adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJS..252...30M (fulltext)
this may be the largest hurdle in adapting numerical magnetohydrodynamics software to use the proposed scheme. 6. Conclusion We have shown that self-gravity source terms, when properly evaluated, are capable of being fully conservative (e.g., Mikami et al. 2008 ; Springel 2010 ; Katz et al. 2016 ; Hanawa 2019 ). The source terms are derived by guaranteeing their equivalence to a corresponding flux (i.e., for momentum source terms, the divergence of the gravitational stress tensor, and for the energy source term, the divergence of a gravitational energy flux). As presented in this work, the fully conservative source-term scheme is formally second-order accurate in space and time, and does not increase the total number of Poisson solves needed per numerical time step compared to Athena++ v19.0 . The scheme can be implemented alongside a broad class of temporal integrators (e.g., VL2, RK2, RK3, etc.), Riemann solvers, or reconstruction methods (e.g., PLM, PPM, etc.). The three test problems presented in Section 4 exemplify the key advantages of the fully conservative source-term scheme. For the 1D Spitzer sheet advection test problems, we see that the the L 1 errors are nearly identical when the momentum equation is integrated via the divergence of the gravitational stress tensor T g and the conservative source terms. The similarities in the errors and error convergence between the two Spitzer sheet runs are a reflection of the two schemes being equivalent via Equations ( 23 ) and ( 24 ). The Jeans linear stable wave tests demonstrate that the scheme can be used in conjunction with a variety of temporal integrators (e.g., VL2, RK2, and RK3) and reconstruction methods (e.g., PLM and PPM). Although we use the HLLC Riemann solver for all test problems, any solver can be employed; the scheme only mandates that the energy source term uses the same mass fluxes applied in evolving the continuity equation (i.e., the energy source term must be consistent with mass conservation). The source terms in Section 2.3 only guarantee spatial second-order accuracy; however, the Jeans linear wave analysis shows that higher-order temporal integrators and reconstruction methods are still capable of significantly lowering errors at a given resolution. The Jeans instability test demonstrates the algorithm’s ability to conserve total energy. Despite significant changes in each component of the volume-integrated energy, total energy is conserved to round-off error. Total energy conservation (to round-off error) requires that the solution to the discretized Poisson equation (Equation ( 26 )) is of round-off error accuracy at the beginning and final stages of the numerical time step. As noted in Section 3.2 , the algorithm does not require machine-accurate evaluations of the potential at intermediate stages for total energy conservation. This could yield interesting strategies when solving the Poisson equation with iterative methods, e.g., perhaps the convergence
Paper: 2019A&A...624A...2D (fulltext)
2018 ; Barr et al. 2018 ; Wang et al. 2017 ; Grimm et al. 2018 ), and we also find the vast majority of the possible structures for planet b have substantial quantities of water (~ 20−70%). The remaining planets are also probably water-rich, most notably planet d, where a volume fraction of H 2 O ≳ 0.25 is required by the mass and radius data. Table 2 summarises the values of T eq , F tidal , F RG , and F glob for planets b through e. Planets d and e have a total heat flux from tidal heating and insolation that is below the critical heat flux for a runaway greenhouse effect, and could be potentially habitable from a geophysical point of view. Planet d avoids a runaway greenhouse effect only if its Bond albedo is greater than or approximately equal to 0.3, indicating a surface at least as reflective as the surface of the Earth, which could be possible if the planet has a thick atmosphere with highly reflective clouds ( Turbet et al. 2018 ). In this case, planet d could have liquid water at its surface at the substellar point provided it is tidally locked in a 1:1 spin–orbit resonance ( Turbet et al. 2018 ). It is not clear whether TRAPPIST-1d is synchronously rotating because perturbations from the other planets may prevent tidal locking which results in a larger illuminated area where surface ice can melt ( Vinson Hansen 2017 ). Since the planet is close to the runaway greenhouse state due tothe received stellar irradiation, the moderate tidal heating rate that is present in the body might transform itinto a “tidal Venus” ( Barnes et al. 2013 ). We find that planet e avoids a runaway greenhouse effect regardless of its albedo, bolstering the already strong case for its habitability based solely on climate modelling ( Wolf 2017 ; Turbet et al. 2018 ). Planet e can maintain global surface oceans through greenhouse warming from H 2 O alone ( Turbet et al. 2018 ); alternatively, Earth-like temperatures can be maintained with an atmosphere of 1 bar of N 2 and 0.4 bar CO 2 , or a pure CO 2 atmosphere with a pressure of 1.3 bar ( Wolf 2017 ). If planets d or e were ice-covered, they could still sustain biospheres fueled by the planet’s interior tidal dissipation, for example clustered around hydrothermal vents at the top of the internally heated rock layer, as has been suggested for Jupiter’s moon Europa ( Vance et al. 2007 ). Since both planets have equilibrium rock mantle temperatures greater than the melting point of silicate minerals, heat dissipation from the rock layer can locally melt the high-pressure ice to provide a warmer environment where the rock interacts with water through the vents and might support simple chemoautolithotropic life ( Vance et al. 2007 ). The surface ice layer would shield the biosphere from the strong stellar flare activity of the TRAPPIST-1 star ( Vida et al. 2017 ; Garraffo et al. 2017 ), but would serve as a barrier to detection of the biosphere through telescopic observations. Hydrothermal activity would be more
Paper: 2016MNRAS.456.3100D (fulltext)
and systematics), the resulting co-added maps will now also contain a simulated signal that is correlated with that present in the FIRST simulations. We have passed these co-added ‘real + simulated’ maps through our power spectrum estimation pipeline, and the results are shown in Fig. 11 . These recovered power spectra have undergone noise bias subtraction and correction of the mask in exactly the same way as before. Fig. 11 shows that the recovery of the SDSS auto-power spectrum is very heavily biased by the presence of unaccounted-for large-scale systematics in the data. 17 However, the right-hand panel of Fig. 11 shows that the recovery of the SDSS–FIRST cross-power spectrum remains unbiased, despite the presence of the SDSS systematics. Note that, although it is unbiased, the cross-power spectrum is not completely unaffected by the presence of the SDSS systematics: their presence results in a significant increase in the error bars of the cross-power spectrum recovery (compare the right-hand panel of Fig. 11 and the central panel of Fig. 10 ), due to chance correlations between the noise in the FIRST simulations and the SDSS systematic effects. We will account for these additional contributions to the errors in our final analysis. Figure 11. The recovered mean SDSS auto-power spectra ( left-hand panel) and SDSS–FIRST cross-power spectra ( right-hand panel) from 100 simulations in the presence of large-scale systematic effects in the real SDSS shear maps. For a definition of the symbols, see Fig. 10 . Systematic effects in the SDSS data severely bias the recovery of the auto-power spectrum, shown as the solid curve in the left-hand panel. The cross-power spectrum recovery is unbiased in the presence of the same systematics, though the errors are amplified. To assess the impact of the large-scale systematics in the FIRST shear maps, we have performed an equivalent analysis to what is described above but now we superimpose the FIRST signal-only simulations on to the real FIRST maps and cross-correlate these with the SDSS simulations. The results of this test are displayed in Fig. 12 where we observe similar effects to what is described above – the FIRST auto-power spectrum is heavily biased by large-scale systematics while the cross-power spectrum remains unbiased. Figure 12. The recovered mean FIRST auto-power spectra ( left-hand panel) and SDSS–FIRST cross-power spectra ( right-hand panel) from 100 simulations in the presence of large-scale systematic effects in the real FIRST shear maps. For a definition of the symbols, see Fig. 10 . Systematic effects in the FIRST data severely bias the recovery of the auto-power spectrum, shown as the solid curve in the left-hand panel. As in Fig. 11 , the systematics contribute additional errors into the cross-power spectrum measurement though it remains unbiased. 6 REAL DATA MEASUREMENTS In the previous section, we have demonstrated, using simulations, that we can extract an unbiased cosmic shear
Paper: 2020ApJ...900L..28D (fulltext)
