adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...889...19G (fulltext)
equation of state (EOS) has been studied for hydrodynamic turbulence and for a polytropic EOS (Federrath Banerjee 2015 ) or an adiabatic EOS (Nolan et al. 2015 ; Mohapatra Sharma 2019 ), indicating additional dependencies, on (for example) the adiabatic index γ , the relation between density fluctuations and . In the magnetized case the majority of studies focus on the isothermal case , finding an additional dependency on the ratio of thermal to magnetic pressure (e.g., Kowal et al. 2007 ; Padoan Nordlund 2011 ; Molina et al. 2012 ). Overall, a highly dynamic picture of turbulence has been found, challenging our ability to examine the general case. In this paper, we present adiabatic magnetohydrodynamic simulations of stationary turbulence in the subsonic ( to 0.6), super-Alfvénic (), and high (ratio of thermal to magnetic pressure with ) regime. This regime approximates the turbulent intracluster medium (ICM; Brunetti Lazarian 2007 ; Brüggen Vazza 2015 ) even though our magnetohydrodynamics (MHD) model neglects effects stemming from low collisionality (Schekochihin Cowley 2006 ; Schekochihin et al. 2008 ). However, studies including effects from low collisionality, e.g., through the Chew–Goldberger–Low MHD model, found that they generally introduce only small differences compared to the MHD model, e.g., a small increase in density fluctuations (Kowal et al. 2011 ; Santos-Lima et al. 2017 ), but leave imprints on Faraday rotation maps (Nakwacki et al. 2016 ). Here, we specifically focus on the effects of departure from an isothermal equation of state by varying the adiabatic index γ and the cooling function between simulations. The rest of this paper is organized as follows. In Section 2 , we introduce the numerical setup and the simulations conducted. In Section 3 , we present results from analyzing the simulations starting with a high-level overview of the energy spectra, to correlations in the stationary regime, to statistics of distribution functions that can potentially be used to break degeneracies between the thermodynamics and sonic Mach number. The results are discussed in Section 4 , and we conclude with a brief outlook in Section 5 on how our findings point the way to future measurements that can be used to better diagnose the properties of turbulence in astrophysical plasmas. 2. Numerical Details In this work we utilize the equations of compressible, ideal MHD equations that are closed by an ideal equation of state , with γ as the ratio of specific heats. The symbols have their usual meaning, i.e., density ρ , velocity , total pressure consisting of thermal pressure and magnetic pressure , and magnetic field , which includes a factor . Cooling is included via and is the total energy density with specific internal energy e . Vector quantities that are not in boldface refer to the L 2 norm of the vector and ⨂ denotes the outer product. The details of the acceleration field that we use to mechanically drive our simulations are
Paper: 2016ApJ...822...35A (fulltext)
again. The velocity is calculated by dividing the distance that the center of the BD moved by the time between recordings. The image was then advanced again and this process is repeated until the BD disappears or merges with another BD. Because the lifetime of the BDs is variable, the time between recordings varies, but as a general rule the number of recordings is maximized (minimizing the time between each recording) but there are at least 11 s between each recording. The average velocity of a BD is the average of these multiple velocity measurements. The speed was measured for only 25 BDs because there were not enough data points for five of the BDs. Figure 3 (i) is a scatterplot between the speed and the ratio of length-to-width of the BDs. We plot this figure in an attempt to find a correlation between how long a BD is relative to how fast it is moving (the thought being that faster BDs might be elongated in the radial direction), but evidently there is no clear relationship; there is perhaps a slight tendency for faster BDs to have less elongation. To investigate the formation mechanism and temperature of these BDs, we obtained their light curves from five AIA channels. Figure 4 shows the light curves of four of our BDs. These light curves are made by tracking the BD from when it first begins to brighten, through peak intensity, and ending when it fades from view or gets dim enough. To measure the intensity, we first find the target BD using the Hi-C 193 Å images and then match the location to the AIA 193 Å images. We center a circle on the BD's brightest part and measure the intensity inside. The frame is advanced and the intensity is measured for the new time, again with the circle centered on the brightest part of