adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...857..121Y (fulltext)
the effects of AGN feedback in the evolution of its host galaxy. The galaxy is an isolated elliptical galaxy, and we assume in this work that the specific angular momentum of the gas is low. Physical processes like star formation and SNe Ia and II are taken into account. The inner boundary of the simulation is chosen so that the Bondi radius is resolved, which is crucial for the precise determination of the mass accretion rate of the AGN. According to the theory of black hole accretion, black hole accretion has two intrinsically different modes: cold and hot. They have quite different radiation and wind outputs and thus naturally correspond to two different feedback modes. They have been carefully discriminated and taken into account in the present work. We consider the feedback effects by both the radiation and wind in each mode. The two feedback modes have quite diverse names in the literature, e.g., the quasar (or radiative) and radio (or kinetic or maintenance) mode. Our present work indicates that these names are not only diverse but sometimes also misleading. For example, in terms of regulating the accretion rate of the black hole and star formation in the galaxy, feedback by the wind is always much more important than that by radiation. So, we suggest simply following the names of the black hole accretion mode and calling them the “cold feedback mode” and “hot feedback mode.” The most important distinctive feature of the present work is that we adopt the most updated AGN physics. This is especially the case for the radiation and wind from hot accretion flows, for which much progress has been made in recent years but has not been taken into account in most AGN feedback works (see a recent review by Yuan Narayan 2014 ). It also includes more precise descriptions of the wind in the cold mode that are obtained from recent new observations. These most updated AGN physics are summarized in the present paper. For the feedback effects, we have investigated the light curve of the AGN, the black hole growth, star formation, the AGN duty cycle, and the surface brightness of the galaxy. We have compared these results with previous works that have a very similar model framework to our work but different AGN physics. Significantly different results have been found in almost every aspect mentioned above. This indicates the crucial importance of having correct AGN physics. The main results obtained in this paper are summarized below. 1. We have compared the energy and momentum fluxes of the wind and radiation in the two feedback modes (Figure 1 ). Roughly speaking, in both modes, the power of radiation is larger than that of the wind, while the momentum flux is the opposite. However, the magnitude of the energy or momentum fluxes is not the only factor to determine which component, radiation or wind, is more important in the feedback. This is because the cross section of photon–particle interaction is orders of magnitude smaller than that of
Paper: 2018A&A...611A...7S (fulltext)
Weisz et al. 2011 ). According to numerical simulations, the tidal-driven changes in star formation are rather short-lived, with a typical duration of the activity of about 10 8 yr ( Di Matteo et al. 2008 ). Strong starbursts during which the star formation rate is increased by a factor larger than 5 are rare. IC 1613 is a good example of a dwarf that calmly produces stars over its entire lifetime, while showing also some fluctuations ( Skillman et al. 2014 ). The galaxy has a small H i mass of 8 × 10 7 and a stellar mass of 1.1 × 10 8 ( Woo et al. 2008 ). The current SFR is 0.003 M ⊙ yr −1 ( Mateo 1998 ), but the SFH reveals some episodes in the galaxy life with theSFR enhancement of about threefold ( Skillman et al. 2014 ). Another aspect of evolution of low-mass galaxies not addressed in numerical experiments to date is the often observed irregular distribution of star-forming regions in galactic disks. Such an asymmetric distribution is evident in for example IC 10, dominated by a giant H ii region in its southern part. The observed morphology and the strength of magnetic field are strongly related to the distribution of H ii regions within the galactic disk ( Chyży et al. 2016 ). So as to address the above issues, in this paper a series of 3D simulations of the CR-driven dynamo for low-mass galaxies are performed. In the first place, we simulate a calm evolution of galaxies at different speeds of rotation and different (but constant in time) levels of star formation activity. Below, we introduce a starburst (a rapid enhancement of star formation) and study its impact on magnetic field, while finally investigating how a localised starburst, restricted to just part of the galactic disk, changes the magnetic field topology, and how this is related to the topology for the global starburst. 