adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...906...98N (fulltext)
spiral-wave wind is here defined as the ejecta computed with the Bernoulli criterion discussed in Section 2.4 , and computed at times after which the dynamical ejecta have saturated. Table 4 Summary Table of the Spiral-wave Wind Properties of Long-lived Remnants Note. The columns contain the following information, starting from the left. Equation of state, mass ratio, available resolutions, inclusion of subgrid turbulence, time of the simulation end, mass of the spiral-wave wind, mass-loss rate via spiral-wave wind, mass-averaged electron fraction, terminal velocity, and finally rms angle for spiral-wave wind. For these four quantities we give the mean value among the resolutions and 1 σ deviations. For binaries for which only one resolution is present, the error is assumed to be 20% of the value. Figure 9 shows the total unbound mass of the wind as a function of time. The wind is monitored after the mass flux of the dynamical ejecta (computed according to the geodesic criterion) has saturated. Mass outflows due to the spiral-wave wind continue throughout the duration of the simulations with no indication of saturation. Indeed, while injection of mass and angular momentum from the high-density core of the remnant into the disk decreases with time as the system becomes more stationary, the mass ejection is expected to continue for as long as the spiral waves persist. Because the m = 1 modes are not efficiently damped (Paschalidis et al. 2015 ; Radice et al. 2016a ; Lehner et al. 2016b ; East et al. 2016a ), the ejection can in principle continue for the timescales that the system needs to reach equilibrium or to collapse to a BH (Section 3 ). Figure 9. Cumulative mass of the spiral-wave wind from long-lived remnants. The wind persists on timescales of ms with mass fluxes ∼0.33–1.23 M ⊙ s –1 . The largest wind masses are obtained for asymmetric binaries such as BLh q = 1.67 and LS220 q = 1.4, which in about ∼50 ms unbind ∼0.02 M ⊙ at a rate of ∼0.5 M ⊙ s –1 . We find that models with softer EOS achieve higher mass flux at lower mass ratios, i.e ., the mass flux of BLh* q = 1.66 is achieved by LS220* with q = 1.22. This might be attributed to softer EOS models having a stronger m = 1 mode in the remnant (see Section 7 ). However, if these remnants collapse, the spiral-wave mechanism shuts down and the outflow terminates. Thus the total mass ejected via spiral-wave wind depends directly on the lifetime of the remnant in addition to the binary parameters, EOS, and mass ratio. Thermal effects play an important role in determining the outflow properties, because high thermal pressures result in more extended disks with material that is easier to unbind. The highest temperatures in our simulations are found for the BLh EOS. On longer timescales than those simulated, the spiral-wave wind from the remnants with stiffer EOS might be larger, also in relation to the larger disk masses (Section 3 ). Overall, the spiral-wave wind from the long-lived remnant
Paper: 2019MNRAS.482L.118M (fulltext)
or the ansae (symmetric enhancements at the ends of the stellar bar) observed in the inner bar. Fig. 1 shows a 3.6 m S 4 G image of NGC 1291 where these stellar structures are visible. Figure 1. S 4 G 3.6 m images of NGC 1291. The left-hand panel shows the main outer structures described in Section 2 , i.e. the outer ring, outer disc, lens, and outer bar. The blue square represents the MUSE 1 arcmin × 1 arcmin field of view. The right-hand panels show a zoom-in (upper panel) and contour (lower panel) images highlighting the central photometric structures, i.e. inner disc, inner bar, and central bulge. They correspond to the actual field of view observed with MUSE, which is slightly offset with respect to the centre of the galaxy. North is up and East is left. 3 TIMER OBSERVATIONS OF NGC 1291 The TIMER project is a survey employing the MUSE integral-field spectrograph to study the central 1 arcmin × 1 arcmin of a sample of 24 nearby ( d 40Mpc) barred galaxies with nuclear rings and other central structures such as nuclear spiral arms, inner bars, and inner discs (see Paper I ). One of the project’s main goals is to study the star formation histories of such structures to infer the cosmic epoch of the formation of the bar and the dynamical settling of the main disc of the host galaxy. The methodology was demonstrated with a pilot study of NGC 4371 (Gadotti et al. 2015 ). The corresponding observations of NGC 1291 were performed during ESO Period 97 (2016 April to September) with a typical seeing on the combined data cube of 1.1 arcsec (spaxel size = 0.2 arcsec), mean spectral resolution of 2.65 Å (full width at half-maximum), and spectral coverage from 4750 Å to 9350 Å. NGC 1291, being one of the closest galaxies in