adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.465.2748P (fulltext)
different cell sizes in the CMDs, they are fixed each time, i.e. they do not vary across the CMDs. From our experience in cleaning the field star contamination in the cluster CMDs, we have identified some situations which still need our attention. It frequently happens that some parts of the CMDs are more populated than others, so that fixing the size of the cells in the CMDs becomes a difficult task. Small cells do not usually carry out a satisfactory job in CMD regions with a scarce number of field stars, while big cells fail in populous CMD regions. Thus, relatively bright field red giants with small photometric errors could not be subtracted and, consequently, the cluster CMD could show spurious red giant features. A compromise between minimizing the residuals left after the subtractions of field stars from the cluster CMDs and maximizing the cleaning of field stars is always desirable. In this paper, we present a comprehensive multiband photometric analysis of Haffner9 from BVI and Washington CT 1 photometry. The cluster has been previously studied from 2MASS and BVI photometry. However, a relatively lower photometric accuracy and shallower limited magnitude, in combination with a not much pinpointed treatment of field decontamination led those studies to discordant results. In Section 2 , we describe the collection and reduction of the available photometric data and their thorough treatment in order to build an extensive and reliable data set. The cluster structural and fundamental parameters are derived from star counts and CMD and colourcolour (CC) diagrams, respectively, as described in Section 3 . The analysis of the results of the different astrophysical parameters obtained is carried out in Section 4 , where implications about the stage of its dynamical evolution are suggested. Finally, Section 5 summarizes the main conclusion of this work. 2 DATA COLLECTION AND REDUCTION We used CCD images 1 obtained on the nights of 2004 December 18 and 19 with a 2048 2048pixel Tektronix CCD attached to the 0.9-m telescope (scale 0.396 arcsecpixel 1 ) at Cerro Tololo Inter-American Observatory (CTIO, Chile). Its field of view (FOV) is 13.6 13.6arcmin 2 . We used the Washington C (Canterna 1976 ) and KronCousins R filters. The latter has a significantly higher throughput as compared with the standard Washington T 1 filter so that R magnitudes can be accurately transformed to yield T 1 magnitudes Geisler ( 1996 ). We used a series of bias, dome and sky flat-field exposures per filter to calibrate the CCD instrumental signature. We also utilized images for the Small Magellanic Cloud cluster Lindsay106, which was previously observed at La Silla (ESO, Chile) with the C and T 1 filters (Piatti etal. 2007 ). Lindsay106 was used here only as control cluster, i.e. to verify the quality of the present CTIO photometry. Table 1 shows the log of the observations with filters, exposure times, airmasses and seeing estimates. A large number (typically 20) of
Paper: 2020MNRAS.496.1941G (fulltext)
and the lack of a large additional population of variable sources beyond those identified with the CRATES catalogue. 4.4 Differential source counts Differential source counts have been intensively studied (e.g. de Zotti et al. 2005 ; de Zotti et al. 2010 ; AMI Consortium et al. 2011 ), having important implications for models of cosmic evolution (e.g. Condon 1984 ; Massardi et al. 2010 ) and provided the first clear evidence against the Steady State Theory (Pooley Ryle 1968 ). However, for the purposes of C-BASS analysis, we calculate the differential source counts to provide an additional cross-check on the statistical properties of our bright-source population. Given that we are concerned with the statistical properties of our catalogue at flux-densities , where the C-BASS catalogue is largely complete, we do not consider correction factors to the differential source counts necessary at lower flux densities to account for catalogue incompleteness and varying flux-density sensitivities across the sky. Sources are put into flux-density bins of width . For each bin, source counts are determined against a central flux density, S c , taken to be the geometric mean of flux densities in that bin. The differential source counts for a given flux-density bin are then given by where N c is the number of sources in the flux-density bin, A is the sky area to which the catalogue corresponds, and Δ S is the bin width. Errors are estimated for the source counts by assuming Poisson statistics. We only consider bins with N c ≥ 9 so as to maintain a SNR ≥ 3 for each bin. The differential source counts for C-BASS North in total intensity, applying the CG30 mask, are plotted in Fig. 12 , alongside the differential source counts for the