adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...872...87L (fulltext)
rotated about 1.1 turns. Accompanied by the rotation, some fine dark threads moved as a whole from the southeast to the northwest to the coronal loops (Figures 2 (a)–(f)). At last, most of the threads interacted with the overlying field (see also Figure 4 (f)). In this process, a circular ribbon developed at the site of filament eruption as indicated in Figures 2 (b) and (e). By 09:04 UT, an interaction between the shifting filament and the coronal loops occurred and a brightening of the filament appeared at the reconnection sites (indicated by the white arrow in Figure 2 (f)). The brightened plasma around the reconnection region may be heated to higher temperature. Meanwhile, the reconnection resulted in the formation of some newly closed hot loops connecting “P” to “N1” (indicated in Figure 2 (i)). From AIA 304 Å observations at 09:06 UT, the filament was split and disintegrated completely. It is clear that the draining process is not freely falling but shaped by the magnetic field, and the bulk of filament mass flowed away along the nearby magnetic field, taking the shape of a jet (Figures 2 (g) and (h)). Figure 2. AIA 94, 304 Å, and H α images showing the eruptive process of the filament and subsequent interaction between the filament and coronal loops. (a–c) H α images showing the filament eruption. The solid arrow in panel (c) denotes the shift movement of the filament during filament rising. (d–g) 304 Å images showing the eruptive filament colliding with coronal loops. The blue and white contours have the same meaning as in Figure 1 . The coronal loops originating from “N1” are superposed with white lines, such as in panel (d) of Figure 1 . (h, i) Close-up views of the interacting region at AIA 304 and 94 Å, for which the FOV is indicated by the dashed line box in panel (f). Panel (i) is a 94 Å image showing newly formed hot loops. The white lines with an arrow (slice1 and slice2) mark the slit position of time slices as shown in Figure 3 (c) and (f), respectively. The white box of panel (g) indicates the FOV for Figure 4 . The animated version of this figure compares the AIA 94, 211, and 304 Å images from 08:40 to 09:10 UT compressed to a 5 s movie. (An animation of this figure is available.) Figure 3. (a–c) H α images showing an unwinding motion of filament (counterclockwise from above). A curved arrow indicates the direction. (d) Time slice taken from H α images at the position marked by slice3 in panel (b). A strand rotating around the filament is highlighted by the white dotted line. Figure 4. (a, b) Normalized light curves of AIA 94, 304, 335, and 1600 Å, and the positive magnetic flux. Fermi HXR 10–14 keV and 14–25 keV fluxes are also plotted. The AIA curves and magnetic flux are calculated from the eruptive region outlined by the white box in panel (f) of Figure 2 . (c) Time-slice plot for the shift motion of the eruptive filament along slice1 in Figure 2 (c), with the white lines marking the filament threads. The two
Paper: 2016MNRAS.460.2853S (acknowledgments)
The authors are thankful for the anonymous referee for his/her comments. JS acknowledges the support from the Capital Fund Management's J.P. Aguilar PhD fellowship and the ETH Post-doctoral Fellowship from the Swiss Federal Institute of Technology (ETH Zurich). The Nice group is thankful to the Agence Nationale pour la Recherche under grant ANR-13-BS05-0003-01 (MOJO). JS and FM acknowledges the support from Universidad Nacional Autónoma de México grant PAPIIT IA101113, FM also from CONACyT's grant 178377. This work has been in part carried out within the frame of the National Centre for Competence in Research ‘PlanetS’ supported by the Swiss National Science Foundation. Computations have been done on the ‘Mesocentre SIGAMM’ machine, hosted by Observatoire de la Côte d'Azur. Part of this work was performed using HPC resources from GENCI [IDRIS] (Grant 2015, [100507]).
Paper: 2020ApJ...888..125B (acknowledgments)
We would like to thank the anonymous referee for their comments, which improved the manuscript. We would like to thank the NuSTAR Galactic Binaries Science Team and G. Vasilopoulos for comments and contributions. M.C.B. acknowledges support from NASA grant Nos. NNX15AV32G and NNX15AH79H. This research made use of NuSTARDAS, developed by ASDC (Italy) and Caltech (USA), XMMSAS developed by ESA, ISIS functions (ISISscripts) provided by ECAP/Remeis observatory and MIT ( http://www.sternwarte.uni-erlangen.de/isis/ ), and the MAXI data provided by RIKEN, JAXA, and the MAXI team.