dispersion as a Doppler shift; Huang et al. 2018 ). Battye et al. ( 2020 ), however, suggested that the line width is dominated by the neutron star’s spinning magnetosphere. We adopt this spinning-mirror model that produces, on average, a bandwidth Δ f / f ≃ Ω r c / c , where Ω is the rotation angular frequency and r c is the axion–photon conversion radius. Hook et al. ( 2018 ) showed that r c depends on the neutron star’s radius r 0 , magnetic field B 0 , angular frequency, polar orientation angle θ , and magnetic axis offset angle θ m :where . For now, we assume that θ = π /2 and θ m = 0 (we deal appropriately with these angles in Section 5 ) to obtain the expected line width:(4.1 μ eV corresponds to 1 GHz as observed). PSR J1745−2900 has a 3.76 s rotation period (Kennea et al. 2013 ) and a magnetic field of 1.6 × 10 14 G (Mori et al. 2013 ). The expected axion–photon conversion line width is thus . This corresponds to 2500 km s −1 at 1 GHz and 215 km s −1 at 40 GHz, which is generally broader than the expected dark matter dispersion, ∼300 km s −1 , except at the highest observed frequencies. Spectra have flagged channels due to RFI and due to spectral window edges that lack the signal-to-noise for calibration. Flagged channels are much narrower than the expected line width, so we interpolate across these channels when smoothing using a Gaussian kernel (Price-Whelan et al. 2018 ). In some bands, such as L , S , and K , entire spectral windows can be flagged and all information is lost. RRL emission lines are seen toward the magnetar in X -band (15A-418), Ku 12–13 GHz, Ku 17–18 GHz, and K 26 GHz. To remove RRLs, two 2 MHz channels are flagged per line, and subsequent smoothing interpolates across the flagged channels (the expected axion conversion line width is 18 MHz in X -band and 24 MHz at 26 GHz). We combine spectra obtained from multiple observing sessions using an error-weighted average, and the sky (noise) spectra are combined in quadrature. When different observing programs overlap in frequency, we select the lowest noise observation. This is effectively the same as combining overlapping spectra in quadrature because the less sensitive spectra contribute negligibly to an error-weighted mean. Table 1 lists synthesized beam parameters, channel widths, and spectral rms noise values. Appendix appendix presents the new magnetar flux, noise, and signal-to-noise spectra. 4. Results After smoothing to the expected axion–photon conversion line width, the new spectra show no significant (>3 σ ) single-channel emission features (Appendix appendix ). The exception is a 3.2 σ channel at 2.34 GHz (9.67 μ eV) in an RFI-affected region of S -band (there also two channels at 3.1 σ and 3.5 σ in previous Ka -band spectra; Darling 2020 ). We obtain single-channel 95% confidence limit flux density spectra from the sky noise spectra. Figure 1 shows the combined limits from this work and Darling ( 2020 ). These limits do depend on the assumed line width
Paper: 2020MNRAS.498.3184C (fulltext)
Figure 4. The (0.3–1 versus 1–4 keV) X-ray soft time lag versus frequency (left) and (3–10 keV) energy spectra (right) fitted with the kynxilrev and kynxillver models, respectively, using the same values of the parameters obtained for spin zero (top) and rapidly spinning (bottom) from Table 2 . Both the a = 0 and the a = 0.99 best-fitting probability values are rather low. The poor quality of the model fits is mainly due to the best-fitting residuals that appear at energies below ∼3.3 keV in the LF energy spectra (possibly due to the presence of a blueshifted, S xvi absorption line at these energies, e.g. Jiang et al. 2018 ). Indeed, if we neglect the points below 3.3 keV in the energy spectra the best-fitting improves significantly, with χ 2 = 430.8 and 439.8 for 403 degrees of freedom when a = 0.99 and 0, respectively. We furthermore investigated if we can get a better fit by allowing the inclination to be a free parameter. The best-fitting inclination values are 47.5° ± 1.8 and for the a = 0.99 and 0 cases, respectively. These values are similar to the one we had originally assumed (45°). Since the fit does not improve significantly (Δχ 2 ∼ 2 − 3 for 414 degrees of freedom) and the new model parameter values are consistent (within the errors) with the previous ones, we decided to keep the original best-fitting results. Looking at the residuals of the best-fits to the time lags (left column panels in Fig. 4 ), we can see that the a = 0 model cannot fit the observed time lags as good as the a = 0.99 model. The best-fitting a = 0 model appears to be consistently overestimating the time lags at frequencies higher than ∼3 × 10 −4 Hz. As a result, Δχ 2 = 10 between the best fit of the a = 0.99 and the a = 0 model to the time lags only (Δχ 2 = 18 when we consider the energy spectra as well). It is difficult to quantify the improvement in the quality of the best fit in the high-spin case, one reason being that the number of degrees of freedom is the same in both model fits. One way to do so is to compute the ‘ratio of likelihoods’, which is equal to (Mushotzky 1982 ). In our case, this is identical to the ‘evidence ratio’, which is based on the Akaike (Akaike 1973 ) information criterion (see equation 8 in Emmanoulopoulos et al. 2016 ). This ratio is a measure of the relative likelihood of one versus the other model, and is equal to ∼150 in our case (∼8100 when we consider the energy spectra as well). This result does not mean that we can reject the hypothesis that the spin zero model is correct, but it does mean that the high spin model is much ‘more likely’ (i.e many hundreds to thousand times) to be the ‘real/correct’ model. We therefore conclude that our results strongly support the hypothesis of a rapidly rotating BH in IRAS 13224-3809. The best-fitting iron abundance and disc density values are consistent with the results from Jiang et al. ( 2018 ), who performed a detailed spectral analysis of the 2016 observations using a similar model to
