adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...846...39H (fulltext)
regions described in Section 4.3 . In Figure 6 , we present again the oxygen abundance and ionization parameter gradients and color-code the data points based on the different groups defined in Figure 5 . Figure 6 further demonstrates the azimuthal variation visible on the oxygen abundance map (Figure 3 ). The H ii regions that belong to the spiral arms have systematically higher oxygen abundances than the other regions at the same galactocentric distances. The upstream regions have oxygen abundances in between the main spiral and downstream H ii regions. A total of 73 of the 96 (76%) main spiral H ii regions are above the best-fit abundance gradient. In contrast, only 13/76 (17%) of the downstream H ii regions are above the best-fit abundance gradient. The distributions of the residuals, after the best-fit radial gradient is subtracted, are statistically different (Figure 6 histogram). Two-sample Kolmogorov–Smirnov tests suggest that the null hypotheses that the residuals of the spiral-downstream, spiral-upstream, and downstream-upstream regions are drawn from the same distributions can effectively be ruled out (, , and , respectively). Figure 6. Oxygen abundance (left panels) and ionization parameter (right panels) gradients in NGC 1365. Each color point corresponds to one H ii region in Figure 3 . The different color symbols correspond to the different groups of H ii regions shown in Figure 5 . The black solid lines are the overall best fits. The red, blue, and green dashed lines are the linear best fits to the “Spiral,” “Downstream,” and “Upstream” H ii regions, respectively. The bottom panels show the residuals, i.e., after subtracting the black solid lines. The best-fit parameters are tabulated in Table 2 . We also perform the same least-squares linear fits to the spiral arm, downstream and upstream H ii regions. The best-fit oxygen abundance gradients shown in Figure 6 demonstrate the systematic differences between the different groups of H ii regions. The best-fit parameters are tabulated in Table 2 . It is worth pointing out that the majority of the upstream H ii regions (21/27; 78%) are in between the best-fit gradients defined by the spiral arm and downstream H ii regions. In Figure 7 , we present the azimuthal variations more clearly by subtracting the best-fit radial gradient from the oxygen abundance map. If there are no azimuthal variations, the residual map should present only random fluctuations. However, Figure 7 shows that the residuals correlate strongly with the spiral pattern. Figure 7. Oxygen abundance map after subtracting the best-fit radial abundance gradient (i.e., Figure 6 ). The azimuthal variations of oxygen abundance are now clearly visible. To summarize, while overall there is a negative oxygen abundance gradient in NGC 1365, we find pronounced azimuthal variations. The H ii regions on the spiral arms present, on average, about 0.2 dex higher oxygen abundance than the downstream H ii regions. The upstream H ii
Paper: 2016MNRAS.457L.103R (fulltext)
focal ratios of these telescopes, along with the use of single, photographic-film size CCDs, allow them to probe large areas of galaxy haloes while reaching surface brightness levels ∼2–3 mag deeper (μ r ∼ 28 mag arcsec −2 ) than the classic photographic plate surveys (e.g. Palomar Observatory Sky Survey) and the available large-scale digital surveys (e.g. the Sloan Digital Sky Survey). Here, we use this amateur telescope discovery approach to report a faint, elongated galaxy projected on to the halo regions of NGC 253 in the Sculptor Group. With a distance of 3.5 Mpc, NGC 253 is one of the nearest large spirals, and has been well studied (e.g. Davidge 2010 ; Bailin et al. 2011 ; Greggio et al. 2014 ; Monachesi et al. 2015 ), but this object, which we call NGC 253-dw2, had been missed. 1 2 OBSERVATIONS We first noticed a candidate satellite galaxy in a visual inspection of several images of NGC 253 available on the internet that were taken by amateur astronomers: Alessandro Maggi using a Takahashi Epsilon 180ED astrograph (18-cm diameter at f /2.8) and Mike Sidonio using a 12-in f /3.8 Newtonian (see Fig. 1 ). In both cases, the pixel scale was coarse and it was not clear if the elongated feature was real or an artefact or reflection as often present in amateur images. Figure 1. Left: amateur images of NGC 253 and its satellite NGC 253-dw2. The grey-scale image is from the 12-in telescope luminance filter, with colour image of NGC 253 from the 0.5 m Black Bird Remote Observatory, and field of view of ∼11 × 10 (67 kpc × 60 kpc). North is up and east is left. The zoom-in on NGC 253-dw2 is from CHART32 and covers ∼8 kpc × 7 kpc. Right: Subaru/Suprime-Cam image, colourized using g and R c bands, and demonstrating that the satellite is composed of red, discrete stars. We investigated the nature of NGC 253-dw2 by follow-up observations using amateur and professional facilities as follows. First, deep imaging of the field