GB6 catalogue. We can see that at low flux densities the C-BASS source counts are significantly lower. This is to be expected from the increasing catalogue incompleteness at these lower flux densities. From the differential source counts, we can again see the catalogue completeness level from the point at which the source count turn-off ends and we have good agreement between the C-BASS and GB6 counts, i.e. over flux densities . It is worth noting here that the errors obtained from assuming Poisson statistics are likely to be somewhat optimistic. These do not account for false positives in the source catalogues, or errors arising from incorrect binning due to uncertain flux-density estimates. These effects become significant at lower flux densities, where the proportion of false positives in the catalogue is greater, and our photometry methods become less reliable. Figure 12. Differential source counts calculated using the C-BASS northern sky total intensity catalogue, plotted alongside the differential source counts for the GB6 catalogue. Source counts were calculated applying the CG30 mask. We also plot as grey-scale the fitted model source counts corresponding to 100 posterior samples for the C-BASS and GB6 models. At low-flux
Paper: 2019MNRAS.483.2778I (fulltext)
generating these artificial leakage parameters, it was ensured that no off-diagonal elements of the Mueller matrix exceeded 1.5 conversion between Stokes parameters at any frequency channel, while reaching this maximum percentage in at least one frequency channel. This limit was chosen based on the following test. From our sample, we chose all pulsars that did not show any RM(φ) variations (e.g. J1048–5832 and J1709–4429). Our assumption was that the imperfections of the receiver could not generate more RM(φ) variations than were already observed. The limit was therefore chosen as the maximum value that did not create additional apparent variations in RM(φ). The systematic uncertainties because of receiver imperfections were quantified for each pulsar by randomly generating 100 receiver imperfections obeying the above description, which then were used to distort the pulsar signal in the process of polarization calibration. 3 For each of these 100 different distortions, we calculated values for RM profile , RM scatt , RM(φ), and Δ( V / I )(φ). The standard deviation of these values was quoted as the systematic uncertainty in Table 1 . The systematic uncertainties determined for the RM(φ) and Δ( V / I ) are displayed for each pulsar, for example in Fig. 1 , in the third and fourth panels as a 1σ contour grey-shaded region overplotted over the RM(φ) values (green in the online version). 4 RESULTS Pulsars described in Sections 4.1 and 4.2 and in Table 1 , for which the phase-resolved RM profile had never been investigated previously in the literature, are marked with an asterisk (*). The plots for the 98 pulsars have been included in the online supplementary material , in Figs A1–A26 and an example is displayed in Fig. 1 . All plots were aligned, so that the total intensity peaked at pulse longitude 180°. For the six pulsars from the sample, which had both a main pulse (MP) and an interpulse (IP), we aligned the MP peak at pulse longitude 90°, and hence the IP peaked around pulse longitude 270°. The results from the analysis described in the Section 3 can be found in Table 1 . The resulting RM(φ) profiles allowed us to classify the pulsars as follows. Pulsars that had were classified as showing significant RM(φ) variations, and they are discussed in more detail, on a case to case basis, in Section 4.1 . Six pulsars, which were initially classified as showing significant RM(φ) variations, were removed from this section based on their high systematic uncertainties. A total of 42 pulsars, of our sample of 98, were classified as showing significant RM(φ) variations. For all cases where we saw RM(φ) variations, we observed deviations from the expected λ 2 dependence (as quantified by , but also by inspecting PA versus λ 2 directly), implying that Faraday law fails to describe the full frequency dependence of the PA, and there must be another frequency and pulse longitude dependent effect present. Therefore, the results obtained from the panel, where
Paper: 2017ApJ...846L..16G (fulltext)
