Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>We report multiepoch radial velocities, rotational velocities, and atmospheric parameters for 37 T-type brown dwarfs observed with Keck/NIRSPEC. Using a Markov Chain Monte Carlo forward-modeling method, we achieve median precisions of 0.5 and 0.9 km s<jats:sup>−1</jats:sup> for radial and rotational velocities, respectively. All of the T dwarfs in our sample are thin-disk brown dwarfs. We confirm previously reported moving group associations for four T dwarfs. However, the lack of spectral indicators of youth in two of these sources suggests that these are chance alignments. We confirm two previously unresolved binary candidates, the T0+T4.5 2MASS J11061197+2754225 and the L7+T3.5 2MASS J21265916+7617440, with orbital periods of 4 and 12 yr, respectively. We find a kinematic age of 3.5 ± 0.3 Gyr for local T dwarfs, consistent with nearby late M dwarfs (4.1 ± 0.3 Gyr). Removal of thick-disk L dwarfs in the local ultracool dwarf sample gives a similar age for L dwarfs (4.2 ± 0.3 Gyr), largely resolving the local L dwarf age anomaly. The kinematic ages of local late M, L, and T dwarfs can be accurately reproduced with population simulations incorporating standard assumptions of the mass function, star formation rate, and brown dwarf evolutionary models. A kinematic dispersion break is found at the L4–L6 subtypes, likely reflecting the terminus of the stellar main sequence. We provide a compilation of precise radial velocities for 172 late M, L, and T dwarfs within ∼20 pc of the Sun.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The period versus mass diagrams (i.e., rotational sequences) of open clusters provide crucial constraints for angular momentum evolution studies. However, their memberships are often heavily contaminated by field stars, which could potentially bias the interpretations. In this paper, we use data from Gaia DR2 to reassess the memberships of seven open clusters with ground- and space-based rotational data, and present an updated view of stellar rotation as a function of mass and age. We use the Gaia astrometry to identify the cluster members in phase space, and the photometry to derive revised ages and place the stars on a consistent mass scale. Applying our membership analysis to the rotational sequences reveals that: (1) the contamination in clusters observed from the ground can reach up to ∼35%; (2) the overall fraction of rotational outliers decreases substantially when the field contaminants are removed, but some outliers persist; (3) there is a sharp upper edge in the rotation periods at young ages; (4) at young ages, stars in the 1.0–0.6<jats:italic>M</jats:italic> <jats:sub>⊙</jats:sub> range inhabit a global maximum of rotation periods, potentially providing an optimal window for habitable planets. Additionally, we see clear evidence for a strongly mass-dependent spin-down process. In the regime where rapid rotators are leaving the saturated domain, the rotational distributions broaden (in contradiction with popular models), which we interpret as evidence that the torque must be lower for rapid rotators than for intermediate ones. The cleaned rotational sequences from ground-based observations can be as constraining as those obtained from space.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present high-sensitivity CH 9 cm ON/OFF observations toward 18 extragalactic continuum sources that have been detected with OH 18 cm absorption in the Millennium survey with the Arecibo telescope. CH emission was detected toward 6 of the 18 sources. The excitation temperature of CH has been derived directly through analyzing all detected ON and OFF velocity components. The excitation temperature of CH 3335 MHz transition ranges from −54.5 to −0.4 K and roughly follows a log-normal distribution peaking within [−5, 0] K, which implies overestimation by 20% to more than 10 times during calculating CH column density by assuming the conventional value of −60 or −10 K. Furthermore, the column density of CH would be underestimated by a factor of 1.32 ± 0.03 when adopting local thermal equilibrium assumption instead of using the CH three hyperfine transitions. We found a correlation between the column density of CH and OH following log <jats:italic>N</jats:italic>(CH) = (1.80 ± 0.49) log <jats:italic>N</jats:italic>(OH) −11.59 ± 6.87. The linear correlation between the column density of CH and H<jats:sub>2</jats:sub> is consistent with that derived from visible wavelengths studies, confirming that CH is one of the best tracers of H<jats:sub>2 </jats:sub>components in diffuse molecular gas.