Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>The Coronal Global Evolutionary Model (CGEM) provides data-driven simulations of the magnetic field in the solar corona to better understand the build-up of magnetic energy that leads to eruptive events. The CGEM project has developed six capabilities. CGEM modules (1) prepare time series of full-disk vector magnetic field observations to (2) derive the changing electric field in the solar photosphere over active-region scales. This local electric field is (3) incorporated into a surface flux transport model that reconstructs a global electric field that evolves magnetic flux in a consistent way. These electric fields drive a (4) 3D spherical magnetofrictional (SMF) model, either at high resolution over a restricted range of solid angles or at lower resolution over a global domain to determine the magnetic field and current density in the low corona. An SMF-generated initial field above an active region and the evolving electric field at the photosphere are used to drive (5) detailed magnetohydrodynamic (MHD) simulations of active regions in the low corona. SMF or MHD solutions are then used to compute emissivity proxies that can be compared with coronal observations. Finally, a lower-resolution SMF magnetic field is used to initialize (6) a global MHD model that is driven by an SMF electric field time series to simulate the outer corona and heliosphere, ultimately connecting Sun to Earth. As a demonstration, this report features results of CGEM applied to observations of the evolution of NOAA Active Region 11158 in 2011 February.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Earth-sized planets in the habitable zones of M dwarfs are good candidates for the study of habitability and detection of biosignatures. To search for these planets, we analyze all available radial velocity data and apply four signal detection criteria to select the optimal candidates. We find 10 strong candidates satisfying these criteria and three weak candidates showing inconsistency over time due to data samplings. We also confirm three previous planet candidates and improve their orbital solutions through combined analyses of updated data sets. Among the strong planet candidates, HIP 38594 b is a temperate super-Earth with a mass of 8.2 ± 1.7 <jats:italic>M</jats:italic> <jats:sub>⊕</jats:sub> and an orbital period of 60.7 ± 0.1 days, orbiting around an early-type M dwarf. Early-type M dwarfs are less active and thus are better hosts for habitable planets than mid-type and late-type M dwarfs. Moreover, we report the detection of five two-planet systems, including two systems made up of a warm or cold Neptune and a cold Jupiter, consistent with a positive correlation between these two types of planets. We also detect three temperate Neptunes, four cold Neptunes, and four cold Jupiters, contributing to a rarely explored planet population. Due to their proximity to the Sun, these planets on wide orbits are appropriate targets for direct imaging by future facilities such as the Habitable Exoplanet Observatory and the Extremely Large Telescope.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). We also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We have matched the ultraviolet (UV) sources in <jats:italic>GUVcat</jats:italic>_AIS with optical databases that have similar depth and wide sky coverage. <jats:italic>GUVcat</jats:italic>_AIS has Galaxy Evolution Explorer (GALEX) far-UV (FUV, <jats:italic>λ</jats:italic> <jats:sub>eff</jats:sub> ∼ 1528 Å) and near-UV (NUV, <jats:italic>λ</jats:italic> <jats:sub>eff</jats:sub> ∼ 2310 Å) photometry of ≈83 million sources, covering 24,788 square degrees of the sky, with a typical depth of FUV = 19.9 and NUV = 20.8 AB mag. Matches with Gaia and the Sloan Digital Sky Survey (SDSS) databases are presented here. Gaia data release 2 (DR2), covering the entire <jats:italic>GUVcat</jats:italic> footprint, detected about one-third of the <jats:italic>GUVcat</jats:italic>_AIS sources. We found 31,925,294 Gaia DR2 counterparts to 30,024,791 <jats:italic>GUVcat</jats:italic>_AIS unique sources, with photometry in the Gaia <jats:italic>G</jats:italic> band and often also in Gaia BP and RP bands; 26,275,572 matches have a parallax measurement, 21,084,628, 18,588,140, and 16,357,505 have a parallax error less than 50%, 30%, and 20%, respectively. The match with SDSS data release 14 (DR14) yields 23,310,532 counterparts to 22,207,563 unique <jats:italic>GUVcat</jats:italic>_AIS sources, 10,167,460 of which are pointlike, over a total overlap area of ≈11,100 square degrees (Bianchi et al. 2019). SDSS adds five optical magnitudes to the UV photometry : <jats:italic>u</jats:italic>, <jats:italic>g</jats:italic>, <jats:italic>r</jats:italic>, <jats:italic>i</jats:italic>, <jats:italic>z</jats:italic>, and optical spectra of 860,224 matched sources. We used a match radius of 3″, consistent with previous works, although the positions agree to ≲15 for the majority of (pointlike) matched sources, in order to identify possible multiple matches whose UV flux could be unresolved in GALEX imaging. The catalogs can be trimmed to a tighter match radius using the provided separation. The multiband photometry is used to identify classes of astrophysical objects that are prominent in UV, to characterize the content of the <jats:italic>GUVmatch</jats:italic> catalogs, where stars in different evolutionary stages, quasi-stellar objects, and galaxies can be separated.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This paper describes the database of optical spectra of BL Lacertae (BLL) objects (Z BLL objects) available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://web.oapd.inaf.it/zbllac/" xlink:type="simple">https://web.oapd.inaf.it/zbllac/</jats:ext-link>. At present, it contains calibrated spectra for 295 BLL. For about 35% of them, we report a firm measure of redshift <jats:italic>z</jats:italic>, while for 35 sources we set a lower limit on <jats:italic>z</jats:italic> based on the detection of intervening absorption systems, mainly ascribed to Mg <jats:sc>ii</jats:sc> (<jats:italic>λ</jats:italic>2800 Å). We report here on the architecture of the database and on its website front-end that permits us to filter, query, and interactively explore the data. We discuss some properties of the objects in the present data set by giving the distribution of the redshifts and reporting on the detected emission lines, which turn out to be mainly forbidden and ascribed to [O <jats:sc>ii</jats:sc>] (<jats:italic>λ</jats:italic>3737 Å) and [O <jats:sc>iii</jats:sc>] (<jats:italic>λ</jats:italic>5007 Å). Finally, we discuss on intervening absorption systems detected in 35 BLLs that allow us to set lower limits to their distance.</jats:p>