adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.503.3867H (fulltext)
al. ( 2015 ). We do not expect our L62 results to match the obervational data, since the TNG model was tuned for the TNG-L25N512 test simulations using the galaxy properties mentioned above (as well as the BH mass–stellar mass relation and the galaxy size at redshift z = 0; Pillepich et al. 2018a ) and it has been demonstrated that the stellar properties of galaxies depend on the simulation resolution in the TNG model (see appendix A of Pillepich et al. 2018a , b , for details). Figure 8. Stellar and gaseous galaxy properties at z = 0 (unless otherwise stated). Upper left: Stellar-to-halo mass ratio as a function of halo mass. The stellar mass is measured within 30 kpc from the halo centre. Upper right: Galaxy stellar mass function measured within 30 kpc from the centre of the halo. Bottom left: The halo gas fraction within r 500 c as a function of the total halo mass. Bottom right: Star formation rate density as a function of redshift. Different colours represent different gravity models as specified in the legend. The solid colour lines show results from our L25 simulations, while the dashed coloured lines are from our L62 boxes. All lower subpanels show the relative differences between the galaxy properties of nDGP and GR models. In all panels, we compare our results with the IllustrisTNG results at three different resolutions (Pillepich et al. 2018a ): TNG-L25N512 (the solid grey curves), TNG-L25N256 (the grey-dashed curves), and TNG-L25N128 (the grey-dotted curves). The blue solid line in the upper panels shows the results from the TNG100 simulation (Pillepich et al. 2018b ). The light grey symbols represent observational measurements from Behroozi, Wechsler Conroy ( 2013 ), Baldry et al. ( 2012 ), Bernardi et al. ( 2013 ), D’Souza, Vegetti Kauffmann ( 2015 ), Giodini et al. ( 2009 ), and Lovisari, Reiprich Schellenberger ( 2015 ). The upper left-hand panel of Fig. 8 shows the stellar-to-halo mass ratio multiplied by the inverse of the baryon fraction (Ω b /Ω m ), as a function of the total host halo mass ( M 200 c ) for our six full-physics simulations at z = 0. The stellar mass was measured within 30 kpc from the halo centre. First, we note that the L62 simulations (the dashed coloured lines) are in good agreement with the TNG-L25N128 (the grey-dotted line) run at the high-mass end, but is lower than the TNG-L25N256 (the grey-dashed line) and TNG-L25N512 (the grey solid line) results, which is as expected given the resolutions of these runs. Our L25 runs (the solid coloured lines) predict higher stellar mass fractions than the TNG-L25N512 (the grey solid line), despite the fact that these simulations have the same resolution. The differences are due to the combination of the different initial condition realizations we used to run our simulations (i.e. cosmic variance) and the small number of high-mass objects in the small boxes. Note that the final TNG100 run (the blue solid line; Pillepich et al. 2018b ) also has disagreements with the
Paper: 2019ApJ...878...27K (fulltext)
coordinate. The photon ring is the gravitationally lensed image of the (unstable) photon orbit. Because the absolute values of angular momenta of the co- and counter-rotating photons on the photon orbit around highly spinning BHs (i.e., blue and red curves in Figure 2 (a)) are distinctly different, the impact parameter is significantly different between them. This results in the positional shift of the photon-ring center in the observer screen (Bardeen 1973 ). On the other hand, photons emitted by the innermost accretion flow produce an SSA-thick ISCO ring and the ISCO ring does not show such a significant positional shift of its center (green and purple curves in Figure 2 (b)). This is because these rays deviate from the photon orbit near the BH and the resultant impact parameter does not strongly depend on the sign of angular momentum of photons. Then, the positional offset between the center of the photon ring and that of the SSA-thick innermost region forms the “dark crescent” in the resultant image. The dark crescent appears in the opposite side of relativistically beamed bright region. Figure 2. Trajectories of photons forming the photon ring (top) and the SSA-thick ISCO ring (bottom), in the vicinity of the Kerr BH with a * = 0.998. The viewing angle is i = 30°. Each trajectory is projected onto the equatorial plane of the Boyer–Lindquist coordinate (we note that we define ( x eq , y eq ) ≡ ( r sin θ cos φ , r sin θ sin φ )). The solid/dashed curves represent the SSA-thin/SSA-thick regions along the rays. The positions of the rays on the observer screen are represented by the dashed lines in Figure 4 . Because the horizontal shift of the photon ring increases with BH spin (as well as the ISCO radius for matter