adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...848...66Z (fulltext)
t b to be 23.0. Figure 3. Multi-band light-curve fitting of SN 2013dy using Equation ( 1 ) for estimating t 0 . The value of is fixed to be 1.90 and t b is fixed to be 23.0 during the fitting procedure. Diamond-shaped data points are included in the fitting, while cross-shaped ones are excluded. After applying the t 0 fit to each SN for each band, and for each case with different ( X varies from 0 to −7), we compare the estimated t 0 with the “known t 0 .” The result is shown in Figure 4 , where the upper panel shows the cases for each SN and each filter with different . For a certain , if there is more than one filter observed for an SN (as shown in Table 1 , except for the three Kepler SNe that were only observed with one broadband filter), a mean value of t 0 from all filters is also calculated. The bottom panel shows the histogram of the t 0 offsets compared with the “known t 0 ”; each filter is color-coded. Figure 4. Difference of first-light time t 0 estimated from Equation ( 1 ) fitting with the real t 0 . The upper panel shows the cases for each SN and each filter with different , arbitrarily shifted along the ordinate for clarity. The bottom panel shows the histogram of t 0 offsets for each filter, also arbitrarily shifted along the abscissa for clarity. In general, the smaller the X in (with earlier data), the smaller the offset. The B (or g ) band has the smallest offset, and appears to not change with X , with a 1 σ systematic error of ±0.7 days. Three important and clear conclusions can be drawn from Figure 4 . First, the offset between the fitting t 0 and the “known t 0 ” becomes smaller when earlier data are included—namely, the smaller the X value in , the smaller the offset. But once X reaches −3, then even earlier data do not help much for t 0 estimation. Second, B interestingly has the smallest offset, and no matter when starting with , the offset is always within ±1.5 days; we estimate a 1 σ systematic error of ±0.7 days by adopting only the B -band estimate of the t 0 offset. Third, the mean offset with multiple filters is similar to that of the B band; thus, for t 0 estimation, one should use either the mean value or the value estimated from B alone. Technically, it is not surprising to see why the B band gives the best estimate of t 0 ; in other bands, the peak is usually broader than that of B , making B better for estimating t 0 . Because of this, in the following we only adopt the t 0 estimate from B , even if the SN was observed with multiple filters. We will also add a systematic error of ±0.7 days for the t 0 estimated from B -band fitting. In addition, Figure 4 shows that the fixed value of is appropriate, giving a mean t 0 offset of 0.1 ± 0.7 days for the B -band, very close to 0 as expected. Furthermore, we performed two other sets of fitting with the value fixed to be 1.50 and 2.30 (respectively), and found t 0 offset by 1.2 ± 0.8 and −0.8 ± 0.7 for B , confirming that the fixed value of is appropriate. 2.4. A
Paper: 2019ApJ...870...84C (acknowledgments)
The project was partially supported by National Science Centre, Poland, grant no. 2017/26/A/ST9/00756 (Maestro 9). V.K. acknowledges Czech Science Foundation no. 17-16287S.
Paper: 2018MNRAS.477.4403W (acknowledgments)
ACKNOWLEDGEMENTS This work is based upon observations obtained with the Georgia State University (GSU) Center for High Angular Resolution Astronomy (CHARA) Array at Mount Wilson Observatory, and the University of Hawai‘i 2.2-m telescope on Maunakea. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. The CHARA Array is supported by the National Science Foundation under Grants Nos. AST-1211929 and AST-1411654. Institutional support has been provided from the GSU College of Arts and Sciences and the GSU Office of the Vice President for Research and Economic Development. Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation. The research was supported by the ASTERISK project (ASTERoseismic Investigations with SONG and Kepler) funded by the European Research Council (Grant agreement no.: 267864). TRW and VSA acknowledge the support of the Villum Foundation (research grant 10118). DH acknowledges support by the Australian Research Council’s Discovery Projects funding scheme (project number DE140101364) and support by the NASA Grant NNX14AB92G issued through the Kepler Participating Scientist Program. LC is supported by the Australian Research Council Future Fellowship FT160100402. MJI was supported by the Australian Research Council Future Fellowship FT130100235. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. This research made use of NASA's Astrophysics Data System Bibliographic Services; the SIMBAD data base (Wenger et al. 2000 ) and VizieR catalogue access tool (Ochsenbein, Bauer Marcout 2000 ), both operated at CDS, Strasbourg, France; and the Jean-Marie Mariotti Center Aspro2 service 1 .