structure than in the spot. Moreover, the smearing due to the point-spread function of the telescope may dilute into the bright structure part of the magnetic signal belonging to the surrounding umbra and penumbral filaments. However, the average estimated errors for the vertical and horizontal component of the field in the structure are about ±35 G and ±70 G, respectively. Thus, the strong horizontal field of about 2000 G appears to be a robust result. The presence of such a significant horizontal component of the magnetic field allows us to exclude the field-free configuration typically observed in LBs. Conversely, it supports the possibility that the magnetic field of the flux rope forming the filaments around the sunspot may have a counterpart in the middle of the sunspot umbra, where it appears to end in the cospatial chromospheric images. This would also provide an explanation for the detection of opposite polarity in the structure with respect to the umbra, as well as an alternative interpretation for the opposite polarity of an apparently filamentary LB with respect to its hosting umbra observed by Bharti et al. ( 2007 ). Note that this phenomenon is different from the magnetic reversals found by Lagg et al. ( 2014 ) and Felipe et al. ( 2016 ) in LBs, due to the bending of the field lines along the atmospheric stratification of the structure. Moreover, this scenario is confirmed by the portions of the bright feature characterized by both upward and downward plasma motions along it, which are compatible with plasma flows along the helical field lines of a flux rope. Actually, the length of each portion of the filamentary structure with coherent plasma motion is of about 5′′, which could correspond to the pitch of a helically twisted flux tube. Therefore, we guess that this bright filamentary structure is not a filamentary LB, but it is due to the accumulation of plasma coming from higher solar atmospheric levels into the photosphere. The presence of stably inclined fields belonging to the flux rope, which are touching part of the sunspot umbra, would set in radiatively driven penumbral magneto-convective mode (Jurčák et al. 2014 , 2017 ). Indeed, high-resolution observations have shown that plasma fallen from chromospheric layers can lead to the formation of penumbra in sunspots (Shimizu et al. 2012 ; Romano et al. 2013 , 2014 ). Furthermore, penumbral-like structures, i.e., so-called orphan penumbrae, have been reported as a photospheric counterpart of a chromospheric filament (Kuckein et al. 2012a , 2012b ; Buehler et al. 2016 ). Observations of similar bright filaments inside the umbra, called UFs, have been recently reported by Kleint Sainz Dalda ( 2013 ). They found that UFs were characterized by a horizontal flow opposite to the Evershed flow and they also found from coronal images the presence of bright coronal loops that seemed to end in UFs. To interpret such phenomena, Kleint Sainz Dalda ( 2013 ) conjectured two different
Paper: 2021ApJ...910..144S (fulltext)
2010 ; Bahramian et al. 2021 ). In addition, very hard spectra in a bright hard state have been observed in a couple of NS LMXBs (Parikh et al. 2017 ) and BH LMXBs including V 404 Cyg (e.g., Kimura et al. 2016 ), V4641 Sgr (e.g., Maitra Bailyn 2006 ), and Swift J1858.6−0814 (Hare et al. 2020 ). All these sources have a strong short-term flux variation by a more than order of magnitude (e.g., Kitamoto et al. 1989 ; Revnivtsev et al. 2002 ; Wijnands et al. 2017 ), also in agreement with the present case. In addition, partial covering absorption is sometimes required in these sources, especially in high-flux phases (Kimura et al. 2016 ), consistent with Swift J0840.7−3516 in a bright phase. We note that the photon index of Swift J0840.7−3516 after the decay, ≲2, is reminiscent of a BH rather than an NS LMXB, which usually shows a larger value, Γ ∼ 3 (Wijnands et al. 2015 ), although the NS LMXB EXO 1745−248 showed a similar hard spectrum in quiescence (Rivera Sandoval et al. 2018 ). On the other hand, an NS LMXB would rather be favored, in that a very small radius of the thermal component, 1–2( D /8 kpc) km, consistent with emission from an NS surface, was obtained from the NuSTAR+Swift/XRT+NICER spectrum in a faint phase. This holds even if the observed power-law component was entirely produced by Comptonized disk photons. Assuming conservation of the number of photons and an isotropic scattered emission (Kubota Makishima 2004 ), and using the intrinsic disk photons estimated from the best-fit diskbb+cutoffpl model, we obtain the inner disk radius as ∼3( D /8 kpc) km (where i = 60° is assumed). Similar thermal emission likely emitted by the NS surface can be seen in NS LMXBs at low X-ray luminosities below ∼10 35 erg s −1 but not in a more luminous hard state, where the power-law component dominates the X-ray flux (e.g., Sakurai et al. 2014 ; Shidatsu et al. 2017 ). The thermal component in Swift J0840.7−3516 was observed at a consistent luminosity; the second rapid decay from 10 5 s since the discovery, in which this component was found, started at ∼10 35 ( D /8 kpc) 2 erg s −1 . Even if an LMXB scenario works for all the properties raised above, the observed absorption feature is difficult to explain. Although ionized Si and S lines from disk winds are sometimes seen at similar energies (e.g., Ueda et al. 2009 ), they are much weaker than the present case and always with strong iron K α lines at ∼7 keV. Assuming the observed feature to be the redshifted Fe K edge, we obtained z = 1.1. If this is the gravitational redshift caused by a Schwarzschild stellar-mass BH, the feature should be produced at ∼1.3 R S , where R S is the Schwarzschild radius. It is unclear that an absorber with such a small size can exist in the vicinity of a BH. Another possibility is that the observed feature is a different blueshifted line produced by an outflow at a relativistic speed, like baryonic jets observed in SS 433. However, it is unclear whether such