decreasing with BH spin), the larger and clearer dark crescent appears when the BH spin is higher: the dark crescent appears in the models with a * = 0.998 and 0.75, while it is not found around the BH with a * = 0.5 in our shadow images. We can find a clearer dark crescent for the model with i = 30° than for i = 15°, because of a more prominent horizontal shift of the photon ring by the frame-dragging effect in the case with larger viewing angle. In Figure 3 , we present the intensity images (top) and τ SSA (bottom) in the observer’s screen at 230 and 350 GHz for i = 30° and 15° in the model with a * = 0.998. This is the first example explicitly displaying τ SSA in the observer’s screen. These spatial distribution maps of τ SSA on the observer screen enable us to understand the formation of the dark crescent in the BH shadow image. By comparing the intensity and τ SSA map at 230 GHz, we can confirm that the bright inner ring is formed via the emission from the SSA-thick innermost accretion flow. We note that the photon ring is τ SSA ≳ 1, because the rays pass close to the photon orbit, which results in the large emission gain and the formation of the bright photon ring. At ν = 350 GHz, the right side of both the photon ring and
Paper: 2017ApJ...847...63O (fulltext)
The standard approach has been to compare measurements of different statistics to cosmological hydrodynamical simulations (Zaldarriaga et al. 2001 ; Theuns et al. 2002b ; Viel et al. 2009 ; Lidz et al. 2010 ; Becker et al. 2011 ; Garzilli et al. 2012 ; Rorai et al. 2013 , 2017a , 2017b ; Iršič Viel 2014 ) to deduce the thermal parameters (e.g., T 0 , γ or ) that best describe the IGM thermal state. As a larger number of high-resolution spectra of quasars have become available at higher redshifts , the same approach has been applied to study the thermal state of the IGM at these redshifts, where the Ly α forest is more sensitive to the timing and nature of hydrogen reionization (Theuns et al. 2002b ; Hui Haiman 2003 ; Cen et al. 2009 ; Furlanetto Oh 2009 ; Becker et al. 2011 ; Viel et al. 2013a ; Lidz Malloy 2014 ; Garzilli et al. 2015 ; Nasir et al. 2016 ). In the light of the new Planck constraints on reionization, as well as the increasing number of quasars discovered at high- z (e.g., Bañados et al. 2014 , 2016 ; Matsuoka et al. 2016 ), it is pertinent to revisit what Ly α observations with current and upcoming facilities can tell us about reionization. This work aims to explore in detail the possibilities of using the Ly α forest 1D flux power spectrum at high- z to constrain H i reionization. For this we have used a new method that has recently been introduced by Oñorbe et al. ( 2017 ). It builds on the Haardt Madau ( 2012 ) model, but enables one to vary the redshifts of and reionization, as well as their associated heat injection, allowing one to consistently simulate a more diverse range of reionization histories. This allows for a much comprehensive and consistent exploration of the space of thermal parameters than previously done. We present here the results of the 1D flux power spectrum at high- z of a new set of hydrodynamical simulations that also improve the resolution used in previous studies at these redshifts. Additionally, we compare these power spectra with some recent measurements at these redshifts (Viel et al. 2013a ). The structure of this paper is as follows. In Section 2 we describe the characteristics of our hydrodynamical simulations and the different reionization models studied in this work. Section 3 presents the thermal evolution of the different reionization models obtained from the cosmological hydrodynamical simulations. In Section 4 we compare the 1D flux power spectrum of each model at as well as with the best available observations. We discuss in Section 5 the relevance of our findings in the context of current observational and theoretical limitations. We conclude by presenting a summary of our results and an outlook in Section 6 . In the Appendix we perform a set of convergence test for the optical depth and the 1D flux power spectrum at . Throughout this paper we assumed a flat ΛCDM cosmology with the following fundamental parameters: , , , h = 0.67038, , and . These values agree within 1 σ with the
Paper: 2021MNRAS.505.5846I (fulltext)