centred on the dwarf candidate was collected remotely with the Chilean Advanced Robotic Telescope (CHART32), an 80-cm f /7 corrected Cassegrain telescope located at the Cerro Tololo Inter-American Observatory, Chile. An FLI PL-16803 CCD camera was used, with a pixel scale of 0.331 arcsec over a 22 arcmin × 22 arcmin field-of-view. Two overlapping sets of 20 × 1200 s individual image frames were obtained through a Baader luminance filter over several photometric nights between 2013 September and November. Each individual exposure was reduced following standard image processing procedures for dark subtraction, bias correction, and flat-fielding (Martínez-Delgado et al. 2010 ). The images were combined to create a final co-added image with a total exposure time of 48 000 s. Part of the resulting CHART32 image is shown as an inset in Fig. 1 (left-hand panel). The candidate object looks fairly regular and diffuse, like a nearby dwarf spheroidal (dSph) galaxy rather than a background galaxy or an instrumental artefact. It is also very
Paper: 2016ApJ...832...90W (fulltext)
The Gini coefficient is strongly dependent on the noise level of imaging data, and suffers from large uncertainties when signal-to-noise ratio (S/N) is not sufficiently high (Lisker 2008 ). For asymmetry A , noise contribution is estimated by measuring A on a nearby blank sky region with the same aperture. However, the noise correction for A overestimates the real noise contribution (see Figure 1 of Shi et al. 2009 ). Here, we present a detailed analysis on the noise effects in quantifying A , and introduce a revised correction for noise contamination. We construct a sample of model galaxies to test the morphological parameter A . The sample consists of 569 isolated galaxies from Wen et al. ( 2014 ) (see Section 3.2 for more details about model generation). An artificial image is obtained through combining a model galaxy image with a background noise image. The parameter A is measured from both the artificial image and the noise-free model image, following the definition given in Conselice ( 2003 ) aswhere I 0 is the intensity of the galaxy and I 180 is the -rotated intensity. The second term refers to the correction for noise contamination. A group of pixels are randomly selected from the background regions to form the background noise image. In the measurement, an aperture of 1.5 times the circular Petrosian aperture is adopted to estimate the noise contamination. Figure 12 presents comparison of the measure of A from noise-added and noise-free images for the 569 model galaxies. We find that noise contamination leads A to be systematically overestimated, and the significant scatter indicates the uncertainties of noise contribution—about 20% for a high-S/N galaxy, and up to 50% for a low-S/N galaxy (Shi et al. 2009 ). The noise correction introduced in Conselice ( 2003 ) overestimates the noise contamination and biases A to be smaller. This is because all background pixels within the Petrosian aperture are used to calculate the noise contamination. As discussed in next section, however, we argue that noise contamination is correlated with the number fraction of noise dominated pixels of a galaxy, indicating that only part of the noise contribution estimated by Equation ( 6 ) is real. Furthermore, the measured A from noise-added images is found to be lower than that from noise-free model images, in a few cases, even if no noise correction is adopted. This is because that noise dramatically changes the segmentation maps of the model galaxies. In our measurements, only pixels within 1.5 times the circular Petrosian aperture are counted to calculate A , and the extended structures of the model galaxies out of the aperture are ignored, leading to an underestimation in A . For a noise-free model galaxy image, all pixels associated with the galaxy are used to compute A . Figure 12. Comparison of asymmetry measurements from galaxy images, with and without noise. Asymmetry of the noise-free model image is marked as . For galaxy image with
Paper: 2020MNRAS.495.4570M (fulltext)
TNG100 parent galaxy sample selection . Red dots show the positions of central disc galaxies in TNG100 with 10 10 ≤ M stell /M ⊙ ≤ 10 11 . Orange dots point to satellite disc galaxies in the same mass range; to avoid Virgo-like clusters, we require that satellite galaxies have no more than one massive () neighbour within 1 Mpc. Galaxies rejected on the basis of these environmental and morphological criteria are shown in black, for reference. In the background, the spatial distribution of all galaxies in TNG100 is shown in blue. In addition to this parent sample, we also defined smaller samples of stellar mass-matched TNG100 galaxies for each DNGS galaxy. This time we applied a more observer-friendly definition of stellar mass – specifically the integral of the stellar mass surface density profile. We denote this ‘observed’ stellar mass as M stell, obs . When calculating TNG100 stellar masses in this way, we only integrated the density