adsabs/FOCAL huggingface dataset:
Understanding how inertial waves interact with co-rotation resonances is thus a key issue in quantifying tidal dissipation, especially since waves may deeply interact with the background flow at this particular location, which in turn may alter the background flow (as was proposed first by Eliassen & Palm 1961, for terrestrial mountain waves). In binary systems and for late-type stars, Goldreich & Nicholson (1989) showed that the angular momentum transported by gravity waves and exchanged at co-rotation can lead to the successive synchronisation of the layers, from the base to the top of the radiative envelope. More generally, a body of work in various domains, from astrophysical disks (e.g. Goldreich & Tremaine 1979; Baruteau & Masset 2008; Latter & Balbus 2009; Tsang & Lai 2009) to geophysical fluid dynamics (e.g. Bretherton 1966; Yamanaka & Tanaka 1984), has tried to understand the properties of wave propagation and dissipation around co-rotation and, more generally, at all special locations in fluids that correspond to singularities in the linear wave propagation equation. We will refer to them as ‘critical levels’ in the following (Maslowe 1986), or as ‘critical layers’ in the case of a viscous medium. This distinction is analogous to the distinction between shear layers and attractors of characteristics that are kinds of singularities for the governing equation of inertial waves in a spherical shell. The aforementioned singularities can act very differently, with either severe absorption at the critical level (as in Booker & Bretherton 1967, for stratified vertical shear flows) or no attenuation if the wave propagates in a peculiar direction (Jones 1967; Acheson 1972; Grimshaw 1975a, for stratifiedvertical shear flows with rotation and magnetism). In other cases, a critical level may even give rise to wave amplification under certain conditions related to the first and second derivatives of the mean flow velocity (Lindzen & Tung 1978; Lindzen & Barker 1985, for barotropic and stratified shear flows, respectively). These studies all used an invariant quantity (the Reynolds stress or thewave action for rotating or magnetic flows) as a diagnostic tool to interpret the role of the critical level in terms of energy transmission and to quantify exchanges between the wave and the mean flow (Eliassen & Palm 1961; Bretherton 1966).
Citation: Bretherton 1966
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Citation: Bretherton 1966
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The launch of the Fermi Gamma-ray Space Telescope has spurred on the search for pulsars in γ-rays (Grenier & Harding 2015), yielding over 2001 detections and triggering multiwavelength observations. While pulsars are common targets in the X-rays, they are very challenging targets in the optical and very few of them have been identified (see Mignani et al. 2016, and references therein). Here, we report on Large Binocular Telescope (LBT) observations of an isolated pulsar, PSR J2043+2740 (Taylor, Manchester & Lyne 1993), detected by both AGILE (Pellizzoni et al. 2009) and Fermi (Abdo et al. 2010; Noutsios et al. 2011). It was discovered as a radio pulsar (Ray et al. 1996) and later on as an X-ray source by XMM–Newton (Becker et al. 2004), although X-ray pulsations have not yet been found. PSR J2043+2740 is one of the very few non-recycled pulsars older than 1 Myr detected in γ-rays, with a characteristic age τc = 1.2 Myr, inferred from the values of its spin period Ps = 0.096 s and its derivative $\dot{P}_{\rm s} = 1.27 \times 10^{-15}$ s s−1 (Ray et al. 1996). This also yields a rotational energy loss rate $\dot{E}_{\rm rot} = 5.6 \times 10^{34}$ erg s−1 and a surface dipolar magnetic field Bs = 3.54 × 1011 G.2 Although PSR J2043+2740 does not have a very large spin-down power compared to young (∼1–10 kyr) pulsars (∼1036–1038 erg s−1), it is still a factor of 2 larger than that of middle aged γ-ray pulsars (∼0.1–0.5 Myr), such as Geminga, PSR B0656+14 and PSR B1055−52, all detected in the optical, thanks to their distances ≲ 500 pc (Abdo et al. 2013). The distance to PSR J2043+2740 is uncertain owing to the lack of a radio parallax measurement. The radio dispersion measure (DM = 21.0 ± 0.1 pc cm−3; Ray et al. 1996) gives a distance of 1.8 ± 0.3 kpc from the NE2001 model of the Galactic-free electron density (Cordes & Lazio 2002). A slightly smaller distance (1.48 kpc) is inferred from the model of Yao, Manchester & Wang (2017). The hydrogen column density towards the pulsar obtained from the X-ray spectral fits (NH ≲ 3.6 × 1020 cm−2; Abdo et al. 2013) suggests a distance of a few hundred pc (He, Ng & Kaspi 2013), although these estimates depend on the model X-ray spectrum. Such a distance would make PSR J2043+2740 a viable target for deep optical observations, never carried out until now, and might be compatible with the debated association (Noutsios et al. 2011) with the Cygnus Loop supernova remnant (SNR) at $540^{+100}_{-80}$ pc (Blair, Sankrit & Raymond 2005).