Paper: 2019ApJ...871..257P (fulltext)
good λ 2 fits for the other RMs measured at different locations as well (some of them are shown in Figure 3 ) and also in the other five data sets not presented in Figure 2 . We omitted the jet and RMs at ≈900 mas from the core at 2 GHz for better visualization; those data are presented in Figure 3 . At lower frequencies, Faraday rotation is observable in more outward regions of the jet due to longer cooling times of the jet plasma. At higher frequencies, Faraday rotation is observable closer to the compact upstream emission thanks to better angular resolution and less depolarization. We note that the RM distributions are patchy at all frequencies because significant linear polarization is detected only in some parts of the jet, possibly due to substantial depolarization in the other parts. We also note that it is unlikely that those patchy RMs are artifacts because we found that the RMs in different epochs at the same observing frequency are detected in similar locations of the jet (see Appendix C ). Figure 2. Color map of the RM distribution, overlaid on contours of the total intensity of the M87 jet in three VLBA data sets (out of eight) at 2 (a), 5 (b), and 8 GHz (c). Contours start at 0.79, 0.54, and 0.53 mJy per beam for the 2 GHz, 5 GHz, and 8 GHz maps, respectively, and increase by factors of 2. The RM colorscale in units of rad m −2 is shown at the top-right corner. Beam sizes are illustrated by the gray shaded ellipses. All maps are rotated clockwise by 23° relative to astronomical R.A.–decl. coordinates for better visualization. EVPA as function of λ 2 , along with the best-fit λ 2 laws at the locations indicated by the black dashed arrows, is shown in (d)–(f). We note that all RMs measured at different locations show good λ 2 fits (see Figure 3 ). We omitted the jet and RMs at ≈900 mas from the core at 2 GHz for better visualization (see Figure 3 ). Figure 3. Same as Figure 2 but including HST -1 at ≈900 mas from the core at 2 GHz and showing EVPA– λ 2 diagrams for various jet locations where significant Faraday rotation is detected. 3.2. Radial RM Profile To obtain a radial RM profile along the jet, we calculated spatially binned RM by taking the weighted mean of all values in each separated region of the map with similar RM values. A priori, taking a weighted mean over a part of a map assumes that all individual pixels are independent from each other, which is not the case here. Pixels values are correlated across the extension of a resolution element (here, the synthesized beam). Thus, we first calculated a mean value, then its formal error (which assumes all pixels to be uncorrelated), and then multiplied this formal error by , where n is the number of the pixels used for taking the mean, Σ RM the size of the map region with RM values, and Σ FWHM the area within the FWHM of the synthesized beam. We present the mean distance from the black hole, the binned RM values, and corresponding RM errors in Table 2 . This data is used
Paper: 2017ApJ...848..116B (fulltext)
to the shortening of the flux tube or slingshot effect of the collapsing field (i.e., the Fermi acceleration of type A or B; Northrop 1963 ), causing an increase in the parallel energy. However, Figure 5 (a) demonstrates that this is only the case for small pitch angles. However, these orbits are reflected at the bottom boundary ( z = 0). This assumption is presumably unrealistic, as discussed in Section 9 . It is also noteworthy that none of the orbits illustrated in Figure 4 have come close enough to the generalized x -line or the separator to have crossed the presumed region of the parallel electric field (within the estimated ion inertial length or resistive scale). Thus, pre-acceleration by the direct electric field is probably not an important factor unless this region were greatly extended, for instance, through turbulent effects. The stepwise energy gain, as illustrated in Figure 5 , can be interpreted in two ways. This becomes obvious from the energy equations (see Giuliani et al. 2005 ; Grady et al. 2012 ), which, for simplicity, we write in its nonrelativistic form, which is derived from Equations ( 7 ) and ( 8 ) with neglect of the inertia term The parallel electric field term is included in Equation ( 28 ) for completeness; although, it is disregarded in our orbit calculations, as discussed in Section 3.4 . The first and last terms on the rhs of Equation ( 27 ) describe the betatron effect from the increasing (or decreasing) magnetic field