adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...647A..70H (fulltext)
T / T c = 10 −2 . ( e ) GC3 WC, T / T c = 10 −4 . ( f ) GC3 WC, T / T c = 10 −6 . ( g ) GC4 LC, T / T c = 10 −2 . ( h ) GC4 LC, T / T c = 10 −4 . ( i ) GC4 LC, T / T c = 10 −6 . ( j ) GC4 WC, T / T c = 10 −2 . ( k ) GC4 WC, T / T c = 10 −4 . ( l ) GC4 WC, T / T c = 10 −6 . The orbital decay time for a wide range of parameters is shown in Fig. 8 for the two GCs inside the core radius of the Fornax dSph, GC3 and GC4, in the two density profiles considered. τ DF generally either attains a minimum value, τ DF, min , or approaches infinity. The minimum values in the region V > c − and δ ρ / ρ 0 1 are summarized in Table 3 , with τ DF in the range 67 Myr−197 Myr. These timescales are considerably smaller than the CDM result assuming the same density profiles, 515 Myr−1327 Myr, with the dynamical friction given by Binney Tremaine (2008) Table 3. Minimum orbital decay time τ DF, min of GC3 and GC4 in the LC and WC models for the Fornax dSph, found in the region V > c − and δ ρ / ρ 0 1 (for which perturbation theory is properly valid), with the CDM result given by Eq. ( 87 ) for comparison. Notes. The listed values are essentially constant for all temperatures for which the assumptions made are valid, T / T c ≲ 0.1. For V c − , perturbation theory instead predicts the dynamical friction to quickly vanish, causing τ DF to become infinite. The criterion on g / m 2 due to the hydrostatic core radius, Eq. ( 82 ), is also listed. The values for g / m 2 are given in units of eV −4 . where Λ ≈ r δ v 2 / G M , , δ v is the velocity dispersion of CDM particles, taken to be δ v ≈ V , and erf is the error function. The decay time remains small even if parameters used to model the Fornax dSph, the GCs, and the dynamical friction are varied, as illustrated in Fig. 6 . A notable exception is the position of the GC, for which τ DF is considerably shorter when closer to the halo center, and likewise longer when further away. This implies that the value for τ DF obtained from Eq. ( 79 ) overestimates the time it takes the GC to fully decay from its current position, but it also implies that the migration towards the halo center was slower in the past when the GCs were at larger radial distances. Indeed, estimates of τ DF, min for GC1, GC2, and GC5, all of which are located at r ≳ 1 kpc, give decay times in excess of 4 Gyr. In the CDM case, the decay times for these GCs are even longer: 17 Gyr and more. These estimates do not suggest a timing problem for the outer GCs, even if their decay times are considerably shorter for SIBEC-DM compared to CDM. However, we note that these GCs are near or outside the radius r s , where the density profile of the dSph falls sharply, and therefore we do not expect the result for the dynamical friction, nor Eq. ( 79 ), to necessarily provide a reasonable estimate of τ DF . Nonetheless, the present results show that for a large region of the relevant parameter space of the SIBEC-DM model considered here, GC3 and GC4 are currently
Paper: 2018MNRAS.478.1141O (fulltext)
but slightly lower mean ellipticity ⟨ε⟩ = 0.38 ± 0.05. Clampitt Jain ( 2016 ) used the technique to measure the quadrupole weak-lensing signal, and applied it to a sample of SDSS clusters. They corrected anisotropic PSF following Reyes et al. ( 2012 ) and Huff et al. ( 2014 ). They considered the noise from the intrinsic shape of galaxies and measurement on each background galaxy following Mandelbaum et al. ( 2013 ) and Sheldon et al. ( 2012 ). They obtained the best-fitting value of the mean ellipticity of ε = 0.19 with 1σ uncertainty of ∼0.05. They ascribed this smaller value to the misalignment between major axis of the BCG and that of cluster halo which is implicitly assumed to be aligned. Given the large uncertainty, their result is broadly consistent with our result. Shin et al. ( 2018 ) applied the quadrupole technique to SDSS clusters. They estimated anisotropic PSF, measurement noise, and noise from the intrinsic shape following Clampitt Jain ( 2016 ). The resulting mean ellipticity value with a prior of the satellite galaxydistribution is ⟨ε⟩ = 0.45 ± 0.09 after correcting for Poisson sampling. They also measured the ellipticity of satellite galaxy distribution as ⟨ε⟩ = 0.42 ± 0.04, and that derived from stacked weak lensing with a prior of the CG major axis as ⟨ε⟩ = 0.25 ± 0.06. By comparing these ellipticity values, they also estimated the rms misalignment angle of 30° between the CG and DM halo and 18° between satellite galaxies and DM halo. These misalignment values are in good agreement with our result (see also Section 6.2 ). van Uitert et al. ( 2017 ) used an estimator similar to Clampitt Jain ( 2016 ) and constrained the average ellipticity of galaxy groups obtained from Galaxy And Mass Assembly (GAMA) survey combined with the weak-lensing signal measured by Hildebrandt et al. ( 2017 ) from the Kilo Degree Survey (KiDS). They did not consider anisotropic PSF, but consider the intrinsic shape noise. They compared different priors for stacking analysis of weak-lensing signals at different scales. Their resulting values of the mean ellipticity are ε = 0.38 ± 0.12 (40 R 250 kpc) and ε = 0.05 ± 0.13 (250 kpc R 750 kpc) for the BCG prior, whereas ε = −0.04 ± 0.11 and 0.349 ± 0.13, respectively for the prior of the satellite galaxy distribution. They concluded that the BCG major axis (satellite galaxy distribution) is aligned (misaligned) with the DM halo orientation on small scales (250 kpc), whereas the BCG major axis (satellite galaxy distribution) is misaligned (aligned) with DM on large scales (>250 kpc). This result appears to be inconsistent with our result which indicates that the distribution of satellite galaxies is aligned better than the major axis of the CG at all scales, 100–1000 kpc. This discrepancies are partly because they use galaxy groups with M 200 ∼ 10 13 M ⊙ rather than galaxy clusters we considered in this paper, M 200 ∼ 10 14 M ⊙ . Nevertheless, further work is needed to explain this inconsistency, for
Paper: 2017A&A...598A.110R (fulltext)
resonant stripes, where comets have experienced secular eccentricity pumping. This sometimes goes in the direction of circularizing the orbits, but very often leads into the sungrazing end state. This structure would be smeared out in the plot by Levison et al. ( 2006 ; their Fig. 1b) but, most likely, in reality it does not exist. The small nudges given to the comets in their integrations would inhibit the long-term action of these resonances in most cases (see Valsecchi et al. 1995 ), while we introduced the artefact of leaving the comets entirely in the hands of the giant planets. We find that deflected comets get dynamically ejected only if their starting semi-major axis is larger than 2.6 au. For smaller values, if Jupiter-crossing occurs, it is only for very high eccentricity, so encounters are rare and inefficient in changing the orbits. In these cases, collision with the Sun is very likely, and many of the survivors are “saved by the bell”, since our integrations stop after 100 000 revolutions. Looking at the statistics of end states for the deflected comets, we find a remarkably high probability (more than 12%) of collision with the Sun, and most of these belong to the above-mentioned category. We derived the steady-state number of comets in the Encke-like domain and made a comparison with the results of Levison et al. (2006) , which are quite different. In fact, using our first approximation to the transfer mechanism, we predict only of the number of Encke-like comets found by Levison et al. (2006) . Thus, our method is severely lacking in transfer efficiency, and several reasons for this are easily identifiable. First, consider the limitation caused by cutting the integrations to a total of 100 000 revolutions. Compare this to the example provided by Levison et al. (2006) of a comet meeting their requirement for an Encke-like comet. Before this happens, the comet spends a million years (roughly 250 000 orbits) in a decoupled state waiting for the completion of its transfer. If this situation is typical, we missed most of the transfers by stopping the integrations too early. Moreover, as in the case mentioned, most transfers may require several interventions by terrestrial planets – something that our procedure forbids. It is true that more than one encounter as close as those that we modelled is not likely to occur for the same object. However, significant perturbations may be caused by the more distant and more frequent encounters that our procedure neglects. The main reason for the inefficiency of our transfer model appears to be the premature cutting of the integrations and the limitation to the closest encounters, which are too rare to have a large effect. If we had integrated all the comets until ejection or collision, some more deflections would have occurred. However, the increase would not be large, as seen from the fact that more than 80% of the deflections that we followed up by further integrations happened before the 50
Paper: 2016A&A...591A..65B (fulltext)
