Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>We analyze the parsec-scale jet kinematics from 2007 June to 2018 December of a sample of <jats:italic>γ</jats:italic>-ray bright blazars monitored roughly monthly with the Very Long Baseline Array (VLBA) at 43 GHz under the VLBA-BU-BLAZAR program. We implement a novel piecewise linear fitting method to derive the kinematics of 521 distinct emission knots from a total of 3705 total intensity images in 22 quasars, 13 BL Lacertae objects, and 3 radio galaxies. Apparent speeds of these components range from 0.01<jats:italic>c</jats:italic> to 78<jats:italic>c</jats:italic>, and 18.6% of knots (other than the “core”) are quasi-stationary. One-fifth of moving knots exhibit nonballistic motion, with acceleration along the jet within 5 pc of the core (projected) and deceleration farther out. These accelerations occur mainly at locations coincident with quasi-stationary features. We calculate the physical parameters of 273 knots with statistically significant motion, including their Doppler factors, Lorentz factors, and viewing angles. We determine the typical values of these parameters for each jet and the average for each subclass of active galactic nuclei. We investigate the variability of the position angle of each jet over the 10 yr of monitoring. The fluctuations in position of the quasi-stationary components in radio galaxies tend to be parallel to the jet, while no directional preference is seen in the components of quasars and BL Lacertae objects. We find a connection between <jats:italic>γ</jats:italic>-ray states of blazars and their parsec-scale jet properties, with blazars with brighter 43 GHz cores typically reaching higher <jats:italic>γ</jats:italic>-ray maxima during flares.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as Galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum <jats:italic>C</jats:italic> <jats:sub> <jats:italic>ℓ</jats:italic> </jats:sub> is smaller than the cosmic variance at <jats:italic>ℓ</jats:italic> &gt; 10. We then apply this method to the current Planck observations, and find that the recovered CMB is quite consistent with that disclosed by the Planck Collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The majority of planetary nebulae (PNs) show axisymmetric morphologies, whose causes are not well understood. In this work, we present spatially resolved kinematic observations of 14 Galactic PNs surrounding Wolf–Rayet ([WR]) and weak emission-line stars (<jats:italic>wels</jats:italic>), based on the H<jats:italic>α</jats:italic> and [N <jats:sc>ii</jats:sc>] emission taken with the Wide Field Spectrograph on the Australian National University 2.3 m telescope. Velocity-resolved channel maps and position–velocity diagrams, together with archival Hubble Space Telescope (HST) and ground-based images, are employed to construct three-dimensional morpho-kinematic models of 12 objects using the program <jats:sc>shape</jats:sc>. Our results indicate that these 12 PNs mostly have elliptical morphologies, with either open or closed outer ends. The kinematic maps show the on-sky orientations of the interior shells in NGC 6578 and NGC 6629, as well as the compact (≤6″) PNs Pe 1-1, M 3-15, M 1-25, Hen 2-142, and NGC 6567, in agreement with the elliptically symmetric morphologies seen in high-resolution HST images. Point-symmetric knots in Hb 4 exhibit deceleration with distance from the central star, which could be due to shock collisions with the ambient medium. The velocity dispersion maps of Pe 1-1 also disclose the shock interaction between its collimated outflows and the interstellar medium. Collimated bipolar outflows are also visible in the position–velocity diagrams of M 3-30, M 1-32, and M 3-15, which are reconstructed by tenuous prolate ellipsoids extending upward from dense equatorial regions in the kinematic models. The formation of aspherical morphologies and collimated outflows in these PNs could be related to the stellar evolution of hydrogen-deficient [WR] and <jats:italic>wels</jats:italic> nuclei, which require further investigation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The complex physics involved in atmospheric turbulence makes it very difficult for ground-based astronomy to build accurate scintillation models and develop efficient methodologies to remove this highly structured noise from valuable astronomical observations. We argue that a deep-learning approach can bring a significant advance to treat this problem because of deep neural networks’ inherent ability to abstract nonlinear patterns over a broad scale range. We propose an architecture composed of long short-term memory cells and an incremental training strategy inspired by transfer and curriculum learning. We develop a scintillation model and employ an empirical method to generate a vast catalog of atmospheric-noise realizations and train the network with representative data. We face two complexity axes: the signal-to-noise ratio (S/N) and the degree of structure in the noise. Hence, we train our recurrent network to recognize simulated astrophysical pointlike sources embedded in three structured-noise levels, with a raw-data S/N ranging from 3 to 0.1. We find that a slow and repetitive increase in complexity is crucial during training to obtain a robust and stable learning rate that can transfer information through different data contexts. We probe our recurrent model with synthetic observational data, designing alongside a calibration methodology for flux measurements. Furthermore, we implement traditional matched filtering (MF) to compare its performance with our neural network, finding that our final trained network can successfully clean structured noise and significantly enhance the S/N compared to raw data and in a more robust way than traditional MF.