adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...620A.103B (fulltext)
has a small impact on the vertical and radial density distributions of the stellar discs. These two last parameters will be constrained with observations and not by the dynamical self-consistency. Thus, these two parameters and are currently fixed before they can be more tightly constrained by kinematical observations. Then we assume that and we fixed for each stellar disc the velocity dispersion ratio σ U / σ W at the solar position, respectively 2.1 for the thin discs ( Gomez et al. 1997 ; Robin et al. 2017 ), 1.47 for the young thick disc, and 1.38 for the old thick disc ( Soubiran et al. 2003 ; Robin et al. 2017 ). Figure 1 (left) shows the vertical density distribution of the old thin disc (scale height around 230 pc) at three Galactic radii 4, 8, and 12 kpc, and Fig. 2 (left) its radial distribution at four z = 0, 200, 400, and 600 pc. The adjustment is satisfying. This is expected at R = 8 kpc since the BGM2014 version is built to be dynamically consistent up to z = 1 kpc. The fact that the fit is also satisfying at other galactic radii confirms that the choice of Einasto profiles in the BGM2014 version for the disc density was valuable with respect to the dynamics. For the other discs, the agreement between the BGM2014 and the new version is correct and is better for the thinner disc components with smaller vertical velocity dispersions. For the old thick disc, the vertical and radial density distributions (corresponding to a 795 pc scale height in the case of a s e c h 2 density law) are shown in Figs. 1 and 2 (right). The agreement between both models is still correct. We note that at R = 8 kpc and below z = 1 kpc the fit is excellent, illustrating the exactness of the dynamical consistency of the BGM2014 version. However, at larger height above the Galactic plane, both models differ, and at two scale-heights, or 1.6 kpc height, the BGM2014 density for the old thick disc is 17 % too small. Concerning the density scale length, the agreement between the two models is nearly perfect for the old thin disc. For the old thick disc, the agreement is obtained only for z lower than 1 kpc. We note that the flare present in the outskirts of the Galaxy could be naturally modelled by adjusting the vertical velocity dispersions at large Galactic radii. Table 1 gives the relative difference in percentage between the new and the old models at different heights z and at R = 8 kpc, for the old thin and old thick discs. The differences increase with z ; they become significant at three scale heights for the old thin disc and beyond 1 kpc for the old thick disc. At high z , the differences become of the order of the expected density of the stellar halo, and this illustrates the necessity of using dynamically self consistent distribution functions to properly identify and separate the Galactic stellar components. However, only observations will tell us whether our choice of nearly isothermal modelling of stellar discs is the correct one since small
Paper: 2018MNRAS.481.2125B (fulltext)
The PM diagrams of Fig. 1 present spike features for high velocity stars. 2 These spikes reflect low-quality astrometric measurements, specifically due to a dishomogeneous scanning pattern of Gaia , and can be identified through the astrometric quality parameter variability_periods_used (see e.g. Lindegren et al. 2018 ). The details of the effects on kinematics of these features are described in a follow up paper (Bianchini et al. in preparation). In this work, we verify that the rotation detections reported in the next sections do not depend on these features: kinematics subsamples that include a cut in the variability_periods_used give results fully consistent with the one obtained in the following sections. 3.2 Global rotation measurements To measure the rotation on the plane of the sky, we convert the stellar positions into cartesian coordinates centred on each cluster (centres from the Harris Catalogue; Harris 1996 ), with the positive x -axis pointing towards West and the positive y -axis pointing towards North, using equation ( 1 ) from van de Ven et al. ( 2006 ). The PMs in the RA and Dec. components are converted accordingly into a cartesian coordinate system using the orthographic projection reported in equation ( 2 ) of Gaia Collaboration et al. ( 2018a ), with positive μ x in the direction of West. 3 Finally, we transform the PMs in radial and tangential components (μ r , μ t ) in polar coordinates, with positive μ r indicating velocities away from the cluster centre and positive μ t indicating counterclockwise movements. In the rest of this paper we will focus on the analysis of the tangential component of the PMs and analyse the radial component in Section 4.2 . We compute the global rotation along the