adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...889..119T (fulltext)
ejecta, we assume the nucleosynthesis pattern following the recent numerical results for the postmerger ejecta which show significantly low production of lanthanide elements and give the mass fraction of 1.64 × 10 −2 for Y (Fujibayashi et al. 2018 ). We first propose that the degree of Eu enhancement indicated by [Eu/Fe] for individual stars (−0.7 ≲ [Eu/Fe ≲ +2) could be basically determined by the different levels of Eu enrichment of gas via associated merger events (cNSNSs or cBHNSs) with widely varying masses ( M tid ) of tidally driven dynamical ejecta (10 −4 M ⊙ ≲ M tid ≲ 0.1 M ⊙ ). Considering the [Eu/Fe] distribution of halo stars, we identify two components: a Gaussian distribution in the range of −0.7 ≲ [Eu/Fe] ≲ +1.3 with a peak around [Eu/Fe] ≈ + 0.3 and an extending Eu-rich tail up to [Eu/Fe] ≈ + 2. Then, considering the predicted mass range of dynamical ejecta, cNSNS and cBHNS are likely to be responsible for producing the former and latter distributions, respectively. Approximate mass values of the mass of tidally driven dynamical ejecta are shown in the upper part of the figure, implying that the average value for cNSNSs should be a few times 10 −3 M ⊙ . If cBHNS should produce more Eu than cNSNS due to larger tidal mass ejection, the Y/Eu plateau could be connected to such events. Considering very low production of light r -process elements in the tidally dominated dynamical ejecta of cBHNSs, owing to a low Y e (≲0.1) (Roberts et al. 2017 ), we argue that Y in r-II stars originates from the postmerger ejecta. The Y/Eu plateau implies that the production ratio of Y to Eu is quite similar in individual cBHNS events. As each element owes its origin to nontidal postmerger ejecta and tidally driven dynamical ejecta, respectively, the masses of the two type of ejecta are suggested to scale linearly with each other. This linearity may also hold even if the postmerger ejecta of cBHNSs produce Eu due to a smaller Y e resulting from lower neutrino irradiation (Fernández et al. 2017 ), though there are complexities in the influence of the neutrino treatment (e.g., Caballero et al. 2012 ; Wu et al. 2017 ). The required condition of a similar mass ratio of light r -process elements to Eu among the postmerger ejecta of cBHNSs needs to be investigated (see also Kyutoku et al. 2015 ). On the other hand, the trend of an increasing [Y/Eu] with decreasing [Eu/Fe] at the low [Eu/Fe] regime is likely to reflect the diversity of nucleosynthesis in cNSNSs with various mass asymmetry in our scheme. The tidal interaction tends to be efficient for asymmetric binaries, and M tid becomes large. At the same time, shock interactions are not very efficient for dynamical mass ejection in asymmetric binaries. Thus, the average values of Y e become lower for more asymmetric binaries (e.g., Sekiguchi et al. 2016 ). These combined effects could explain the observed Y/Eu trend. According to this view, cNSNSs with massive (≳0.01 M ⊙ ) tidally driven dynamical
Paper: 2016MNRAS.456.3886B (fulltext)
orders, which yields a sufficient number of data points for our analysis and still benefits from a low-dimensional covariance. The fact that we do not find a decrease of parameter uncertainty with increasing number of orders shows that the first few orders indeed contain all the relevant information (see Fig. 7 for more details). The measured data points for both angular intervals are presented in Fig. 8 . Unlike correlation function measurements, these Υ data points cannot be interpreted easily. However, it is clear that in the large-scale interval the model (see Section 2 ) is a very good fit to the data regardless of the cosmological parameters used. 4 This is not the case for the smaller scale interval, where one of the clustering data points is several σ away from the best-fitting model. Clearly, the assumption of linear bias on these non-linear scales is not valid for the clustering data. This is partly due to the small uncertainties in these measurements as well as the fact that we neglect the model uncertainties. As can be seen in Fig. 9 , a change of b of about 5 per cent within this interval would already be enough to reconcile the data with the model. If model uncertainties had been included in this figure, it is likely that data and model would be in line again. Another possible explanation of the discrepancy between data and model in this case could be that the fiducial cosmology is wrong. Furthermore, we also investigate if an indication for a scale-dependent bias can be found in the γ t (θ) data in Fig. 10 and find no such preference. Figure 7. Left: the measured parameter values as a function of maximum Υ-order n for b and r . No significant difference in the values is visible, from which we conclude