adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...826...96P (fulltext)
light curve, which may help discriminate between different explosion scenarios. 4.1. Circumstellar Setup For some guidance in what sort of mass distributions to consider, we turn to studies of post-merger density distributions as presented by Pakmor et al. ( 2012 ), Schwab et al. ( 2012 ), and Shen et al. ( 2012 ), and summarized in Figure 9 . These calculations roughly show how circumstellar material evolves from immediately following the merger (∼seconds), to many viscous times (∼hours), to many thermal times (∼1000 years), respectively. Even over this large range of timescales, this comparison demonstrates that a profile is a good description of all of these density profiles. This is because the large heating near the base of the material leads to a roughly constant flux and a radiative envelope with an n = 3 polytropic index. Motivated by this, here we consider similar density distributions for extended material of , so that the mass of the extended material is simplywhere is the radius of the underlying WD and is a normalization constant for the density profile. Strictly speaking, the models that evolve for large periods of time following the merger are not expected to necessarily make SNe Ia, and instead lead to accretion-induced collapse (Saio Nomoto 1998 ; Shen et al. 2012 ; Schwab et al. 2015 ). Nevertheless, in the spirit of this work, we will still explore a variety of density distributions with different values of M e and R e , even for those cases that might not reach an SN Ia, to see what the corresponding observational signatures should be. Figure 9. Example density profiles showing a diversity of possible outcomes for the circumstellar material following a double WD merger. These profiles are a WD (green dashed line), equatorial material during a violent merger (Pakmor et al. 2012 , purple dot–dashed line), the nearly spherical profile after the material has viscously relaxed (Schwab et al. 2012 , red solid line), and the material after it has expanded to a red supergiant-like size (Shen et al. 2012 ; blue dotted line). 4.2. Extended Material Light Curves Our first set of circumstellar calculations are shown in Figure 10 . Here, we set the external mass to and vary the outer radius from up to . The explosion is triggered in the same way as all previous models, and the 56 Ni mixing is kept low with a boxcar width of , that is, the same as the red curves in Figures 1 , 2 , 5 , 7 , and 8 . The impact of the extended material on the light curve is dramatic, resulting in a bright first peak from the shock cooling of this extended material. This is reminiscent of the double-peaked light curves of some SNe IIb, which have also been attributed to extended material around the progenitor star (Bersten et al. 2012 ; Nakar Piro 2014 ). Furthermore, the roughly parabolic shape is similar to the analytic light curve expected for this material (Piro 2015 ). The observation of such a feature would be extremely useful for constraining the
Paper: 2016A&A...591A.143G (fulltext)
width of 0.26 Å for H and [OIII]. As an example, two performed fits are presented in Fig. 2 , including the central spaxel (with a ) and the lowest S/N spaxel () of the spatially binned MUSE observations. To quantify the impact of our fitting parameters on our results, we performed similar analyses but varying one parameter at a time. Firstly, we used additive polynomials of the 4th order, commonly used for fitting spectra on narrower, SAURON-like, wavelength range. Secondly, we masked all the potential emission lines from the galaxy, e.g. H, [OIII], H, [NII] and [SII]. Finally, we fitted the MUSE spectra on a restricted wavelength range (50005500 Å) that contains the Fe lines and the Mg triplet, which on their own strongly constrain the derived stellar kinematics. In all cases, we measured similar kinematics structures, with mean differences of only a few kilometers per second for the two first LOSVD moments, and , and mean differences of 0.001 to 0.02 for and . We should emphasise, however, that there are still clear systematic errors mostly regarding the third- and fourth-order moments and . The corresponding maps show some odd features, for example a clear offset of the level at the edges of the field (see Figs. 3 and 4 ). This is not surprising considering the original strong variation of the sky background (see Sect. 2.2 ), between exposures and also within the field of view of individual exposures (residual gradient). Assuming symmetric (odd or even) moment maps for and (with respect to the photometric minor axis), we estimate these systematic errors to 0.02 for and 0.05 for . 