Paper: 2017MNRAS.472.2670S (fulltext)
the position angle and distance modulus measurements with no further adjustment. In the Appendix, we discuss the validity of the approximation used in equation ( 8 ) by comparison to models which explicitly employ the posterior samples from Martin etal. ( 2016c ). We find that the posterior distributions on the model parameters are very similar when using the posterior samples compared to the approximation employed throughout the paper. Additionally, in the absence of similar sets of posterior samples for the other M31 dSphs and all of the MW dSphs, we are forced to employ an approximation. We now detail each of the models employed. The first model assumes the dSphs are randomly aligned with respect to the observer, so does not utilize the position angle information. The second model assumes the major axis of the dSphs are preferentially aligned with the galactocentric radial vector. Our final model assumes the major axes of the dSphs preferentially lie in a plane. 3.2 Random alignment We attempt to infer the distribution of T and E given the observed shapes and associated uncertainties under the assumption that there is no preferred orientation for the dSphs. Implicitly, this assumes the distribution of observed position angles is uniform. Our model is described as where is the modified normal distribution to account for the asymmetric uncertainties, and subscript i corresponds to the i th of N dSphs. is a uniform distribution between a and b and is a Cauchy distribution centred on a with scale parameter b . From dark-matter-only simulations, the triaxiality and ellipticity distribution are well approximated by Gaussians (Allgood etal. 2006 ; Knebe etal. 2008a ). The natural choice for the prior on a scale parameter would be a Jeffreys prior of 1/. Instead, we heed the advice of Gelman ( 2006 ) and adopt a half-Cauchy distribution as a weakly informative prior. Very large values of are disfavoured, whilst very small values are not strongly favoured. This matches nicely our expectation that the intrinsic triaxiality and ellipticity distributions will not be -function spikes nor will they lack any structure at all. The choice of the scale as unity is inspired by the finite range of both the triaxiality and the ellipticity. One complication is that some of our variables have a discrete range so the pdf that describe them must be normalized over this range. In the case of the uniformly distributed variables, the normalization is a constant factor but for the normally distributed variables (i.e. T i and E i ) the normalization depends on the parameters and that we are attempting to constrain. Therefore, any distributions f ( x ) for a variable x constrained to lie in the interval ( a, b ) that are quoted in this paper implicitly contain a factor of N 1 where We additionally introduce the pdf given by For clarity, the log-likelihood for this model is given by where p (cos, , T, E ) is a -function related to equation ( 2 ). 3.2.1 Results The
Paper: 2017ApJ...846...60C (fulltext)
into comoving space. The underlying halos are the same. Left panels: a slice of the HCN temperature cube, in transverse (on-sky) directions. Middle panels: slices of the HCN and CO temperature cubes, with one axis along the line of sight. Note the difference in comoving volume and location. Right panels: a slice of the CO temperature cube, again in transverse directions. 2.3. Power Spectra from Observations We work with the spherically averaged, comoving 3D power spectrum P ( k ) and the average of all 2D sky angular power spectra from each frequency channel in the data cube. We calculate P ( k ) by calculating the full 3D power spectrum of the temperature cube in comoving space, binning it in radial “shells” of , and averaging the full spectrum in each bin. We obtain in each frequency channel through similar binning of the 2D power spectrum in space. As Figure 2 shows, the comoving volume is not perfectly rectangular, but we approximate it as such for P ( k ) calculations here. As in Li et al. ( 2016 ) and Breysse et al. ( 2015 ), we present the spherical 3D power spectra as and the averaged 2D power spectra in the conventional form, using a flat-sky approximation as in Chiang Chen ( 2012 ) and Breysse et al. ( 2015 ). While the 3D analog to is just P ( k ) rather than the rescaled , and the 2D analog to is the suitably rescaled rather than just , we use these rather disparate presentations to follow the conventions used in previous literature. 