that the data compression is indeed working and only a few orders contain all the information from the measured signals. Right: the parameter uncertainty in per cent for b and r , again as a function of n . Here, we do not find a significant difference, which again shows that the data compression of Υ( n ) is robust. Figure 8. The top panels show the measured Υ gm and Υ gg and the best fit using one of the two cosmologies. The magenta and dark blue lines are the connections between the predicted data points using the Planck or the CFHTLenS cosmology. In the bottom panels, we show the residuals (Υ obs − Υ model )/ΔΥ obs , where ΔΥ obs is the uncertainty in the measured Υ. Left: measurements for the 3-20 arcmin interval. Clearly, on these scales the model we adopt to describe the galaxy bias is not a good description of the data shown here, especially the clustering data. For more details, see Section 3.4 Right: measurements for the 20-70 arcmin interval. Figure 9. Top: the angular correlation function within the small-scale interval, the corresponding model for b = 1, and the best-fitting model. This best-fitting model has been determined from a joint fit of Υ gm and Υ gg . Bottom: the square root of the ratio between the
Paper: 2016ApJ...833..288T (fulltext)
of signal-to-noise ratio (S/N), and found it to be well described by a power-law function, . is about 0.01 mas in the innermost jet region where , and 0.1 mas downstream of the jet where , corresponding to 1/50 and 1/5 of the beam size, respectively. Therefore, we consider the upper limit of the imaging error to be because the jet radii are measured where the jet is detected when (see also Homan et al. 2002 , Appendix A for similar analysis). Note that we assume the time variability of the jet radius is negligible. 3.3. Radius Profile of the Jet Figure 2 shows the distribution of jet radius ( r ) as a function of the deprojected distance to the core ( z ), which we refer to as the radius profile of the jet. The jet viewing angle ( θ ) for deprojection is uncertain within an upper limit of 47° (Jones Wehrle 2002 ). We adopt θ = 19° derived from a detection of a sub-parsec scale counter-jet (Sudou et al. 2000 ). An independent model of the spectral energy distribution (SED) suggests a consistent result of θ ≃ 18° (Chiaberge et al. 2003 ). We use the deprojected length scale ( mas −1 ) along the jet throughout the rest of the paper. Note that the jet radius, as defined perpendicular to the jet axis, is not affected by projection effects. Figure 2. Top panel: the radius profile of the NGC 6251 jet in units of Schwarzschild radius ( r s ) and parsecs, using archival VLBA data at 15 and 5 GHz (green and blue triangles), and our EVN(-i, ii, iii) data at 1.6 GHz with three weighting schemes (violet square, cross, and circle). The single (orange solid line) and double (brown solid line) power-law models are fitted to the profile, where we show the case of the sharpness parameter n = 100 here. Statistics ( f -test) shows that the data are better fitted by double power-law models (with all n ) than by a single power law (see also Section 3 ). Furthermore, the double power-law model is also supported by the VLA data (gray plus signs) and VLBI cores (gray triangles and squares), as discussed in Section 4.1 . Note that the VLA data and VLBI cores are not included in the regression analysis. Middle and bottom panels: the relative residuals of the jet radii for the single () and double () power-law models. The single power law shows a systematic deviation at the two ends of the distance scale, while the double power laws (i.e., for all n ) show uniformly distributed relative residuals on all distance scales. We conclude that the jet structure is described by a parabolic shape upstream () and a conical shape downstream () with a transition located at a characteristic distance z 0 = (1–2) × 10 5 r s (brown dashed line), which is fairly close to the sphere of gravitational influence (SGI) of the SMBH (, black dashed line) in order of magnitude. 3.4. Collimation Profile by Power-law Models Collimation profiles are generally described by power-law functions with some local features, as a manifestation of the global evolution of jet structure. We perform
Paper: 2021ApJ...909..118Z (fulltext)