the TIMER sample, the MUSE 1 arcmin × 1 arcmin field of view (FoV) corresponds to a (almost) square region of ≈2.5 kpc on a side (i.e. 1 arcsec corresponds to 42 pc). The MUSE pipeline (version 1.6) was used to reduce the data set (Weilbacher et al. 2012 ), correcting for bias and applying flat-fielding and illumination corrections, as well as wavelength calibration. The exposures were flux-calibrated through the observation of a spectrophotometric standard star, which was also used to remove telluric features. Dedicated empty-sky exposures and a PCA methodology were employed to remove signatures from the sky background. Finally, the exposures were also finely registered astrometrically, so that the point spread function of the combined cube is similar to that in individual exposures. More details can be found in Gadotti et al. ( 2018 ). 3.1 Stellar kinematics The fully reduced data cube was spatially binned using the Voronoi scheme of Cappellari Copin ( 2003 ) to achieve a signal-to-noise ratio of approximately 40 per pixel on each spatial bin. We used the penalized pixel fitting ( ppxf ) code developed by Cappellari Emsellem ( 2004 ) to extract the stellar kinematics from the binned spectra, masking emission lines and using the
Paper: 2017MNRAS.466.2939V (fulltext)
net yield of the chemical element X per stellar generation, y X , and the yield of X from Type Ia SNe, m X , Ia . Other fundamental assumptions in the model are given by the DTD for the Type Ia SN rate and the prescription for the SFR law. An observational constraint for the calibration of the gas infall history is given by the radial profile of the present-day total surface mass density, tot ( r , t G ), which is very difficult to retrieve from an observational point of view. In this work, we apply our model to reproduce the observed chemical abundance patterns at the solar neighbourhood, where r S 8kpc; hence, we normalize the infall law, as given in equation ( 12 ), by requiring the following constraint: where we assume t G 14Gyr, a single infall episode, and tot ( r S , t G )54M pc 2 , which represents the present-day total surface mass density at the solar neighbourhood (see also Micali, Matteucci Romano 2013 , which refers to the work by Kuijken Gilmore 1991 ). The best model is defined as the one capable of reproducing the observed trend of the [O/Fe] versus [Fe/H] chemical abundance pattern, which represents the best observational constraint for the chemical evolution models of the MW. Our best models are characterized by the following values of the free parameters: L 2Gyr 1 , for our best model with the linear SchmidtKennicutt law, and SFR 0 2M pc 2 Gyr 1 , for our best model with the non-linear SchmidtKennicutt law; the infall time-scale for the gas mass growth of the MW disc is 7Gyr; the mass loading factor is 0.4. Finally, the best models assume the single-degenerate scenario for the Type Ia SN rate. We remark on the fact that the predicted [Fe/H]-[O/Fe] relation in the MW is fitted by construction using fixed values for the key free parameters L (or SFR 0 ), and . It is worth noting that the values of our best parameters are in agreement with previous recent studies (see e.g. Minchev, Chiappini Martig 2013 ; Nidever etal. 2014 ; Spitoni etal. 2015 ); in particular, a typical infall time-scale 7Gyr and star formation efficiency 1Gyr 1 are necessary to reproduce also the observed age-metallicity relation and the metallicity distribution function of the G-dwarf stars in the solar neighbourhood (Matteucci 2001 , 2012 ; Pagel 2009 ). Once the best set of free parameters is determined, we fix them and explore the effects of assuming different DTDs for the Type Ia SN rate and different prescriptions for the relation between the SFR and the surface gas mass density, gas ( r , t ). In this way, we can demonstrate the flexibility of our method for solving the chemical evolution of galaxies by taking into account the major systematics in the theory. 3 THE OBSERVED DATA SET The data set for the observed [Fe/H][O/Fe] and [Fe/H][Si/Fe] relations in the solar neighbourhood is shown in Fig. 1 (a) and Fig. 1 (b), respectively, where the data from different works are drawn with different colours. Our set of data includes both MW halo stars from
Paper: 2020MNRAS.494.1263M (fulltext)