3.2. Stellar kinematics results Fig. 4 One-dimensional curve of the first four line-of-sight velocity distribution (LOSVD) moments: a) stellar radial velocity, V; b) stellar velocity dispersion, ; c) third-order Gauss-Hermite moment, ; d) fourth-order Gauss-Hermite moment, . The points were extracted from the two-dimensional maps of the respective quantities (see Fig. 3 ), by taking the light-weighted mean value within a 2 wide slit, aligned with the major axis (orange points), and offset by +8 (green points) and +20 (blue points) along the minor axis. We present in Fig. 3 the stellar kinematics maps of NGC 3115, obtained as described in Sect. 3.1 from our MUSE reduced cube. The mean radial stellar velocity map (Fig. 3 , panel a) exhibits a fast-rotating structure, similar to a thin disc, embedded in a slower spheroidal component, but still rotating fairly rapidly. The thin disc structure has an apparent vertical extent (with respect to the major axis) widening from a few arcseconds near the centre to (i.e. 930 pc) at about two effective radii (i.e. 70 or 3.4 kpc) where it becomes blended with the spheroidal part of the galaxy. The thinness of the kinematic disc also confirms that NGC 3115 is viewed almost edge-on. Along the major axis, the mean radial stellar velocity curve exhibits a double maximum (see Fig. 4 , panel a) with a first steep rise to 150 km s
Paper: 2018MNRAS.481.4300S (acknowledgments)
ACKNOWLEDGEMENTS The authors are grateful to Joop Schaye and the anonymous referee for helpful comments and suggestions. Furthermore, we would like to thank Volker Springel for providing the particle data of snapshot 54 of the MXXL simulation. JS thanks Daniele Sorini for helpful discussions and acknowledges financial support from the Heidelberg Graduate School of Fundamental Physics (HGSFP). CMB acknowledges receipt of a Leverhulme Trust Research Fellowship. This work was supported by the Science and Technology Facilities Council [ST/L00075X/1]. We used the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-Infrastructure capital grant ST/K00042X/1, STFC capital grants ST/H008519/1 and ST/K00087X/1, STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure.
Paper: 2017MNRAS.468.2382V (fulltext)
select dimensions corresponding to non-zero diagonal values of . A similar selection of dimensions on gives of size below the dimension of the oversampled discrete Fourier grid, thus leading to the dimensionality reduction operator We see that the full measurement operator preserves the null space of , following from , thus retaining any original NSP. The diagonal dominated nature of has already been shown, which leads to an appropriate modelling of the noise. Also, applying is fast owing to the sparsity of . Note that in the context of this dimensionality reduction with , the weighting matrix , in fact designed for optimal weighting of the embedded visibilities, also operates as uniform weighting. Indeed, the diagonal values of are a measure of the density of continuous visibilities at each discrete grid point. As with , we also approximate the initial noise covariance matrix in this case by assuming , leading to the dimensionality reduction . This variant is included in comparisons of reconstruction quality. 3.4 Feature comparison A comparison of the different dimensionality reduction methods , and is shown in Table 1 . We note in the listing that provides a good combination of the desired final dimension, the guarantees for compressed sensing-based imaging to reconstruct images and a largely diagonal noise covariance matrix that enables us to embed this technique in the convex optimization algorithm we employ for imaging. embeds the data to image size while maintaining any original NSP of , and has a fast implementation; however, it fails to appropriately model the noise and is therefore less suitable for the minimization problem which requires i.i.d. Gaussian embedded noise to enable a simple 2 -norm data fidelity term. continues to maintain the NSP of and the i.i.d. Gaussian properties of the noise, which are essential for image reconstruction using our convex optimization algorithms. However, the data reduction is limited to a size . As mentioned in Sections 3.2 and 3.3 , for each of , and , an attempt is also made to further approximate the initial noise covariance matrix by the identity matrix in order to render the application of the respective dimensionality reduction methods even faster. Equations ( 19 ), ( 22 ) and ( 25 ) are then simplified to , and , respectively. However, this approximation is seen to be inappropriate, leading to poorer modelling of the noise and consequently lowering image reconstruction quality. , , and their respective variants are used to reduce dimensionality before performing image reconstruction in different settings and with varying data sizes. 3.5 Further reduction by thresholding As a conservative dimensionality reduction method, would embed to a final data size under the assumption that there are very few zero-valued singular values of , within limits of numerical precision, and all corresponding singular vectors are thus necessary to retain the information content of . Similarly, embeds to a size
Paper: 2015ApJ...803...23D (acknowledgments)
Support for Program number GO–12600 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. B.B. received partial support from NSF grant AST-0808201.