the BD. In this way, we track the BD's peak brightness over its lifetime or over its brightest peak during its lifetime. This intensity is then normalized to the highest intensity over the period. Figure 4. Normalized light curves for four example BDs from a set of five AIA bands. Each set of light curves is from tracking the brightest part of the BD as it drifts laterally during its peak brightening. As denoted in each panel by different colored lines, orange is for 193 Å, pink is for 211 Å, yellow is for 171 Å, green is for 1600 Å, and red is for 304 Å. Please note that the time on the x -axis is based on the AIA 193 Å channel; the closest available data in time for other channels is taken at each time step. Therefore, a few seconds’ difference in the observation of different channels is not seen. Asterisks on the 193 Å light curves mark data points. We use four AIA wavelengths in addition to the 193 Å: 211, 171, 1600, and 304 Å. The AIA 171, 193, and 211 Å channels are typically coronal, as their response functions peak at temperatures of roughly 1, 1.5, and 2.5 MK, respectively (Lemen et al. 2012 ). However, all of these channels have cooler-TR lines in the passband and hence have some response in the cooler-TR
Paper: 2016MNRAS.461.3683I (fulltext)
in the W 1 and W 2 bands and are characterized by W 1 − W 2 0.5 mag. Instead, the red W 1 − W 2 > 1 of our objects imply a considerable contribution of the warm and hot dust to their mid-infrared luminosity (Izotov et al. 2014a ). Three out of five galaxies were detected by WISE in the W 4 band at 22 μm. The estimated ratio of the 22 μm luminosity, which is a characteristic of warm dust, to the luminosity absorbed at 1500 Å in these galaxies is of order of unity (Table 9 ). As the luminosity of the cold dust emitting in the far-infrared range of extreme starbursts (not accessible to WISE ) is less or comparable to the luminosity of the warm dust (Izotov et al. 2014b ), we conclude that the UV luminosity absorbed in visible star-forming regions of our galaxies is consistent with the luminosity emitted by dust in the infrared range. Optical, near- and mid-infrared observations of other low-metallicity SFGs with similar properties suggest that they are relatively transparent (Izotov Thuan 2011 , 2016 ). That there is not much dust-obscured hidden star formation is also implied by the observed thermal free–free cm radio emission in dwarf galaxies with optical and infrared observations. Its flux density is consistent with the value derived from the flux density of the H β emission line (Izotov et al. 2014b ), indicating that there is not a considerable amount of additional star formation, not seen in the UV range, in our galaxies. 10 DISCUSSION To the best of our knowledge, our experiment is the first where compact SFGs with strong emission lines and high [O iii ]λ5007/[O ii ]λ3727 ratios are observed in the LyC. Furthermore, the entire sample (five out of five) at z ∼ 0.3 is found to emit LyC radiation, with absolute escape fractions ranging between 6 per cent and 13 per cent. Our observations thus significantly expand the sample of LyC leakers at low redshift, from four previously known galaxies (Leitet et al. 2013 ; Borthakur et al. 2014 ; Leitherer et al. 2016 ) to nine. The newly discovered sources also show significantly higher LyC escape fractions, compared to f esc (LyC) ≈ (1–4.5) per cent previously found. Apparently, the selection criteria we adopted are very efficient in finding LyC leakers. As such, this finding is probably one of the most important results of our study. Compactness and a peculiar emission line ratio as selection criteria, were previously already suggested to be related to LyC leakage. Jaskot Oey ( 2013 ) and Nakajima Ouchi ( 2014 ) have proposed that extreme [O iii ]λ5007/[O ii ]λ3727 ratios may be due to high escape fractions of ionizing photons and have argued that this could be the case for some ‘GP’ galaxies at z ∼ 0.1–0.3 or in z ∼ 2–3 LAEs. For comparison, Heckman et al. ( 2011 ) examined so-called Lyman-break analogues (LBAs) and suggested that a subset of them, those with a compact massive (∼10 9 M ⊙ ) dominant central object, could be LyC leakers. The LBAs have lower excitation and higher masses compared to
Paper: 2019A&A...632A.129W (fulltext)