2 Numerical model and setup 2.1 Cosmic-ray-driven dynamo The CR-driven dynamo model in dwarf/irregular galaxies has been implemented into a numerical code called godunov-mhd ( Kowal et al. 2009 ). The code solves the system of resistive magnetohydrodynamic (MHD) equations in a conservative form in 3D space (e.g. Goedbloed Poedts 2004 ): where ρ , p , and v are the gas density, pressure, and velocity, respectively, p cr is the cosmic-ray pressure, A is the vector potential, B ≡∇× A is the magnetic field, φ is the gravitational potential, η is the magnetic turbulent diffusivity and I is the identity matrix. To close the system an isothermal equation of state is assumed: where c s is the isothermal speed of sound and γ is the adiabatic index of the gas. Cosmic rays form a key component of the model. In our case they are approximated as a fluid, whose evolution is described by the diffusion-advection transport equation following ( Schlickeiser Lerche 1985 ): where e cr is the CR energy density, K̂ is the diffusion tensor, v is the plasma velocity, p cr is the CR pressure, and Q is the source term of e cr . CR pressure is related to the e cr via the adiabatic CR
Paper: 2016MNRAS.458.1190C (fulltext)
The effect of different thicknesses of H atmosphere is discussed in Section 3.1 . The effect of different values of effective temperature and He layer mass is discussed in Section 3.2 . The effect of different values of total stellar mass are discussed in Section 3.3 . The average period spacing for DAV star models is analysed in Section 4 . In Section 5 , we preliminarily study the dispersion of period spacing for a DAV star KUV03442+0719. In Section 6 , discussion and conclusions are summarized. 2 INPUT PHYSICS AND MODEL CALCULATIONS In this section, we discuss the input physics and model calculations. In Paxton et al. ( 2011 ) reported modules for experiments in stellar astrophysics ( mesa ) which can evolve a star from main sequence (MS) stage to WD stage. mesa is updated quickly on the web site of ‘ http://mesa.sourceforge.net/ ’. The core compositions of a WD star are results of thermonuclear burning. The thermonuclear reaction rates are from Caughlan Fowler ( 1988 ) and Angulo et al. ( 1999 ) for mesa . With mesa version 4298, we evolve a group of MS stars, as shown in the first column in Table 1 , to WD stage by a module named make_co_wd. The input settings in the module are default by mesa . When these progenitor stars evolve to the WD cooling curve on the Hertzsprung-Russell ( H − R ) diagram, the WD models are took out. The core composition structures of these WD models, including mass, radius, luminosity, pressure, temperature, entropy, and carbon profile, are took out together. The location of maximal carbon profile is used as the boundary of the core for approximate calculations. The evolved WD masses in mesa and their core masses are showed in Table 1 . Using the module make_co_wd to evolve WDs is quick but a little rough. In the future work, we will try to evolve WDs in the module named 1M_pre_ms_to_wd and refine the input settings. Table 1. Masses of MS stars, WD stars evolved by mesa , corresponding cores, and corresponding WD stars evolved by wdec . The structure parameters are inserted into white dwarf evolution code ( wdec ). wdec was first written by Schwarzschild and subsequently modified by Kutter Savedoff ( 1969 ) ( wdec 1.0), Lamb van Horn ( 1975 ) ( wdec 2.0), Winget ( wdec 3.0), Kawaler ( wdec 4.0), Wood 1990 ( wdec 5.0), and Bradley ( wdec 6.0). The program is from White Dwarf Research Corporation ( http://whitedwarf.org/ ). wdec can evolve groups of DAV star models. The model parameters are adopted as total stellar mass, effective temperature, He layer mass fraction, and H atmosphere mass fraction. With the core composition structures from WD models evolved by mesa , groups of WD models are evolved by wdec . The He layer and the H atmosphere are very thin for a DAV star. Therefore, we take the total stellar mass of WD models in wdec the same as the WD core masses in mesa , as shown in Table 1 . The carbon profiles of those WD models are results of thermonuclear burning. The element diffusion scheme of Thoul,
Paper: 2018ApJ...855L..11O (fulltext)