profiles down to a floor of kpc −2 , as this corresponds to the typical depth of the DNGS observations (see Section 5.1 for more details). The physical radius corresponding to this surface density threshold varies between ∼20 and 70 kpc for the DNGS sample, although we note that this is an extrapolation (from a last observed point of kpc −2 ) in two of the eight galaxies. TNG100 galaxies exhibit an even larger variation in physical extent under this definition, ranging from ∼40 to 140 kpc. We can infer from these numbers that TNG100 galaxies are more extended than DNGS galaxies, and we will return to this idea in Sections 6 and 8.2 . As a consequence of the shape of the stellar mass function, if we were to simply select all TNG100 galaxies that fall within the observational errors (see Table 1 ), each mass-matched sample would remain strongly weighted towards low-mass galaxies by number. To alleviate this issue, we instead used rejection sampling (Press et al. 1992 ) to obtain a normal distribution of TNG100 galaxy stellar masses for each DNGS galaxy. We calculated the probability P ( M stell, obs ) of drawing each TNG100 stellar mass from a Gaussian distribution with a mean and standard deviation set by M stell, DNGS and , respectively, and accepted the first 50 TNG100 galaxies with P ( M stell, obs ) less than a random number drawn from the same distribution. We emphasize that, in addition to the definition of the mass-matched samples, all comparisons between observations and simulations were done using quantities measured from TNG100 galaxies (e.g. surface density profiles or stellar halo mass fractions) in a manner identical to the DNGS sample. When exploring parameter space that contains unobservable properties, however, we will occasionally use the ‘true’ stellar mass instead. In the following sections we will explicitly distinguish between the parent and mass-matched samples, and between ‘true’ and ‘observed’ stellar masses where applicable. 3 FROM STELLAR PARTICLES TO IMAGES The first step towards
Paper: 2018MNRAS.475..585M (fulltext)
with the power-law function. The fits included wavelength intervals that were free of BALs and ranged from approximately 1260–1350, 1370–1480, 1530–1800, and 1870–2500 Å. We integrated the P v BAL apparent optical depth profiles using the following equation, in Gaussian units: where f is the oscillator strength, λ is the rest wavelength, and τ( v ) is the apparent optical depth at a given velocity (Savage Sembach 1991 ). Rest wavelengths and oscillator strength values were taken from Morton ( 2003 ), and we treat the P v λλ1117,1128 doublet as a single line at λ = 1121 Å with an average oscillator strength of f = 0.705. For sources that showed non-variable P v BALs, we integrated τ( v ) in accordance with equation ( 1 ). For sources that showed variable P v BALs, we integrated the optical depth difference to generate lower limits for the change in P v column density. 4 RESULTS AND DISCUSSION 4.1 Absorption line variability We detect definite (category Y, see Section 3.1 ) variability from any BAL (i.e. from C iv , Si iv , or P v ) in 10 out of 71 BAL quasars in our sample (see Appendix A for notes on individual objects). Spectroscopic comparisons in our sample probe rest-frame time-scales between 0.5 d and 3.8 yr, and we measure BAL variability time-scales over ≤ 0.2–3.8 yr for our Y sources. Tentative (T) variations from any BAL are detected in 44 sources, and 17 objects exhibit no (N) BAL changes. Table 3 lists the associated ion, measured variability time-scale, LOS velocity of the variable region, and variability direction for each variable (Y) BAL in our sample. BALs not presented in Table 3 show no significant absorption line changes over the listed time-scales. For example, there are four cases of P v and/or Si iv BAL variations with no associated C iv BAL changes in the same time-scale comparison (see 013652 + 122501, 104059 + 055524, 104247 + 061521, and 0.5 yr for 122654−005430 in Table 3 ). We detect four cases of C iv BAL variability with non-variable P v absorption at similar velocities (see 2.4 yr for 122654−005430, 0.8 yr for 140532 + 022947, along with 2.0 and 0.8 yr for 221326 + 003846 in Table 3 ). There are five cases of C iv BAL changes with likely no associated P v absorption at similar velocities (see 102154 + 051646, 114548 + 393746, 2.4 and 0.8 yr for 122654−005430, and 2.4 yr for 140532 + 022947 in Table 3 ). In two cases (025042 + 003536 and 230721 + 011118; see Table 3 ), it is unclear if the C iv BAL variations are associated with P v absorption at the same velocity. Table 3. Variable BAL properties. Information listed for each BAL quasar includes (from left to right): ions associated with variable BALs, measured time-scales of BAL variability (upper limits that are limited by temporal sampling), LOS velocities corresponding to maximum flux changes within the BALs, and whether the BAL depths increased (+) or decreased (−). Multiple, distinct time-scales listed for the same ion are in chronological order.