Citation: Noutsios et al. 2011
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Citation: Noutsios et al. 2011
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For completeness, we also supplement our reaction scheme with grain-surface association reactions extracted from the publicly available Ohio State University (OSU) network3 (Garrod et al. 2008). For those species important in grain-surface chemical reaction schemes, e.g., the CH3O radical, which are not included in Rate12, we also extract the corresponding gas-phase formation and destruction reactions from the OSU network. The grain-surface network has been further updated to include all studied routes to water formation under interstellar and circumstellar conditions (Cuppen et al. 2010a; Lamberts et al. 2013). The grain-surface reaction rates are calculated assuming the Langmuir-Hinshelwood mechanism only, and using the rate-equation method as described in Hasegawa et al. (1992). We limit the chemically “active” zone to the top two monolayers of the ice mantle. We assume the size of the barrier to surface diffusion is 0.3× the binding energy; in this way, volatile species diffuse at a faster rate than strongly bound species. This value lies at the optimistic end of the range determined by recent off-lattice kinetic Monte Carlo simulations of surface diffusion of CO and CO2 on crystalline water ice (Karssemeijer & Cuppen 2014). This allows the efficient formation of complex organic molecules via radical-radical association reactions at ≳20 K (see, e.g., Vasyunin & Herbst 2013; Walsh et al. 2014). For the lightest reactants, H and H2, we use either the classical diffusion rate or the quantum tunnelling rate depending on which is fastest (see, e.g., Tielens & Hagen 1982; Hasegawa et al. 1992). For the latter rates, we follow Garrod & Pauly (2011) and adopt a rectangular barrier of width 1.5 Å. We also include reaction-diffusion competition in which the reaction probability is determined by the relative rates between the barrier-mediated reaction and thermal diffusion (see, e.g., Chang et al. 2007; Garrod & Pauly 2011). Although still relatively simplistic, this takes into account the increased probability of reaction in the limit where the thermal diffusion of the reactants away from a common binding site is slow compared with the barrier-mediated reaction rate.