and 180° to the phase space densities at 90° are greater than three. It is easy to see that strong depletions occur within the first bar, and the depletion CSE interval was removed when we estimated the percentages of CSEs. One can also see that the bars in the rear half have two short intervals, which are caused by scattering of strahl electrons. We therefore identify the two short intervals as CSE strahls, and the percentage of CSE strahls is about 35%. Fig. 1. Suprathermal electron pitch angle distributions of 272 eV, magnetic field and plasma data measured by ACE during the October 21C22, 1999 ICME passage. The two vertical dashed lines denote the boundaries of the ICME. Furthermore, this study examined all the suprathermal electron PADs of the 272 ICMEs and estimated the percentages of CSEs. The results are listed in the sixth column of Table A.1 . Only 10 (9.9%) of the 101 MCs have no CSE, and the ratio is consistent with the results of Shodhan et al. (2000) , who examined the CSE signatures of 52 MCs and concluded that 6 MCs have no CSEs. As for the other 91 MCs, the percentage of CSEs varies from 6 to 100%, with a mean value of 57%. Meanwhile, 75 of the 171 nonMC ICMEs (43.9%) do not contain CSEs. The fraction of ICMEs without CSEs is thus clearly larger in nonMC ICMEs than in MCs. The extensive difference may imply that the two groups have different magnetic structures. For the other 96 nonMC events, their mean CSE percentage is 45%, which is smaller than for the MCs (57%). Among the 96 nonMC ICMEs, 21 have greater than 70% CSEs. Examination of the magnetic field component curves of these 21 ICMEs reveals that fields of most events have neither apparent smooth rotations nor disorder, but their magnetic field component curves have only slight rotations, and they are relatively stable or even close to the line. It is well known that most MCs can be described with a constant force-free field configuration, for example, the Lundquist solution ( Lepping et al. 1990 ; Feng et al. 2006 ). According to the Lundquist solution, the shapes of measured field component curves depend on the distance d 0 between the spacecraft trajectory and the rope axis (see Figs. 2–4 of Feng et al. 2006 ). The spacecraft will measure center-enhanced and bipolar curves if the spacecraft trajectory is close to the rope axis; otherwise, the bipolar curves will disappear. Figure 2 shows the variations of the magnetic field components for the case of d 0 = 0.9 R 0 , where R 0 is the radius of the flux rope, z is the axial direction, x is the radial direction pointing to the satellite contact position, and the direction y is obtained from the cross product of z and x . In Fig. 2 , all the three magnetic field components are relatively stable and exhibit only slight rotations. These trends indicate that the spacecraft that passes through the flank of a magnetic flux rope will measure relatively stable magnetic field variations. Therefore, the 21 highest percentage (more
Paper: 2017MNRAS.466.1709T (fulltext)
calculations and the applicability of their formula for C-rich AGB stars with Z ini 10 4 should be checked by using the appropriate values. Also, the dependence of the mass-loss rate on the velocity amplitude of pulsation should be investigated, since the dependence is considered to be more sensitive for stars with larger C and R . The mass-loss rate derived from the DDW model is significantly smaller than the mass-loss rate assumed in stellar evolution calculations. If we use the derived mass-loss formula after the necessary condition for the efficient DDW is satisfied on the TP-AGB, the dredged-up carbon accumulates in the surface regions; accordingly, the effective temperature decreases and the mass-loss rate could increase. Although in the present calculations the derived mass-loss rate is inconsistent with the assumed mass-loss rate, it should be recalled here again that the hydrodynamical model can derive the properties of the DDW by specifying a set of input parameters, being independent of the stellar evolution model. Thus, the derived formulae, together with a necessary condition for the efficient DDW presented in Section 5.1 , could enable us to evaluate the mass-loss rate and dust yield during the C-rich AGB phase of stars with Z ini 10 4 in a manner consistent with stellar evolution, including whether the efficient DDW can operate on the C-rich AGB. 5.3 Implication for evolution of C-rich AGB stars and the dust-driven wind in the early Universe Investigations on the formation of stars in low-metallicity environments have revealed that the critical metallicity Z cri for the transition from Population III to Population II stars is as low as 10 9 10 7.5 , depending on the depletion factor of metal into dust (e.g. Omukai etal. 2005 , Schneider etal. 2006 , Chiaki etal. 2015 ). Although there is no information available for the initial mass function at the present time, intermediate-mass AGB stars with Z ini 10 4 can contribute to the enrichment of dust in the early Universe if the condition for the efficient DDW derived in the previous subsection is satisfied during the TP-AGB phase. However, the possibility of developing dust formation and a resulting DDW on the AGB is strongly influenced by the mass-loss history during the evolution, on which the time evolution of effective temperature as well as the elemental composition and opacity in the surface layer depend strongly through the number of TDU episodes and/or the occurrence of HBB. At present, we have no knowledge of the mass-loss mechanism and mass-loss rate before the onset of the DDW for the extremely low-metallicity stars considered in this article. In recent investigations focused on low-metallicity (1.6[Fe/H] 0.5) AGB stars observed in distant galaxies, Rosenfield etal. ( 2014 , 2016 ) showed that a mass-loss rate higher than SC05 is required during the AGB phases previous to the onset of the DDW, to reproduce the observed TP-AGB luminosity function as well as the number
