Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is the microlensing Einstein radius crossing time (<jats:italic>t</jats:italic> <jats:sub>E</jats:sub>) distribution. However, systematics in individual light curves as well as oversimplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian process model. We used light curves for nearly 10,000 OGLE-III and -IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the <jats:italic>t</jats:italic> <jats:sub>E</jats:sub> distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and the previous analyses overestimated the number of <jats:italic>t</jats:italic> <jats:sub>E</jats:sub> &gt; 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Population studies of exoplanets are key to unlocking their statistical properties. So far, the inferred properties have been mostly limited to planetary, orbital, and stellar parameters extracted from, e.g., Kepler, radial velocity, and Gaia data. More recently an increasing number of exoplanet atmospheres have been observed in detail from space and the ground. Generally, however, these atmospheric studies have focused on individual planets, with the exception of a couple of works that have detected the presence of water vapor and clouds in populations of gaseous planets via transmission spectroscopy. Here, using a suite of retrieval tools, we analyze spectroscopic and photometric data of 25 hot Jupiters, obtained with the Hubble and Spitzer Space Telescopes via the eclipse technique. By applying the tools uniformly across the entire set of 25 planets, we extract robust trends in the thermal structure and chemical properties of hot Jupiters not obtained in past studies. With the recent launch of the James Webb Space Telescope and the upcoming missions Twinkle and Ariel, population-based studies of exoplanet atmospheres, such as the one presented here, will be a key approach to understanding planet characteristics, formation, and evolution in our galaxy.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The direction of parsec-scale jets in active galactic nuclei (AGNs) is essential information for many astrophysical and astrometric studies, including linear polarization and magnetic field structure, frequency-dependent synchrotron opacity, proper motion, and reference-frame alignment. We developed a rigorous, simple, and completely automated method to measure the directions from calibrated interferometric visibility data at frequencies ranging from 1.4 to 86 GHz. We publish the results for 9220 AGNs with the typical accuracy below 10°. An internal check of the method comparing the directions between different observing frequencies as well as with previous publications verifies the robustness of the measured values.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present an overview of best practices for publishing data in astronomy and astrophysics journals. These recommendations are intended as a reference for authors to help prepare and publish data in a way that will better represent and support science results, enable better data sharing, improve reproducibility, and enhance the reusability of data. Observance of these guidelines will also help to streamline the extraction, preservation, integration and cross-linking of valuable data from astrophysics literature into major astronomical databases, and consequently facilitate new modes of science discovery that will better exploit the vast quantities of panchromatic and multidimensional data associated with the literature. We encourage authors, journal editors, referees, and publishers to implement the best practices reviewed here, as well as related recommendations from international astronomical organizations such as the International Astronomical Union for publication of nomenclature, data, and metadata. A convenient Checklist of Recommendations for Publishing Data in the Literature (Appendix A) is included for authors to consult before the submission of the final version of their journal articles and associated data files. We recommend that publishers of journals in astronomy and astrophysics incorporate a link to this document in their Instructions to Authors.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The prediction of the Dst index is an important subject in space weather. It has significant progress with the prevalent applications of neural networks. The selection of input parameters is critical for the prediction model of the Dst index or other space-weather models. In this study, we perform a layer-wise relevance propagation (LRP) method to select the main parameters for the prediction of the Dst index and understand the physical interpretability of neural networks for the first time. Taking an hourly Dst index and 10 types of solar wind parameters as the inputs, we utilize a long short-term memory network to predict the Dst index and present the LRP method to analyze the dependence of the Dst index on these parameters. LRP defines the relevance score for each input, and a higher relevance score indicates that the corresponding input parameter contributes more to the output. The results show that Dst, <jats:italic>E</jats:italic> <jats:sub> <jats:italic>y</jats:italic> </jats:sub>, <jats:italic>B</jats:italic> <jats:sub> <jats:italic>z</jats:italic> </jats:sub>, and <jats:italic>V</jats:italic> are the main control parameters for Dst index prediction. In order to verify the LRP method, we design two more supplementary experiments for further confirmation. These results confirm that the LRP method can reduce the initial dimension of neural network input at the cost of minimum information loss and contribute to the understanding of physical processes in space weather.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>A small number of extragalactic radio sources disclose a pair of low-surface-brightness radio lobes, known as “wings,” aligned at a certain angle to the primary jets. Such exotic sources are known as “winged” radio sources. Here we report the new identification of a total of 26 “winged” radio sources from the LOFAR Two-meter Sky Survey First Data Release (LoTSS DR1). Out of the 26 “winged” sources, 14 are identified as X-shaped radio galaxies and the remaining 12 as Z-shaped radio galaxies. The available optical counterpart of each radio galaxy is cataloged along with its estimated redshift. Among the 26 sources, 15 candidates are classified as FR-II radio galaxies, and two are classified as FR-I type. For nine candidates, no conclusions are drawn due to their complex morphology. We also calculate the physical parameters such as spectral index, radio luminosity, and power of the sources. We have made a statistical study of the spectral index by combining our estimated value with the spectral index collected from previous works. A mean value of spectral index of 0.71 is obtained.