effect of photoionization feedback may be further reduced for realistic accretion disc spectra, compared to the case of the single power-law spectrum usually assumed in most works (Takeo et al. 2019 ). As mentioned in Section 3 , for accretion rates satisfying the DPT condition, photon trapping due to electron scattering is also naturally realized. In the case of DPT, radiation feedback at radii larger than the dust sublimation radius is effectively suppressed, while the entire accretion disc radiation may be efficiently trapped. Under such extreme trapping conditions, most of the infalling matter may be accreted (Volonteri et al. 2015 ). Since the accretion rate leading to DPT is much larger than the Eddington rate, it should be associated with super-Eddington accretion and a rapid build-up of the BH mass. For instance, adopting the DPT accretion rate as the minimal value, the enhancement factor could be of a few orders of magnitude compared to the Eddington-limited value. It is known that photon trapping also modifies the emergent spectral energy distribution. Higher energy photons originating in the inner accretion flow are more efficiently trapped than lower energy photons produced from the outer regions. As a result, the peak frequency of the spectral energy distribution shifts towards lower energies, and the active galactic nucleus (AGN) spectrum becomes softer with increasing accretion rate (Ohsuga, Mineshige Watarai 2003 ). In the case of DPT, the whole disc radiation can be efficiently trapped, and the overall luminosity output may be considerably reduced, leading to faint AGNs. This may not be the case for photon trapping due to electron scattering, where only the inner parts of the accretion disc remain trapped (unless the accretion rate is extremely high). Since UV/optical photons are trapped within the dusty accretion flow, the central AGN will be obscured. In this context, X-ray photons would be even more readily trapped than UV/optical photons, and thus super-Eddington accretion will be likely obscured at both X-ray and UV/optical wavelengths (Wyithe Loeb 2012 ). In our picture, DPT accretion is indeed associated with significant obscuration, with large column densities expected at early times. Cosmological hydrodynamic simulations also suggest heavy obscuration on different physical scales around the growing BHs in the early Universe (Trebitsch, Volonteri Dubois 2019 ; Ni et al. 2020 ). Assuming a standard radiative efficiency of ε ∼ 0.1, it has been estimated that the obscured fraction at z > 7 could be of the order of (Davies, Hennawi Eilers 2019 ). In fact, much of the early growth of massive BHs could be obscured, and a large number of such obscured quasars may be missed in current optical surveys (Hickox Alexander 2018 ; Inayoshi et al. 2020 , and references therein). On the other hand, obscuration by dusty gas may cause higher energy photons to be reprocessed into lower energy photons, leading to an increase in the
Paper: 2020ApJ...896..100A (fulltext)
projected orbital arrangements in motion in Figure 17 . Figure 5. Smoothed representation of the pole vision image of the circularized orbit distribution shown in Figure 4 (f). We subtracted the distribution expected from 39 randomly oriented orbits. Here the 39 orbits are generated, assuming a sin i uniform distribution for the inclinations, and a circular uniform distribution of the longitude of ascending node after setting e = 0 and a = const. As in Figure 4 (f), the black lines indicate the directions of the disks and the white line the direction of the Galactic plane. The two orbital disk systems are well separable (see above) but rather thick. Furthermore, the orthogonal X-shaped disk structure becomes apparent only in alternating zones in the position angle histogram (see Figure 8 ) and in Ω diagrams as well (see Figure 6 ). This leads to the fact that they cannot easily be recognized in polar diagrams as used by, e.g., Gillessen et al. ( 2017 ) in their Figure 12 or Yelda et al. ( 2014 ) in their Figure 21. In Figure 6 , we show the inclination of the stars as a function of the longitude of the ascending node Ω. The color indicates their membership in either the red or the black disk. It becomes clear that the two disks cannot easily be identified as the angular momentum vectors of the disk members point in opposite directions. Comparing Figure 8 and Figure 6 , one can also see that the two disks can be better separated by evaluation in the position angle histogram instead of evaluating the longitude of the ascending node Ω. Figure 6. The inclination as a function of the (LOAN) longitude of the ascending node Ω. However, compared to Figure 6 , the inclined and face-on representations of the disk members as shown in Figure 4 are better grouped together, because the direction of the angular momentum vector is not relevant in this representation. Here, looking at circularized orbits as described above is more successful in searching for face-on orbits that bunch close to the circumference of the sky-projected distributions as in Figures 4 (g) or (h). In Figures 7 (a) and (b), we show the inclinations of the two stellar systems. On the right-hand side of Figure 7 , we show the distribution of inclinations for all stars within the central acrseconds for which we can provide Newtonian orbital fits. In particular, the distributions for the two disks do not follow a sin i distribution as one might have expected for a fully randomized scenario. There is a clear clustering of inclinations around a mean value of ∼90° with the bulk of the higher inclined stellar orbits contained within an interval width of 50° (red disk with bulk between 80° and 130°) or even a width of only 40° (black disk with bulk between 70° and 110°). In comparison to the expected width of about 100° for a sin i distribution, this implies that the two separate disk are highly biased toward high inclinations. There are also no stars with inclinations in the intervals 0°–20°