Paper: 2016MNRAS.458.2596F (fulltext)
the tail in the velocity distribution is produced independently of η, with velocities up to ∼1000 km s −1 . However, different eccentricities do not change the shape of the distribution function with respect to the case of the circular orbit. Therefore, these results suggest that the energy exchange between the generic star of the GC and the BHB is almost independent on the eccentricity, depending only on the initial total energy of the binary. Figure 3. Comparison among the velocity distributions of escaping stars for an SMBHB with mass ratio ν = 1/4 and different eccentricities (η = 0, 0.25, 0.75). The distributions are cut on the left side at 212 km s −1 , which corresponds to the escape velocity from the system evaluated at 20 pc. For what regards the branching ratios, Table 2 shows the branching ratios as function of η. From this table, it is clear that the branching ratio remains nearly constant for different eccentricities. Therefore, not only the shape of the velocity distribution is preserved, but also the probability of star ejection. Table 2. The values of the Branching Ratios (BR) for ν = 1/4 and different BHB orbital eccentricities, η. To conclude, the BHB orbital eccentricity does not affect neither the shape of the velocity distribution nor the branching ratio of ejected stars. Therefore, the main features of the ejected stars depend on the binary mass ratio ν, but not on the binary eccentricity. 3.3 The role of the GC orbital inclination In our scattering experiments, the GC orbits are coplanar with the BHB orbital plane. This means that the angular momentum of the GC and that of the BHs are aligned. In order to explore the role of the GC orbital inclination, we performed a set of simulations in the extreme case of GC orbits perpendicular with respect to the BHB orbital plane, in the case of mass ratios ν = (1/20, 1/10, 1/5, 1/3). The results of our scattering experiments reveal that the GC orbital inclination plays an important role both for the branching ratio and the resulting velocity distribution. Fig. 2 shows the branching ratios for both the coplanar and perpendicular cases. In the perpendicular case, the branching ratios of the ejected stars are reduced. Actually, the angle between the two angular momentum vectors results to have an important role in the output of the scattering experiments, as the relative inclination of the angular momenta of the GC and of the test star has. Therefore, the actual value of the branching ratio comes from a combination of two effects due to the two-mentioned relative inclinations. Calling β and γ the angles between the pairs of (GC, BHB) and (test star, GC), respectively, the higher probabilities are obtained when β ∼ 0 and γ ∼ 0. Finally, when ν ≲ 1/20, the branching ratio tends to the value of the single BH case, for which β is not defined and consequently only γ has a role in the output. The effect of the GC orbital inclination is significant also in the velocity distribution of the
Paper: 2020MNRAS.499.1356G (fulltext)
the numerical code. The second term is the source term, , which describes the injection of new power-law particles into the causal volume. In general, S ′ ( p e ) receives contributions from other processes, such as Compton scattering, pair-production and annihilation. The advection coefficient for the adiabatic cooling of particles is given by Here, we have used the scaling of particle energy with comoving volume due to adiabatic expansion, such that where is the adiabatic index for a relativistic (non-relativistic) particle distribution. In general, the advection coefficient includes contributions from other processes that change the energy of particles, e.g. Compton scattering, synchrotron cooling, Coulomb interactions, and particle heating (as described below). The adiabatic cooling timescale is obtained from the rate of volume expansion, where where ζ = 7/3 for r r s when Γ ∝ r 1/3 , and ζ = 2 for r ≥ r s when Γ = Γ ∞ . By expressing the LHS of equation ( A3 ) using the number density, we find where we have conveniently defined the comoving growth time of the causal volume, , through Next, we use equation ( A2 ) to express the rate of change of the