Einstein radius, and σ SIS is the velocity dispersion of the star in the periphery of the lens galaxy due to the lens mass distribution in the SIS model. In general, σ SIS does not equal the observed stellar velocity dispersion σ 0 (White Davis 1996 ). To express the difference, researchers use a phenomenological free parameter f e defined by the relation σ SIS = f e σ 0 (Kochanek 1992 ; Ofek et al. 2003 ; Cao et al. 2012 ), where (0.8) 1/2 f e (1.2) 1/2 . In this case, the systematic error is caused by σ 0 as σ SIS , the deviation of the SIS model, the effects of secondary lenses (nearby galaxies), the line-of-sight contamination (Ofek et al. 2003 ), etc. The uncertainty of Equation ( 5 ) can be written by (Liao et al. 2016 )In Equation ( 6 ), and are the fractional uncertainties of σ sis and θ E , respectively. To test the CDDR, the left term of Equation (ref5), R A ( z l , z s ), should be expressed as the ratio of luminosity distance, . We transform R A ( z l , z s ) into the ratio of comoving distance ( D C ) or dimensionless distance H 0 D C / c . In a flat space, the dimensionless distance satisfiesBy using the equation, R A ( z l , z s ) can be written as In a nonflat space, the expression of R A ( z l , z s ) is more complicated; one can refer to Räsänen et al. ( 2015 ). Fortunately, most cosmological tests today support a flat cosmic space (Planck Collaboration et al. 2016 ). In this work, we test the CDD relation in a case where we assume that spacetime is flat. To test the CDDR, Equation ( 1 ) is rewritten by the parameterization of the deviation:Combining Equations ( 9 ) and ( 10 ), R A ( z l , z s ) can be written asBy using Equation ( 4 ), of Equation ( 11 ) can be rewritten aswhere the absolute magnitude of the SNe Ia subsample is offset, z l is the redshift of the lens, and z s is the redshift of the sources. 3. Method and Results In this section, we introduce a distance-deviation consistency method of data selection and show the result. 3.1. Method of Data Selection: A Distance-deviation Consistency Method To test the CDDR, D L and D A at the same redshift z need to be provided simultaneously. However, redshifts of subsamples from the SGLS and the SNe Ia are different. To take full advantage of the data, one needs to pair the subsamples efficiently. In this section, we provide a distance-deviation consistency method to pair subsamples, which outperforms the conventional method. The redshift-difference of subsample pairs in this work is not fixed, and it decreases with the redshifts to ensure the distance deviation of the sources stays the same. The relation between Δ z and coordinate distance with a cosmology model readswhere R h = ct and flat ΛCDM model with Ω m = 0.31 (Planck Collaboration et al. 2016 ) are used. By setting Δ d c / d c equal to 5% and combining Equation ( 13 ), Δ z ( z ) can be calculated:where the distance formulas of the two cosmology model, ΛCDM and R h = ct are used. The R h = ct cosmological model was
Paper: 2016ApJ...822...99C (fulltext)
are sub-Keplerian by ≈20%, in good agreement with other studies (e.g., Wade Horne 1988 ). Note that for q ≳ 0.25, the 3:1 resonance lies beyond ≈0.9 R L 1 and the outer disk radius is then limited by the largest non-intersecting orbits allowed by three-body interactions (Paczyński 1977 ). Under these circumstances the dependence of the outer disk radius on q is very weak (Frank et al. 2002 ) and therefore a nearly constant evolution of DP/FWHM versus q is expected for q ≳ 0.25. For practical purposes, in Figure 3 we display the variation of DP/FWHM versus q in logarithmic units. A least-squares linear fit yieldswith a Pearson correlation coefficient r = 0.95. To estimate the error in q implied by this relation, we computed the difference with respect to the true observed values for our 11 SXTs. The distribution of differences can be approximated by a normal function with σ ( q ) = 0.015. This indicates that mass ratios can be realistically obtained from Equation ( 6 ) with a typical ∼25% uncertainty. Figure 3. Correlation between q and the parameter DP/FWHM in log units and the best linear fit. 4. ORBITAL EFFECTS The spectra that we used to produce the DP/FWHM– q correlation are orbital averages. This is party because individual spectra rarely possess enough signal-to-noise for the technique to be applicable. But also because orbital means average out possible asymmetries in individual spectra from, for example, hot spots or disk eccentricities that could potentially bias the determination of q . At this point we decided to explore the impact of line asymmetries in the results of our technique. Since this can only be tested on data with sufficient signal-to-noise, we have focused on the 154 high-quality GTC spectra of XTE J1118+480 obtained along four different orbits over two years. We have performed Gaussian fits on every individual spectrum and computed mass ratios using Equation ( 6 ). The distribution of q values is found to peak at q = 0.024 (the bottom panel in Figure 4 ), with 68% of the values contained between q = 0.021 and 0.045. This indicates that if q were to be obtained from a single individual spectrum, the typical uncertainty would be about ∼56%. Figure 4. Bottom: histogram of q values derived through Equation ( 6 ) from 154 GTC spectra of XTE J1118+480. Top: variation q with the V / R parameter of the H α profile (see the text for details). In order to trace the effect of line asymmetries we also extracted the V / R parameter from each spectrum, with V / R defined as the ratio of equivalent widths between the blue and the red part of the H α profile. The two halves of the line are set from the rest wavelength till ±2500 km s −1 . For example, indicates a line with the red part stronger by 20% while implies a symmetric profile. A plot of q versus V / R (see top panel in Figure 4 ) seems to show a trend, with asymmetric profiles preferring slightly lower q values, although the large scatter prevents from drawing a firm