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Precise correction of dust reddening is fundamental to obtain the intrinsic parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D extinction maps are widely used for reddening correction. In this work, using accurate reddening determinations of about 2 million stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 5 and Gaia DR2, we check and calibrate the SFD and Planck maps in the middle and high Galactic latitudes. The maps show similar precision in reddening correction. We find small yet significant spatially dependent biases for the four maps, which are similar between the SFD and Planck2014-R maps, and between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear dependence on the dust temperature and extinction for the SFD and Planck2014-R maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak dependence on the dust temperature, they both strongly depend on the dust spectral index. Finally, we present corrections of the SFD and Planck extinction maps within the LAMOST footprint, along with empirical relations for corrections outside the LAMOST footprint. Our results provide important clues for the further improvement of the Galactic all-sky extinction maps and lay a significant foundation for accurate extinction correction in the era of precision astronomy.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The discovery of the electromagnetic counterpart to the binary neutron star (NS) merger GW170817 has opened the era of gravitational-wave multimessenger astronomy. Rapid identification of the optical/infrared kilonova enabled a precise localization of the source, which paved the way to deep multiwavelength follow-up and its myriad of related science results. Fully exploiting this new territory of exploration requires the acquisition of electromagnetic data from samples of NS mergers and other gravitational-wave sources. After GW170817, the frontier is now to map the diversity of kilonova properties and provide more stringent constraints on the Hubble constant, and enable new tests of fundamental physics. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time can play a key role in this field in the 2020s, when an improved network of gravitational-wave detectors is expected to reach a sensitivity that will enable the discovery of a high rate of merger events involving NSs (∼tens per year) out to distances of several hundred megaparsecs. We design comprehensive target-of-opportunity observing strategies for follow-up of gravitational-wave triggers that will make the Rubin Observatory the premier instrument for discovery and early characterization of NS and other compact-object mergers, and yet unknown classes of gravitational-wave events.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Circular-ribbon flares (CFs) are a special type of solar flares owing to their particular magnetic topology. In this paper, we conducted a comprehensive statistical analysis of 134 CFs from 2011 September to 2017 June, including 4 B-class, 82 C-class, 40 M-class, and 8 X-class flares. The flares were observed by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory spacecraft. The physical properties of CFs are derived, including the location, area (<jats:italic>A</jats:italic> <jats:sub>CF</jats:sub>), equivalent radius (<jats:italic>r</jats:italic> <jats:sub>CF</jats:sub>) assuming a semispherical fan dome, lifetime (<jats:italic>τ</jats:italic> <jats:sub>CF</jats:sub>), and peak soft X-ray (SXR) flux in 1–8 Å. It is found that all CFs are located in active regions, with the latitudes between −30° and 30°. The distributions of areas and lifetimes could be fitted with a lognormal function. There is a positive correlation between the lifetime and area. The peak SXR flux in 1–8 Å is well in accord with a power-law distribution with an index of −1.42. For the 134 CFs, 57% of them are accompanied by remote brightenings or ribbons. A positive correlation exists between the total length (<jats:italic>L</jats:italic> <jats:sub>RB</jats:sub>) and average distance (<jats:italic>D</jats:italic> <jats:sub>RB</jats:sub>) of remote brightenings. About 47% and 51% of the 134 CFs are related to type III radio bursts and jets, respectively. The association rates are independent of flare energies. About 38% of CFs are related to minifilament eruptions, and the association rates increase with flare classes. Only 28% of CFs are related to coronal mass ejections (CMEs), meaning that a majority of them are confined rather than eruptive events. There is a positive correlation between the CME speed and peak SXR flux in 1–8 Å, and faster CMEs tend to be wider.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In this paper, we describe a project we initiated to investigate individual pixels in downloaded Kepler apertures in order to find objects in the background of the main targets with variable brightness. In the first paper of this series, we discovered and investigated 547 short-period eclipsing binaries. Here we present the independent discovery of 26 new RR Lyrae stars in the Kepler background pixels obtained during the primary mission and provide continuous and precise photometry for these objects. Twenty-one of these stars were already noted by the Gaia or the Pan-STARRS survey. This new population of dominantly faint and distant RR Lyrae stars increases by 50% and complements nicely the 52 already known main target RR Lyrae stars in the original Kepler field. Despite their faintness, the four-year quasi-uninterrupted light curves of these stars allow an unprecedented view of these faint halo objects. We present an analysis of the light curves of the new RR Lyrae sample, verify their classification using Fourier parameters, and discuss the properties of these newly found pulsating variable stars. Most notably, this is the first time that such faint RR Lyrae stars have been investigated with the help of a photometric data set with outstanding cadence and precision. Interestingly, these objects share the properties of their brighter siblings in terms of subclass characteristics, additional mode content, and modulation occurrence rates.</jats:p>