Paper: 2021A&A...645A.132V (fulltext)
every system, the highest mass is generally achieved when pebble accretion is included. While the systems in which pebble accretion is neglected fail to build super-Earths with our input parameters, the formation of such planets becomes possible when pebble accretion is included. While the number of active embryos decreases when pebble accretion is included (see Fig. 6 ), their masses increase drastically. 4.3 Active number and total mass Figure 6 shows the total number of embryos and the total mass that is in embryos over time for the setups from Figs. 2 – 4 . We also give the fraction of total embryo mass M Emb over the mass that was given to the planetesimaldisk after 1 Myr ( M D ) for each setup. The first embryos always form in the systems in which pebble accretion is enabled. However, the number of active embryos during the simulation is almost a factor of 2 below the number of embryos in the systems without pebble accretion. The mass in embryos differs even more strongly than the active number of embryos for the corresponding systems. The fraction M Emb ∕M D consistentlyincreases for higher total masses and steeper Σ P -profiles, respectively. In the systems in which pebble accretion is included, it can exceed unity. This means that the mass in planetary embryos can be higher than the mass that is transformed into planetesimals through pebble accretion. 4.4 Orbital separation In Fig. 7 we compare the mean orbital separation of embryos over time for the systems from Figs. 2 – 4 . The orbital separation is expressed in units of the embryos Hill radii. The mean orbital separation after 1 Myr converges to ≈ 10R Hill for each setup. The simulations in which pebble accretion is included show a smoother and more stable behavior over time than the systems in which pebble accretion is neglected. The explanation of these differences lies in the fact that the first embryos can start growing farther apart from each other in the runs that only consider planetesimal accretion. Therefore numerous embryos are needed in order to converge for a characteristic orbital Hill spacing. Fig. 2 Time over semimajor-axis evolution of the N -body simulation in LIPAD. The time and location at which an object has first reached lunar mass is indicated by the black dots in the plot. The subsequent growth of the embryo is tracked and connected with the gray lines, and its mass is given by the color bar. The mass in planetesimals after 1 Myr is given by 6 M ⊕ in these runs, the planetesimal surface density slope is varied (Σ P ∝ r −1.0 , Σ P ∝ r −1.5 , and Σ P ∝ r −2.0 ). Left panels : system without pebble accretion. Right panels : system in which pebble accretion is included. The red line indicates the time after which the analytic model presented in Voelkel et al. (2021) states that embryo formation is possible. Fig. 3 Time over semimajor-axis evolution of the N -body simulation in LIPAD. The time and location at which an object has first reached lunar mass is
Paper: 2016A&A...589A..10T (fulltext)
scales linearly with the gas mass density and is proportional to the logarithm of the ratio of the spatial extents of the forming wake and the shock’s stand-off distance. 7.2. Minimum spatial interaction scale The numerical experiments allow us to compute the total dynamical friction acting on the body and also to investigate the individual contributions to the dynamical friction induced as a function of the distance to the perturber. The outcome of this analysis reveals that the region within the stand-off distance of the shock does not contribute to the dynamical friction of a gaseous medium (see Fig. 5 and associated main text for details). At radii smaller than the stand-off distance, a spherically symmetric stratified atmosphere forms around the moving body, which (because of its symmetry) induces no net gravitational pull or pressure force. Only at radii above the stand-off distance does a wake of higher density form behind the moving body and induce a gravitational drag force. Hence, in the well-known formulae by Ruderman Spiegel (1971) or Ostriker (1999) , it is the stand-off distance of the shock that determines the minimum length scale required in the so-called Coulomb logarithm : The formation of a spherically symmetric atmosphere (with radius ) around a gravitating object implies that the hydrodynamical drag force vanishes in contrast to the non-gravitating case, but for very high Mach numbers the shock moves very close to the object so that . In this case, no spherically symmetric envelope around the object forms and the hydrodynamical drag becomes important again. 