luminosity of 3D H α clumps ranges from ∼10 per cent–95 per cent, while the contribution to H α luminosity from individual 3D clumps ranges from less than 1 per cent to ∼90 per cent. This large range results from the short time-scale of star formation traced by H α and thus the clumpiness of H α emission. Most individual 3D clumps contribute less than 10 per cent to total H α luminosity, while only one or two largest clumps contribute most of the H α flux. The gas in H α clumps is not bound, with virial parameters (equation 1 ) larger than 1 by orders of magnitude. 4.4 Origins of giant stellar clumps Gravitational instability can induce fragmentation of the galactic disc, thus leading to growth of clumps. Stability criterion to linear axisymmetric perturbations for gaseous discs can be described by the Toomre parameter (Safronov 1960 ; Toomre 1964 ): where κ is the epicycle frequency, σ is the velocity dispersion, and Σ is the mass surface density. A disc is unstable if Q ≲ 1. Observations (e.g. Puech 2010 ; Girard et al. 2018 ) found that clumpy galaxies are marginally stable, with Toomre Q ∼ 1. Most H α clumps in high-redshift galaxies are located in regions where Q is low (Genzel et al. 2011 ; Wisnioski et al. 2012 ; Mieda et al. 2016 ). In galaxy formation simulations (Inoue et al. 2016 ; Oklopčić et al. 2017 ) gas clumps also coincide with regions of Q 1, albeit with large scatter of Q value. Inoue et al. ( 2016 ) also found that stellar clumps coincide with regions of low stellar Q . In Meng et al. ( 2019 ) we studied the distribution of Q in our simulations, accounting for different phases of the gas and stars. Such multicomponent Q more accurately describes the linear stability criterion in realistic galaxies. We found that strong feedback from young stars disperses gas around them, leading to spatial anticorrelation of dense gas and stars up to 50 Myr old. Here we analogously find that locations of 2D UV clumps do not coincide with the regions of low Q . This is because low Q regions closely trace high gas density, while rest-frame UV clumps trace young stars. The average value of Q in 2D UV clump regions ranges from 2.1 to 3.6, which is larger than the median value of Q = 0.5–1.0 weighted by molecular gas mass in Meng et al. ( 2019 ). For H α clumps Q = 1.4–3.3 is closer to the gas values, as expected for very young stars. The discrepancy in location of gas and young stars is also seen in the simulations of Oklopčić et al. ( 2017 ), where they find that the gas clumps coincide with the location of instantaneous star formation, but not of the SFR averaged over more than 10 Myr. If clumps form out of instability of a self-gravitating disc, the wavelength of the fastest growing perturbation is The effective radius of a fully formed clump may not exactly correspond to this scale, but it can still be used as a rough guide. If we take λ T /2 for R clump , and approximate the disc rotation velocity profile as flat, then , and we obtain
Paper: 2018MNRAS.479L..23H (fulltext)
within these haloes, we can quantify the expected rate of DCBH mergers that will be detectable by LISA due to SMS multiplicity. 2 NUMBER DENSITY DISTRIBUTION OF DCBHS In order to obtain the abundance of DCBH seeds, we use the results of A12, D14 and H16. We focus on two scenarios with J c = 30 J 21 1 and 300 J 21 respectively, where J c is the critical Lyman–Werner (LW) flux required to induce DCBH formation. The exact value of J c that can facilitate DCBH formation in an ACH is still unknown. What complicates the matter further is that a single value of J c is not representative of the chemothermodynamical processes that lead to the formation of a DCBH, namely the photodestruction of H − and H 2 (Wolcott-Green Haiman 2012 ; Sugimura, Omukai Inoue 2014 ; Agarwal Khochfar 2015 ; Agarwal et al. 2016 ). Therefore, we use abundance estimates from the literature and employ two values of J c as our extreme limits. In doing so, we are implicitly assuming that other process may either augment or replace the effect of an external LW field to produce similar results (e.g. streaming velocities, see Tanaka Li 2014 ; Hirano et al. 2017 ; Schauer et al. 2017 , or rapid accretion, see Yoshida et al. 2003 ). Thus, J c = 30 J 21 can be viewed as a scenario in which DCBHs are relatively common and would result in a sufficient number of massive seeds for the entire MBH population. On the other hand, J c = 300 J 21 would result in a number density of heavy seeds that could only seed a subpopulation of massive black holes (perhaps the highest redshift quasars) and can thus be viewed as a scenario where large initial mass black holes are rare. We now describe our steps going from the theoretical estimates of abundance of DCBH seeds to the observed number of events. Using the models and predictions from the redshift distributions of the newly formed DCBH seeds per unit comoving Mpc 3 found in A12, D14, H16 we calculate a ‘rate’ of formation at time-step ‘i’ in units of cMpc −3 where N new is the number of newly formed DCBH sites identified in the simulations for a given value of J c , and d V denotes the comoving volume normalization. To get the DCBH formation rate per unit time per unit redshift from any given redshift in the observer’s frame of reference (i.e. as seen at z = 0), we follow Magg et al. 2016 (equation 8) where R z is the comoving distance at a given redshift. In Fig. 1 we plot the number of DCBH seeds per unit time per unit volume i.e. equation ( 1 ), generating a fit using a second-order polynomial. The fit is obtained from the estimate for the number density of DCBHs based on J c = 30 J 21 (in blue filled circles, A12 , D14 , and H16 ), and can be written as where t is the age of the Universe at which the rate is computed. The number density of pristine ACHs scales approximately as J (D14, Inayoshi Tanaka 2015 ), hence rescaling from J c = 30 to J c = 300 requires a normalization factor of 10 −5 . This is shown using the red filled circles. The