strength or the drift motion into the spatially increasing (or decreasing) magnetic field. Similarly, the last term in Equation ( 28 ) describes the gain (or loss) from the motion in a curved field, which is the first-order Fermi acceleration (type B; Northrop 1963 ). However, these last terms can also be re-written as where and are the gradient and curvature drifts, as defined in Equation ( 7 ). Thus, the betatron and Fermi acceleration of the electrons in the collapsing field are equivalent to the gradient B and curvature drift components, respectively, antiparallel to the electric field (see, Northrop 1963 ; Birn et al. 2013 ). These terms dominate the acceleration. It occurs at the loop tops because both the drift and the field gradients are largest there. 6. Parameter Effects Choosing the high-shear, initially force-free case as an example, we have explored the influence of various parameters on the amount and location of particle acceleration. Figure 6 provides a comparison of the flux enhancements for t = 110 at z = 1, which is slightly above the photospheric boundary, for different energies, pitch angles, and relativistic parameters ξ (representing the ratio of the characteristic energy to the electron rest energy). Panels (a)–(d) of Figure 6 show the relative energy gains, as a proxy of the flux enhancement, for different parameters indicated in the margin, while panels (e)–(g) show MHD quantities that might be related to the energetic particle fluxes: downward Poynting flux S z ,
Paper: 2020MNRAS.495.3276L (fulltext)
more evident the lower is the observed stellar mass of the galaxy, because of the low chemical enrichment predicted by the more truncated IGIMF. Therefore, the IGIMF does not seem to work in the regions of very low SFR and metallicity typical of UFDs. In the case of CVn I, which is considered more as a dSph galaxy, a short infall time-scale, typical of UFDs, does not allow us to reproduce the cumulative SFH derived by observations, since it predicts a too step rise of the cumulative SFH. However, if a longer infall time-scale is assumed, the MDF and the [α/Fe] abundances are not well reproduced, for both the two IMFs. Therefore we are not able to determine which IMF better reproduces the four observational constraints of this galaxy. More data on this galaxy are necessary before drawing firm conclusions. We are forced to conclude that the models with the Salpeter IMF are able to better reproduce the data than the IGIMF, at least for the version adopted here ( R14 ). Perhaps an IGIMF with a weaker dependence on the SFR and a stronger dependence on metallicity would better fit the properties of these extremely small galaxies. Recently, a new IGIMF has been proposed by Yan, Jerabkova Kroupa ( 2017 ) and Jeřábková et al. ( 2018 ) which enhances the dependence of the slopes of IMF on metallicity, extending it to the low mass range. Such IMF has been applied to the chemical evolution of elliptical galaxies by Yan et al. ( 2019 ). Nevertheless, the models assuming the Salpeter IMF that we have selected as the ones best reproducing the present-day stellar mass match well the [α/Fe] trends but, for a large number of galaxies, they predict too many metal-poor stars than what have been derived by observations. This is similar to the G -dwarf problem but, with the exception of CVnI which has a longer SF episode, it persists even for longer infall time-scales than the one adopted here (τ inf > 0.005 Gyr). The explanation of such a discrepancy could be an observational bias: the samples we have used to build-up the MDFs could be influenced by the instrumentation capability to detect extremely low-metallicity stars because of the weak spectral lines characterizing these stars. Comparing the abundances of UFD stars with the Galactic ones, we suggest that at least a fraction of UFDs could not be the building blocks of the halo given the different [α/Fe] trends observed for UFDs and the Galactic halo. To draw firmer conclusions more data about UFDs are necessary and more elements should be studied such as barium (Spitoni et al. 2016 ). For future projects, it will be interesting to put more stringent constraints on the stellar mass and IMF of UFDs by using photo-chemical evolution models that reproduce at the same time the observed chemical abundances and photometric properties (including the CMD) of UFD stars, taking into account the intrinsic incompleteness of observations (see also Vincenzo et al. 2016 ). Finally, another promising follow-up will be to study