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>With continuous measurements from space-borne cosmic-ray detectors such as AMS-02 and PAMELA, precise spectra of galactic cosmic rays over the 11 yr solar cycle have become available. For this study, we utilize proton and helium spectra below 10 GV from these missions from 2006 to 2017 to construct a cosmic-ray transport model for a quantitative study of the processes of solar modulation. This numerical model is based on Parker’s transport equation, which includes four major transport processes. The Markov Chain Monte Carlo method is utilized to search the relevant parameter space related to the drift and the diffusion coefficients by reproducing and fitting the mentioned observed spectra. The resulting best-fit normalized <jats:italic>χ</jats:italic> <jats:sup>2</jats:sup> is mainly less than 1. It is found that (1) when reproducing these observations the parameters required for the drift and diffusion coefficients exhibit a clear time dependence, with the magnitude of the diffusion coefficients anticorrelated with solar activity; (2) the rigidity dependence of the resulting mean free paths varies with time, and their rigidity dependence at lower rigidity can even have a larger slope than at higher rigidity; (3) using a single set of modulation parameters for each pair of observed proton and helium spectra, most spectra are reproduced within observational uncertainty; and (4) the simulated proton-to-helium flux ratio agrees with the observed values in terms of its long-term time dependence, although some discrepancy exists, and the difference is mostly coming from the underestimation of proton flux.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present radio observations (1–40 GHz) for 36 classical novae, representing data from over five decades compiled from the literature, telescope archives, and our own programs. Our targets display a striking diversity in their optical parameters (e.g., spanning optical fading timescales, <jats:italic>t</jats:italic> <jats:sub>2</jats:sub> = 1–263 days), and we find a similar diversity in the radio light curves. Using a brightness temperature analysis, we find that radio emission from novae is a mixture of thermal and synchrotron emission, with nonthermal emission observed at earlier times. We identify high brightness temperature emission (<jats:italic>T</jats:italic> <jats:sub> <jats:italic>B</jats:italic> </jats:sub> &gt; 5 × 10<jats:sup>4</jats:sup> K) as an indication of synchrotron emission in at least nine (25%) of the novae. We find a class of synchrotron-dominated novae with mildly evolved companions, exemplified by V5589 Sgr and V392 Per, that appear to be a bridge between classical novae with dwarf companions and symbiotic binaries with giant companions. Four of the novae in our sample have two distinct radio maxima (the first dominated by synchrotron and the later by thermal emission), and in four cases the early synchrotron peak is temporally coincident with a dramatic dip in the optical light curve, hinting at a common site for particle acceleration and dust formation. We publish the light curves in a machine-readable table and encourage the use of these data by the broader community in multiwavelength studies and modeling efforts.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Solar flare formation mechanisms and their corresponding predictions have commonly been difficult topics in solar physics for decades. The traditional forecasting method manually constructs a statistical relationship between the measured values of solar active regions and solar flares that cannot fully utilize the information related to solar flares contained in observational data. In this article, we first used neural-network methods driven by the measured magnetogram and magnetic characteristic parameters of the sunspot group to learn the prediction model and predict solar flares. The prediction fusion model is based on a deep neural network, convolutional neural network, and bidirectional long short-term memory neural network and can predict whether a sunspot group will have a flare event above class M or class C in the next 24 or 48 hr. The real skill statistics (TSS) and F1 scores were used to evaluate the performances of our fusion model. The test results clearly show that this fusion model can make full use of the information related to solar flares and combine the advantages of each independent model to capture the evolution characteristics of solar flares, which is a much better performance than traditional statistical prediction models or any single machine-learning method. We also proposed two frameworks, namely F1_FFM and TSS_FFM, which optimize the F1 score and TSS score, respectively. The cross validation results show that they have their respective advantages in the F1 score and TSS score.