Paper: 2016ApJ...826...48M (fulltext)
Saturn's bow shock one of the strongest in the Solar System (e.g., Russell 1985 ), and such occasions occur under rare solar wind conditions where the near-Saturn Interplanetary (solar) Magnetic Field (IMF) strength drops to ∼0.1 nT and the shock Alfvén Mach number increases to order 100. On such occasions Cassini has witnessed electron acceleration at a quasi-parallel shock crossing (Masters et al. 2013 ), and provided evidence for shock reformation controlled by specular ion reflection (Sulaiman et al. 2015 ). The electron acceleration result indicates that there may not be an electron injection problem at high Mach number quasi-parallel collisionless shocks, in agreement with some theories (e.g., Amano Hoshino 2010 ). Here we reveal the full picture of suprathermal electrons at Saturn's bow shock revealed by Cassini . We show that the sum of all Cassini electron observations made during hundreds of shock crossings is consistent with electron acceleration theory that involves interactions with whistler waves excited by the reflected thermal electrons just upstream of the shock. The implication for the strong shocks surrounding young SNRs is that they may be able to inject thermal electrons into an acceleration process under any upstream magnetic field orientation. 2. SURVEY OF SUPRATHERMAL ELECTRON SIGNATURES AT SATURN'S BOW SHOCK The Cassini spacecraft has been in Saturn orbit since 2004 July. During the orbital tour the spacecraft has crossed Saturn's bow shock hundreds of times. The location of the shock is highly variable, and the boundary moves at speeds much greater than that of the spacecraft (Achilleos et al. 2006 ). As a result, multiple shock crossings are typically made on an inbound/outbound pass of any orbit where the spacecraft enters the region of space defined by the range of possible shock locations. Two of the sensors carried by instruments mounted on the three-axis stabilized spacecraft are particularly relevant for a survey of electron acceleration at Saturn's bow shock. The first is the Electron Spectrometer (ELS) of the Cassini Plasma Spectrometer (CAPS), which detects electrons in the (lower) energy range 0.5 eV to 26 keV (Young et al. 2004 ). The second is the Low Energy Magnetospheric Measurements System (LEMMS) of the Magnetospheric Imaging Instrument, which detects electrons in the (higher) energy range 18 keV to ∼1 MeV (Krimigis et al. 2004 ). Both ELS and LEMMS have a limited field of view (FOV). In addition, measurements of the local magnetic field vector made by the fluxgate magnetometer of the Cassini dual-technique magnetometer (MAG; Dougherty et al. 2004 ) provide an essential diagnostic of shock structure. This study is motivated by recently reported observations made by Cassini at a single (quasi-parallel) shock crossing, where suprathermal electrons were detected (Masters et al. 2013 ). The high-energy electron signature of this event is most pronounced in data taken by the more sensitive LEMMS
Paper: 2016MNRAS.456.2722K (fulltext)
specified by the photon index Γ of the underlying power law. Under the assumption of isotropic emission, this provides the bolometric luminosity, L bol . We express the luminosity as the Eddington ratio: the ratio of L bol to the Eddington limited luminosity, L Edd . The latter is determined as L Edd = (2.1 ± 0.5) × 10 45 erg s −1 using a black hole mass of (14 ± 4) × 10 6 M ⊙ (Peterson et al. 2004 , see also Grier et al. 2012a ) as well as a solar hydrogen abundance of X = 0.71. The Eddington ratio's uncertainty is dominated by systematics. The relation of Zhou Zhao ( 2010 ) introduces a substantial systematic uncertainty from the spread in the observations that they employed. Including this uncertainty and the uncertainty in the Eddington luminosity, the mean 1σ uncertainty in log L bol / L Edd is 0.77. Without these systematic uncertainties, and only including the uncertainties in Γ and the 2–10 keV luminosity, the mean error in log L bol / L Edd is 0.10. As the in-band flux is well measured, the uncertainty is mostly set by the error in Γ. We are interested in the correlation between parameters of a single source, so we omit the systematics from the uncertainty reported in each data point. The systematic error would only shift the observed trends with Eddington ratio. 