tangential component within 3 R h using equation ( 2 ), as described in Section 2 . The results are reported in Table 1 and plotted in Fig. 2 . Out of the 51 GCs analysed, 11 display an average value of μ t significantly different than zero at a confidence level >3σ. These clusters are indicated as red symbols in Fig. 2 . We confirm the detection of rotation in NGC 104, NGC 5139, NGC 5272, NGC 5904, NGC 6656, NGC 6752, NGC 6809, and NGC 7078 reported by Gaia Collaboration et al. ( 2018a ), and additionally we detect rotation in NGC 4372, NGC 6273, and NGC 7089. Figure 2. Mean PM along the tangential component measured in a three half-light radii field of view around the cluster centres. The absolute value of μ t ( 3 R h ) is plotted as a function of the distance of the clusters. Red stars indicate clusters for which rotation is detected at least with a 3σ confidence level. Dotted lines indicate PM values in km s −1 for a given distance (, with μ measured in mas yr −1 , v measured in km s −1 , and d in kpc). Gaia Collaboration et al. ( 2018a ) and Lindegren et al. ( 2018 ) reported that systematics errors are present in Gaia DR2, in particular a ∼1° small-scale component, related to the Gaia scan pattern. Following the procedure
Paper: 2016ApJ...830L..22A (fulltext)
transverse MHD wave derived from 3D MHD numerical simulations and forward modeling. The main factors affecting the intensity modulation in the host loops are the KHI and resonant absorption. Importantly, the KHI vortices (TWIKH rolls) carry over characteristics of the resonant absorption and phase mixing mechanisms, allowing these to be detected with current instrumentation. We have found that the KHI produces significant intensity changes and that emission lines with different temperature sensitivity to either the loop core or the external medium provide insights into different wave modes. At a high spatial resolution and especially in the hot line, the fine strand-like structure generated by the TWIKH rolls can be observed, as described in Antolin et al. ( 2014 ). At a lower spatial resolution corresponding to AIA, we have shown that the detected damping and period can vary depending on the emission line, which can lead to an out-of-phase behavior between the core and boundary lines. These results can be understood from the fact that the boundary line is more sensitive to the KHI dynamics, to resonant absorption and phase mixing. A decay-less oscillation is obtained in the boundary line for any LOS angle and this is the result of periodic brightenings and the coherent motion of the vortices. The vortices, in turn, result from unstable azimuthal Alfvén waves. Therefore, this decay-less oscillation is more reflective of the Alfvén waves coupled resonantly to the kink mode than the global kink mode itself. Resonant absorption transfers the energy of the kink mode to the Alfvén waves, whose dampings are expected to be much lower than that of the kink mode, since they rely on phase mixing and the turbulence resulting from the instability. The Alfvén waves therefore act as an energy reservoir for the TWIKH rolls, which persist over time, leading to the decay-less oscillation. A shorter period is found for the boundary line and is due to phase mixing. Indeed, the azimuthal Alfvén waves in the boundary layer have an increasingly higher phase speed the farther they are from the loop core. Since it is these waves that are enhanced due to resonant absorption and that become K-H unstable, their signal becomes dominant in the boundary line. The fit to the density from the numerical model ends up with a shorter period than that obtained from the core line. This is because the density profile is influenced by the KHI dynamics, which broaden the flux tube, even though the small-scale structure produced by the instability is unresolved. Accordingly, when fitting only the central section of the flux tube along the oscillation axis, which is minimally influenced by resonant absorption and by the KHI, we obtain a period of 256 s, closer to that obtained from the core line. The out-of-phase behavior between the core and boundary lines is ultimately due to the same effect that leads to the out-of-phase behavior between the LOS velocity and the POS motion seen in
Paper: 2018A&A...620A...1M (fulltext)