from the singlet ground state (1 1 S) because radiative transitions between them are strongly suppressed. Due to relativistic and finite-wavelength corrections to the magnetic dipole transition formula, the 2 3 S triplet helium can radiatively decay into the singlet ground state with an exceptionally long lifetime of 2.2 hr (Drake 1971 ). The 2 3 S state can be populated by recombination 4 4 Around 75% of helium recombinations result in the triplet configuration (Osterbrock Ferland 2006 ). or by collisional excitation from the ground state (see Figure 1 ). Depopulation of this state progresses slowly making it metastable, and hence a promising origin of absorption lines. The resonance scattering from the metastable state to the 2 3 P state produces an absorption line at 10830 Å. This transition consists of three lines, two of which—at 10830.34 Å and 10830.25 Å—are practically indistinguishable, whereas the third component is at 10829.09 Å. Figure 1. Atomic structure of the helium atom, indicating the radiative and collisional transitions included in our analysis. The transition shown in red depicts the 10830 Å absorption line. The helium 10830 Å line has been used to probe the dynamics of stellar winds (e.g., Dupree et al. 1992 ; Edwards et al. 2003 ) and outflows from active galactic nuclei (e.g., Leighly et al. 2011 ), as well as the structure of the solar chromosphere and transition region (Avrett et al. 1994 ; Andretta Jones 1997 ; Mauas et al. 2005 ). Even though this line has been identified as one of the most promising spectral signatures of exoplanet atmospheres (Seager Sasselov 2000 ), no firm detections have been reported thus far (see, e.g., Moutou et al. 2003 ). Most previous models of escaping atmospheres have focused on hydrogen or tracked helium only in terms of neutrals and ions, without modeling the metastable state. Turner et al. ( 2016 ) used cloudy to compile a list of ∼60 potentially interesting absorption lines for probing upper exoplanet atmospheres, including the 10830 Å line. 3. Methods and Results 3.1. Isothermal Parker Wind Our model is based on the assumption that upper layers of a hydrodynamically escaping atmosphere can be described by an isothermal Parker wind driven by gas pressure (Parker 1958 ; Watson et al. 1981 ). Winds in which heating and cooling are due to radiative processes are close to being isothermal (Lamers Cassinelli 1999 ). If the radiative heating/cooling processes operate on short timescales compared to the dynamical time of the system, the gas can self-regulate at a constant temperature. A time-independent and spherically symmetric wind has a constant mass-loss rate determined by the equation of mass conservation:For an isothermal wind, the energy equation iswhereas the momentum equation is given byWe ignore the gravitational influence of the star and the Coriolis force. The momentum equation has a singularity at the so-called critical point, which in our case coincides with the sonic point,
Paper: 2016A&A...589A..62P (fulltext)
model fitting is extensively detailed in ( Chen et al. 2016 ). In Table 2 , the refined planet parameters for CoRoT-29b are given. The orbital period and the zero epoch are calculated by fitting a linear function to our two transit epochs together with literature epochs. The rest of parameters are calculated by individually fitting the light curve of each epoch, but also by jointly fitting both transits together. These values are compared to those in the discovery paper by Cabrera et al. (2015) . Fig. 3 White light curve of CoRoT-29b. It is a summation of all the color-shaded passbands as shown in Fig. 2. From top to bottom : (1) normalized flux ratio between CoRoT-29 and its reference star, together with the best-fitting model; (2) white light curve after removing the modeled systematics, for display purpose; (3, 4) best-fitting light-curve residuals. Fig. 4 Color light curves of CoRoT-29b. Left and right panels : data from July 31st, 2014 and July 8th 2015, respectively. The light curves are shown after removing the modeled systematics, and overplotted are the best-fitting light-curve models. Fig. 5 Transmission spectrum of CoRoT-29b. The dashed line indicates the best-fitting derived from the white light curve. Blue and red colors indicate the two observing epochs. The gray-shaded area marks the scale height. Within error bars the spectrum is flat. 4. Discussion and conclusions Figure 3 presents the white light curve for the two datasets. In the top panel the raw flux ratios between CoRoT-29 and the comparison star are shown, where effects of airmass and seeing are still present. In the middle panel of the same figure, the light curves have been normalized and all systematic effects, except the analytic transit model, have been removed. Overplotted to the data are our best fits to each of the transits. It is remarkable that even at the faint magnitude of the CoRoT-29 (), using GTC we are able to reach precisions down to 300 ppm in 5 min exposure time; this is a factor 2.2 above photon noise. The residuals of the transit fit do not show any clearly identifiable features attributable to red noise, or an unaccounted for planetary signal. To be sure that the decorrelation process is not removing real asymmetries in the transit curve, we have generated mock light curves introducing an asymmetry in the form of a stellar spot (not shown). In that case using the same fitting methodology and a symmetric transit, the asymmetry is recovered, and only when using an asymetric transit profile, taking into accout the stellar spot effects, are the residuals of the fit similar to those in Fig. 3 . To further explore possible transit anomalies, we plot in Fig. 4 the color light curves for each of our spectral bins for the two observed epochs. It can be seen that the symmetry of the transit is maintained over all spectral bins and in both epochs. The only exception would be on the bluest light curves from July 8th 2015, where deviations from the transit model