difference for the XIS detector normalization constants between both observations. We attribute this difference to the different data extraction modes and the lacking attitude correction for the first observation, as the simulation of the effective area of XIS, which is performed by the FTOOL xissimarfgen in the course of data reduction, is highly sensitive to the source position and extent. Possible deviations of the apparent source position due to thermal wobbling might therefore introduce an unknown systematic uncertainty to the flux measured by XIS. For this reason, we normalized the detector constants with respect to PIN. The fitted fluxes in the 15–50 keV band are about 10% higher than the corresponding NuSTAR observations, which agrees with flux calibration uncertainties ( Madsen et al. 2015 ). For future reference, we also determined the continuum flux for other energy bands than those given in Table 2 . For the first observation, the measured fluxes are and in the 2–10 keV and 3–60 keV band, respectively, and for the second observation and for the corresponding energy bands. Fig. 7 a) Unfolded phase-averaged spectra of Obs. I (red) and Obs. II (blue) and b) ratio of the phase-averaged spectra of Obs. I and Obs. II. For clarity, only XIS3, PIN and GSO data are shown in both panels. The ratios of the individual detectors have been corrected for the respective cross-calibration constants given in Table 2 . Fig. 8 Panels a) and b) : phase-averaged spectrum of XIS0 (blue), XIS1 (red), XIS3 (magenta), PIN (green), and GSO (brown) of Obs. I with best-fit model and residuals. Panels c) through g) : count rates of the respective phase-resolved spectra (1) to (5), divided by the count rate of the phase-averaged spectrum. Figure 7 shows the unfolded spectra of Obs. I and Obs. II in comparison and the photon ratio of both observations. We observe a slight change in spectral slope below 2.5 keV, while the ratio is mainly flat above this energy with slight differences in the Fe band. Above the cyclotron line energy the spectra are very similar in shape. Comparing the fit parameters of both Suzaku observations, we find a decrease of the photon index and folding energies toward higher flux. Fürst et al. (2014) observed the opposite trend regarding the first and second NuSTAR observation. There is, however, an artificial correlation between these two parameters, which is not taken into account here, so we cannot conclude that the Suzaku and NuSTAR results contradict each other. The excess of soft photons in Obs. I below 2.5 keV is at least partly explained by the increase of the blackbody temperature from Obs. I to Obs. II, while the relative normalization of the blackbody component is very similar in both observations. This behavior is consistent with NuSTAR . See Fürst et al. (2014) for a more detailed investigation of the parameter evolution over the outburst. The fitted value is comparable to the value of given by the Leiden/Argentine/Bonn
Paper: 2019ApJ...872..191S (fulltext)
). CSM densities and radii of well-known Type Ia SNe and confirmed Type Ia SNRs studied in radio or X-rays, along with SN 1885A and G1.9+0.3 from this work (shown in red). The blue dashed and dotted lines indicate different CSM profiles of the form ( is mass-loss rate, u w is wind velocity, μ = 1.4) created by isotropic mass loss at constant rates and constant velocities given in the legend box. The horizontal gray bars show examples of cavity sizes created by accretion-driven outflows from the progenitor due to fast and slow winds (Badenes et al. 2007 ). The ruler on top shows the timescales on which these CSM profiles formed in the pre-supernova stage. The gray region shows the typical extent of the warm ISM phase (Ferrière 2001 ). The red dashed line indicates the average ISM density of the M31* circumnuclear region where SN 1885A is located (Li et al. 2009 ). Object references: SN 2011fe (Chomiuk et al. 2012 ), SN 2014J (Pérez-Torres et al. 2014 ), Tycho (Slane et al. 2014 ), SN 1006 (Raymond et al. 2007 ). Our upper limit on density of 0.04 ± 0.03 (sys) cm −3 derived in Section 5.1 suggests that, on average, SN 1885A has interacted with very little CSM material. A significantly denser CSM, if present, would have been captured in our measurements of n 0 in Figure 3 (a), and could have produced detectable radio emission. A low-density CSM (≲0.1 cm −1 ) around SN 1885A was also inferred from the non-detection of X-rays from SN 1885A (Perets et al. 2011 ). Our inferred density is lower than that of well-known Type Ia SNRs, and well-studied, recent nearby Type Ia SNe such as SN 2011fe and SN 2014J, although the density values are in the range of the warm ISM phase (Figure 5 ). The strong density limit rules out some portion of the SD parameter space for SN 1885A. For example, if the progenitor binary emitted a wind, the mass-loss rate, , would have been 10 −4 M ⊙ yr −1 (assuming wind velocity u w = 1000 km s −1 ) or 10 −6 M ⊙ yr −1 (assuming u w = 20 km s −1 , see Figure 5 ). Modeling the shock dynamics in such an r −2 profile can lead to even deeper constraints on , because the SNR would have evolved in much denser CSM in the past, and this would have left an observable effect on the radius and velocity of the SNR. However, as noted in Badenes et al. ( 2007 ), it is likely that the CSM structure on parsec scales resulting from an SD configuration is more complex than either a uniform or r −2 profile. Accounting for realistic CSM would require detailed modeling to predict the radius, velocity, and radio luminosity of the SNR, and is beyond the scope of this paper. In the future, it would be productive to revisit the observational constraints from this paper and Fesen et al. ( 2015 ) when a grid of models become available, predicting the CSM structure out to ∼10 pc for a variety of SN Ia progenitors (C. Badenes et al. 2019, in preparation). Such an analysis would allow us to make definitive statements about the progenitor of SN 1885A. As an