7.3. Relating the stand-off distance to the accretion radius In a second step, to allow an easy computation of the stand-off distance for general dynamical friction problems, we derived the relation of the shock’s stand-off distance to the common definition of the accretion radius, depending only on the mass and velocity of the perturber. The stand-off distance is directly proportional to the accretion radius and the proportionality constant only depends on the Mach number and the adiabatic index of the gas: Additionally, the geometrical radius of the moving body yields a lower limit for the stand-off distance . Via an analytical estimate of the stand-off distance and further fitting of the remaining free parameters, we gave an approximate relation of the dependence of the shock’s stand-off distance on the adiabatic index of the gaseous medium with and values of and . 7.4. Updated formula of dynamical friction Finally, we combined the derived scaling relations and the finding on the stand-off distance to give an update of the well-known formula of dynamical friction acting on a body of mass moving at supersonic speed through a gaseous homogeneous medium of mass density , where denotes the extent of the wake and can be obtained using the derived formulae above. Appendix A: Hydrostatic equilibrium stratification As mentioned in the text, behind the shock front and
Paper: 2020ApJ...897...12A (fulltext)
ejecta and a shock-heated ISM (Park et al. 2003 , 2012 ; Uchida et al. 2015 ). It is also notable that N49 is in an overionized state (Uchida et al. 2015 ) and is interacting with dense molecular clouds on the eastern side (Banas et al. 1997 ; Yamane et al. 2018 ). Due to the interaction with molecular clouds, the thermal X-ray emission of N49 is particularly bright on the southeastern rim. N49 is an attractive object since Kaastra Mewe ( 1995 ) pointed out that the effect of RS can show up if an SNR has such a spherically asymmetric structure. Here, we present a high-resolution X-ray spectroscopy of N49 with the Reflection Grating Spectrometer (RGS) on board XMM-Newton. The obtained G-ratio of O vii He α and other line ratios as well imply a nonnegligible contribution of RS in N49. Throughout the paper, errors are given at a 68% confidence level. We assume the distance to N49 (LMC) to be 50 kpc (Pietrzyński et al. 2013 ). 2. Observation and Data Reduction N49 was observed with the XMM-Newton satellite (Jansen et al. 2001 ) in 2001 (Obs.ID 0113000201) and 2007 (Obs.ID 0505310101). The observation in 2007 was performed with a roll angle which placed N49 and a nearby SNR, N49B, along the dispersion direction of the RGS, making it difficult to extract RGS spectra of N49. We thus analyzed only the data obtained in 2001. For our spectral analysis, we used the RGS (den Herder et al. 2001 ) and the European Photon Imaging Camera MOS (Turner et al. 2001 ) data. We reduced the data using XMM Science Analysis Software version 16.1.0. The RGS data were processed with the RGS pipeline tool rgsproc . To discard periods of background flares, we applied good time intervals based on the count rate in CCD9, which is the closest to the optical axis of the mirror and has the least X-ray counts from the source and most affected by the background flares. The resulting effective exposure time is 11 ks for both RGS1 and RGS2. Since the second-order spectra are of low statistical quality, we analyzed only the first-order spectra. Figure 1. EPIC-MOS image (0.4–8.0 keV) of N49. The cross-dispersion width of the RGS (5 arcmin) is in between the white solid line. The spectral extraction region is enclosed by the white dashed line. N49B is another SNR near N49. 3. Analysis We analyzed the spectra using version 3.04.0 of the SRON SPEX package (Kaastra et al. 1996 ) with the maximum likelihood C-statistic (Cash 1979 ; Kaastra 2017 ). The RGS spectra were fitted simultaneously with those of MOS1 and 2. To account for the spatial broadening of the source, we multiplied spectral models with the SPEX model lpro , to which we input the MOS1 image of the source. Our models have two absorption models: one for the Milky Way and the other for the LMC. The column density of the former was fixed to (Dickey Lockman 1990 ) whereas that of the latter is left free. The elemental abundances for the LMC absorption were fixed to values found in the literature (∼ 0.3 solar; Russell Dopita