3. Results 3.1. Simulated Temperature Cubes For 496 different pairs of CO and HCN light cones, we generate temperature cubes for CO emission and HCN emission, which we then add together in observed voxels to obtain a simulated map for CO emission plus HCN contamination. Approximately halos in the CO light cones and approximately halos in the HCN light cones typically fall above the cutoff mass of 10 10 and within a patch. Since we used only 100 light cones populated with CO emitters and 100 light cones populated with HCN emitters, there is some redundancy in the light cones used. But since temperature maps were generated anew for each new pairing, even two maps from the same light cone are subject to some differences due to reapplication of halo-to-halo scatter in SFR and luminosity. On top of the halo-to-halo scatter in each light cone, sample variance in large-scale structure between light cones results in light cone–to–light cone scatter in temperature and power spectra. Figure 2 shows slices of a sample pair of temperature data cubes for a given pair of light cones. Note that these slices are generated with the fiducial model (the turnaround model with 0.3 dex log scatter in luminosity). The power-law model maps appear much sparser than maps from any of the other models, especially when only of sufficiently massive halos host emitters. Table 3 lists the mean line temperatures over each simulated CO map and HCN map. Variations in do not appear to significantly affect , and any differences present
Paper: 2021MNRAS.502.2984W (fulltext)
we cannot predict how the vapour production mass will scale with impact energy for very energetic giant impacts. Although the amount of mass in vapour is relatively independent of b and μ there is a strong dependence on these parameters when determining which body (target or projectile) the vapour comes from (Fig. 3 ). As expected for equal mass impacts, the vapour mass is produced equally from both the target and projectile, which is true for all impact angles (parameter b ). However, as μ decreases, the fraction of vapour production from the projectile is higher than that of the target. This behaviour is also sensitive to the impact angles as shown in Fig. 3 . Figure 3. The difference between the escaping vapour mass from the projectile normalized by projectile mass and the escaping vapour mass from the target normalized by the target mass versus the specific energy of the collision normalized by the catastrophic disruption criteria. Symbols and colour bar are same as in Fig. 2 . 3.2 Initial vapour velocity distribution Planetary embryo impacts produce a variety of post-collision dust distributions, none are isotropic as has been assumed in previous work (e.g. Jackson Wyatt 2012 ; Jackson et al. 2014 ; Wyatt et al. 2017 ). To be able to compare different distributions, we need to characterize the anisotropic nature of the collisionally generated dust. We separate the velocity of each escaping particle into two velocity components, the velocity parallel ( v ∥ ) and perpendicular ( v ⊥ ) to the direction of the impact. For example, the direction of the impact is the same (along the x -axis) for the two collisions shown in Fig. 1 . To understand and compare different distributions of particles, we choose to take the range between the 16 per cent and 84 per cent percentiles to compute the velocity dispersion for both velocity components in each collision (Δ v ∥ and Δ v ⊥ ). This allows us to compare the velocity distribution of different distributions without assuming a given distribution for any collision. We choose to take the 16 per cent and 84 per cent percentiles as this would be comparable to taking the 1σ values either side of the mean of a normal distribution. In Fig. 4 , we show the absolute velocity distributions of vapour condensates parallel (blue) and perpendicular (orange) to the direction of the collision and absolute velocity (black) for three different giant impacts. Fig. 4(a) shows sim. 168, a collision between 1.11 and 0.1 at 15 and b = 0.4. We choose to show sim. 168 as it is the closest simulated impact we have to the Moon-forming impact. Comparing to fig. 1 of Jackson Wyatt ( 2012 ), we have an overall similar distribution of absolute velocities and we find only a small difference between the direction vapour condensates are launched (close to isotropic). Fig. 4(b) shows sim. 8, a collision between two 0.1 at 10 and b = 0. Fig. 4(c) shows sim 88, a collision between two 0.1 at 10 and b = 0.8. There is a stark difference