model the angular mask. Different assumptions on the angular selection used to construct the random catalogue might impact the measurement at small scales ( s ≲ 10 h −1 Mpc), although the effect is within the current measurement uncertainties (see Appendix A.4 ). To minimise the impact of shot noise error due to the finite number of random objects, we construct our random catalogue to be 100 times larger than the XXL cluster sample. 3.4 Covariance matrix The covariance matrix, C i , j , which measures the variance and correlation between 2PCF bins, is defined as where the subscripts i and j run over the 2PCF bins, k refers to the 2PCF of the k th of N R catalogue realisations, and is the mean 2PCF of the N R samples. The normalisation factor, , which takes into account the fact that the N R realisations might not be independent, is , , and for the subsample, jackknife, and bootstrap methods, respectively ( Norberg et al. 2009 ). We assess the XXL 2PCF covariance matrix with the bootstrap method, using 1000 realisations obtained by resampling galaxy clusters from the original catalogue, with replacement. The impact of this choice is discussed in Appendix A.3 . 4 Cosmological analysis We perform a Bayesian statistical Markov chain Monte Carlo (MCMC) analysis of the 2PCF by sampling the posterior distribution of Ω M , the only free parameter in the assumed flat ΛCDM model considered. As we verified, the current clustering uncertainties do not allow us to consider more general cosmological scenarios, for example models with free dark energy equation of state parameters, by exploiting only the XXL cluster clustering. The joint cosmological analysis of XXL cluster number counts and clustering will be presented in a forthcoming paper. We consider the commonly used likelihood function, , defined as where is the inverse of the covariance matrix estimated from the data with the bootstrap method (Eq. 5 ), N is the number of comoving separation bins at which the 2PCF ( ξ ) is estimated, and the superscripts d and m stand for data and model. The likelihood is estimated at the mean pair separations in each bin (see Sect. 3.2 ). The 2PCF model in redshift space, ξ m ( s ), is computedas where ξ DM ( r , z ) is the real-space dark matter 2PCF, which we estimated by Fourier transforming the power spectrum, P DM ( k , z ), computed with the software CAMB ( Lewis Bridle 2002 ). Since the present cluster clustering analysis focuses at sufficiently large scales, the dark matter power spectrum can be safely computed in linear theory, , with marginal effects on our results. The model depends on two free quantities, fσ 8 and b eff σ 8 (since ), and on the reference background cosmology used both to convert angles and redshifts into distances and to estimate the real-space dark matter 2PCF (see e.g. Marulli et al. 2017 ). The geometric distortions caused by an incorrect assumption of the background cosmology are modelled by the α parameter, i.e. the ratio between the
Paper: 2021ApJ...916...89R (fulltext)
relative astrometry between the initial image and each of the later sets using Astroalign . To uncover or place limits on transient emission on these timescales, we perform digital image subtraction using the HOTPANTS software package (Becker 2015 ). If an optical or NIR source is detected within the GRB localization regions (Evans et al. 2009 ; Lien et al. 2016 ), we perform aperture photometry on the residual images to determine the source brightness using the IRAF/ phot package. We determine zero-points by using point sources in common with the SDSS Data Release 12 (Alam et al. 2015 ), Pan-STARRS DR1 and DR2 (Chambers et al. 2016 ), and 2MASS (Skrutskie et al. 2006 ) or USNO-B1 (Monet et al. 2003 ) catalogs. If there are no significant residuals at or near the localization region, we obtain an upper limit from sources detected at 3 σ significance in the field. As we obtain the limit from the initial image, our values do not include additional noise from the subtraction. In total, we present 52 unpublished upper limits and three unpublished detections of 23 bursts, 10 with known redshifts (Table A1 ). 2.3. Observations from GCNs We search the GCNs of all short GRBs detected between 2013 and 2020 for constraining upper limits to ensure that our catalog covers all Swift short GRBs to date (2005–2013 is covered by the literature sample in Section 2.4 ). Considering that the range of peak kilonova luminosities based on models is ν L ν ≈ (1−5) × 10 42 erg s −1 (∼10 times the peak luminosity of AT 2017gfo; see Figure 1 ), we only include data that could feasibly fall into the range of kilonova luminosities, or fainter than a given depth, corresponding to griz ≳ 22.5 mag and JHK ≳ 21.0 mag (AB system) at z ≈ 0.25. We choose z ≈ 0.25, as 80% of known redshifts in our sample fall above this cut. Observations significantly brighter than this are not included, as they are likely dominated by afterglow emission. In total, we collect deep optical and NIR limits of 18 bursts, seven with confirmed redshifts, from the GCNs (Table A1 ). Figure 1. Light curves of AT 2017gfo and four diverse kilonova models considered in this work in the griJ bands. Light curves are shown in ν L ν (left y -axis) and absolute magnitude (right y -axis) units, and rest-frame time in days. In each panel, we show the interpolated, smoothed AT 2017gfo data set (solid dark-blue line; Villar et al. 2017 ), an NSBH kilonova model (blue dotted line; Kawaguchi et al. 2020b ), an infinite-lifetime NS remnant kilonova model (red dashed line; Kasen et al. 2015 ), an extreme optimistic magnetar-boosted kilonova model (turquoise solid line; Fong et al. 2021 ), and a model for an optimistic scenario motivated by GW190425 (magenta dashed–dotted lines; Barbieri et al. 2020 ). Black and open triangles denote upper limits of short GRBs with known and unknown redshifts, respectively, in the respective rest-frame bands at δt rest = 0.1–10 days. The most constraining limits of bursts with known