3 SIMPLE POWER-LAW FIT Previous analyses of these observations required a complex spectral model that includes ionized absorption at energies ≲3 keV as well as photoionized reflection (e.g. Parker et al. 2014 ; Wilkins Gallo 2015 ). Before we apply a similar detailed spectral model in Section 4 , however, we first fit the data with a simple power law. The observed correlation of the photon index Γ and the X-ray flux (Sarma et al. 2015 ), suggests that we can gain some insight into the qualitative behaviour of the source with a simple spectral model. Following Parker et al. ( 2014 ), we fit a power law to the spectra in the energy intervals 3–4, 8.5–10, and 40–50 keV. This avoids absorption at low energies as well as the Fe Kα line and the Compton hump, which are prominent reflection features. These fits provide a relatively clean look at the underlying power law. We include absorption by the Galactic interstellar medium using the Tübingen–Boulder model (Wilms, Allen McCray 2000 ) with an equivalent hydrogen column of N H = 3.6 × 10 20 cm −2 (Kalberla et al. 2005 ), and we use solar abundances as reported by Grevesse Sauval ( 1998 ). A cosmological redshift is applied (Section 2.3 ). The spectral fits are performed using xspec v.12.8.2 (Arnaud 1996 ), and all presented errors in the fit parameters are at 1σ. For each spectrum, we determine Γ and the 3–10 keV power-law flux. We reproduce the logarithmic relation of Γ and the X-ray flux with a break at higher fluxes (Sarma et al. 2015 ), which has also been observed in other AGN (e.g. Perola et al. 1986 ; Done, Madejski Życki 2000 ; Shih, Iwasawa Fabian 2002 ), and we will quantify this relation with the detailed spectral
Paper: 2020ApJ...902..148S (fulltext)
in the downstream. In PIC simulations of nonrelativistic shocks, Bohdan et al. ( 2020 ) found similar values of while Park et al. ( 2015 ) found even more effective thermalization with . This heating via plasma processes of the thermal electrons requires to be large. In Figure 6 , we show as a dashed vertical cyan line, representing the value found by Crumley et al. ( 2019 ) (). This value of would overshoot that radio data by orders of magnitude. Observations of supernova remnants also find the ratio to be between 0.03 and 1, albeit the velocities in those cases are much lower (Ghavamian et al. 2013 ; Vink et al. 2015 ). Very small values of help alleviate the constraint on . However, small values of are problematic in the sense of UHECR acceleration. The dotted–dashed horizontal lines shown in Figure 6 correspond to minimum values of required for UHECR acceleration. They are shown for different values of the best-fit rigidities, similarly to Figure 3 . The lower limit on is calculated using aswhere is the internal energy of the downstream. The lines are plotted for completely stripped iron using acceleration efficiency (thin) and (thick). It is possible that the magnetization decreases with distance to the shock front (e.g., Lemoine et al. 2013 ), something which is not considered in the present work. Inspection of Figure 6 shows that, while , the radio flux is only weakly dependent on . Therefore, for these values of , our conclusion would not change if a varying magnetization was included. As previously mentioned, is suggested by PIC simulations (Sironi Spitkovsky 2011 ; Park et al. 2015 ; Crumley et al. 2019 ; Bohdan et al. 2020 ). The constraints shown in Figure 6 are valid for the microphysical parameters at the forward shock. In principle, the reverse shock could be different from the forward shock, which means that could be sufficiently small in the forward shock as to not overshine the radio, while large enough at the reverse shock for UHECR acceleration to be possible. As both the reverse and the forward shocks are transrelativistic, this might imply that , but this also depends on the magnetization around the two shocks, which can be different. One can estimate as follows. Assuming cosmic ray acceleration occurs during the first shell crossing of the shock, the shell crossing radius is estimated as , where is the shell width and is the central engine duration that we set equal to T 90 (Nakar Piran 2004 ). With an initial Lorentz factor of , the ratio of the ejecta density to the circumburst density is . Thus, the Lorentz factor of the downstream of the reverse shock as seen from a laboratory observer can be estimated as , where has been used (Panaitescu Kumar 2004a ; Zhang Murase 2019 ). 14 14 This approximation for is not very accurate in this case, but it has been used to show the parameter dependence. A more precise value is given by . The magnetic field is calculated as . The value of sufficient to accelerate UHECR can be