Paper: 2019ApJ...882..102G (fulltext)
include in our list lines with intensity values that are above some fraction (typically 50%) of the maximum value. Of course, the choice of this threshold is arbitrary, but we find it works well for our needs, and can be easily modified as needed. In a companion paper (A. Gal-Yam, O. Yaron N. Knezevic 2019, in preparation), we describe in more detail our survey of other methods to compile line lists from the NIST databases and other tests and applications of this method. With the line lists in hand, we attempt to determine for each spectrum the expansion velocity by matching features to ions with numerous transitions (see. e.g., Figure 1 ). Once we have determined a convincing value for the expansion velocity, we inspect other ions using the fixed velocity (i.e., we assume explicitly that the photosphere is narrow). Figure 1. High S/N red spectra of the nearby SLSN-I PTF12dam (left; from Nicholl et al. 2013 ) and Gaia16apd (right; from Yan et al. 2017b ). Both spectra (black curves) have been binned and host narrow emission lines have been excised; the original spectra are shown in blue. Uncorrected Telluric features in the spectrum of PTF12dam are marked. The spectrum of PTF12dam shows pronounced lines of O i and C ii with distinct sharp absorption troughs, all at the same expansion velocity of V exp = 11,000 km s −1 ; O ii lines probably begin to shape the spectrum blueward of 5000 Å while C iii may also contribute. The same is true for Gaia16apd (right) though absorption lines are of lower contrast and have higher expansion velocity, V exp = 14,000 km s −1 . 2.1. Application to Peak Spectra of SLSNe-I We begin our examination of SLSN-I peak spectra by inspecting high signal-to-noise ratio (S/N) spectra of nearby events. In particular, we begin by focusing on the red side of the optical range ( λ > 5000 Å). This wavelength range has been relatively neglected in studies of SLSN-I spectra as it does not contain strong features, and is redshifted out of visible-light spectra of SLSNe at moderate or high redshifts, which are typical for SLSNe. Figure 1 (left) shows our analysis results for a high S/N spectrum of PTF12dam from Nicholl et al. ( 2013 ) obtained on 2012 May 24, 25 restframe days prior to peak according to the analysis of Vreeswijk et al. ( 2017 ). This and all other spectra discussed here are available from WISeREP (Yaron Gal-Yam 2012 ). 5 5 http://wiserep.weizmann.ac.il/ Multiple features consistent with O i and C ii lines all align using a single expansion velocity of V exp = 11,000 km s −1 ; these two ions explain most features in this wavelength range. C iii may explain two weak features, and may also contribute to the observed spectrum. O ii has a distinct line spectrum with a multitude of lines all with very similar intensities. While a few single lines seen around 6500 Å appear to be negligible compared to the stronger C ii lines, accumulations of multiple overlapping O ii lines begin to shape the spectrum bluewards of 5000
Paper: 2021A&A...647A.130B (fulltext)
of star formation, with the younger and older parent stellar population dominating the brighter and fainter PNe respectively. Indeed Valenzuela et al. (2019) showed theoretically that the shape of the PNLF at the faint-end is quite sensitive to the distribution of masses of the PN central stars, which is a product of the SFH in any galaxy. In Bhattacharya et al. (2019b , hereafter Paper I), we observed a rise in the faint-end of the PNLF in the central 16 sq. deg. of M 31 conceivably linked to the SFH of M 31. Owing to the homogeneous spatial coverage and photometry of the current imaging survey, we detect PNe associated with all the stellar populations in a 54 sq. deg area centred on M31. Thanks to the sensitivity and accuracy, we can quantify the imprints of the variations of these stellar populations on the PNLFs in distinct regions. In Sect. 2 , we describe our PN survey of M 31 and set the geometrical boundaries around any regions with RGB overdensities where the PNLFs are measured. In Sect. 3 , we obtain the PNLFs, compute the parameters that best describe them, and carry out the quantitative comparisons via statistical methods. In Sect. 4 , we correlate the stellar population parameters with the properties of the PNLFs at both their bright and faint ends in the different subregions. In Sect. 5 , we discuss the implications of our results on the origin of the M31 inner halo substructures. We draw our conclusions in Sect. 6 . 