Paper: 2019MNRAS.484.3356G (fulltext)
which suffers from persistence and non-linearity effects, we created pixel-to-pixel maps that we used to pre-process and correct the raw images (details in Section A ). Following standard data reduction steps (Section 2.1 ) and calibration, we were able to achieve excellent image registration resulting in a sharp and stable PSF of 0.25 arcsec over the entire 4 × 4 arcmin LUCI 2 field of view. The PSF size only increases by out to the detector edges (Section B ). This superb spatial resolution enables to resolve the star cluster candidates and bring down contamination from background galaxies to a minimum. In a forthcoming paper we will present the detailed analysis of the star cluster system of NGC 6384 (cf Section 3.1 ), while in this paper we mainly focus our analysis to the central 3.6 × 3.6 kpc (∼36 × 36 arcsec) overlap region between our NIR LBT and archival optical HST data. The high spatial NIR imaging is crucial for breaking age–metallicity–reddening degeneracies when combined with the optical HST data. We reprocessed the HST /ACS and WFPC 2 data to the same plate scale resolution of 0.05 arcsec pixel −1 (cf. Section 2.2 ). We performed a 2D MCMC image fitting with imfit (Erwin 2015 ) by using a PSF model built from stars in the image for both the LUCI 2 and HST images, as well as a tinytim (Krist et al. 2011 ) PSF model drizzled in identical manner as the science images. We also used a pixel mask of the central obscuring disc-like dust lane (Section 3.2 , Fig. 5 ). Our main findings can be summarizes as follows: We uncover the presence of two inner Sersić (low n − index, disc?) components (Section 3.2 , Fig. 4 and Section 4.1 ) with effective radii of ∼100 and 400 pc in which the NSC with r eff ≃ 10 pc is embedded. NGC 6384 has a large-scale boxy bulge (∼1 kpc) and disc (∼6 kpc). The effective radius of NGC 6384 NSC increases with wavelength (cf. Fig. 11 , which suggests for a more centrally concentrated younger stellar population (see Section 4.2 ). The NSC effective radius is smaller in size by and magnitude/mass by ∼0.4 mag/dex compared to Georgiev Böker ( 2014 ) due to the unaccounted inner Sersić components. This highlights the need to resolve and model well the central regions for the correct mass and dynamical modelling of the NSC when follow-up spectroscopy is included. The NSC SED is described by an old population (10.8 ± 1.3 Gyr) with a stellar mass of , and a young population of 230 ± 140 Myr, which is of its mass. Both populations have slightly sub- and supersolar metallicity, respectively (cf. Table 2 , Sections 3.3 and 4.3 ). We obtained the SED radial surface mass density, age, metalicity, and reddening (cf. Fig 10 , Sections 3.3 and 4.3 ) We find negligible, 1 per cent fraction of the light, to be coming from a weak AGN (Section 4.4 ). If an MBH in the NSC of NGC 6384 is present, then it might not be accreting, similarly to those in the MW and M 31. All structural and stellar population evidences suggest that the NGC
Paper: 2017ApJ...849L..34P (fulltext)
geometry, but, e.g., if we assume a 3D isotropic velocity dispersion it would reduce the dynamical time by ). So far, we have no measurement of the shell’s velocity dispersion, but estimates from the literature suggest for similar shells in other galaxies (Quinn 1984 ). This would give a dynamical timescale of t dyn ≈ 192 Myr. Detailed simulations of shells in other galaxies suggest that survival time could be even larger that 1 Gyr, depending on the assumed scenario (Pop et al. 2017 ). The survival time of the shell could be used as an upper limit for the time the minor merger took place, i.e., t dyn ≥ t merg , so we estimate t merg ≲ 200 Myr. If the BNS was formed as such in a shell, then we would have expected to see evidence for recent star formation, but we find no indication of this. In the absence of star formation, it is plausible that the BNS coalescence was triggered by a dynamical process, e.g., NS–NS capture or the destabilization of a pre-existing wide-separation binary. These processes will be quite sensitive to the stellar density, which, given the Sérsic index and the luminosity from the residual image found in Section 3.2 , is high in