Paper: 2017ApJ...845...77H (fulltext)
is , these IC emissions are detectable with CTA, provided that the distance is within a few tens of Mpc and that the dimensionless accretion rate is in the range . If , they are detectable with CTA, provided that the distance is within a few Mpc and that . 6.1. Improvement from H16 In the present work, there are two major improvements from H16 , which formulated the BH gap model and applied it to various BH masses in . First, the distribution functions of electrons and positrons are solved as a function of the Lorentz factor, γ , in the present work, whereas a monoenergetic approximation was adopted in H16 . It is found that the Lorentz factors broadly distribute below the saturated value that was estimated in the monoenergetic approximation. This fact causes an important impact on the gap electrodynamics. Since only the highest-energy IC photons contribute in the gap closure (Section 4.2.5 ), the monoenergetic approximation has overestimated the pair creation in the gap, thereby underestimating the gap width and luminosity (cf. Figure 10 of the present paper and Figure 23 of H16 ). Moreover, the reduced Lorentz factors significantly suppress the curvature process, whose power is proportional to . Also, the primary IC spectrum is softened to peak between 1 and 10 TeV in the present work, whereas it peaked between 10 and 100 TeV in H16 . Since CTA increases its sensitivity with decreasing photon energy around 10 TeV, this result encourages us to observe nearby low-luminosity AGNs in VHE. Second, in the present work, we take into account an anisotropic and inhomogeneous RIAF specific intensity in computing the IC and pair creation. In particular, it is found that the polar region is less efficiently illuminated by the RIAF photon field compared to the middle latitudes . It leads to a harder and stronger VHE emission along the rotation axis than that into the middle latitudes. On the contrary, in H16 it was simply assumed that the RIAF photon field was constant for θ . Thus, although the VHE flux decreased with θ owing to the reduced near the equatorial boundary, it decreased more slowly than the present analysis. 6.2. Comparison with Pulsar Emission Models Let us compare the present BH gap model with the pulsar outer gap model. In both gap models, the gap appears around the null-charge surface where the GJ charge density vanishes, as the stationary solution of the Maxwell–Boltzmann equations. There are, however, differences as described below. In pulsar magnetospheres, the neutron star (NS) emits X-ray photons from its surface, losing its thermal energy. The luminosity of these soft photons decreases as the NS ages. The decreased soft-photon density in the outer magnetosphere results in an extended gap along the magnetic field line. For middle-aged pulsars, w becomes comparable to the radius of the outer light surface, within which the special relativistic GJ charge density changes substantially owing to the convex geometry of the magnetic field
Paper: 2021MNRAS.502..390H (fulltext)
predictions. The luminosity of X-ray emissions ( L X ) from pulsars, sometimes mixed with their PWN, has indeed been found to strongly correlate with a pulsar’s spin-down power . In Seward Wang ( 1988 ), with Einstein data for nine pulsars, it was found that L X (0.2–4 keV) is proportional to , where the luminosity L X is contributed from both the pulsars and their nebulae. With ROSAT data of 27 pulsars, Becker Trümper ( 1997 ) reported that L X (0.1–2.4 keV) is about . This luminosity counts only the emissions from the pulsar. To reduce the contribution from surface thermal emissions, Possenti et al. ( 2002 ) considered an energy range of 2–10 keV and reported that , with a sample of 39 pulsars whose PWNe, if present, were not separated from their pulsars. Therefore, in that study, the luminosity includes a contribution from PWNe if they exist. Another relation of (2–10 keV) was reported in Mattana et al. ( 2009 ) for 14 TeV-emitting PWNe, some of which are mixed with the pulsar’s luminosity. In the works mentioned above, there seems to be an indication that the relation between the X-ray luminosity L X and the spin-down power has a larger power