From a sample of 279 stars with previous high-resolution determinations of metallicity, and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of ~ 135-150 K over the range 4250 <= Teff <= 6500 K, logg with an accuracy of ~ 0.25-0.30 dex over the range 1.0 <= logg <= 5.0 dex, and [Fe/H] with an accuracy ~ 0.15-0.20 dex over the range -4.0 <= [Fe/H] <= +0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs, and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys.
Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend its usage to
determine absolute magnitudes. We have constructed a library of 167 medium-resolution stellar spectra (R ~ 1200) covering the stellar temperature range of 4200 to 8000 K, log g range of 0.5 to 5.0, and [Fe/H] range of -3.0 to +0.3 dex. This empirical spectral library was used to train ANNs, yielding an accuracy of 0.3 dex in [Fe/H], 200 K in temperature, and 0.3 dex in log g. We found that the independent calibrations of near-solar metallicity stars and metal-poor stars decreases the errors in T_eff and log g by nearly a factor of two. We calculated T_eff, log g, and [Fe/H] on a consistent scale for a large number of field stars and candidate metal-poor stars. We extended the application of this method to the calibration of absolute magnitudes using nearby stars with well-estimated parallaxes. A better calibration accuracy for M_V could be obtained by training separate ANNs for cool, warm, and metal-poor stars. The current accuracy of M_V calibration is (+-)0.3 mag. A list of newly identified metal-poor stars is presented. The M_V calibration procedure developed here is reddening-independent and hence may serve as a powerful tool in studying galactic structure.
Ultracool dwarfs (UCDs) are objects with spectral types equal or later than M7. Most of them have been discovered using wide-field imaging surveys. The Virtual Observatory (VO) has proven to be of great utility to efficiently exploit these astronomic
al resources. We aim to validate a VO methodology designed to discover and characterize UCDs in the J-PLUS photometric survey. J-PLUS is a multiband survey carried out with the wide angle T80Cam optical camera mounted on the 0.83-m telescope JAST/T80 in the Observatorio Astrofisico de Javalambre. In this work we make use of the Internal Data Release (IDR) covering 528 deg$^2$. We complement J-PLUS photometry with other catalogues in the optical and IR using VOSA, a VO tool that estimates physical parameters from the spectral energy distribution fitting to collections of theoretical models. Objects identified as UCDs are distinguished from background M giants and highly reddened stars using parallaxes and proper motions from Gaia DR2. We identify 559 UCDs, ranging from i=16.2 to 22.4 mag, of which 187 are candidate UCDs not previously reported in the literature. This represents an increase in the number of known UCDs of about 50% in the studied region of the sky, particularly at the faint end of our sensitivity, which is interesting as reference for future wide and deep surveys such as Euclid. Three candidates constitute interesting targets for exoplanet surveys because of their proximity (<40 pc). We also analyze the kinematics of UCDs in our catalogue and find evidence that it is consistent with a Galactic thin-disk population, except for 6 objects that might be members of the thick disk. The results obtained validate the proposed methodology, which will be used in future J-PLUS and J-PAS releases. Considering the region of the sky covered by the IDR used, we foresee to discover 3,000-3,500 new UCDs at the end of the J-PLUS project.
We present results from a medium-resolution (R ~ 2, 000) spectroscopic follow-up campaign of 1,694 bright (V < 13.5), very metal-poor star candidates from the RAdial Velocity Experiment (RAVE). Initial selection of the low-metallicity targets was bas
ed on the stellar parameters published in RAVE Data Releases 4 and 5. Follow-up was accomplished with the Gemini-N and Gemini-S, the ESO/NTT, the KPNO/Mayall, and the SOAR telescopes. The wavelength coverage for most of the observed spectra allows for the determination of carbon and {alpha}-element abundances, which are crucial for con- sidering the nature and frequency of the carbon-enhanced metal-poor (CEMP) stars in this sample. We find that 88% of the observed stars have [Fe/H] <= -1.0, 61% have [Fe/H] <= -2.0, and 3% have [Fe/H] <= -3.0 (with four stars at [Fe/H] <= -3.5). There are 306 CEMP star candidates in this sample, and we identify 169 CEMP Group I, 131 CEMP Group II, and 6 CEMP Group III stars from the A(C) vs. [Fe/H] diagram. Inspection of the [alpha/C] abundance ratios reveals that five of the CEMP Group II stars can be classified as mono-enriched second-generation stars. Gaia DR1 matches were found for 734 stars, and we show that transverse velocities can be used as a confirmatory selection criteria for low-metallicity candidates. Selected stars from our validated list are being followed-up with high-resolution spectroscopy, to reveal their full chemical abundance patterns for further studies.
Throughout this paper we present a new method to detect and measure emission lines in J-PAS up to $z = 0.35$. J-PAS will observe $8000$~deg$^2$ of the northern sky in the upcoming years with 56 photometric bands. The release of such amount of data br
ings us the opportunity to employ machine learning methods in order to overcome the difficulties associated with photometric data. We used Artificial Neural Networks (ANNs) trained and tested with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. We carry out two tasks: firstly, we cluster galaxies in two groups according to the values of the equivalent width (EW) of $Halpha$, $Hbeta$, $[NII]{lambda 6584}$, and $ [OIII]{lambda 5007}$ lines measured in the spectra. Then, we train an ANN to assign to each galaxy a group. We are able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Secondly, we utilize another ANN to determine the values of those EWs. Subsequently, we obtain the $[NII]/Halpha$, $[OIII]/Hbeta$, and ion{O}{3}ion{N}{2} ratios recovering the BPT diagram . We study the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We can reproduce properly the main sequence of star forming galaxies from the determination of the EWs. With the CALMa training set we reach a precision of 0.093 and 0.081 dex for the $[NII]/Halpha$ and $[OIII]/Hbeta$ ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an AGN. We also show the importance of the dataset used for both training and testing the model. ANNs are extremely useful to overcome the limitations previously expected concerning the detection and measurements of the emission lines in surveys like J-PAS.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