Paper: 2019MNRAS.488..559Z (fulltext)
zero frequency lag component. Similarly, the autocorrelation of the (unfiltered) 2019-03-07 epoch (left-hand panels of Fig. 7 ) shows a strong zero frequency lag peak centred at 5.447 ± 0.008 h. Note that the quoted uncertainties are estimates based on the sample distribution of periods from the simple top-hat pulse model described in Sections 3.1.1 and 3.1.2 . Figure 7. Two-dimensional autocorrelation function, and the corresponding zero frequency lag components for the 2019-03-07 (left-hand panels) and 2018-10-02 epochs (right-hand panels). Dashed lines indicate the time lag of the major peak in the autocorrelation, which measures the periodicity of the pulses. The dash–dotted lines in the autocorrelation of 2019-03-07 are spaced every h from the zero time lag component, and around the major peak at a time lag of 5.447 h, and show the quasi-periodic nature of the pulses 19-1A, B, C and 19-2A, B, and C. The pulse periods measured between the two epochs are consistent with one another, and consistent with the 5.4432 ± 0.0072 h period measured by Barnes et al. ( 2017 ). We note that this disambiguates which component of the L726-8AB binary the emission originates from – the primary component BL Ceti (L726 − 8A) is also known to be active at radio frequencies (e.g. Gary et al. 1982 ), and is separated from UV Ceti by ∼4′′, well below the ∼15′′ resolution of our ASKAP observations. However, BL Ceti has a rotational period of 5.832 ± 0.012 h (Barnes et al. 2017 ). Along with the h periodicity revealed by the autocorrelation of the 2019-03-07 observation, there are also three minor peaks in the autocorrelation spaced every h from the zero time lag component, and to either side of the primary period of h. This gives strong evidence for the quasi-periodic nature of the faint pulses 19-1A, B, C and 19-2A, B, C. 3.3 Comparison of the two epochs We find notable similarities between the pulses occurring in both epochs when inspecting the dynamic spectra side by side. Most striking is the faint pulses occurring in both epochs: 18-1A, 18-2A, and 19-1A, B, C, 19-2A, B, C – all these pulses have very similar drift rates (, virtually indistinguishable within the bandwidth of our observations), durations (∼12 min), and are similar in their average flux densities (∼40–50 mJy). However, in the 2019-03-07 epoch, three faint pulses with equal drift rates occur every rotation, where as in 2018-10-02 only one faint, negative-drifting pulse occurs per rotation. Pulses 19-1B and 19-2B are morphologically most similar to the faint pulses 18-1A and 18-2A occurring once per rotation in the 2018-10-02 epoch, 18-1A and 18-2A. Furthermore, the time difference between 19-1B and 19-1D, and 19-2B and 19-2D (∼60 min) is similar to the time difference between 18-1A and 18-1B (∼50 min). While there are some morphological similarities between the bright pulses in both epochs, the bright burst in 2018-10-02 has a slower bulk drift rate, a longer duration, and overall a more complex
Paper: 2019MNRAS.486.3849Z (acknowledgments)
ACKNOWLEDGEMENTS This work is supported by the National Natural Science Foundation of Chinaunder Grant No. 11474079, the Science and Technology Platform and Talent Team Program of Guizhou province (No. 2017-5610), and the Major Research Project of innovative Group of Guizhou province (No. 2018-013).
Paper: 2015MNRAS.451.2174T (acknowledgments)
Numerical computations and analyses were carried out on Cray XC30 and computers at Center for Computational Astrophysics, National Astronomical Observatory of Japan, respectively. This work was supported in part by Grant-in-Aid for Scientific Research (B) by JSPS (15H03638).
Paper: 2018ApJ...863...67G (acknowledgments)
We thank Timothy Brandt for helpful comments and discussion on earlier drafts of this paper. We would also like to thank the anonymous referee for their comments in improving the clarity of the paper. S.G. acknowledges the National Science Foundation Graduate Research Fellowship under grant No. DGE1745303. C.C. acknowledges support from NASA grant AST-1313280, and the Packard Foundation. This paper is based upon work supported by the National Aeronautics and Space Administration (NASA) under Contract No. NNG16PJ26C issued through the WFIRST Science Investigation Teams Program. Some of this material is based upon work supported by the National Science Foundation under Award No. 1501205. We would also like to thank Bill Paxton and the MESA community for making this work possible.