Paper: 2021MNRAS.505.1311H (fulltext)
GHz were respectively, obtained based on the integrated profile of 2.25/8.60 GHz with typical SNRs >10.28 in the middle of each segment. Then, the time-of-arrivals (ToAs) of pulses were obtained by cross-correlating integrated profiles with the standard template of corresponding frequencies with pat command. Next, we combined the ToA sets from 2.25/8.60 GHz after removing the delay of the receiver system, and carried out timing analysis with TEMPO2 software package (Hobbs, Edwards Manchester 2006 ) to measure the spin properties of the magnetar. The length of each segment lasted until there were obvious changes in the integrated profiles or the error of fitted exceeded 2.70 × 10 −12 which was about 10 per cent of mean (Champion et al. 2020a ). In Table 1 , two adjacent segments are distinguished with different background colours. The top three panels of Fig. 1 show the fitted ν, , and DM changing with the time along with some previous published results (Borghese et al. 2020 ; Champion et al. 2020a , b ; Enoto et al. 2020 ; Hu et al. 2020b ; Joshi Bagchi 2020 ; Lower et al. 2020b , c ; Lower Shannon 2020 ; Rajwade et al. 2020 ; Ridolfi et al. 2020 ). The data points from Champion et al. ( 2020a )’s work were captured from the published plots with the open-source software engauge digitizer . 2 Figure 1. The plots about ν, , DM, W 50 , S 2.25 , S 8.60 , and α of Swift J1818.0–1607 changing with the time. The best fit long-term ν for our observation data and all observation data are shown with solid and dot line, respectively, in top panel. Table 1. Parameters of Swift J1818.0–1607 observations. a Start time. b Mean flux density at 2.25/8.60 GHz. c Spectral index. d Integrated profile morphology: single (S), double (D), and triple (T) peaks. It is clear that the long-term decline trend of ν previously reported by Champion et al. ( 2020a ) and Hu et al. ( 2020a ) went on in our observations, though still changes erratically on short time-scale. We did a linear fit to the ν measurements with the least-square method base on only ours and all observation results shown in the top panel of Fig. 1 . The best-fitted was about and , respectively. Champion et al. ( 2020a ) also reported a similar fitted of using the data spanning nearly 100 d. Combined with all observations, the showed continuous oscillations instead of random variations after the discovery burst. It also was noticed that the amplitude of the oscillation seems to decrease in time. The DM values obtained with TMRT are consistent with previous values. The DM range is between 699 and 705 except for three observations before MJD 59040 (Joshi Bagchi 2020 ; Karuppusamy et al. 2020 ; Lower et al. 2020b ). 3.2 Integrated profiles Integrated profiles of pulsars provide important information of the structure of the radiation beam. With updated ephemeris, the integrated profiles for each epoch of 2.25/8.60 GHz observations were obtained and showed chronologically in Fig. 2 . For easier comparison,
Paper: 2019ApJ...871...32K (fulltext)
y -axis, and time is given on the x -axis in days. 2.1. Perturbation in an Accretion Disk One purpose of our model is to simulate the long-term variability in SMBHBs. The variability can be caused by the dynamical parameters of the system, as is shown in Figure 1 , but also by the intrinsic variability of one of the components. As often observed in the light curves of single active galactic nuclei (AGNs), under long-term monitoring, the flux perturbations are present in the form of outbursts (Shapovalova et al. 2010 ; Graham et al. 2017 ), in the form of long-lasting flares as in the case of binary black hole candidates NGC 4151 (see Figure 2 in Shapovalova et al. 2008 ) and E1821+643 (Shapovalova et al. 2017 ), or as remarkably low states or minimum states characterized by an exceptionally weak continuum and line fluxes, also seen in the case of binary black hole candidates NGC 4151 (Shapovalova et al. 2008 ) and NGC 5548 (Bentz et al. 2007 ). The long-term variability of some objects has been successfully modeled by the variety of disk perturbations ranging from the precession of an elliptical disk, or a disk with a spiral arm, to bright spots, highly ionized fast accretion disk outflows, and rotated, sheared, and decayed bright spots (see Jovanović et al. 2010 ; Popović et al. 2012 , and references therein) and cold spots (Kasliwal et al. 2017 ). The perturbations are well localized in the light curve, which possibly reflects the sharp edges of the