Paper: 2019A&A...626A..10G (acknowledgments)
This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ); data from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation; and the SIMBAD and Vizier databases, operated at the CDS, Strasbourg, France. We thank the anonymous referee for his/her useful comments and suggestions. ESV thanks “Estrategia de Sostenibilidad, Universidad de Antioquia”. DGD acknowledge support from the Plataforma de Movilidad Estudiantil y Académica of Alianza del Pacífico and its chilean headquarters in the Agencia Chilena de Coperación Internacional para el Desarrollo - AGCID, as well as the support from call N° 785 of 2017 of the colombian Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS.
Paper: 2019MNRAS.482.1545S (fulltext)
sample of SNe included in this data release, our adopted classifications and dates of peak brightness (if available), and the values we used to perform redshift and dust-reddening corrections. Table 3 provides a description of the spectra included in our sample. Our research group utilizes a database of observations and associated metadata (the SNDB; Silverman et al. 2012a ) to orchestrate our observational campaigns, to facilitate access to and analysis of our large repository of data, and to share those data publicly – all of the spectra described herein are publicly available through the SNDB web page (see footnote 1). Table 3. The spectra presented in this paper. Notes. (Truncated; full table available online) α Days relative to peak brightness, if date of peak is constrained. β Estimated average resolution across the spectrum. γ Original publications for already-published spectra. δ This historical spectrum was observed under unique conditions – we exclude from our analyses below but we include it here for completeness. The majority of SNe in this sample have only a few spectral observations each, though several well-observed examples of each subtype are also included – Fig. 3 shows the distribution of observation count across SNe in our sample. Our sample includes spectra taken within days of core collapse out to several years [for those SNe that show ongoing, luminous interaction with circumstellar material (CSM)]. Fig. 4 illustrates the phase coverage for spectra of those SNe having either a photometrically or SNID -measured peak-brightness date. Figure 3. The distribution of spectral observation counts for SNe in our sample. Totals are binned over five-spectra intervals, and we group spectra of peculiar subtypes and of stripped-envelope SNe without a clearcut subtype classification together into the ‘other’ category. Figure 4. The phase distribution of spectra having a measured date of peak brightness, grouped by spectral type and binned over 50-d intervals. Spectra having photometric peak dates are shown in solid, while the lighter shade also includes those with snid -determined peak dates. This plot is truncated at 450 days; a small number of observations exist at later phases. 4 RESULTS Below we examine the evolution, relative to date of peak brightness, of several different observational signatures in order to probe the kinematics and composition of the ejecta and to compare to previous results. In summary, we confirm that the O i λ7774 feature has both a higher pseudo-equivalent width (pEW) and a higher characteristic velocity in SNe Ic than in SNe Ib or IIb, supporting the interpretation that SNe Ic are stripped further down toward the C-O core than SNe Ib/IIb but they explode with similar energies. However, we do not find supporting evidence for that scenario from an analysis of the nebular spectra: one may expect that SNe Ic, having lost more of their envelope but exploding at similar energies, would exhibit wider full width at
Paper: 2018MNRAS.476.4362S (acknowledgments)
Acknowledgements The authors would like to thank the referee Francisco-Shu Kitaura and Stefan Gottlöber for providing comments that helped clarified the paper as well as Sergey Pilipenko for very useful discussions. JS acknowledges support from the Astronomy ESFRI and Research Infrastructure Cluster ASTERICS project, funded by the European Commission under the Horizon 2020 Programme (GA 653477) as well as from the ‘l’Oréal-UNESCO Pour les femmes et la Science’ and the ‘Centre National d’études spatiales (CNES)’ postdoctoral fellowship programmes. ET was supported by ETAg grants IUT40-2, IUT26-2 and by EU through the ERDF CoE grant TK133. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( http://www. gauss-centre.eu ) for providing computing time on the GCS Supercomputers SuperMUC at LRZ Munich and Jureca at JSC Juelich.