Paper: 2019ApJ...874...34Y (fulltext)
for Different Dark Matter Density Profiles in NSCs Figure 9 shows the final masses of IMBHs as a function of the initial ones. For those IMBHs that could deplete NSs during one Hubble time, the final masses include the contribution from all the stellar BHs and NSs in their host NSCs. Figure 9. Same as Figure 6 but for the IMBHs in NSCs. The circles, at about 1500 M ⊙ when α = 1.5 and 7800 when α = 2.0 and 7/3, mark the masses with which an IMBH could deplete all the stellar BH in the same cluster. The solid dots are the masses corresponding to NS depletion, which is about 1500 M ⊙ when α = 2.0 and 3200 M ⊙ when α = 7/3. 4.2. Merger Rate in Nuclear Star Clusters Per Mpc<sup>3</sup> The calculation of the merger rate per unit Mpc 3 for NSCs is almost the same as the previous calculation for GCs, but with two crucial modifications. (1) For the mass function of IMBHs, we consider two models. The first is suggested by Babak et al. ( 2017 ), which isAnother model, suggested by Gair et al. ( 2017 ), is more conservative,In this latter one, the exponent is positive, which significantly reduces the number of IMBHs. (2) When α ≥ 2.0, to calculate the M min in the integration of the mass function is determined by the solid dots in Figure 9 , because smaller IMBHs in NSCs would have depleted all the NSs. The corresponding merger rates for BHs and NSs per Mpc 3 are presented in Table 6 . Again, we find an enhancement of BH and NS merger rates in the presence of DM minispikes. Table 6 The Merger Rate per Mpc 3 of IMRIs or EMRIs with BHs R BH and Neutron Stars R NS Formed by IMBH for Different Dark Matter Density Profiles From the calculations we can also find that the results are sensitive to the BH and NS supply. With DM minispikes, the duty cycle of BHs and NSs may be shorter than the Hubble time, as the dramatically increased merger rate can lead to a high efficiency of consumption of compact objects. As a result, the appearance nowadays strongly depends on the total BH and NS supply. 5. Could Stellar Dynamics Deplete the Minispikes? We have shown in Figures 1 – 3 that dynamical hardening, by ejecting intruding stars, is important for the initial evolution of IMRI/EMRIs, when pc. It is worth discussing here whether the same process could eject DM particles and hence deplete the DM minispikes. The difference between hardening against DM and hardening against stars is that the interloper stars that we considered are gravitationally unbound to the IMBHs but the DM particles are deep in the potential well of the IMBH. The DM particles are more difficult to deplete. To show this more clearly, we calculate the hardening timescale associated with DM using the ejection timescale derived in Sesana et al. ( 2008 ) for the stars gravitationally bound to binary MBHs. They showed that it is , where P 0 is the orbital period of the binary. For example, the pink solid line in Figure 10 illustrates the dependence of this timescale on the IMBH mass. Figure 10. Same as
Paper: 2016MNRAS.456.2900A (fulltext)
one. The choice of S depends on the sampling strategy, and we recommend a fine scanning of the complete velocity spectrum which corresponds to S = 1 and would be covered by 1 + N − R bins. Then, we calculate the median for the spatial coordinates as well as for the radial velocity for each data bin. Median, as a central value estimator, is more robust than mean against the presence of outliers, and we expect to find some points away from the central value in most cases. A robust estimator of the standard deviation is given by: (see Gott et al. 2001 , for a detailed discussion of median statistics), where MAD is the median of the absolute deviations from the data median. The error of this central value can then be approached as . The b parameter is dependent on the distribution function underlying the data. In the following we will take b = 1.4826 as default. The next step is to obtain the MST graph for the R objects in each bin. The median of the edge length distribution is the first indicator of the group compactness (equation 2 ). As discussed by Maschberger Clarke ( 2011 ), median is the best estimator of the central value of the edge length when