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We report 541 new open-cluster candidates in Gaia EDR3 through revisiting the cluster results from an earlier analysis of Gaia DR2, which revealed nearly 1000 open-cluster candidates in the solar neighborhood (mostly <jats:italic>d</jats:italic> &lt;3 kpc) residing at Galactic latitudes ∣<jats:italic>b</jats:italic>∣ &lt; 20°. A subsequent comparison with lists of known clusters shows a large increase of the cluster samples within 2 kpc from the Sun. We assign membership probabilities to the stars through the open-source pyUPMASK algorithm, and also estimate the physical parameters through isochrone fitting for each candidate. Most of the new candidates show small total-proper-motion dispersions and clear features in the color–magnitude diagrams. Besides, the metallicity gradient of the new candidates is consistent with those found in the literature. The cluster parameters and member stars are available at CDS via anonymous ftp to <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://cdsarc.u-strasbg.fr(130.79.128.5)" xlink:type="simple">cdsarc.u-strasbg.fr(130.79.128.5)</jats:ext-link> or via <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS/260/8" xlink:type="simple">https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS/260/8</jats:ext-link>. The discovery of these new objects shows that the open-cluster samples in Gaia data is still not complete, and more discoveries are expected in future research.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>A hybrid two-stage machine-learning architecture that addresses the problem of excessive false positives (false alarms) in solar flare prediction systems is investigated. The first stage is a convolutional neural network (CNN) model based on the VGG-16 architecture that extracts features from a temporal stack of consecutive Solar Dynamics Observatory Helioseismic and Magnetic Imager magnetogram images to produce a flaring probability. The probability of flaring is added to a feature vector derived from the magnetograms to train an extremely randomized trees (ERT) model in the second stage to produce a binary deterministic prediction (flare/no-flare) in a 12 hr forecast window. To tune the hyperparameters of the architecture, a new evaluation metric is introduced: the “scaled True Skill Statistic.” It specifically addresses the large discrepancy between the true positive rate and the false positive rate in the highly unbalanced solar flare event training data sets. Through hyperparameter tuning to maximize this new metric, our two-stage architecture drastically reduces false positives by ≈48% without significantly affecting the true positives (reduction by ≈12%), when compared with predictions from the first-stage CNN alone. This, in turn, improves various traditional binary classification metrics sensitive to false positives, such as the precision, F1, and the Heidke Skill Score. The end result is a more robust 12 hr flare prediction system that could be combined with current operational flare-forecasting methods. Additionally, using the ERT-based feature-ranking mechanism, we show that the CNN output probability is highly ranked in terms of flare prediction relevance.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We propose a generalization of our previous kernel density estimation (KDE) method for estimating luminosity functions (LFs). This new upgrade further extends the application scope of our KDE method, making it a very flexible approach that is suitable to deal with most bivariate LF calculation problems. From the mathematical point of view, usually the LF calculation can be abstracted as a density estimation problem in the bounded domain of <jats:inline-formula> <jats:tex-math> <?CDATA $\{{Z}_{1}\lt z\lt {Z}_{2},\,L\gt {f}_{\mathrm{lim}}(z)\}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">{</mml:mo> <mml:msub> <mml:mrow> <mml:mi>Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:mi>z</mml:mi> <mml:mo>&lt;</mml:mo> <mml:msub> <mml:mrow> <mml:mi>Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> <mml:mspace width="0.25em" /> <mml:mi>L</mml:mi> <mml:mo>&gt;</mml:mo> <mml:msub> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>lim</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mi>z</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo stretchy="false">}</mml:mo> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac596aieqn1.gif" xlink:type="simple" /> </jats:inline-formula>. We use the transformation-reflection KDE method (<jats:inline-formula> <jats:tex-math> <?CDATA $\hat{\phi }$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mover accent="true"> <mml:mrow> <mml:mi>ϕ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac596aieqn2.gif" xlink:type="simple" /> </jats:inline-formula>) to solve the problem, and introduce an approximate method (<jats:inline-formula> <jats:tex-math> <?CDATA ${\hat{\phi }}_{1}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>ϕ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac596aieqn3.gif" xlink:type="simple" /> </jats:inline-formula>) based on one-dimensional KDE to deal with the small sample size case. In practical applications, the different versions of LF estimators can be flexibly chosen according to the Kolmogorov–Smirnov test criterion. Based on 200 simulated samples, we find that for both cases of dividing or not dividing redshift bins, especially for the latter, our method performs significantly better than the traditional binning method <jats:inline-formula> <jats:tex-math> <?CDATA ${\hat{\phi }}_{\mathrm{bin}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>ϕ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>bin</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac596aieqn4.gif" xlink:type="simple" /> </jats:inline-formula>. Moreover, with the increase of sample size <jats:italic>n</jats:italic>, our LF estimator converges to the true LF remarkably faster than <jats:inline-formula> <jats:tex-math> <?CDATA ${\hat{\phi }}_{\mathrm{bin}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>ϕ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>ˆ</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mi>bin</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac596aieqn5.gif" xlink:type="simple" /> </jats:inline-formula>. To implement our method, we have developed a public, open-source Python toolkit, called <jats:monospace>kdeLF</jats:monospace>. With the support of <jats:monospace>kdeLF</jats:monospace>, our KDE method is expected to be a competitive alternative to existing nonparametric estimators, due to its high accuracy and excellent stability. <jats:monospace>kdeLF</jats:monospace> is available online at GitHub with further extensive documentation available.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>All available experimental data on the spectrum of singly ionized carbon have been critically analyzed. Measurement uncertainties of all published studies have been reassessed. The scope of observational data includes laboratory emission spectra of arcs, sparks, electrodeless discharges, and hollow cathode lamps recorded with grating and Fourier transform spectrometers, laboratory photoabsorption spectra, and emission spectra of planetary nebulae. The total number of observed spectral lines included in this compilation is 597. These lines participate in 972 transitions. From this list of identified transitions, we have derived a set of 414 energy levels, which are optimized using a least-squares fitting procedure. The identifications are supported by parametric calculations with Cowan’s codes. The existing tables of critically evaluated transition probabilities have been extended with our newly calculated data. The ionization energy has been derived from the newly optimized energy levels with improved precision. Data on the isotope shifts and hyperfine structure have also been compiled.</jats:p>