Paper: 2017A&A...606A..58D (fulltext)
in their targets. Hence, we can conclude that in stars belonging to the transitional branch, the cycle length and the rotation period are uncorrelated. 5.2. Relationship between <italic>P</italic><inline-formula specific-use="simple-math"><sub>cyc</sub></inline-formula> and global stellar parameters We investigated how the cycle lengths are related to the global stellar parameters of our targets. We evaluated the degree of the correlation between and a given parameter by computing the Spearman rank-order correlation coefficient () ( Press et al. 1992 ). This coefficient is a nonparametric measure of the monotonicity of the relationship between two datasets. A value of close to 0 implies that the two datasets are poorly correlated, whereas the closer is to 1 the stronger the monotonic relationship between the two variables. The statistical significance of a given value can be evaluated by computing the two-sided -value, i.e., the probability under the null hypothesys that two invesigated datasets are non-monotonically correlated ( Press et al. 1992 ). In Table 2 we reported the Spearman correlation coefficients between the length of the primary cycles and the various stellar parameters. The corresponding -values are also reported. The values of are very close to 0 in all the cases indicating a poor correlation between and the different variables. 5.3. Relationship between <inline-formula specific-use="simple-math">⟨ <italic>IQR</italic> ⟩</inline-formula> and global stellar parameters As remarked in Sect. 2, the index is proportional to the amplitude of the rotational modulation signature and is related to the non-axisymmetric part of the spot distribution. The amplitude of rotational modulation is a widely used activity index and can be regarded as a robust proxy of the surface magnetic activity. García et al. (2013) demonstrated that the variance of the TSI, computed on a 60-d sliding window, closely mimics the variations of the 10.7 cm radio flux that in turn is a good indicator of the solar magnetic activity (see Bruevich et al. 2014 , for details). We computed the Spearman coefficient between and different stellar parameters to investigate how the mean level of the stellar surface magnetic activity is linked to the stellar properties. In Table 3 we reported the values of for the different parameters. In this case, values are significantly higher then those computed for . A good correlation () is seen between and , , , and . The sign of correlation is positive for , and and negative for and . In Fig. 9 we reported versus the various stellar parameters for which a significant correlation is seen (). Despite the scatter in the data, the different plots clearly show that tends to increase with , , and and has a negative correlation with and . Table 2 Spearman correlation coefficient between and different stellar parameters. Notes. The parameters are ordered by decreasing . Table 3 Spearman correlation coefficient between and different stellar
Paper: 2019ApJ...877L..18Z (fulltext)
x -axis in all the panels are the observation wavelength. The horizontal dashed line in the lower right panel shows where τ obs = 0.6. On the other hand, we caution that the DSHARP opacity adopted here is for compact spheres without porosity, and that the mixture of different compositions are handled with the Bruggeman rule. Changing composition or porosity can change the opacity and albedo dramatically, as alluded to in Birnstiel et al. ( 2018 ). For example, the dust becomes more reflective with more water ice. Porosity reduces the resonant opacity features, making the opacity curve smoother. Carbonaceous materials have a substantial effect on the dust opacity. The maximum absorption feature can be shifted to much longer wavelengths than 2 πs max with some choices of carbonaceous materials. On the other hand, the peak of albedo is still at 2 πs max with different choices of carbonaceous materials (Birnstiel et al. 2018 ), making both χ and α measurements (Section 3.3 ) less sensitive to the choices of carbonaceous materials. More detailed calculations exploring different dust compositions, porosities, and size distributions are needed in future. 3.3. Dust Properties and the Spectral Index <italic>α</italic> Just based on the DSHARP opacity, we want to explore how we can constrain the dust size distributions using the measured χ and the spectral index α . The spectral index α iswhere the approximation on the right is with the Rayleigh–Jeans approximation. Clearly, the measured α depends on totally different quantities in the optically thick and thin regimes. In the more familiar optically thin regime, it depends on how the absorption coefficient changes with the frequency. With κ abs ∝ ν β , α is β + 2. In the optically thick regime, α depends on χ . Assuming that χ ∝ ν γ , α is γ + 2. As shown in Figure 1 , ω changes monotonically with 1/ χ that changes as λ γ . Thus, if the albedo ω increases with wavelength ( γ > 0), α = γ + 2 measured at this wavelength span will be larger than 2, and vice versa. To be more specific, if the disk is optically thick, we can use the measured α from observations to derive γ . Then we can constrain the relationship between ω ν 1 and usingwhich is based on Equation ( 13 ). Figure 9 shows how κ abs , χ and the spectral index α change with different dust populations for different ALMA and VLA bands, using the DSHARP opacity. The panels in the second row show χ in the optically thick limit. The third row shows α in the optically thin limit (thus denoted as α thin ), while the bottom row is α in the optically thick limit ( α thick ). Here, we simply use β + 2 or γ + 2 to calculate α , and thus do not consider the deviation of the blackbody radiation from the Rayleigh–Jeans limit (Huang et al. 2018a ). χ and both α s have sharp transitions around s max ∼ λ /2 π . When s ≪ λ /2 π , scattering is not important and χ = 1, α thick = 2, α thin ∼ 4. When s ≳ λ /2 π , χ becomes less than 1, α thick becomes larger than 2,