emission region, and usually resemble a Gaussian-like form. If the angular dimension of the emission region is much smaller than the viewing angle, a distant observer could not detect the anisotropy of its radiation. Based on the above reasons, the perturbations in the light curves have been modeled with Gaussian, exponential, and various other functional forms (e.g., Valtaoja et al. 1999 ; Kudryavtseva et al. 2011 ; Angelakis et al. 2015 ). Kaulakys Alaburda ( 2009 ) and Kelly et al. ( 2011 ) proposed models of light curves based on superpositions of exponentially decaying perturbations occurring at random times and with random amplitudes (the latter is also recognized as Gaussian process regression). Gaussian-like perturbations could arise from the intrinsic quasar variability, for example, the convergence of the Poissonian process once applied to the AGN light curve as reported by Pecháček et al. ( 2008 , 2013 ) can be understood in the general statistical sense as convergence to the Gaussian random process (Fageot et al. 2017 ). Based on previous discussion, there is some evidence that the Gaussian-shaped perturbations are seen in AGNs and even in magnetohydrodynamical simulations, though it is not the only type that can emerge. Usually considered perturbation is in the form of an outburst, which is obtained directly from the magnetohydrodynamic simulation with a hot spot in the disk (Balbus Hawley 1991 ; Abramowicz et al. 1992 ; Poutanen Fabian 1999 ; Życki 2002 ; Armitage Reynolds 2003 ;
Paper: 2019MNRAS.484..510N (fulltext)
offering a realistic quantification of the effect, we resort to a simulation of an isolated halo of mass . The simulation is similar to those used by Nusser ( 2018 ) to study the effect of dynamical friction of the population of the star clusters in NGC 1052-DF2 . The simulation contains 2.4 × 10 5 particles of equal mass distributed according to the Einasto mass profile. The particles were advanced forward for 10 Gyr using the publicly available treecode by Barnes Hut ( 1986 ). Further details can be found in Nusser ( 2018 ). We select those particles found at distances of less than a trimming radius r tr = 10 kpc from the halo centre in all 50 snapshots covering the last 5 Gyr of the simulation. We assume that those particles were never at a farther distance and we identify them as composing the halo surviving a tidal stripping process. We then project the particle velocities along a single axis (the l.o.s) and compute the corresponding velocity dispersion, σ u , at a 3D distance r from the centre. We compare this with the velocity dispersion computed from all original particles (i.e. including pre-trimming particles passing by from distances beyond 10 kpc). The red and blue solid curves in the top panel of Fig. 2 plot square velocity dispersions versus r for the trimmed (tidally stripped) and the untrimmed haloes. The l.o.s velocity variance is represented by the solid curves. As r approaches the trimming radius of 10 kpc, of the trimmed halo (red solid curve) falls substantially below the untrimmed halo (blue solid). The ratio of between the two curves approaches unity only at r 2 kpc. The dotted curves represent the variance, , of the radial (from the halo centre) velocity component. The red dotted curve actually drops to zero at 10 kpc, since there are no particles moving beyond that radius. The dashed curves are the tangential velocity variance divided by 2, . Trimming also reduces the tangential dispersion but the reduction in the is mainly due to the lowered dispersion in the radial direction. Note that the velocity dispersion ellipsoid in the original halo is skewed to the tangential direction with an anisotropy parameter for 2 ≲ r ≲ 10 kpc. The reduction in σ u clearly depends on the parameter β of the original halo. We emphasize that the numerical experiment presented here is simply for illustration purposes. In fact, haloes in simulations with generic initial conditions based on the ΛCDM model have a marked tendency at large radii towards a positive β (e.g. Colín, Klypin Kravtsov 2000 ; Mamon Łokas 2005 ; Ascasibar Gottlöber 2008 ; Mamon, Biviano Murante 2010 ; Lemze et al. 2012 ; Sparre Hansen 2012 ). This implies both a more significant reduction in σ u and also a more isotropic velocity ellipsoid in the trimmed halo at r r tr . Figure 2. Top: The variance of velocity, versus the 3D radius, of particles inside a radius of r tr = 10 kpc in a simulated halo of . The blue curve is the variance from particles in the pre-trimmed