we could be facing a clumpy distribution for a given velocity range. The same is applied to a set of R randomly extracted objects from the complete sample. We repeat this step 500 times and calculate the mean and the standard deviation of the 500 medians. We chose 500 repetitions to get a reliable average of the edge length median for the R sized random sample, as recommended by Allison et al. ( 2009 ). This way we estimate the edge length median of the MST for a reference set of R objects, representing the whole sample. Then we estimate the kinematic segregation index, , as where j is the bin identification and i is the order of the first element in the bin, both being connected through the relation i = 1 + ( j − 1) × S . Supra-index 500 indicates that the average is calculated over 500 stochastic realizations. The total number of bins is given by the integer part of 1 + (( N − R )/ S ). A plot of versus the median of the radial velocity in the RV j interval for all j , shows what we name the spectrum of kinematic groupings which provides an interesting and useful tool for the exploratory analysis of the stellar system phase space. Those that verify the inequality mark the radial velocity channels, RV j , where a kinematic segregation has been detected. Here, we decided to multiply by two the standard deviation term as a conservative criterion in the detection of groupings (confidence level greater than 95 per cent). Note that is not the median of the 500 standard deviations but the standard deviation of the 500 medians. Figure 1. Schematic representation of the six test cases. From top to bottom and from left to right they are: (1) two spatially separated clusters with the same central velocity of 1 km s −1 ; (2) two clusters separated both in space and in velocity, where the mean velocities
Paper: 2021ApJ...913..134K (fulltext)
z sample imply the presence of a puffed-up torus in the low- N H NLRGs, suggesting that L / L Edd extends to lower values than those in the high- z sample. 7.2. <italic>N</italic><sub>H</sub> Distribution The distributions of the intrinsic N H in the medium- and high- z 3CRR samples are presented in Figure 17 . The high- z sample (on the right) shows a bimodal distribution, where quasars have N H 10 22.5 cm −2 , consistent with low obscuration at face-on inclination angles, while the NLRGs show N H > 10 22.5 cm −2 , implying higher obscuration at higher inclination angles, consistent with unification schemes. There are two quasars with moderate column densities (10 22.5 N H /cm −2 10 23 ) and hard HRs (0 HR 0.5) in this sample. In the medium- z sample, the distributions of quasars and NLRGs overlap. Although the quasars show N H 10 22.5 cm −2 , similar to quasars at high redshifts, the NLRGs have a much broader range of column densities that extend to lower, quasar-like values in the low- N H NLRGs. These NLRGs possibly have low L / L Edd , which allows clouds with low column density to form in the vicinity of the central engine (Section 6.3 ). Such low L / L Edd NLRGs are missing from the high- z sample. Figure 17. Histograms of the X-ray equivalent intrinsic hydrogen column density N H for the medium- z 3CRR sample (left) and the high- z 3CRR sample (Wilkes et al. 2013 ; right). Quasars are shown in blue and NLRGs in red. Upper limits, mostly for quasars with no evidence for intrinsic absorption, are indicated by arrows. CT NLRGs in the medium- z sample with no measurement of N H due to low S/N are indicated by an underlined 3C identification. Note the low- N H (≤10 22 cm −2 ) NLRGs in the medium- z sample that are not present in the high- z sample. Although a simple unification model was sufficient to explain the X-ray data and the bimodal N H distribution in the high- z sample, this is not the case in the medium- z sample. An additional parameter, a range of L / L Edd , is required to explain the large range of N H in NLRGs seen at intermediate inclination angles, skimming the edge of the torus or accretion disk atmosphere/wind. As a result, the broad range of L / L Edd smears the N H distribution for NLRGs, removing the bimodality that was found in the high- z sample. Turning this argument around, because the unification model was sufficient for the high- z 3CRR sample, producing a bimodal and narrow N H distribution, the L / L Edd ratio must have a narrower range and higher values compared to the medium- z sample, allowing orientation effects to dominate the properties of the high- z sample. To test this hypothesis, we compiled spectra of the high- z 3CRR quasars (from the SDSS archive; Barthel et al. 1990 ; M. Vestergaard D. Stern 2021, private communication) and measured the black hole masses from the widths of the C iv and Mg ii emission lines. The masses (measured in 12 out of 20 high- z quasars) are in the range of M BH = 10 7.7 to