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We attempt to visually classify the morphologies of 18,190 molecular clouds, which are identified in the <jats:sup>12</jats:sup>CO(1–0) spectral line data over ∼450 deg<jats:sup>2</jats:sup> of the second Galactic quadrant from the Milky Way Imaging Scroll Painting project. Using the velocity-integrated intensity maps of the <jats:sup>12</jats:sup>CO(1–0) emission, molecular clouds are first divided into unresolved and resolved ones. The resolved clouds are further classified as nonfilaments or filaments. Among the 18,190 molecular clouds, ∼25% are unresolved, ∼64% are nonfilaments, and ∼11% are filaments. In the terms of the integrated flux of <jats:sup>12</jats:sup>CO(1–0) spectra of all 18,190 molecular clouds, ∼90% are from filaments, ∼9% are from nonfilaments, and the remaining ∼1% are from unresolved sources. Although nonfilaments are dominant in the number of the discrete molecular clouds, filaments are the main contributor of <jats:sup>12</jats:sup>CO emission flux. We also present the number distributions of the physical parameters of the molecular clouds in our catalog, including their angular sizes, velocity spans, peak intensities of <jats:sup>12</jats:sup>CO(1–0) emission, and <jats:sup>12</jats:sup>CO(1–0) total fluxes. We find that there is a systematic difference between the angular sizes of the nonfilaments and filaments, with the filaments tending to have larger angular scales. The H<jats:sub>2</jats:sub> column densities of them are not significantly different. We also discuss the observational effects, such as those induced by the finite spatial resolution, beam dilution, and line-of-sight projection, on the morphological classification of molecular clouds in our sample.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Observing nearby galaxies with submillimeter telescopes on the ground has two major challenges. First, the brightness is significantly reduced at long submillimeter wavelengths compared to the brightness at the peak of the dust emission. Second, it is necessary to use a high-pass spatial filter to remove atmospheric noise on large angular scales, which has the unwelcome side effect of also removing the galaxy’s large-scale structure. We have developed a technique for producing high-resolution submillimeter images of galaxies of large angular size by using the telescope on the ground to determine the small-scale structure (the large Fourier components) and a space telescope (Herschel or Planck) to determine the large-scale structure (the small Fourier components). Using this technique, we are carrying out the HARP and SCUBA-2 High Resolution Terahertz Andromeda Galaxy Survey (HASHTAG), an international Large Program on the James Clerk Maxwell Telescope, with one aim being to produce the first high-fidelity high-resolution submillimeter images of Andromeda. In this paper, we describe the survey, the method we have developed for combining the space-based and ground-based data, and we present the first HASHTAG images of Andromeda at 450 and 850 <jats:italic>μ</jats:italic>m. We also have created a method to predict the CO(<jats:italic>J</jats:italic> = 3–2) line flux across M31, which contaminates the 850 <jats:italic>μ</jats:italic>m band. We find that while normally the contamination is below our sensitivity limit, it can be significant (up to 28%) in a few of the brightest regions of the 10 kpc ring. We therefore also provide images with the predicted line emission removed.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Data from the Transiting Exoplanet Survey Satellite (TESS) have produced of the order of one million light curves at cadences of 120 s and especially 1800 s for every ∼27 day observing sector during its two-year nominal mission. These data constitute a treasure trove for the study of stellar variability and exoplanets. However, to fully utilize the data in such studies a proper removal of systematic-noise sources must be performed before any analysis. The TESS Data for Asteroseismology group is tasked with providing analysis-ready data for the TESS Asteroseismic Science Consortium, which covers the full spectrum of stellar variability types, including stellar oscillations and pulsations, spanning a wide range of variability timescales and amplitudes. We present here the two current implementations for co-trending of raw photometric light curves from TESS, which cover different regimes of variability to serve the entire seismic community. We find performance in terms of commonly used noise statistics meets expectations and is applicable to a wide range of intrinsic variability types. Further, we find that the correction of light curves from a full sector of data can be completed well within a few days, meaning that when running in steady state our routines are able to process one sector before data from the next arrives. Our pipeline is open-source and all processed data will be made available on the websites of the TESS Asteroseismic Science Operations Center and the Mikulski Archive for Space Telescopes.