We present results from a medium-resolution (R∼2000) spectroscopic follow-up campaign of 1694 bright (V<13.5), very metal-poor star candidates from the RAdial Velocity Experiment (RAVE). Initial selection of the low-metallicity targets was based 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 α-element abundances, which are crucial for considering the nature and frequency of the carbon-enhanced metalpoor (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 GroupI, 131 CEMP GroupII, and 6 CEMP GroupIII stars from the A(C) versus [Fe/H] diagram. Inspection of the C a [ ] abundance ratios reveals that five of the CEMP GroupII 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.
We present results from an observing campaign to identify low-metallicity stars in the Best & Brightest Survey. From medium-resolution (R ∼ 1, 200 − 2, 000) spectroscopy of 857 candidates, we estimate the stellar atmospheric parameters (T eff , log g, and [Fe/H]), as well as carbon and α-element abundances. We find that 69% of the observed stars have [Fe/H] ≤ −1.0, 39% have [Fe/H] ≤ −2.0, and 2% have [Fe/H] ≤ −3.0. There are also 133 carbon-enhanced metal-poor (CEMP) stars in this sample, with 97 CEMP Group I and 36 CEMP Group II stars identified in the A(C) versus [Fe/H] diagram. A subset of the confirmed low-metallicity stars were followed-up with high-resolution spectroscopy, as part of the R-process Alliance, with the goal of identifying new highly and moderately r-processenhanced stars. Comparison between the stellar atmospheric parameters estimated in this work and from high-resolution spectroscopy exhibit good agreement, confirming our expectation that mediumresolution observing campaigns are an effective way of selecting interesting stars for further, more targeted, efforts.
The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
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