the center of NGC 4993 and around the transient position. If this dynamical hypothesis is true, then the delay time Δ t NSM between the BNS formation and coalescence is ≲200 Myr. On the other hand, Blanchard et al. ( 2017 ) find a median delay time of under the assumption that the binary was formed through secular SF. 5.2. Galaxy Environment If the binary formation is related to dynamical processes in galaxy merging, as we are investigating here, then this is most likely to happen in galaxy groups and low-mass clusters. According to the 2MASS catalog (Tully 2015 ), NGC 4993 resides in a group of which we analyze the remaining seven galaxies. A spectral analysis shows that NGC 4993 is not the only galaxy showing AGN activity (see Figure 3 ), but it is peculiar in terms of age, metallicity, and mass-to-light ratio. It shows an older stellar population (the mean age of the other 13 galaxies is Log (Age) = 9.56 ± 0.17), lower metallicity (mean: M / H = −0.31 ± 0.11), and higher M / L r (mean: 2.41 ± 0.45) than the average. The group has a projected virial radius of R vir = 0.36 Mpc and a line-of-sight velocity dispersion (Tully 2015 ). The crossing time is therefore . If galaxy mergers are correlated to BNS coalescence, future GW studies could possibly concentrate on galaxy groups (but note that these are crowded regions and therefore matching candidates to a host could be difficult). In order to have precise measurements of H 0 , one needs to identify the host galaxy redshift clearly. When the match is clear, the properties of the type of host galaxy found could help future studies to select the right host galaxy or create galaxy catalogs of likely hosts for GW–EM follow-up and untriggered kilonova searches (Doctor et al. 2017 ). In fact, large photometric surveys such as DES, LSST, or WFIRST are
Paper: 2018MNRAS.474.4347C (fulltext)
fitted separately. Posterior distributions of { M disc , γ, R c , h c , ψ} are presented in Figs 2 and 3 , while the best-fitting models are shown in Fig. 4 . Best-fit parameters are listed in Table 2 . Figure 2. Posterior distributions of each disc parameter, including their marginalized distributions for V2775 Ori, V1647 Ori, and NY Ori. The vertical dashed lines represent the 16th, 50th, and 84th percentiles. Contours correspond to 68 per cent, 95 per cent, and 99.7 per cent confidence regions. These plots were produced using the python module corner (Foreman-Mackey et al. 2013 ). Figure 3. Same as Fig. 2 , but for V883 Ori and HBC 494. Figure 4. The ALMA 1.3 mm continuum data (left), synthetic best-fitting models (middle), and the residuals (right) for the five resolved discs in our sample. Table 2. Disc parameters from radiative transfer modelling. 4.2.1 Radiative transfer results We find that, at the observed resolution of 0.2 arcsec, V2775 Ori, V1647 Ori, and NY Ori are well described by our parametric models: the posterior distributions of the disc parameters are relatively narrow and single peaked (see Fig. 2 ). However, V883 Ori and HBC 494 have disc parameters that are less well defined. In the case of V883 Ori, the posterior distributions for M disc , R c , and Ψ are double peaked (see Fig. 2 , left-hand panel). We speculate that this is due to the particular structure of V883 Ori, showing a very bright core, 0.2 arcsec in diameter, surrounded by a much more tenuous outer disc. The boundary between these two regions has been interpreted as the water snowline, resulting in a discontinuity in the disc properties (Cieza et al. 2016 ). The water snowline is typically at ∼3–5 au for solar-mass stars and would be unidentifiable at the 80 au resolution of our observations, but given the extreme accretion rate of V883 Ori, it has been displaced to a radius of ∼40 au. Detailed modelling of the V883 Ori disc at ∼0.03 arcsec (15 au) resolution will be presented in a follow-up paper. Similarly, in the case of HBC 494, the posterior distributions for M disc and R c are wider than for the other sources (see Fig. 3 , right-hand panel). This is likely due to the fact that the disc is not radially symmetric, preventing the MCMC exploration of parameters to converge to a narrower range of values. The disc asymmetry in HBC 494 can be seen in Figs 1 and 4 as an elongation of the disc on the south-east direction. We speculate that this asymmetry might indicate the presence of two barely resolved discs (e.g. the system might be a binary as V1118 Ori and FUor itself; Hales et al. 2015 ; Liu et al. 2017 ) or a non-axisymmetric disc structure. Fig. 5 shows a ∼8 mJy residual in our ALMA image after subtracting the best-fitting model, but in different scaling than Fig. 4 . This residual has the same intensity level ( ∼ 7 per cent of the total flux) as in other sources, but is compact and not radially symmetric. A particularly interesting possibility for this