index when emissions from PWNe are included and a smaller one when only the emission from the pulsar is considered. Indeed, in Cheng et al. ( 2004 ), with a sample of 23 pulsars in the energy range between 2 and 10 keV, the non-pulsed luminosity was found to be , while the pulsed one was . In Li, Lu Li ( 2008 ), with 24 PWNe and 27 pulsars, it was reported that, at 2–10 keV, and , where L X, pwn is the luminosity from PWNe and L X, psr is that from pulsars. It is one of the purposes of this paper to examine these relations with a larger sample. The spectral photon indices Γ pwn and Γ psr in the power-law spectra of the non-thermal X-ray emissions from PWNe and from pulsars are another important property of the emission. With nine bright Crab-like pulsars, relations between Γ pwn and Γ psr , between Γ pwn and , and between Γ psr and were reported (Gotthelf 2003 ). These relations were, however, not confirmed in Li et al. ( 2008 ) with a larger sample of pulsars and their PWNe. None the less, Cheng et al. ( 2004 ) reported an indication of a positive correlation between Γ pwn and , and Li et al. ( 2008 ) found moderately strong positive correlations between Γ pwn and L X, pwn and between Γ pwn and . On the other hand, Γ psr was not found to correlate with luminosities or other pulsar timing properties (Cheng et al. 2004 ; Li et al. 2008 ). In this paper, we report correlations among properties of the PWN/pulsar power-law component of the X-ray emissions and of pulsar rotations, using a sample of 35 PWNe, all with detected pulsars whose X-ray emission can be separated from that of PWNe. To have a more uniform comparison basis, we consider the flux in the energy range from 0.5–8 keV for all the sources in the sample. This flux is derived from the power-law component of the corresponding best-fitting
Paper: 2020A&A...636A..66P (fulltext)
shown to scale. The temperature and abundance maps of the ∇ − T case are almost the same, except the scale height of the atmosphere is smaller. Fig. 5 Abundance profiles for H 2 O and H 2 plotted for a range of temperature between 2000 and 3000 K. At a given pressure, H 2 O and H 2 dissociation depends strongly on the temperature. The higher the temperature, the lower the abundance in the atmosphere. At a fixed temperature the variation of the dissociation rate is a function of P and P 2 , respectively, for H 2 and H 2 O. 2.3 Retrieval model: TauREx The spectral retrievals has been computed with the TauREx retrieval code ( Waldmann et al. 2015b , a ). TauREx consists in aBayesian retrieval code which uses the ExoMol line lists ( Yurchenko et al. 2011 , 2014 ; Tennyson Yurchenko 2012 ; Barton et al. 2013 , 2014 ), HITRAN ( Rothman et al. 2009 ; Gordon et al. 2013 ) and HITEMP ( Gordon et al. 2010 ). TauREx assumes a plane-parallel exoplanetary atmosphere with 1D distribution elements and temperature-pressure profile. We assume the radius at the base of the model of Wasp-121b as the value for which the atmospheric pressure is 10 bar. We simulate the exoplanetary atmosphere assuming a pressure between 10 6 and 10 −4 Pascals. We consider an atmosphere dominated by hydrogen and helium, with a mean molecular weight of 2.3 amu. We also suppose the presence of CO and H 2 O as trace gases. An important assumption is that TauREx does not take into account thermal dissociation of species and assumes that molecular abundances are constant with the atmospheric pressure (see Changeat et al. 2019 for a detailed discussion on the effect of this assumption). We set up the prior range for the molecular volume mixing ratios of CO and H 2 O between 10 −12 and 10 −1 , which allows us to explore a wide range of solutions. Even if we never add clouds in our input simulation, we set up the prior range for clouds between 10 −12 and 10 −1 to allow theretrieval code to have more freedom. We also feel that this better simulates a real situation where clouds would be put in the retrieval without prior knowledge about their occurrence in the real planet. The allowed range for the planetary radius and the atmospheric temperature (assumed constant in the vertical) are, respectively, 1.3 R Jup − 4.0 R Jup and 400−3600 K, with R Jup the radius of Jupiter. The range for the temperature is linked to the minimum and maximum temperature in the GCM simulation of Wasp-121b, as shown in Table 1 . As we simulate JWST observations, we set up the range of wavelengths between 0.6 to 10 μ m. For the non-retrieved parameters such as masses and stellar radius, we use those describe in Table 2 . Table 1 Parameters used in the three simulation configurations of WASP-121b. Table 2 Mass, radius, and temperature parameters of the WASP-121 star (top) and its planet Wasp-121 b (bottom). Notes. They are used for the observable generations and for the retrievals. We note that the TauREx retrieval