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<jats:title>Abstract</jats:title> <jats:p>In this paper, the multiwavelength data from radio to X-ray bands for 2709 blazars in the 4FGL-DR3 catalog are compiled to calculate their spectral energy distributions using a parabolic equation <jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}(\nu {f}_{\nu })={P}_{1}{\left(\mathrm{log}\nu -{P}_{2}\right)}^{2}+{P}_{3}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>ν</mml:mi> <mml:msub> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:msup> <mml:mrow> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>log</mml:mi> <mml:mi>ν</mml:mi> <mml:mo>−</mml:mo> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msub> <mml:mrow> <mml:mi>P</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn1.gif" xlink:type="simple" /> </jats:inline-formula>. Some important parameters including spectral curvature (<jats:italic>P</jats:italic> <jats:sub>1</jats:sub>), synchrotron peak frequency (<jats:italic>P</jats:italic> <jats:sub>2</jats:sub>, <jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}{\nu }_{{\rm{p}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:msub> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn2.gif" xlink:type="simple" /> </jats:inline-formula>), and peak luminosity (<jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}{L}_{{\rm{p}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:msub> <mml:mrow> <mml:mi>L</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn3.gif" xlink:type="simple" /> </jats:inline-formula>) are obtained. Based on those parameters, we discussed the classification of blazars using the “Bayesian classification” and investigated some mutual correlations. We came to the following results. (1) Based on the Bayesian classification of synchrotron peak frequencies, the 2709 blazars can be classified into three subclasses, i.e., <jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}({\nu }_{{\rm{p}}}/\mathrm{Hz})\lt 13.7$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi>Hz</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>&lt;</mml:mo> <mml:mn>13.7</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn4.gif" xlink:type="simple" /> </jats:inline-formula> for low synchrotron peak blazars (LSPs), <jats:inline-formula> <jats:tex-math> <?CDATA $13.7\lt \mathrm{log}({\nu }_{{\rm{p}}}/\mathrm{Hz})\lt 14.9$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>13.7</mml:mn> <mml:mo>&lt;</mml:mo> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi>Hz</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>&lt;</mml:mo> <mml:mn>14.9</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn5.gif" xlink:type="simple" /> </jats:inline-formula> for intermediate synchrotron peak blazars (ISPs), and <jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}({\nu }_{{\rm{p}}}/\mathrm{Hz})\gt 14.9$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi>Hz</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>&gt;</mml:mo> <mml:mn>14.9</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn6.gif" xlink:type="simple" /> </jats:inline-formula> for high synchrotron peak blazars (HSPs), and there are 820 HSPs, 750 ISPs, and 1139 LSPs. (2) The <jats:italic>γ</jats:italic>-ray emission has the closest relationship with radio emission, followed by optical emission, while the weakest relationship is that with X-ray emission. The <jats:italic>γ</jats:italic>-ray luminosity is also correlated with the synchrotron peak luminosity. (3) There are strong positive correlations between the curvature (1/∣<jats:italic>P</jats:italic> <jats:sub>1</jats:sub>∣) and the peak frequency (<jats:inline-formula> <jats:tex-math> <?CDATA $\mathrm{log}{\nu }_{{\rm{p}}}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:msub> <mml:mrow> <mml:mi>ν</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac7debieqn7.gif" xlink:type="simple" /> </jats:inline-formula>) for all subclasses (FSRQs, (high, intermediate, and low) BL Lacertae objects). For different subclasses, the correlation slopes are different, which implies that there are different acceleration mechanisms and emission processes for different subclasses of blazars.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Convection is ubiquitous in stars and occurs under many different conditions. Here we explore convection in main-sequence stars through two lenses: dimensionless parameters arising from stellar structure and parameters that emerge from the application of mixing length theory. We first define each quantity in terms familiar to both the 1D stellar evolution community and the hydrodynamics community. We then explore the variation of these quantities across different convection zones, different masses, and different stages of main-sequence evolution. We find immense diversity across stellar convection zones. Convection occurs in thin shells, deep envelopes, and nearly spherical cores; it can be efficient or inefficient, rotationally constrained or not, transsonic or deeply subsonic. This atlas serves as a guide for future theoretical and observational investigations by indicating which regimes of convection are active in a given star, and by describing appropriate model assumptions for numerical simulations.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present a catalog of 948,216 stars with mass labels and a catalog of 163,105 red clump (RC) stars with mass and age labels simultaneously. The training data set is crossmatched from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR5, and high-resolution asteroseismology data, mass, and age are predicted by the random forest (RF) method or a convex-hull algorithm. The stellar parameters with a high correlation with mass and age are extracted and the test data set shows that the median relative error of the prediction model for the mass of the large sample is 3%, and for the mass and age of RC stars is 4% and 7%. We also compare the predicted age of RC stars with recent works and find that the final uncertainty of the RC sample could reach 18% for age and 9% for mass; meanwhile, the final precision of the mass for the large sample with different types of stars could reach 13% without considering systematics. All of this implies that this method could be widely used in the future. Moreover, we explore the performance of different machine-learning methods for our sample, including Bayesian linear regression and the gradient-boosting decision tree (GBDT), multilayer perceptron, multiple linear regression, RF, and support vector regression methods. Finally, we find that the performance of a nonlinear model is generally better than that of a linear model, and the GBDT and RF methods are relatively better.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We analyze 5108 AFGKM stars with at least five high-precision radial velocity points, as well as Gaia and Hipparcos astrometric data, utilizing a novel pipeline developed in previous work. We find 914 radial velocity signals with periods longer than 1000 days. Around these signals, 167 cold giants and 68 other types of companions are identified, through combined analyses of radial velocity, astrometry, and imaging data. Without correcting for detection bias, we estimate the minimum occurrence rate of the wide-orbit brown dwarfs to be 1.3%, and find a significant brown-dwarf valley around 40 <jats:italic>M</jats:italic> <jats:sub>Jup</jats:sub>. We also find a power-law distribution in the host binary fraction beyond 3 au, similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs. Our work also reveals nine substellar systems (GJ 234 B, GJ 494 B, HD 13724 b, HD 182488 b, HD 39060 b and c, HD 4113 C, HD 42581 d, HD 7449 B, and HD 984 b) that have previously been directly imaged, and many others that are observable at existing facilities. Depending on their ages, we estimate that an additional 10–57 substellar objects within our sample can be detected with current imaging facilities, extending the imaged cold (or old) giants by an order of magnitude.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We describe a large simulation of the stars to be observed by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The simulation is based on the <jats:monospace>TRILEGAL</jats:monospace> code, which resorts to large databases of stellar evolutionary tracks, synthetic spectra, and pulsation models, added to simple prescriptions for the stellar density and star formation histories of the main structures of the Galaxy, to generate mock stellar samples through a population synthesis approach. The main bodies of the Magellanic Clouds are also included. A complete simulation is provided for single stars, down to the <jats:italic>r</jats:italic> = 27.5 mag depth of the coadded Wide–Fast–Deep survey images. A second simulation is provided for a fraction of the binaries, including the interacting ones, as derived with the <jats:monospace>BinaPSE</jats:monospace> module of <jats:monospace>TRILEGAL</jats:monospace>. We illustrate the main properties and numbers derived from these simulations, including: comparisons with real star counts; the expected numbers of Cepheids, long-period variables, and eclipsing binaries; the crowding limits as a function of seeing and filter; the star-to-galaxy ratios. Complete catalogs are accessible through the NOIRLab Astro Data Lab, while the stellar density maps are incorporated in the LSST metrics analysis framework.</jats:p>