Paper: 2017MNRAS.468.1551Z (fulltext)
the interaction between the outgoing shock and the CSM. The X-ray data of SN 1978K prior to 2006 have been analysed; however, whether its flux has remained constant since the first detection by ROSAT or started to decline was still under debate (e.g. Lenz Schlegel 2007 ; Smith etal. 2007 ). It is worth noting that there are another two ULXs (X-1 and X-2) in NGC 1313. Thus, this region has been frequently visited by X-ray missions in order to explore the spectral evolution of these sources (e.g. Feng Kaaret 2006 ; Bachetti 2013 ; Weng, Zhang Zhao 2014 ). In the past decade, some additional long observations were carried out with XMMNewton (Table 1 ), which is a focusing X-ray telescope having a large effective area in the soft X-ray band. In this paper, we study the XMMNewton observations covering the period from 2000 to 2015 (spanning 5277d), aiming to explore the evolution in X-ray emission of SN 1978K at very late times. The XMMNewton data reduction is described in the next section, and the results are presented in Sections 3 . Discussion and conclusion follow in Section 4 . Table 1. XMMNewton observations of SN 1978K. Net exposure: clean exposure after background flares excluded. 2 DATA REDUCTION The detailed study presented here was on data from the main camera on board XMMNewton , the European Photon Imaging Camera (EPIC), which consists of two MOS arrays and one PN CCD array (Strder etal. 2001 ; Turner etal. 2001 ). We analyse all of the available XMMNewton observations of SN 1978K made from 2000 October to 2015 March; however, we discard those data that were affected by the strong background flares or when the source falls in the CCD gaps. The observational details of the data used in this paper are listed in Table 1 . The XMMNewton observations of SN 1978K are quite clean except two short data observed in 2003 December (ObsIDs = 0150280401, 0150280501), and only less than 12percent of data on average are excluded. The data are reduced with the Science Analysis System software ( sas ) version 14.0.0. We create the light curve above 10keV, and exclude background flares with a count-rate cut-off criterion. The data from good time intervals are filtered by setting FLAG = 0 and PATTERN 4 for the PN data, and PATTERN 12 for the MOS data to ensure the best quality spectrum (Snowden etal. 2016 ). The source photons are extracted from a circle aperture with a radius of 30arcsec, while the background is taken from the same CCD chips as the source and at a similar distance from the readout node. Since the source is relatively faint ( flux 10 12 erg cm 2 s 1 ), the pile-up effect can be neglected. We produce the response files with the sas tasks rmfgen and arfgen to facilitate subsequent spectral analysis. The task specgroup is carried out to rebin all spectra to have at least 20 counts per bin and not to oversample the instrument energy resolution by more than a factor of 3. We fit the spectra in the 0.510keV range using xspec with a 2percent
Paper: 2017ApJ...851..102M (fulltext)
of the ion spectra, here taken to be 30 MeV nucleon −1 . The figure suggests that neutron production is more effective for steep ion spectra, and this is true but only if the number of ions above this normalizing energy is kept constant as the index is changed. Figure 6. Total angle-integrated neutron yield, , as a function of the accelerated-ion spectral index for accelerated α /proton = 0.1 and 3 He/ 4 He = 0.0, 0.5, 1.0, 5.0, and 10.0. Even for accelerated 3 He/ 4 He ratios as large as 1, neutron production is seen to be increased only by about a factor of 2. Only for much larger 3 He enhancement factors would the neutron yield be significantly affected. Most of the impact is for ion spectral indexes near the middle of the index range (3–6). We can understand this by considering Figure 7 , showing the fraction of the total neutron yield contributed by each of the 11 reaction types (p+ 4 He, 4 He+H, p+H, 4 He+ 4 He, p+[CNO], [CNO]+H, 4 He+[CNO], [CNO]+ 4 He, 3 He+H, 3 He+ 4 He, and 3 He+[CNO], where “[CNO]” refers to all nuclear species heavier than 4 He). For spectral indexes of 3–5, the 3 He + H reaction contributes more than 10% of the total (and the new 3 He + [CNO] reactions, shown in red, are not significant). For flatter spectra, neutron production is dominated by proton reactions so the impact of enhanced 3 He is less. For steeper ion spectra, the contribution of the 3 He + H reaction falls due to its relatively high threshold energy and the 3 He + [CNO] reactions increase to contribute up to about 10%. We note that for steep spectra, the contribution of the 4 He + [CNO] reactions and their inverse reactions [CNO] + 4 He are both strong. As noted above, the corresponding 3 He inverse reactions ([CNO] + 3 He) are not included because of the very low abundance of 3 He in the ambient solar atmosphere ( 3 He/H ∼ 10 −5 ). Figure 7. Fraction of the total neutron yield contributed by the 11 reaction types for accelerated α /proton = 0.1 and 3 He/ 4 He = 0.5. The red curve shows the fraction from 3 He + [CNO] reactions. Figure 8 shows the total angle-integrated neutron yield as a function of accelerated-ion spectral index for two values of accelerated α /proton: 0.1 and 0.5. For each value, total yields are shown for (1) coronal accelerated-ion composition and 3 He/ 4 He = 0 (solid), (2) coronal accelerated-ion composition and 3 He/ 4 He = 0.5 (dotted), and (3) impulsive-flare accelerated-ion composition and 3 He/ 4 He = 0.5 (dashed). For the α /proton = 0.1 case, the impact of the impulsive-flare accelerated-ion composition via the inverse reactions can be seen for the steeper spectra because the cross sections for neutron production by α -particle (and 3 He) reactions with heavy elements tend to have lower threshold energies than those of proton reactions; these reactions are emphasized by steep accelerated-ion spectra. The impact of the impulsive-flare accelerated-ion composition is not as large for the α /proton = 0.5 case because
Paper: 2018ApJ...864...96C (fulltext)
for both efficient and inefficient conduction appear to follow the general shape of the data. High velocities also correspond to high q values, reflecting the effects of shocks on both the cloudlets within a cooling run and the filaments in the conduction runs to compress the gas. 5. Application: Down-the-barrel Outflow Observations As an illustration of the types of studies enabled by our results, we consider an application of our tabulated fits. We consider the observations in Chisholm et al. ( 2018 ) in particular as many of our assumptions, including radiation background and ionization parameter, are derived from these observations. Chisholm et al. ( 2018 ) report absorption profiles from down-the-barrel observations, of a lensed galaxy with z ≈ 2.9. Observations are made of both low and high ionization profiles that indicate that the two phases are cospatial, much like the wind-cloud interaction considered here. We aim to determine an appropriate scaling of our column density profiles by accounting for two factors that can influence the optical depth of the absorbing clouds. To approximate an absorption profile for these ions, we estimate the observed optical depth for a particular ion aswhere is the average number density of the ion, where x is the number of points within the column density profile and N i is the column density for the i th point. Here τ is approximated with the center of the absorption profile at the average velocity of the cloud, v 0 , with a velocity dispersion determined by the b parameter estimated for each ion, and v is the velocity bin within the absorption profile. We consider velocities between −600 and 200 km s −1 offset from line center. This average optical depth approximates a single cloud. There are then two ways to parameterize the absorption profile, we consider the two parameters independently. The first method is altering covering fraction, which describes the fraction of the area within the sightline that is obscured by the cloud. With this covering fraction parameter, the observed flux from the derived column density profiles can be expressed aswhere τ ( v ) is the average optical depth above and f is the free parameter describing the covering fraction. The second way to parameterize the absorption profile considers scaling the optical depth either to represent multiple clouds along the sightline or one cloud with scaled density. For this, we assume that the intervening cloud has the average optical depth of a single cloud from Equation ( 16 ), which is then scaled by the parameter α , which describes the number of clouds within the sightline or the scaling factor of the density for a single cloud. In this case, the observed flux can be described aswhere τ ( v ) is again the average optical depth above and α is the second free parameter we consider, describing the number of clouds. Best-fit covering fraction and α are found by performing χ 2 minimizations for each of the parameters independently