Paper: 2015MNRAS.446.1551S (acknowledgments)
The SAMI Galaxy Survey is based on observation made at the Anglo-Australian Telescope (AAT). The Sydney–AAO Multiobject Integral field spectrograph (SAMI) was developed jointly by the University of Sydney and the Australian Astronomical Observatory. The SAMI input catalogue is based on data taken from the Sloan Digital Sky Survey, the GAMA Survey and the VST ATLAS Survey. The SAMI Galaxy Survey is funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO, CE1100010200) and other participating institutions. The SAMI Galaxy Survey website is http://sami-survey.org . We thank the dedicated staff at the AAT whose support in interfacing SAMI with AAOmega is invaluable. MSO and JTA acknowledge the funding support from the Australian Research Council through a Super Science Fellowship (ARC FS110200023 and FS110200013). SMC acknowledges the support of an ARC future fellowship (FT100100457). ISK is the recipient of a John Stocker Postdoctoral Fellowship from the Science and Industry Endowment Fund (Australia). LC acknowledges support under the Australian Research Councils Discovery Projects funding scheme (DP130100664). CJW acknowledges support through the Marie Curie Career Integration Grant 303912. This research made use of astropy , a community-developed core python package for Astronomy (Astropy Collaboration, 2013).
Paper: 2019MNRAS.482.3989R (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to thank the anonymous reviewer for his comments, and Jerry Sellwood, Jim Stone, and Charles Gammie for advice and encouragement. This work was partially funded by STFC grant ST/L000636/1.
Paper: 2015MNRAS.450..807L (acknowledgments)
We thank the Korea Institute for Advanced Study for providing computing resources (KIAS Center for Advanced Computation Linux Cluster System). We thank Seokcheon Lee and Graziano Rossi for many helpful discussions.
Paper: 2016MNRAS.458.2122D (fulltext)
require high sodium abundances at low oxygen abundances. In the following, we list the three quantitative discrepancies between AGB nucleosynthesis yields and observed abundance patterns that are solved if we allow for smaller mass-loss rates. (1) The oxygen depletion in the most massive super-AGBs is not large enough. To address this problem, we have invoked deep mixing in the giant progenitors of the most extreme population, a mixing which should be favoured by the large helium abundance of these stars (D'Antona Ventura 2007 ), and we adopt this solution also here (see Section 4.1 ). Nevertheless, smaller mass-loss rates in the super-AGB phase would lead to the required large depletion of oxygen and would provide an additional possible solution to this problem. (2) Apart from 24 Mg depletion, some other HBB proton captures require a longer time than allowed by the mass-loss rates chosen in our Ventura et al. ( 2013 ) paper and in the previous computation. In particular, the silicon increase by proton captures on aluminium is found in Ventura et al. ( 2013 ) at the level of ∼0.05 dex, while ∼0.15 dex are required by Carretta ( 2015 ) data for NGC 2808. Also in this case, a larger production requires longer evolutionary times. (3) Taking at face value the modest δ Y implied by the observational width of the MS of some clusters in recent observations, Bastian, Cabrera-Ziri Salaris ( 2015 ) found a discrepancy between the range covered by data in O–Na plane and the range allowed by models. Below, we discuss some limitation of this analysis, and other possible ways to overcome this problem. However, taking this result at face value, also in this case the difficulty is removed if the models have a larger oxygen depletion for a given T HBB , and this can be achieved assuming smaller mass-loss rates during the AGB evolution. If models with smaller T HBB can describe well the O–Na data, they are progeny of lower initial masses and have smaller Y yield, as required by the comparisons in Bastian et al. ( 2015 ), solving the discrepancy. The mass-loss rates in the AGB models by Ventura et al. ( 2013 ) were originally calibrated on a comparison with the luminous lithium-rich stars in the Magellanic Clouds (Ventura, D'Antona Mazzitelli 2000 ), but this calibration is also able to provide a positive sodium yield, in spite of the contemporary burning of sodium and oxygen. If we allow for a mass-loss rate smaller, say by a factor of 3 – that is, we triple the time for p-captures in the AGB envelope – we may find a solution for the four problems listed above, at the expenses of burning too much sodium (see e.g. Ventura et al. 2011 ; Doherty et al. 2014 ). The problem of the sodium destruction is examined in detail in Renzini et al. ( 2015 ), where it is suggested that a reduction by a factor of 5 of the reaction rate 23 Na(p,α) 20 Ne would allow sodium to be destroyed at a slower rate, so that its global yield may be in the range of the observed sodium