Paper: 2017ApJ...834..161S (fulltext)
of the mean pitch angle remains similar over the whole energy range . However, evolution timescales and peak amplitudes change as expected. Next, the influence of the amplitude of the magnetic field of the plasma wave has been studied (Section 5 ). Again, simulation results and analytic predictions have been compared. Since QLT works with the assumption that the wave’s amplitude is negligible compared to the background magnetic field B 0 , it could be expected that the analytic model fails to describe simulations with larger . We have shown that this is the case, although the model still yields a surprisingly good approximation. However, the magnetostatic QLT model fails to describe the interaction of particles and several waves correctly, as further simulations have shown. In a study of the trajectories of individual particles in a simulation with only one plasma wave we have found that particles follow closed trajectories in pitch angle space, i.e., that their motion is deterministic rather than diffusive (Section 6 ). This situation only changes if several plasma waves with different wave numbers or frequencies are included. 7.2. Conclusions In the fully kinetic PiC simulations the propagating waves lead to changes of the absolute momenta p of the individual particles in the plasma frame. This momentum diffusion is not included in the magnetostatic QLT model, where is assumed. However, a comparison of magnetostatic and full PiC simulations, as well as an analysis of the change of particle energy over time in a PiC simulation have shown that momentum diffusion plays only a minor role. The assumption of constant particle energy, which was a prerequisite for the analytic expressions developed by Lange et al. ( 2013 ), can still be applied in the electrodynamic model of PiC simulations. This explains why the electric fields can still be neglected and why the magnetostatic model describes PiC results reasonably well after the transformation of the model equations into the plasma frame has been performed. We found that the analytic model has difficulties in describing the effect of several waves on particle scattering. In Sections 5.2 and 5.3 we present data from simulations with two and four plasma waves. It can be seen that the predicted scattering amplitudes only match the simulation data if the resonances for the different waves are well-separated in pitch angle space. Looking at single particles in pitch angle space, as illustrated in Figure 11 in Section 6 , it can be seen that in the case of single waves (or sufficiently separated resonances) each particle travels along a closed, elliptical trajectory. In the case of single plasma waves we therefore conclude that a particle starting at some pitch angle will be scattered to different pitch angles, but will always come back to at some point in time. This is consistent with the idea of particle trapping developed by Sudan Ott ( 1971 ): the magnetic field of the wave leads to effective
Paper: 2019MNRAS.489..132M (fulltext)
outward flux is not conserved; its losses are a source for the photon angular momentum flux (Fig. 4 ). But in the model solution, the disc can only supply the flux from the relatively small amount of material within the ISCO, because just outside the ISCO the disc velocity is outward: it is not accreting! This cannot go in indefinitely – unless the outward flux is somehow being fed externally. The fact that the integrated total luminosity of our solutions formally diverges as t → ∞ is a clue: something must be feeding this. This could in principle be a magnetic couple to the black hole itself (or a vertical accretion process), but how would a turbulent/viscous model resolve this issue? Our solutions must not be the true t → ∞ disc solutions; they represent instead an extended phase of disc evolution in which the accretion is stalled. While this may be very useful for understanding observations, it raises an important point of physics: how does a real disc ‘unstall’ itself? Figure 4. The normalized angular momentum flux divergences, stemming from the three terms in equation ( 47 ), at a particular time (τ = 0.67) in the disc evolution. Throughout the bulk of the disc, the angular momentum loss balance is predominantly between the radial flow (the first term) and the turbulent stresses (second term). In the region r ≲ 8 r g , however, the photon losses (third term) dominate over the radial flow stress. This balance can be maintained as an extended phase, but not indefinitely. 4.3 Modelling the angular momentum gradient in the inner disc The presence of a non-zero radial velocity means that the azimuthal motion is only approximately Kerr metric circular. We have argued that the correction is second order in the fluctuation amplitude, but , the derivative of the angular momentum, appears as a singular denominator in the evolution equation. Since strict adherence to the idealized model leads to long-term unsustainable behaviour, it is of interest to consider a generic change to the angular momentum gradient of the form where now U φ indicates the mean fluid angular momentum of equation ( 10 ), and is the idealized angular momentum of a Kerr circular orbit. The function ε( r ) is left somewhat arbitrary, but it must satisfy two simple properties: (i) it reaches a maximum at the ISCO, and (ii) it vanishes sharply at large radius. The idea is that the assumption of purely circular orbits is a good approximation at large radii, but become progressively worse as the ISCO is approached. The development of this theory is the topic of the companion paper. We note here, however, that a modification of the angular momentum gradient in the inner disc regions would also affect the steady-state modelling of relativistic thin discs. The steady-state solution of equation ( 10 ) leads to a disc temperature profile where F 1 and F 2 are constants related to the mass and angular momentum flux throughout the disc, respectively. A substantial modification of in the