Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>The Vera C. Rubin Observatory will carry out its Legacy Survey of Space and Time (LSST) with a single-exposure depth of <jats:italic>r</jats:italic> ∼ 24.7 and an anticipated baseline of 10 yr, allowing access to the Milky Way’s old halo not only deeper than, but also with a longer baseline and better cadence than, e.g., PS1 3<jats:italic>π</jats:italic>. This will make the LSST ideal to study populations of variable stars such as RR Lyrae stars (RRL). Here, we address the question of observing strategy optimization of LSST, as survey footprint definition, single-visit exposure time, as well as the cadence of repeat visits in different filters are yet to be finalized. We present metrics used to assess the impact of different observing strategies on the reliable detectability and classification of standard candle variable stars, including detection of amplitude, period, and phase modulation effects of RRL (the so-called Blazhko effect), by evaluating metrics for simulated potential survey designs. So far, due to the depths and cadences of typical all-sky surveys, it has been nearly impossible to study this effect on a larger sample. All-sky surveys with relatively few observations over a moderately long baseline allow only for fitting phase-folded RRL light curves, thus integrating over the complete survey length and hiding any information regarding possible period or phase modulation during the survey. On the other hand, surveys with cadences fit to detect slightly changing light curves usually have a relatively small footprint. LSST’s survey strategy, however, will allow for studying variable stars in a way that makes population studies possible.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Current and future optical and near-infrared wide-field surveys have the potential to find kilonovae, the optical and infrared counterparts to neutron star mergers, independently of gravitational-wave or high-energy gamma-ray burst triggers. The ability to discover fast and faint transients such as kilonovae largely depends on the area observed, the depth of those observations, the number of revisits per field in a given time frame, and the filters adopted by the survey; it also depends on the ability to perform rapid follow-up observations to confirm the nature of the transients. In this work, we assess kilonova detectability in existing simulations of the Legacy Survey of Space and Time strategy for the Vera C. Rubin Wide Fast Deep survey, with focus on comparing rolling to baseline cadences. Although currently available cadences can enable the detection of &gt;300 kilonovae out to ∼1400 Mpc over the 10 year survey, we can expect only 3–32 kilonovae similar to GW170817 to be recognizable as fast-evolving transients. We also explore the detectability of kilonovae over the plausible parameter space, focusing on viewing angle and ejecta masses. We find that observations in redder <jats:italic>izy</jats:italic> bands are crucial for identification of nearby (within 300 Mpc) kilonovae that could be spectroscopically classified more easily than more distant sources. Rubin’s potential for serendipitous kilonova discovery could be increased by gain of efficiency with the employment of individual 30 s exposures (as opposed to 2 × 15 s snap pairs), with the addition of red-band observations coupled with same-night observations in <jats:italic>g</jats:italic> or <jats:italic>r</jats:italic> bands, and possibly with further development of a new rolling-cadence strategy.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Remote imaging of plasmas in the heliosphere and very local interstellar medium is possible with energetic neutral atoms (ENAs), created through the charge exchange of protons with interstellar neutral atoms. ENA observations collected by the Interstellar Boundary Explorer (IBEX) revealed two distinctive sources. One source is the globally distributed flux (GDF), which extends over the entire sky and varies over large spatial scales. The other source encompasses only a narrow circular band in the sky and is called the IBEX ribbon. Here, we utilize the observed difference in spatial scales of these two ENA sources to separate them. We find that linear combinations of spherical harmonics up to degree <jats:inline-formula> <jats:tex-math> <?CDATA ${{\ell }}_{\max }=3$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="italic">ℓ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>max</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2f47ieqn1.gif" xlink:type="simple" /> </jats:inline-formula> can reproduce most of the ENA fluxes observed outside the ribbon region. We use these combinations to model the GDF and the difference between the observed fluxes and the GDF yields estimation of the ribbon emission. The separated ribbon responds with a longer time delay to the solar wind changes than the GDF, suggesting a more distant source of the ribbon ENAs. Moreover, we locate the direction of the maximum plasma pressure based on the GDF. This direction is 17°.2 ± 0°.5 away from the upwind direction within the plane containing the interstellar flow and interstellar magnetic field vectors. This deflection is consistent with the expected position of the maximum external pressure at the heliopause. The maps with separated ribbon and GDF are posted concurrently with this paper and can be used to further study these two sources.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The Sun's motion through the interstellar medium leads to an interstellar neutral (ISN) wind through the heliosphere. Several ISN species, including He, moderately depleted by ionization are observed with pickup ions and directly imaged. Since 2009, analyzed Interstellar Boundary Explorer (IBEX) observations returned a precise 4D parameter tube associated with the bulk velocity vector and the temperature of ISN flow distribution. This 4D parameter tube is typically expressed in terms of the ISN speed, the inflow latitudinal direction, and the temperature as a function of the inflow longitudinal direction and the local flow Mach number. We have used IBEX observations and those from other spacecraft to reduce statistical parameter uncertainties: <jats:inline-formula> <jats:tex-math> <?CDATA ${V}_{\mathrm{ISN}\infty }=25.99\pm 0.18$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>25.99</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.18</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn1.gif" xlink:type="simple" /> </jats:inline-formula> km s<jats:sup>−1</jats:sup>, <jats:inline-formula> <jats:tex-math> <?CDATA ${\lambda }_{\mathrm{ISN}\infty }=75\buildrel{\circ}\over{.} 28\pm 0\buildrel{\circ}\over{.} 13$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>λ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>75</mml:mn> <mml:mo>.°</mml:mo> <mml:mn>28</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.°</mml:mo> <mml:mn>13</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn2.gif" xlink:type="simple" /> </jats:inline-formula>, <jats:inline-formula> <jats:tex-math> <?CDATA ${\beta }_{\mathrm{ISN}\infty }={\rm{-5}}\buildrel{\circ}\over{.} 200\pm 0\buildrel{\circ}\over{.} 075$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>β</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mi mathvariant="normal">−5</mml:mi> <mml:mo>.°</mml:mo> <mml:mn>200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.°</mml:mo> <mml:mn>075</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn3.gif" xlink:type="simple" /> </jats:inline-formula>, and <jats:inline-formula> <jats:tex-math> <?CDATA ${T}_{\mathrm{ISN}\infty }=7496\pm 172$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>7496</mml:mn> <mml:mo>±</mml:mo> <mml:mn>172</mml:mn> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn4.gif" xlink:type="simple" /> </jats:inline-formula> K. IBEX ISN viewing is restricted almost perpendicular to the Earth–Sun line, which limits observations in ecliptic longitude to ∼130° ± 30° and results in relatively small uncertainties across the IBEX parameter tube but large uncertainties along it. Operations over the last three years enabled the IBEX spin axis to drift to the maximum operational offset (7°) west of the Sun, helping to break the ISN parameter degeneracy by weakly crossing the IBEX parameter tubes: the range of possible inflow longitudes extends over the range <jats:inline-formula> <jats:tex-math> <?CDATA ${\lambda }_{\mathrm{ISN}\infty }=75\buildrel{\circ}\over{.} {28}_{-2.21}^{+2.27}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>λ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>75</mml:mn> <mml:mo>.°</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>28</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>2.21</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>2.27</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn5.gif" xlink:type="simple" /> </jats:inline-formula> and the corresponding range of other ISN parameters is <jats:inline-formula> <jats:tex-math> <?CDATA ${V}_{\mathrm{ISN}\infty }={25.99}_{-1.76}^{+1.86}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>25.99</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1.76</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>1.86</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn6.gif" xlink:type="simple" /> </jats:inline-formula> km s<jats:sup>−1</jats:sup>, <jats:inline-formula> <jats:tex-math> <?CDATA ${\beta }_{\mathrm{ISN}\infty }={\rm{-5}}\buildrel{\circ}\over{.} {200}_{-0.085}^{+0.093}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>β</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mi mathvariant="normal">−5</mml:mi> <mml:mo>.°</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>200</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.085</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.093</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn7.gif" xlink:type="simple" /> </jats:inline-formula>, and <jats:inline-formula> <jats:tex-math> <?CDATA ${T}_{\mathrm{ISN}\infty }={7496}_{-1528}^{+1274}$?> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>T</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>ISN</mml:mi> <mml:mo>∞</mml:mo> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>7496</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1528</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>1274</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjsac2fa9ieqn8.gif" xlink:type="simple" /> </jats:inline-formula> K. This enhances the full <jats:italic>χ</jats:italic> <jats:sup>2</jats:sup> analysis of ISN parameters through comparison with detailed models. The next-generation IBEX-Lo sensor on IMAP will be mounted on a pivot platform, enabling IMAP-Lo to follow the ISN flow over almost the entire spacecraft orbit around the Sun. A near-continuous set of 4D parameter tube orientations on IMAP will be observed for He and for O, Ne, and H that cross at varying angles to substantially reduce the ISN flow parameter uncertainties and mitigate systematic uncertainties (e.g., from ionization effects and the presence of secondary components) to derive the precise parameters of the primary and secondary local interstellar plasma flows.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The Transiting Exoplanet Survey Satellite (TESS) space telescope is collecting continuous, high-precision optical photometry of stars throughout the sky, including thousands of RR Lyrae stars. In this paper, we present results for an initial sample of 118 nearby RR Lyrae stars observed in TESS Sectors 1 and 2. We use differential image photometry to generate light curves and analyze their mode content and modulation properties. We combine accurate light-curve parameters from TESS with parallax and color information from the Gaia mission to create a comprehensive classification scheme. We build a clean sample, preserving RR Lyrae stars with unusual light-curve shapes, while separating other types of pulsating stars. We find that a large fraction of RR Lyrae stars exhibit various low-amplitude modes, but the distribution of those modes is markedly different from those of the bulge stars. This suggests that differences in physical parameters have an observable effect on the excitation of extra modes, potentially offering a way to uncover the origins of these signals. However, mode identification is hindered by uncertainties when identifying the true pulsation frequencies of the extra modes. We compare mode amplitude ratios in classical double-mode stars to stars with extra modes at low amplitudes and find that they separate into two distinct groups. Finally, we find a high percentage of modulated stars among the fundamental mode pulsators, but also find that at least 28% of them do not exhibit modulation, confirming that a significant fraction of stars lack the Blazhko effect.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We first derive the stellar population properties: age and metallicity for ∼43,000 low redshift galaxies in the DR7 of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey, which have no spectroscopic observations in the Sloan Digital Sky Survey (SDSS). We employ a fitting procedure based on the small-scale features of galaxy spectra so as to avoid possible biases from the uncertain flux calibration of the LAMOST spectroscopy. We show that our algorithm can successfully recover the average age and metallicity of the stellar populations of galaxies down to signal-to-noise ratio ≥5 through testing on both mock galaxies and real galaxies comprising LAMOST and their SDSS counterparts. We provide a catalog of the age and metallicity for ∼43,000 LAMOST galaxies online. As a demonstration of the scientific application of this catalog, we present the Holmberg effect on both age and metallicity of a sample of galaxies in galaxy pairs.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The PHANGS program is building the first data set to enable the multiphase, multiscale study of star formation across the nearby spiral galaxy population. This effort is enabled by large survey programs with the Atacama Large Millimeter/submillimeter Array (ALMA), MUSE on the Very Large Telescope, and the Hubble Space Telescope (HST), with which we have obtained CO(2–1) imaging, optical spectroscopic mapping, and high-resolution UV–optical imaging, respectively. Here, we present PHANGS-HST, which has obtained NUV–<jats:italic>U</jats:italic>–<jats:italic>B</jats:italic>–<jats:italic>V</jats:italic>–<jats:italic>I</jats:italic> imaging of the disks of 38 spiral galaxies at distances of 4–23 Mpc, and parallel <jats:italic>V</jats:italic>- and <jats:italic>I</jats:italic>-band imaging of their halos, to provide a census of tens of thousands of compact star clusters and multiscale stellar associations. The combination of HST, ALMA, and VLT/MUSE observations will yield an unprecedented joint catalog of the observed and physical properties of ∼100,000 star clusters, associations, H <jats:sc>ii</jats:sc> regions, and molecular clouds. With these basic units of star formation, PHANGS will systematically chart the evolutionary cycling between gas and stars across a diversity of galactic environments found in nearby galaxies. We discuss the design of the PHANGS-HST survey and provide an overview of the HST data processing pipeline and first results. We highlight new methods for selecting star cluster candidates, morphological classification of candidates with convolutional neural networks, and identification of stellar associations over a range of physical scales with a watershed algorithm. We describe the cross-observatory imaging, catalogs, and software products to be released. The PHANGS high-level science products will seed a broad range of investigations, in particular, the study of embedded stellar populations and dust with the James Webb Space Telescope, for which a PHANGS Cycle 1 Treasury program to obtain eight-band 2–21 <jats:italic>μ</jats:italic>m imaging has been approved.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data have been collected in the COSMOS field. This paper describes the collection, processing, and analysis of these new imaging data to produce a new reference photometric redshift catalog. Source detection and multiwavelength photometry are performed for 1.7 million sources across the 2 deg<jats:sup>2</jats:sup> of the COSMOS field, ∼966,000 of which are measured with all available broadband data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, <jats:sc>The Farmer</jats:sc>, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The <jats:italic>i</jats:italic> &lt; 21 sources have subpercent photometric redshift accuracy and even the faintest sources at 25 &lt; <jats:italic>i</jats:italic> &lt; 27 reach a precision of 5%. Finally, these results are discussed in the context of previous, current, and future surveys in the COSMOS field. Compared to COSMOS2015, it reaches the same photometric redshift precision at almost one magnitude deeper. Both photometric catalogs and their photometric redshift solutions and physical parameters will be made available through the usual astronomical archive systems (ESO Phase 3, IPAC-IRSA, and CDS).</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/vladlanda/Low-Dimensional-Convolutional-Neural-Network-For-Solar-Flares-GOES-Time-Series-Classification" xlink:type="simple">https://github.com/vladlanda</jats:ext-link>.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The limiting temporal resolution of a time-domain survey in detecting transient behavior is set by the time between observations of the same sky area. We analyze the distribution of visit separations for a range of Vera C. Rubin Observatory cadence simulations. Simulations from families v1.5–v1.7.1 are strongly peaked at the 22 minute visit pair separation and provide effectively no constraint on temporal evolution within the night. This choice will necessarily prevent Rubin from discovering a wide range of astrophysical phenomena in time to trigger rapid follow-up. We present a science-agnostic metric to supplement detailed simulations of fast-evolving transients and variables and suggest potential approaches for improving the range of timescales explored.</jats:p>