Paper: 2020A&A...639A..58M (fulltext)
in the Upper Scorpius region, assuming only an age evolution from one region to another. Fig. 6. Comparison between median and percentiles of the mass accretion rates as a function of disk mass for the Lupus and Upper Scorpius regions ( left ), and for the Upper Scorpius region and the expectations from viscous models at 1 Myr and 8 Myr ( right ). 5.2. The impact of photoevaporation and variable accretion The observations are even more discrepant from models predicting the Ṁ acc − M disk relation by means of both viscous evolution and internal photoevaporation. As shown by Somigliana et al. (2020) , the effect of photoevaporation is the reduction in the number of accreting targets at low disk masses and mass accretion rates to an extent that, by ∼10 Myr, only a fraction of massive disks still survive. This is not observed here, where we see low-mass disks with high Ṁ acc . It is unclear whether this disagreement is due to the fact that the models assume only one stellar mass and two fixed mass-loss rates, or whether this is an issue of internal photoevaporation models in general. On the other hand, external photoevaporation would predict that the disks have low mass, while still low values of M disk / Ṁ acc ∼ 0.1 Myr (see Fig. 4 , and Rosotti et al. 2017 ; Sellek et al. 2020 ), more in line with what is observed here. In this context, it is worth mentioning that the environment of Upper Scorpius is different than the one of Chamaeleon I and Lupus, having more nearby massive stars (e.g., de Zeeuw et al. 1999 ). In such an environment, the effect of external photoevaporation could have been relevant for the evolution of disks, possibly more than dynamical interactions (e.g., Winter et al. 2018 ). Whether this effect has been dominant for the evolution of the disks observed here is still an open question. Further modeling is mandatory here, but it is nevertheless puzzling how the mass accretion rates can be retained for such long time with so little disk mass available. A possible solution to the fact that the accretion rates measured here are high given the measured M disk might be variable accretion. However, studies in younger star-forming regions have shown that, in general, typical variation of Ṁ acc are 0.4 dex in most disks (e.g., Costigan et al. 2014 ; Venuti et al. 2014 ), with only a small fraction of targets showing extreme variability of Ṁ acc > 1 −2 dex (e.g., Audard et al. 2014 ). We could imagine that the variability is larger in the old region of Upper Scorpius, but in this case we could expect a larger dispersion in accretion rates than in younger regions, which, however, was not observed. Further studies on variability in older star-forming regions is needed. The mass-budget issue given the observed Ṁ acc and M disk is therefore important. Indeed, under the assumption that Ṁ acc is constant with time, these high values of Ṁ acc would imply that over the lifetime of the disk 10 −8 M ⊙ yr −1 ×10 7 yr ∼ 0.1 M ⊙ of disk
Paper: 2019MNRAS.490..752T (acknowledgments)
ACKNOWLEDGEMENTS We acknowledge useful comments and suggestions from the anonymous referee. This study is supported by the National Natural Science Foundation of China (Nos 11873085, 11673059, and 11521303), the Chinese Academy of Sciences (Nos QYZDB-SSW-SYS001, KJZD-EW-M06-01, and Y9XB016001), and the Yunnan Province (Nos 2017HC018, 2018FB005).
Paper: 2018A&A...619A..55M (acknowledgments)
We would like to thank the referee for the comments and suggestions. The authors thank very much Richard Harrison for the careful reading and comments on the manuscript. C. M. and L. X. thank the National Natural Science Foundation of China (41474150 and 41627806). The AIA and HMI data are provided courtesy of NASA/SDO and corresponding science teams. The AIA and HMI data have been retrieved using the Stanford University’s Joint Science Operations Centre/Science Data Processing Facility. M.M. and K.G. thank the ISSI Bern for the support to the team “Observation-Driven Modelling of Solar Phenomena”.