Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>In this work, we present a catalog of cataclysmic variables (CVs) identified from the sixth data release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 H<jats:italic>α</jats:italic> emission spectra, and obtain a classification accuracy of the algorithm of over 99.6% from the cross-validation set. We then apply the template-matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which help us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all of the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further classify the new CV candidates in subtypes based on their spectral features, including five DN subtypes during outbursts, five NL subtypes, and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry and confirm their previous classification with the LAMOST spectra.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present a performance test of the point-spread function (PSF) deconvolution algorithm applied to astronomical integral field unit (IFU) spectroscopy data for restoration of galaxy kinematics. We deconvolve the IFU data by applying the Lucy–Richardson algorithm to the 2D image slice at each wavelength. We demonstrate that the algorithm can effectively recover the true stellar kinematics of the galaxy, by using mock IFU data with a diverse combination of surface brightness profile, signal-to-noise ratio, line-of-sight geometry, and line-of-sight velocity distribution (LOSVD). In addition, we show that the proxy of the spin parameter <jats:inline-formula> <jats:tex-math> <?CDATA ${\lambda }_{{R}_{e}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>λ</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>e</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2828ieqn1.gif" xlink:type="simple" /> </jats:inline-formula> can be accurately measured from the deconvolved IFU data. We apply the deconvolution algorithm to the actual SDSS-IV MaNGA IFU survey data. The 2D LOSVD, geometry, and <jats:inline-formula> <jats:tex-math> <?CDATA ${\lambda }_{{R}_{e}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>λ</mml:mi> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>e</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2828ieqn2.gif" xlink:type="simple" /> </jats:inline-formula> measured from the deconvolved MaNGA IFU data exhibit noticeable differences compared to the ones measured from the original IFU data. The method can be applied to any other regular-grid IFU data to extract the PSF-deconvolved spatial information.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines. To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line. In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter). The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy. We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions. These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines. In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present the deepest Spitzer/InfraRed Array Camera (IRAC) 3.6, 4.5, 5.8, and 8.0 <jats:italic>μ</jats:italic>m wide-area mosaics yet over the Great Observatories Origins Deep Survey (GOODS)-N and GOODS-S fields as part of the GOODS Reionization Era wide-Area Treasury from Spitzer (GREATS) project. We reduced and mosaicked in a self-consistent way observations taken by the 11 different Spitzer/IRAC programs over the two GOODS fields from 12 yr of Spitzer cryogenic and warm-mission data. The cumulative depth in the 3.6 <jats:italic>μ</jats:italic>m and 4.5 <jats:italic>μ</jats:italic>m bands amounts to ∼4260 hr, ∼1220 hr of which are new very deep observations from the GREATS program itself. In the deepest area, the full-depth mosaics reach ≳200 hr over an area of ∼100 arcmin<jats:sup>2</jats:sup>, corresponding to a sensitivity of ∼29 AB magnitude at 3.6 <jats:italic>μ</jats:italic>m (1<jats:italic>σ</jats:italic> for point sources). Archival cryogenic 5.8 <jats:italic>μ</jats:italic>m and 8.0 <jats:italic>μ</jats:italic>m band data (a cumulative 976 hr) are also included in the release. The mosaics are projected onto the tangential plane of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey/GOODS at a 0.″3 pixel<jats:sup>−1</jats:sup> scale. This paper describes the methodology enabling, and the characteristics of, the public release of the mosaic science images, the corresponding coverage maps in the four IRAC bands, and the empirical point-spread functions (PSFs). These PSFs enable mitigation of the source blending effects by taking into account the complex position-dependent variation in the IRAC images. The GREATS data products are in the Infrared Science Archive. We also release the deblended 3.6–8.0 <jats:italic>μ</jats:italic>m photometry 9192 Lyman-break galaxies at <jats:italic>z</jats:italic> ∼ 3.5–10. GREATS will be the deepest mid-infrared imaging until the James Webb Space Telescope and, as such, constitutes a major resource for characterizing early galaxy assembly.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present the results of a multiplicity survey for a magnitude-limited sample of 31 classical Be stars conducted with the Navy Precision Optical Interferometer and the Mark III Stellar Interferometer. The interferometric observations were used to detect companions in 10 previously known binary systems. For two of these sources (66 Oph and <jats:italic>β</jats:italic> Cep) new orbital solutions were obtained, while for a third source (<jats:italic>υ</jats:italic> Sgr) our observations provide the first direct, visual detection of the hot companion to the primary star. Combining our interferometric observations with an extensive literature search, we conclude that an additional four sources (o Cas, 15 Mon, <jats:italic>β</jats:italic> Lyr, and <jats:italic>β</jats:italic> Cep) also contain wider binary components that are physical companions to the narrow binaries, thus forming hierarchical multiple systems. Among the sources not previously confirmed as spectroscopic or visual binaries, BK Cam was resolved on a number of nights within a close physical proximity of another star with relative motion possibly suggesting a physical binary. Combining our interferometric observations with an extensive literature search, we provide a detailed listing of companions known around each star in the sample, and discuss the multiplicity frequency in the sample. We also discuss the prospects for future multiplicity studies of classical Be stars by long-baseline optical interferometry.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Perhaps the most exciting promise of the Rubin Observatory Legacy Survey of Space and Time (LSST) is its capability to discover phenomena never before seen or predicted: true astrophysical novelties; but the ability of LSST to make these discoveries will depend on the survey strategy. Evaluating candidate strategies for true novelties is a challenge both practically and conceptually. Unlike traditional astrophysical tracers like supernovae or exoplanets, for anomalous objects, the template signal is by definition unknown. We approach this problem by assessing survey completeness in a phase space defined by object color and flux (and their evolution), and considering the volume explored by integrating metrics within this space with the observation depth, survey footprint, and stellar density. With these metrics, we explore recent simulations of the Rubin LSST observing strategy across the entire observed spatial footprint and in specific Local Volume regions: the Galactic Plane and Magellanic Clouds. Under our metrics, observing strategies with greater diversity of exposures and time gaps tend to be more sensitive to genuinely new transients, particularly over time-gap ranges left relatively unexplored by previous surveys. To assist the community, we have made all of the tools developed publicly available. While here we focus on transients, an extension of the scheme to include proper motions and the detection of associations or populations of interest will be communicated in Paper II of this series. This paper was written with the support of the Vera C. Rubin LSST Transients and Variable Stars and Stars, Milky Way, Local Volume Science Collaborations.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>With their emission mainly coming from a relativistic jet pointing toward us, blazars are fundamental sources for studying extragalactic jets and their central engines, consisting of supermassive black holes fed by accretion disks. They are also candidate sources of high-energy neutrinos and cosmic rays. Because of the jet orientation, the nonthermal blazar emission is Doppler beamed; its variability is unpredictable, and it occurs on timescales from less than 1 hr to years. Comprehension of the diverse mechanisms producing the flux and spectral changes requires well-sampled multiband light curves over long time periods. In particular, outbursts are the best test bench for shedding light on the underlying physics, especially when studied in a multiwavelength context. The Vera C. Rubin Legacy Survey of Space and Time (Rubin-LSST) will monitor the southern sky for 10 yr in six photometric bands, offering a formidable tool for studying blazar variability features in a statistical way. The alert system will allow us to trigger follow-up observations of outstanding events, especially at high (keV-to-GeV) and very high (TeV) energies. We here examine the simulated Rubin-LSST survey strategies with the aim of understanding which cadences are more suitable for blazar variability science. Our metrics include light curve and color sampling. We also investigate the problem of saturation, which will affect the brightest and many flaring sources, and will have a detrimental impact on follow-up observations.</jats:p>