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Massive stars play key roles in many astrophysical processes. Deriving the atmospheric parameters of massive stars is important to understanding their physical properties, and thus the atmospheric parameters are key inputs to trace the evolution of massive stars. Here we report our work on adopting the data-driven technique called stellar label machine (<jats:monospace>SLAM</jats:monospace>) with the nonlocal thermal equilibrium TLUSTY synthetic spectra as the training data set to estimate the stellar parameters of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) optical spectra for early-type stars. We apply two consistency tests to verify this machine-learning method and compare stellar labels given by <jats:monospace>SLAM</jats:monospace> with the labels in the literature for several objects having high-resolution spectra. We provide the stellar labels of effective temperature (<jats:italic>T</jats:italic> <jats:sub>eff</jats:sub>), surface gravity (<jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}g$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn1.gif" xlink:type="simple" /> </jats:inline-formula>), metallicity ([M/H]), and projected rotational velocity (<jats:inline-formula> <jats:tex-math> <?CDATA $v\sin i$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>v</mml:mi> <mml:mi>sin</mml:mi> <mml:mi>i</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn2.gif" xlink:type="simple" /> </jats:inline-formula>) for 3931 and 578 early-type stars from the LAMOST low-resolution survey (LRS) and medium-resolution survey (MRS), respectively. To estimate the average statistical uncertainties of our results, we calculated the standard deviation between the predicted stellar label and the prelabeled published values from the high-resolution spectra. The uncertainties of the four parameters are <jats:italic>σ</jats:italic>(<jats:italic>T</jats:italic> <jats:sub>eff</jats:sub>) = 2185 K, <jats:inline-formula> <jats:tex-math> <?CDATA $\sigma (\mathrm{log}g)=0.29$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>σ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.29</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn3.gif" xlink:type="simple" /> </jats:inline-formula> dex, and <jats:inline-formula> <jats:tex-math> <?CDATA $\sigma (v\sin i)=11\,\mathrm{km}\,{{\rm{s}}}^{-1}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>σ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>v</mml:mi> <mml:mi>sin</mml:mi> <mml:mi>i</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>11</mml:mn> <mml:mspace width="0.25em" /> <mml:mi>km</mml:mi> <mml:mspace width="0.25em" /> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn4.gif" xlink:type="simple" /> </jats:inline-formula> for MRS, and <jats:italic>σ</jats:italic>(<jats:italic>T</jats:italic> <jats:sub>eff</jats:sub>) = 1642 K, <jats:inline-formula> <jats:tex-math> <?CDATA $\sigma (\mathrm{log}g)=0.25$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>σ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0.25</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn5.gif" xlink:type="simple" /> </jats:inline-formula> dex, and <jats:inline-formula> <jats:tex-math> <?CDATA $\sigma (v\sin i)=42\,\mathrm{km}\,{{\rm{s}}}^{-1}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>σ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>v</mml:mi> <mml:mi>sin</mml:mi> <mml:mi>i</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>42</mml:mn> <mml:mspace width="0.25em" /> <mml:mi>km</mml:mi> <mml:mspace width="0.25em" /> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn6.gif" xlink:type="simple" /> </jats:inline-formula> for LRS spectra, respectively. We note that the parameters of <jats:italic>T</jats:italic> <jats:sub>eff</jats:sub>, <jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}g$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn7.gif" xlink:type="simple" /> </jats:inline-formula>, and [M/H] can be better constrained using LRS spectra than using MRS spectra, most likely due to their broad wavelength coverage, while <jats:inline-formula> <jats:tex-math> <?CDATA $v\sin i$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>v</mml:mi> <mml:mi>sin</mml:mi> <mml:mi>i</mml:mi> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2dedieqn8.gif" xlink:type="simple" /> </jats:inline-formula> is constrained better by MRS spectra than by LRS spectra, probably due to the relatively accurate line profiles of MRS spectra.</jats:p>