comoving number density with comoving time in terms of and the causal volume , so that which in turn implies where the comoving particle injection time into the causal volume is defined by and is the particle injection rate per unit volume. Note that the causal comoving volume grows faster than the total comoving volume V ′ since it occupies an increasing fraction of it, , where . Now, using equations ( A3 ) and ( A6 ) lead to the equation for the number density evolution where . By integrating the above equation over p e , it can be shown that the adiabatic cooling term vanishes and , where so that the equation reads . The comoving rate of particle injection per unit volume per unit dimensionless momentum comprises of two terms, where a fraction ξ of the total is accelerated into a power-law (non-thermal) distribution and the remaining fraction (1 − ξ) forms a thermal distribution, where represents the normalized momentum distribution. The total energy density per unit rest mass energy of the injected distribution can be expressed as where 〈γ e 〉 th is the mean energy per rest mass energy of the thermal distribution and for the power-law distribution 〈γ e 〉 nth is given in equation ( 10 ). Here, we make the assumption that the injected thermal particles are cold, so that . The momentum distribution of power-law electrons is given by The normalization is obtained by equating the rate of energy injection per unit volume to that given to the electrons due to dissipation, such that which for γ M > γ m ≫ 1 gives where we take γ M = γ max , the maximum LF corresponding to the outer boundary of the particle momentum grid in the simulation. The efficiency of acceleration is controlled by the mean energy of the power-law electrons, which depends on ξ, and ultimately by the minimal energy of power-law
Paper: 2018MNRAS.476.4814S (fulltext)
in the magnitude range where our analysis is conducted ( g 24). This procedure was repeated 10 4 times and the corresponding distributions of N syn and μ V, syn were computed. The number of stars contained within the same magnitude limits defined above was counted in the observed CMD and the density of stars per square degree Σ obs was calculated by dividing by the sampled area. The fractions f N of random extractions with N syn Σ obs and the fractions with μ V, mod > μ V, max were then computed. If the MS star density estimated around the sample of observed GCs were compatible with that predicted by models, the distribution of percentiles would be uniform. In Fig. 7 , the cumulative distributions of percentiles of the eight GCs at d ⊙ 45 kpc with respect to the N syn distribution of the five Aquarius simulations are shown. On average, the distribution of percentiles is consistent with a uniform distribution (although a vast range of lumpiness is covered by the five considered simulations), indicating a general agreement with model predictions for three out of five simulations. A Kolmogorov-Smirnov (KS) test indicates probabilities 0.1 P 0.2 for these simulations . However, the simulations Aquarius D and Aquarius E show opposite trends with distributions of percentiles that are incompatible with observations. Note that Aquarius D is the most massive halo among the five simulations of the Aquarius sample, with a virial mass M = 2.52 × 10 12 M ⊙ , exceeding the current estimate for the Milky Way (Patel, Besla Mandel 2017 ). It is therefore likely that the number density of the halo is also overestimated in this simulation. On the other hand, simulation Aquarius E is the one with the lowest fraction of stars in the diffuse halo at distances d GC > 20 kpc, with the majority of stars in this distance range contained in bound satellites. For this reason, the number density outside satellites turns out to be lower than those in other haloes. Figure 7. Cumulative distribution of percentiles in the number density domain for the eight GCs at d ⊙ 45 kpc. Different lines refer to the various cosmological simulations. The probabilities associated with the KS test are also indicated. The same conclusion can be drawn when the entire sample of 14 GCs is considered in the μ V space. The distributions of extracted surface brightness from each of the considered simulations are shown as a function of heliocentric distance in Fig. 8 . We note that the background surface brightness distribution is peaked at values in the range 30 μ V, syn 35 mag arcsec −2 following a declining trend with distance. At large distances, the surface brightness distribution becomes skewed with a tail towards bright values at μ V, syn 34 mag arcsec −2 . This is a consequence of the increasing degree of lumpiness of the halo at large distances: GCs forming within satellites in an early stage of dissolution are still surrounded by the compact remnant of their host galaxy. The upper limits