Paper: 2020MNRAS.498.3601B (fulltext)
also listed in Table 2 . The decay time-scale is in these cases of the order of or shorter than the decay time of the previous stages, meaning that the coalescence would supposedly happen promptly once the hard binary is formed. None the less, the hardening and inspiral time-scales we inferred in Table 2 are definitely longer than the ≈15 Myr found by Khan et al. ( 2016 ), who addressed the pairing of ∼10 8 M ⊙ MBHs in a massive early-type galaxy at z ≈ 3, which would become a BCG by z ≈ 0. The discrepancy in the time-scales has to be attributed to the large central density in their merging systems, which is nearly 10 5 M ⊙ pc −3 at 10 pc (their fig. 6), i.e. ∼100 times larger than in our case (see also the discussion in Khan et al. 2018 ). 13 Note that the system they explored is not a main-sequence galaxy as the one presented in this paper, but instead it is a more massive and relatively rare galaxy. For this reason, we expect the time-scales we obtain in this paper to be more representative for the bulk of the galaxy population. Note that the hardening and inspiral time-scales extrapolated for our system do not account for the effect of gas (which is expected to assist the orbital decay at scales larger than a pc, but could hinder it at smaller scales; Souza Lima et al. 2020 ), and the possibility that the fragmentation of a non-resolved circumnuclear disc could render even this smaller-scale decay very stochastic (Fiacconi et al. 2013 ; Souza Lima et al. 2017 ). If we neglect these complications, our estimates suggest that the whole pairing process could take as short as a few hundred Myr and probably less than a Gyr for the majority of the studied MBHs; that is, the MBH merger is likely going to occur at z = 5–3.5, except for the MBH that undergoes the bar-induced ejection. Concerning the fate of this latter object, one could wonder how likely this scattering event is. Even if our statistical sample is too limited to make strong statements here, it is worth mentioning that a test simulation with virtually the same initialization as the current one also showed evidence for a bar slingshot, suggesting that the event can be relatively common. 4.5 Caveats and limitations Our simulation has the important limitation that MBH accretion and feedback were not taken into account. However, it is reasonable to expect that feedback would render DF even less efficient by evacuating the gas mass around an MBH (see e.g. Park Bogdanović 2017 ; Souza Lima et al. 2017 ; del Valle Volonteri 2018 for a smaller scale analysis of this aspect). Each inspiralling MBH may also enhance its mass by accreting gas along the decay: in this respect, it is worth remembering that the MBH masses we adopted in the current run are relatively large for z ≳ 6, thus we do not expect this to represent a major issue. We also note that accretion on to MBHs may be used in future high-redshift surveys to sample offset or dual active galactic nuclei (e.g. Capelo et al. 2017 ; De
Paper: 2021MNRAS.505..689J (fulltext)
i.e. brighter quasars may live in a region of higher overdensities (e.g. see Guimarães et al. 2007 , and references therein). In order to check this effect, we devised a subsample of low and high luminosity. In the low- and high-luminosity bins, we have taken 25 lowest and 25 highest (∼29 per cent of the total sample) luminosity quasars of our sample. As can be seen from Fig. 7 , the excess pixel optical depth ratio is more pronounced for the subsample of higher luminosity with log L 912 > = 31.85 ergs s −1 Hz −1 as compared to the subsample of lower luminosity with log L 912 > = 31.12 ergs s −1 Hz −1 . The integrated value under the curve (e.g. see Fig. 7 ) between −6 ≤ r ≤ 0 Mpc is found to be higher by a factor of 3.70 ± 0.22 and 1.62 ± 0.15 w.r.t. the corresponding integrated value of curve for high- and low-luminosity subsamples, respectively. However, we do notice that higher luminosity bin has a higher median redshift of z em > = 4.06 in comparison to z em > = 3.85 for the lower luminosity bin (e.g. see grey shade). However, the above R τ ( r ) difference, especially in the −2 ≤ r ≤ 0 Mpc bins, cannot be merely explained by the redshift difference in these two subsamples. Figure 7. Left-hand panel : Same as right-hand panel of Fig. 6 , but for the extreme 25 low- and 25 high-luminosity quasars in our sample (e.g. see text in Section 4 ). The Lyman continuum luminosity was calculated using V -band magnitude (e.g. see Section 2.1 .) Right-hand panel : The plot shows radius versus flux (similar to Fig. 4 ) for the transmitted flux of the subsample of extreme 25 low- and 25 high-luminosity quasars. The transmitted flux of the control sample of IGM for the higher luminosity and lower luminosity quasars is shown in red-dashed and blue dot–dashed lines, respectively. Additionally, we also used our sample to check for any difference of density profile among radio-loud (RL) and radio-quiet (RQ) quasars (e.g. see Sopp Alexander 1991 ; Dunlop et al. 2003 ; Gopal-Krishna, Mangalam Wiita 2008 ; Retana-Montenegro Röttgering 2017 ) where the former is generally associated with the presence of jets. Such bimodality is quantified by a radio-loudness parameter ( i.e. ) defined as a ratio between the rest-frame flux densities at 5 GHz and 2500 Å (see also Ivezić et al. 2002 ; Kellermann et al. 2016 , and references therein), with its value ≥ 23 for RL sources as adopted by Ganguly et al. ( 2013 ). Out of the 85 quasars in our sample, 62 have information about its radio-loudness parameter with 12 being RL (i.e. 23) quasars and 50 being RQ (i.e. ) quasars (e.g. see Perrotta et al. 2016 ). We plot R τ ( r ) for these two sub-samples (i.e. RL and RQ quasars) as shown in Fig. 8 . From this figure, it is evident that their R τ ( r ) profile is quite similar in both sub-samples, though the sample size of RL quasars is small here (12 quasars). Here, we also note that the typical luminosity (median) among these two sub-samples is almost similar being 2.23 × 10 31
Paper: 2018ApJ...860...71D (fulltext)
et al. ( 2006 ) and Kuwabara et al. ( 2009 ) in a numerical model best fitted to the measurements taken by the muon network at Earth (rigidities 50–100 GV) and was utilized to determine the orientation of the interplanetary FR. The diffusion–expansion approach was also implemented by Subramanian et al. ( 2009 ) and Arunbabu et al. ( 2013 ) in an analytical model and was compared to the GRAPES-3 muon telescope (rigidities 12–42 GV). These authors estimated the radial perpendicular diffusion coefficient to be of the order of 10 21 . However, it should be noted that their studies are adjusted to large events associated with a shock and observed at high particle rigidities. At rigidities of about 1 GV, Cane et al. ( 1995 ) estimated a diffusion coefficient of about 10 19 by utilizing spacecraft measurements and less energetic ejecta. Both are roughly in agreement with values obtained from the typical empirical expression used in numerical models (see Equations (23) and (24) in Potgieter 2013 ). Therefore, these studies outline a rough constraint on the diffusion coefficient used to model FR-associated FDs. Since both diffusion and expansion are processes that presumably start close to the Sun, FRs, that is, CME initial properties, need to be considered in the model. This ejecta-only FD model primarily describes FDs associated with ICMEs without a shock/sheath region, because a shock/sheath region presumably introduces an additional decrease via different physical mechanisms, but could also be applied to explain the ejecta part of an FD in two-step FDs. The aim of this study is to take these considerations into account in the diffusion–expansion model to improve our understanding on the cause, formation, and evolution of FDs. 2. Basic Diffusion Model First we consider an analytical FD model based on the perpendicular diffusion of GCRs into the FR, similar to Cane et al. ( 1995 ). In this basic diffusion model, the FR is regarded as a closed magnetic structure, rooted at the Sun, that is initially empty of GCRs (Figure 1 (a)). The FR is of cylindrical form (hereafter approximated by a long cylinder), it moves with constant velocity, and as it moves, it does not vary in shape or size (Figure 1 (b)). We note that the assumption of constant velocity is in general not valid, as it was shown that CMEs slower than the solar wind accelerate, whereas CMEs faster than the solar wind decelerate (e.g., Sheeley et al. 1999 ; Gopalswamy et al. 2000 ), which is attributed to the magnetohydrodynamical drag (see, e.g., Vršnak et al. 2013 ; Hess Zhang 2014 ; Sachdeva et al. 2015 , and references therein). However, since the acceleration/deceleration depends on the difference between the CME and solar wind speed, for a substantial subset of CMEs (and especially the slower ones that do not drive shocks), a constant velocity can be taken as a relatively fair approximation. We also note that the assumption of constant shape and size might not hold true, as it was