2. Survey of Planetary Nebulae in M31 2.1. Survey characteristics In Paper I, we identified PNe candidates in a 16 sq. deg. uniform imaging survey of the disc and disc–halo interface of M 31 using the MegaCam wide-field imager ( Boulade et al. 2003 ) mounted on the 3.6-meter Canada-France-Hawaii Telescope (CFHT). M 31 was observed through a narrow-band [O III ] filter ( λ c = 5007 Å, Δ λ = 102 Å, on-band) and a broad-band g -filter ( λ c = 4750 Å, Δ λ = 1540 Å, off-band). An additional 38 sq. deg. were obtained in late-2019, providing uniform coverage of the inner halo of M 31 and its substructures reaching a uniform sensitivity down to fluxes equivalent to m 5007 = 26.2 mag over the entire area. Details of these observations are presented in Bhattacharya (2020) and Bhattacharya et al. (in prep.). The zero points for each of the [O III ] and g -band frames in AB magnitudes, normalised to a 1 s exposure, are Z [O III ] = 23.4 and Z g = 26.5. The photometry was calibrated using observations of spectrophotometric standard stars. The photometric uncertainty (err m , 5007 ) is well below 0.01 mag at m 5007 = 22.5 mag; it increases to err m , 5007 ∼ 0.15–0.2 mag at m 5007 = 26.2 mag. The PN identification in each of these pointings was also carried out as described in Paper I using the on-off band technique developed and validated in Arnaboldi et al. (2002 , 2003) , later optimised for large imaging surveys by Longobardi et al. (2013) . The PNe are identified as point-like sources with an excess [O III ] – g colour. The
Paper: 2016ApJ...822...41M (fulltext)
accuracy and precision of the density and the potential depends on the choice of the parametrized density model used. Both the Gamma and the Einasto profiles produce nearly unbiased density-potential pairs when compared to observables, while the Einasto profiles are most sensitive to how the halo centers are defined. As noted in the Introduction, Serra et al. ( 2011 ) attribute the lower numerically integrated potential (Equation ( 3 )) compared to the NFW potential (Equation ( 4 b )) to mass outside the cluster that is not accounted for by the NFW profile shape. We show that the opposite is true. The NFW model density profile overpredicts the true density profile outside r 200 and thus overpredicts the true gravitational potential via the Poisson equation by 10%–15%. 3.3. Mass and Redshift Dependence Next, we examine how the edge varies as a function of halo mass and redshift. In this case, we use a new sub-set of the Millennium simulation with the 100 most massive halos halos smaller than 1 × 10 14 M . The minimum mass of this new subset is M , i.e., a factor of 10 smaller than the previous sample. We measure the spherically averaged density profiles and infer the escape edges via the potential from Equation ( 6 ). The edges are measured using the same algorithm as applied to the more massive subset studied previously. We find no statistical or systematic difference between the high mass and low mass halo data sets. The density profile predicts the escape edge via the Poisson equation to the same level of accuracy and precision for over two orders of magnitude in cluster halo mass. We study the low mass clusters at four different simulation snapshot outputs, corresponding to , and 0.75. We keep the 100 most massive halos in each snapshot as provided by the “millimil” subset of the Millennium data. At , the deceleration parameter goes from negative to positive. As the cluster density profiles evolve with redshift, we fit the profiles separately for each halo at each redshift. Instead of physical coordinates, we use radial coordinates with respect to the r 200 of each cluster for the profiles, due to the fact that the cluster sizes also evolve with redshift. In Figure 4 we show that within r 200 , the edge can be accurately predicted from the density and Poisson equation to z = 0.75. However, outside the virial radius, the edge is increasingly underpredicted compared to the model, as a function of increasing redshift. We find the same result when using the mean background density as opposed to the critical background density when defining the cluster masses and radii. Figure 4. The variation in the fractional difference between the measured v esc edge and the predictions from the density profile from redshift 0 to 0.75. The light (dark) gray bands represent the 67% (90%) scatter of the individual halos. We can explain this by the growing influence of the halo gravitational potential well on the dynamics of the infall regions around the