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G 29 − 38 (TIC 422526868) is one of the brightest (V = 13.1) and closest (d = 17.51 pc) pulsating white dwarfs with a hydrogen-rich atmosphere (DAV/ZZ Ceti class). It was observed by the TESS spacecraft in sectors 42 and 56. The atmosphere of G 29 − 38 is polluted by heavy elements that are expected to sink out of visible layers on short time-scales. The photometric TESS data set spans ∼51 d in total, and from this, we identified 56 significant pulsation frequencies, that include rotational frequency multiplets. In addition, we identified 30 combination frequencies in each sector. The oscillation frequencies that we found are associated with g-mode pulsations, with periods spanning from ∼ 260 to ∼ 1400 s. We identified rotational frequency triplets with a mean separation δνℓ = 1 of 4.67 μHz and a quintuplet with a mean separation δνℓ = 2 of 6.67 μHz, from which we estimated a rotation period of about 1.35 ± 0.1 d. We determined a constant period spacing of 41.20 s for ℓ = 1 modes and 22.58 s for ℓ = 2 modes. We performed period-to-period fit analyses and found an asteroseismological model with M⋆/M⊙ = 0.632 ± 0.03, $T_{\rm eff}=11\, 635\pm 178$ K, and log g = 8.048 ± 0.005 (with a hydrogen envelope mass of MH ∼ 5.6 × 10−5M⋆), in good agreement with the values derived from spectroscopy. We obtained an asteroseismic distance of 17.54 pc, which is in excellent agreement with that provided by Gaia (17.51 pc).

 

The ages of the oldest stars shed light on the birth, chemical enrichment, and chemical evolution of the universe. Nucleocosmochronometry provides an avenue to determining the ages of these stars independent from stellar-evolution models. The uranium abundance, which can be determined for metal-poorr-process enhanced (RPE) stars, has been known to constitute one of the most robust chronometers known. So far, U abundance determination has used asingleUiiline atλ3859 Å. Consequently, U abundance has been reliably determined for only five RPE stars. Here, we present the first homogeneous U abundance analysis of four RPE stars using two novel Uiilines atλ4050 Å andλ4090 Å, in addition to the canonicalλ3859 Å line. We find that the Uiilines atλ4050 Å andλ4090 Å are reliable and render U abundances in agreement with theλ3859 U abundance, for all of the stars. We, thus, determine revised U abundances for RPE stars, 2MASS J09544277+5246414, RAVE J203843.2–002333, HE 1523–0901, and CS 31082–001, using multiple Uiilines. We also provide nucleocosmochronometric ages of these stars based on the newly derived U, Th, and Eu abundances. The results of this study open up a new avenue to reliably and homogeneously determine U abundance for a significantly larger number of RPE stars. This will, in turn, enable robust constraints on the nucleocosmochronometric ages of RPE stars, which can be applied to understand the chemical enrichment and evolution in the early universe, especially ofr-process elements.

 

We present the identification and photometric analysis of 30 new low mass ratio (LMR) totally eclipsing contact binaries found in Catalina Sky Survey data. The LMR candidates are identified using Fourier coefficients and visual inspection. We perform a detailed scan in the parameter plane of mass ratio (q) versus inclination (i) using phoebe -0.31 scripter to derive the best (q, i) pair for the initial models. The relative physical parameters are determined from the final model of each system. A Monte Carlo approach was adopted to derive the parameter errors. The resulting parameters confirm the identification. The approximate absolute physical parameters of the systems are estimated based on the light-curve solutions and Gaia Early Data Release 3 distances. 12 out of 30 new systems have fill-out factors $f\gt 50{{\ \rm per\ cent}}$ and q ≤ 0.25 (deep contact LMR systems), and eight of them, to within errors, are extreme LMR deep systems with q ≤ 0.1. We discuss the evolutionary status of the 30 LMR systems in comparison with the most updated catalogue of LMR systems from the literature. The scenario of the LMR systems as pre-merger candidates forming fast rotating stars is investigated for all systems, new and old, based both on Hut’s stability criteria and critical instability mass ratio (qinst) relation. CSS$\_$J075848.2+125656, with q/qinst = 1.23 ± 0.23, and CSS$\_$J093010.1−021624, with q/qinst = 1.25 ± 0.23, can be considered as merger candidates. $$\begin{eqnarray} \_ \end{eqnarray}$$

 

This paper presents a new optical imaging survey of four deep drilling fields (DDFs), two Galactic and two extragalactic, with the Dark Energy Camera (DECam) on the 4-m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO). During the first year of observations in 2021, >4000 images covering 21 deg2 (seven DECam pointings), with ∼40 epochs (nights) per field and 5 to 6 images per night per filter in g, r, i, and/or z have become publicly available (the proprietary period for this program is waived). We describe the real-time difference-image pipeline and how alerts are distributed to brokers via the same distribution system as the Zwicky Transient Facility (ZTF). In this paper, we focus on the two extragalactic deep fields (COSMOS and ELAIS-S1) characterizing the detected sources, and demonstrating that the survey design is effective for probing the discovery space of faint and fast variable and transient sources. We describe and make publicly available 4413 calibrated light curves based on difference-image detection photometry of transients and variables in the extragalactic fields. We also present preliminary scientific analysis regarding the Solar system small bodies, stellar flares and variables, Galactic anomaly detection, fast-rising transients and variables, supernovae, and active Galactic nuclei.

 

We present DELIGHT, or Deep Learning Identification of Galaxy Hosts of Transients, a new algorithm designed to automatically and in real time identify the host galaxies of extragalactic transients. The proposed algorithm receives as input compact, multiresolution images centered at the position of a transient candidate and outputs two-dimensional offset vectors that connect the transient with the center of its predicted host. The multiresolution input consists of a set of images with the same number of pixels, but with progressively larger pixel sizes and fields of view. A sample of 16,791 galaxies visually identified by the Automatic Learning for the Rapid Classification of Events broker team was used to train a convolutional neural network regression model. We show that this method is able to correctly identify both relatively large (10″ <r< 60″) and small (r≤ 10″) apparent size host galaxies using much less information (32 kB) than with a large, single-resolution image (920 kB). The proposed method has fewer catastrophic errors in recovering the position and is more complete and has less contamination (<0.86%) recovering the crossmatched redshift than other state-of-the-art methods. The more efficient representation provided by multiresolution input images could allow for the identification of transient host galaxies in real time, if adopted in alert streams from new generation of large -etendue telescopes such as the Vera C. Rubin Observatory.

 

Abstract Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multipurpose 10 yr optical survey of the Southern Hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the solar system, exploring the transient optical sky, and mapping the Milky Way. The survey’s massive data throughput will be transformational for many other astrophysics domains and Rubin’s data access policy sets the stage for a huge community of potential users. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue.