adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.477.3343S (fulltext)
K and log g = 3.55 model atmosphere calculated with different treatments of the hydrogen-line opacity, using the codes DETAIL (solid curve), SynthV_NLTE (dotted curve), and SYNTHMAG (dash-dotted curve). The position of the Ca ii IR triplet lines is marked with triangles. The vertical lines indicate the wavelength range covered by the Gaia spectra. Table 5 summarises the results of calcium abundance determination from different groups of reliable lines, i. e. lines of Ca i , lines of Ca ii in the visible spectrum range, and lines of Ca ii in the IR spectrum range 8912−27, 9890 Å. We also show in Fig. 9 the NLTE and LTE abundance differences between these groups of lines. For each star, NLTE leads to average abundances from lines of Ca ii in the visible and the IR spectrum range consistent within 0.1 dex, while the abundance discrepancy between these groups of lines varies from 0.6 dex down to −0.5 dex in LTE (Fig. 9 , top panel). The exception is 21 Peg, where NLTE leads to better agreement between these two groups of lines compared to LTE, however, a discrepancy of 0.2 dex remains. For HD 160762, the LTE abundance difference is not shown in Fig. 9 , because Ca ii 8912−27, 9890 Å lines are observed in emission and cannot be fitted in LTE. Figure 9. NLTE (filled circles) and LTE (open circles) abundance differences between Ca ii lines in the visible and IR spectral range (top panel) and between Ca i lines and Ca ii lines in the visible spectral range (bottom panel) in the investigated stars. Fig. 9 , bottom panel, shows the NLTE and LTE abundance differences between lines of Ca i and Ca ii in the visible spectrum range. For the majority of the investigated stars, an abundance difference = log A(Ca i )–log A(Ca ii , vis) does not exceed 0.1 dex in absolute value, independent of NLTE or LTE. However, for A-type stars, the absolute abundances from Ca i and Ca ii lines are higher in NLTE compared with LTE. 4.3 Calcium NLTE abundance determination with SME code As it was written in Section 3.1 , we calculated departure coefficients of Ca i and Ca ii levels for a grid of LLmodels atmospheres (Shulyak et al. 2004 ). Earlier, the same calculations were performed for grids of MARCS plane-parallel models (Gustafsson et al. 2008 ). These NLTE data are implemented in the current version of the SME (Spectroscopy Made Easy) package of automatic parameters and abundance determination based on high-resolution stellar spectra (Piskunov Valenti 2017 ). Atmospheric parameters and element abundances in SME are derived by the interpolation inside the grid models. For cool stars (MARCS model grids), the accuracy of authomatic NLTE Ca abundance determinations was tested in the paper by Piskunov et al. ( 2017 , see Figs 1 and 3 of this paper). Here, we tested the automatic method for hotter stars with HD 32115 and HD 72660. For HD 32115, the SME abundances are −5.68±0.07 (Ca i ) and −5.60±0.14 (Ca ii ). For HD 72660, the corresponding values are −5.44±0.13 (Ca i ) and
Paper: 2017A&A...606A.125S (acknowledgments)
The authors acknowledge support by Allegro, the European ALMA Regional Center node in The Netherlands, and expert advice from Luke Maud in particular. This work was partially supported by grants from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Research School for Astronomy (NOVA). This paper makes use of the following ALMA data: ADS/JAO.ALMA# 2013.1.01268.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ.
Paper: 2020ApJ...891...99A (fulltext)
of the potential field energy E p ( t ) (thick curve with diamonds) and the free energy E f ( t ) (thin curve with crosses), both normalized to their maximum, the loop elimination ratio q e , and cross-correlation coefficient CCC. Note that the potential field energy and the free energy are uncorrelated. 5. Interpretation and Discussion 5.1. Instrumental Oscillatory Effects As these observations report for the first time oscillations in the free (magnetic) energy of solar flares, we discuss first possible effects of instrumental origin. The range of observed oscillations (in 11 flare events) cover the range of P ≈ 10–22 minutes and have a mean and standard deviation of As the SDO spacecraft is positioned in the Lagrangian point L1, there are no eclipsing time intervals of the spacecraft orbit that could modulate the observed flux. Because the Sun is rotating, any tracking of a particular heliographic position of a solar flare or an active region needs to be interpolated between two pixels, which could possibly introduce a pixel-step modulation. We estimate the timescale of a pixel shift based on the synodic solar rotation rate,which amounts for a synodic rotation time of P syn = 27.27 days, an HMI pixel size of Δ x pix = 05, and an average solar radius of R ⊙ ≈ 960′′, to a time interval of Δ t pix = 3.2 minutes for HMI, or Δ t pix = 3.9 minutes for AIA pixels. The beat frequency between the two instruments amounts to a timescale ofAlthough the beat frequency is close to the frequency of the observed mean oscillation period (Equation ( 17 )), subpixel variations of the sensitivity (or modulation transfer function) of AIA are unlikely to explain the observed oscillations, as the width of the point-spread function (PSF) is substantially wider (15) than the pixel size (06) (Boerner et al. 2012 ; Grigis et al. 2012 ; Lemen et al. 2012 ). The PSF may also imply a slight subpixel variation of the sensitivity of CCD pixels (between the pixel center and pixel edge). We calculated this variation by pixel-wise superposition of PSFs, and found an interpixel variation of ≲3%. Moreover, the rms variation in the AIA flat field is found to be of order 2% (Boerner et al. 2012 ). The PSF was also measured during a lunar transit, finding that the PSF of AIA/ SDO is better by a factor of two than the EUV telescope on board TRACE (Poduval et al. 2013 ). In summary, no instrumental effect is known from AIA or HMI that could modulate the observed free energy evolution E f ( t ) with periods of P ≈ 10–20 minutes (J. Lemen et al. 2020, private communication). Incidentally, we did choose a cadence of 3 minutes for the AIA images with the VCA4-NLFFF code, while a cadence of 12 minutes is available from the W-NLFFF code using HMI data (Aschwanden 2019c ). The cadence of 12 minutes is close to the mean observed period of P obs = 15.1 ± 3.9 minutes, and thus the observed oscillations are fully resolved with the 3-minute cadence of the VCA4-NLFFF code only, while they
Paper: 2020MNRAS.493.5369P (fulltext)
region. The distribution under the hypothesis that HMXBs are displaced from their parent star-forming region (the grey histogram) is extended compared with the distribution under the null hypothesis (the yellow histogram). We next quantify the observed displacement of HMXBs, which is attributed to kicks after an asymmetric SN explosion during the formation of the compact object (Fryer Kalogera 1997 ). The contribution of natal kicks is treated as a Gaussian smearing function of standard deviation σ that broadens the spatial distribution of HMXBs compared with their birth distribution. We simulate the vertical distribution of HMXBs from the mid-plane as the convolution P ( z |σ, z sfr ) between this Gaussian and the exponential distribution of SFR (equation 2 ) with a scale height of z sfr = 330 pc .: We apply the maximum likelihood method for finding the best estimate of the Gaussian smearing parameter σ. The likelihood function L ( z |σ, z sfr ) derived from the convolution P is where N = 41 is the number of HMXBs and z i is the vertical distance of the i th HMXB from the mid-plane of NGC 55. We perform 100 000 iterations and in order to account for the estimated AGN contamination, in each iteration our HMXB sample consists of all ‘verified HMXBs’ (Table 3 ) as well as randomly selected combinations of 22 out of 37 ‘candidate HMXBs’ (Table 3 ). In Fig. 10 we show the likelihood of the dispersion of the Gaussian smearing function (normalized to the maximum likelihood). The maximum at σ = 0.57 ± 0.07 kpc is the difference between the scale height of the vertical distribution of HMXBs and the vertical distribution of star-forming activity. In Fig. 11 we show the simulated vertical distribution of HMXBs (black dashed line) as the convolution between a Gaussian smearing function of standard deviation σ = 0.57 kpc (green line) that represents the contribution of natal kicks, on top of their birth distribution of 0.33 kpc (red line). Figure 10. The likelihood of the dispersion of the Gaussian smearing function (normalized to the maximum likelihood σ), described in Section 3.2 . The maximum at σ = 0.57 kpc is the scale height corresponding to the displacement between HMXBs and star-forming regions. Figure 11. Simulated vertical distribution of HMXBs (black dashed) as the convolution between a Gaussian smearing function of standard deviation σ = 0.57 kpc (green) that represents the contribution of kicks, on top of the their birth distribution of 0.33 kpc (red). The grey and yellow histograms show the observed vertical distribution of candidate and verified HMXBs, respectively. 3.3 Constraining the displacement of HMXBs The result of the previous section mainly provides an upper limit on the scale height of HMXBs. There are other factors such as contamination, incompleteness and in situ formation of HMXBs that would bias the result of this analysis toward larger scale heights. We investigate these factors in an effort to constrain the lower limit on
Paper: 2015ApJ...804...14T (acknowledgments)
This work was supported by the U.S. National Science Foundation (NSF) through award AST-1109257. D.C.J. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1401708. J.C.P. is supported by an NSF Astronomy and Astrophysics Fellowship under award AST-1302774. This work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the MWA comes from the NSF (awards: AST-0457585, PHY-0835713, CAREER-0847753, and AST-0908884), the Australian Research Council (LIEF grants LE0775621 and LE0882938), the U.S. Air Force Office of Scientific Research (grant FA9550-0510247), and the Centre for All-sky Astrophysics (an Australian Research Council Centre of Excellence funded by grant CE110001020). Support is also provided by the Smithsonian Astrophysical Observatory, the MIT School of Science, the Raman Research Institute, the Australian National University, and the Victoria University of Wellington (via grant MED-E1799 from the New Zealand Ministry of Economic Development and an IBM Shared University Research Grant). The Australian Federal government provides additional support via the Commonwealth Scientific and Industrial Research Organisation (CSIRO), National Collaborative Research Infrastructure Strategy, Education Investment Fund, and the Australia India Strategic Research Fund, and Astronomy Australia Limited, under contract to Curtin University. We acknowledge the iVEC Petabyte Data Store, the Initiative in Innovative Computing and the CUDA Center for Excellence sponsored by NVIDIA at Harvard University, and the International Centre for Radio Astronomy Research (ICRAR), a Joint Venture of Curtin University and The University of Western Australia, funded by the Western Australian State government.
Paper: 2015ApJ...805...40M (acknowledgments)
The authors thank Dara Norman for her efforts in performing all observations made during the DECam science verification phase, including those of Abell 3128 used in this study. The authors also thank the anonymous referee for suggesting many useful improvements to the analysis. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaborating institutions: Argonne National Lab, University of California Santa Cruz, University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, University of Chicago, University College London, DES-Brazil consortium, University of Edinburgh, ETH-Zurich, Fermi National Accelerator Laboratory, University of Illinois at Urbana-Champaign, Institut de Ciencies de l’Espai, Institut de Fisica d’Altes Energies, Lawrence Berkeley National Lab, Ludwig-Maximilians Universitat, University of Michigan, National Optical Astronomy Observatory, University of Nottingham, Ohio State University, University of Pennsylvania, University of Portsmouth, SLAC National Lab, Stanford University, University of Sussex, and Texas AM University. Funding for DES, including DECam, has been provided by the U.S. Department of Energy, National Science Foundation, Ministry of Education and Science (Spain), Science and Technology Facilities Council (UK), Higher Education Funding Council (England), National Center for Supercomputing Applications, Kavli Institute for Cosmological Physics, Financiadora de Estudos e Projetos, Fundao Carlos Chagas Filho de Amparo a Pesquisa, Conselho Nacional de Desenvolvimento Cientfico e Tecnolgico and the Ministrio da Cincia e Tecnologia (Brazil), the German Research Foundation-sponsored cluster of excellence “Origin and Structure of the universe,” and the DES collaborating institutions.
Paper: 2017ApJ...838..114K (acknowledgments)
The authors thank the referee for their careful and constructive comments which improved our paper. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This paper makes use of the following ALMA data: ADS/JAO.ALMA# 2013.1.00187.S. ALMA is a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. This research also made use of NASA’s Astrophysics Data System (ADS) Abstract Service and the IDL Astronomy Library hosted by the NASA Goddard Space Flight Center. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France (Wenger et al. 2000 ). The JCMT has historically been operated by the Joint Astronomy Center on behalf of the Science and Technology Facilities Council of the United Kingdom, the National Research Council of Canada and the Netherlands Organisation for Scientific Research. Additional funds for the construction of SCUBA-2 were provided by the Canada Foundation for Innovation. The identification number for the programme under which the SCUBA-2 data used in this paper is MJLSG32. Facilities: ALMA , Spitzer , JCMT , Herschel . Software: CASA.
Paper: 2018A&A...616A..87M (fulltext)
Earth-Sun distance by changing the pixel scale accordingly. After the telemetric problems which occurred in January 2008, the images of the synoptic program were binned by a factor of two. Fortunately, this is not a problem in this work, since the images taken before this date were binned by a factor of two for analysis. So the size of all the analyzed images is 2048 × 1024 pixels. The program ended in February 2016, when both cameras broke down. All images were calibrated with the standard solar reduction software distributed under SSWIDL, which corrects for bad pixels, readout errors, dark current and flat field. A 1024 × 512 pixel subfield of a calibrated image is shown in Fig. 1 . In each image, the left and the right square subfields are analyzed separately; this helps us, by comparing the results in each of them, to disentangle solar variations from instrumental effects, which is crucial for our investigation (see the next section and Muller et al. 2011 ). Fig. 1. A 1024 × 512 (114′′ × 57′′) subfield of a 2048 × 1024 (228′′ × 114′′) blue continuum HINODE/SOT image from the synoptic program. 3. Granulation images analysis As mentioned in Sect. 2 , a significant number of images from the synoptic program appear to be blurred to a varying degree. In order to get a reliable variation, the sharpest images must be selected, and only these ones used for further analysis. About 25% of the images have been actually removed because of blurring. The method of selection is described in detail in Muller et al. (2011) and only a summary is presented here. In a first step, the images containing dark columns, sunspots pores, abnormal granulation 1 were removed visually; the images which clearly appear out of focus were also removed visually. In the second step the granulation contrast and scale of the selected images were derived from the power spectrum ( Muller et al. 2007 , 2011 ), separately in the left and right half fields of view of size 114′′ × 114′′. The root mean square of the intensity fluctuations, Irms, which, for simplicity, we refer to as contrast, is the square root of the integral, above the noise level, of the power spectrum in the granulation range, between 0′′̣3 (in order to remove the high frequency noise) and 4′′ (in order to remove non-granular larger scale fluctuations, like the 5-min oscillations and the mesogranulation). The scale is defined as the inverse of the mean wavenumber of the power spectrum in the granulation range, converted in arcseconds. The contrast and the scale derived in this way are about 12% and about 1′′̣6 (1160 km) respectively. When the contrast of the left field-of-view (FOV) of the selected images is plotted versus the contrast of the right FOV, they appear correlated: when the contrast is large (small) in one FOV it is also large (small) in the other FOV (see Fig. 5 in Muller et al. 2011 , which shows the correlation for the images selected in January 2007). But as the granulation evolves independently
Paper: 2017MNRAS.464.1220H (acknowledgments)
The authors wish to thank the anonymous referee for his insightful suggestions. NH thanks Evgeni Grishin for pointing out the matched filter technique to him. AC acknowledges support from CIDMA strategic project UID/MAT/04106/2013.
Paper: 2018ApJ...863..195Y (fulltext)
The Kepler Space Telescope was launched in 2009 March and its main scientific goal was to search for the terrestrial planets using the transit method (Borucki et al. 2010 ). The asteroseismology program is an extremely important byproduct of the Kepler mission (Gilliland et al. 2010 ). Due to the unprecedented photometric precision on the order of a few μ mag with a high duty-cycle, and continuous collection of data over about 4 years, Kepler data allows one to study the internal structure and physical process of stars with an ultra-high precision than that obtained with any telescope on earth (Koch et al. 2010 ). The Kepler telescope is in a 372.5 day Earth-trailing orbit and its field of view covers 105 deg 2 in the constellations of Cygnus and Lyra. Kepler provides two observation strategies for its targets, i.e., short cadence (SC) and long cadence (LC) with sampling times of 58.85 s and 29.424 minutes, respectively. The SC observations are adopted for the primary mission as they are able to obtain more data points during a candidate object passing in front of its host star. However, most of the targets are observed with LC in order to maximize the target’s number through long exposure time. Consequently, more than 170,000 targets are available in LC observations (Jenkins et al. 2010 ). More than 2000 δ Sct stars have been detected through the Kepler mission (Balona Dziembowski 2011 ; Balona 2014 ; Bowman et al. 2016 ). One of them, KIC 5950759, has been classified as a double-mode high-amplitude δ Sct (HADS) star by analyzing the LC data (Bowman et al. 2016 ). The δ Sct stars are typical A- and F-type variable stars with period from 0.02 to 0.25 days. They are usually on or above the main sequence on the H-R diagram, and are situated in the lower classical instability strip (Breger 2000 ). Their oscillations are mainly driven by the κ mechanism that occurs in the partial ionization zone of He ii (Gautschy Saio 1995 ; Breger 2000 ). The δ Sct stars can oscillate in both pressure and gravity modes (Breger 1995 , 2000 ; Balona 2016 ). As a subclass of δ Sct stars, the HADS stars usually pulsate with a light amplitude larger than 0.3 mag and generally rotate slowly with v sin i ≤ 30 km s −1 (Breger 2000 ). Compared with the low-amplitude δ Sct stars, the HADS stars possess a more restrictive instability strip with a width in temperature of about 300 K and tend to shift to a lower temperature with evolution (McNamara 2000 ). Lee et al. ( 2008 ) reveal that only about 0.24 percent of the stars suited in the δ Sct region belong to HADS stars. The majority of HADS stars are typically young and metal-rich Population I stars; some have been confirmed to be SX Phe variables, and are Population II metal-deficient stars (Breger 2000 ; Balona Nemec 2012 ). In general, the HADS stars pulsate with only one or two modes (e.g., AE UMa, Niu et al. 2017 ; YZ Boo, Yang et al. 2018 ; etc.), and most of their pulsations belong to radial modes. AI Vel (Walraven
Paper: 2019ApJ...880L..28N (fulltext)
to the lepton number transport; indeed, the neutrino contribution is just a few percent. This is simply because the number density of electron/positron is much larger than that for the neutrinos. The neutrino diffusion in the fluid rest frame is even subtler, although it does exist (see the blue line in Figure 7 ). It should be noted, however, that the momentum feedback from neutrinos is still comparable to the momentum imparted by the matter-pressure gradient, as discussed above. This occurs because the lateral matter-pressure gradient is partially compensated by the temperature asymmetry: the temperature in the northern hemisphere is ∼1% higher than in the southern hemisphere. Although the temperature asymmetry is much smaller than that of Y e , it still reduces the total asymmetry in the matter pressure substantially, because the baryon and photon pressure are dominant in the total pressure. Figure 7. Radial distributions of angle-average lateral number flux of measured in the laboratory frame (red) and fluid rest frame (blue). The counter part of electron/positron gas corresponds to the black line in the panel. It is also important to note that Y e decreases via deleptonization as the matter moves toward the north pole. The Y e -depleted matter then submerges in the PNS envelope near the north pole. When it hits the PNS core, it is deflected and flows southward along the surface of the PNS core, increasing Y e this time by ν e absorptions. The matter emerges again to the PNS surface by the local PNS convection near the south pole. Although the strong downflow induced by the dislocation of PNS tends to suppress the convection around the north pole, the convection activity continues to occur in the vicinity of the south pole and supplies the Y e -rich material to the PNS envelope there. This closes the cycle and creates the coherent lateral circular motion, with the Y e asymmetry sustained. Then the circulation will continue until the PNS convection shuts off or the neutrinos in PNS have dried up, both of which may take more than a few seconds. If this is in fact the case, the PNS may be accelerated to more than a hundred km s −1 , which is the typical proper motion velocity of pulsars. Finally, we give some comments on other works on the asymmetric Y e distributions. Glas et al. ( 2018 ) found a circular motion in the envelope of PNS in their 3D simulations, which produced the LESA phenomenon. They speculated that the thermal instability of spherical shells discussed by Chandrasekhar ( 1961 ) may be responsible for the sustained asymmetric Y e distribution. It seems, however, that the circular motion found in their study was not as coherent as what we observed in our study; indeed, they look more like a superposition of some smaller eddies instead (see Figure 6 in Glas et al. 2018 ). Powell Müller ( 2018 ) also observed LESA in their simulations and suggested that LESA is in effect a manifestation of PNS convections under a different