Citation: Lamberts et al. 2013
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“Wiggling” outflows are often observed among young stellar jets and protostellar molecular outflows (Eisloffel et al. 1996; Terquem et al. 1999). Terquem et al. (1999) investigated such binary systems where the accretion disk, from which the jet originates, is inclined to the binary orbital plane. They concluded that the observed jet “wiggling” is a consequence of the jet precession caused by tidal interactions in such non-coplanar binary systems. Nichols & Slavin (2009) as well as Hollis & Michalitsianos (1993) suggested that the precession of the accretion disk around the WD may be responsible for the bending of the wide-angle outflow found in the previous studies. In analogy to these observations of young stellar jets, we suggest that the “wiggling” that we also find here for the R Aqr jet may result from disk precession as well. We estimated the precession period using the theory from Terquem et al. (1999) and the binary parameters taken from Gromadzki & Mikołajewska (2009) – Mh = 0.8M⊙ (the mass of the hot WD companion), Mp∕Mh = 1.65 (where Mp is the mass of the primary companion), D = 15.5 AU (the mean semi-major axis of the system), and e = 0.25 (eccentricity). The value of Rd is unknown in our case and we used Rd = 5 AU giving D∕Rd ≈ 3 which corresponds to the average value of 2 ≤ D∕Rd ≤ 4 (the range taken from Terquem et al. 1999). For this calculation, we assumed that the angle δ between the disk plane and that of the binary orbit is small enough (10°) and we adopted cosδ = 1. Using Eq. (1) from Gromadzki & Mikołajewska (2009), we derived the precession time of T ≈ 530 yr. This value is quite large for the wiggling waves that we see. We estimated the projected spatial wavelength λproj of the “wiggling” wave according to λ = λproj∕sin i, where i is the angle between the jet symmetry axis and the line of sight, and T = λ∕υ, where υ is the jet velocity, from Gromadzki & Mikołajewska (2009). Using i = 72° and υ ~ 100 km/s, we derive λproj ≈ 10 500 AU which is more than 20 times larger than the projected length of the observed wiggling outflow (2′′ ≈ 440 AU). However, we should note that the precessing time strongly depends on the D∕Rd; the T decreases significantly with increasing R. It may also be the case that the “wiggling” model developed for YSO jets is not fitting for R Aqr which consists of evolved objects, and both the WD and the disk where the jet probably forms are much hotter than YSO systems. Furthermore, we cannot exclude that the steady wiggling might be a sequence of dynamical interactions of the two collimated flows tilted to each other.
Citation: Gromadzki & Mikołajewska (2009)
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Citation: Gromadzki & Mikołajewska (2009)
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Citation: Gromadzki & Mikołajewska (2009)
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Erwin (2018) showed that, in a sample drawn from the Spitzer Survey of Stellar Structure in Galaxies (S4G), the bar fraction is constant over a range of (g −r) colours and gas fractions. Their bar fraction does not increase, but rather decreases for stellar masses higher than ∼ 109.7M⊙. These results are in contrast to many SDSS-based studies cited above. Erwin (2018) argues that this apparent contradiction can be explained if SDSS-based studies miss bars in low-mass blue galaxies. In Figs 5 and 6, we showed that the newly detected bars in GZD (compared to GZ2) are weak bars in low-mass blue galaxies. Nevertheless, the ‘combined’ bar fraction in Fig. 6 is not constant over (g −r) colour and agrees well with Masters et al. (2011) for redder colours [(g −r) colour > 0.5]. Additionally, our ‘combined’ bar fraction remains roughly constant over stellar mass. As mentioned before, we conclude that strong bars drive the trends of bar fraction with (g −r) colour, stellar mass, and SFR observed in other studies (Nair & Abraham 2010b; Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017). However, the addition of weak bars in low-mass blue galaxies is insufficient to resolve the apparent disagreement between Erwin (2018) and many SDSS-based studies (Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017; Kruk et al. 2018), which instead seems likely to be due to the very different sample selection of the S4G and SDSS galaxy samples. For example, the median stellar mass of the sample used in Erwin (2018) is ∼109.6M⊙ (based on their Fig. 4 and the bins in the top left-hand panel of their Fig. 5). However, the median stellar mass of our sample is 1010.6M⊙. As stellar mass correlates with many parameters (including bar length), this can have major consequences. Additionally, as Erwin (2018) notes, there is also the issue of resolution to consider. With an r-band FWHM of 1.18 arcsec from DECaLS (Dey et al. 2019) and a mean redshift of 0.036, the mean linear resolution of our sample is approximately 834 pc, which is higher than the 165 pc of Erwin (2018). This explains why they observe many sub-kpc bars, while we do not. These differences in stellar mass and resolution will manifest themselves in the conclusions, so a more detailed analysis is needed for a proper comparison with Erwin (2018).
Citation: Masters et al. 2011
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Citation: Masters et al. (2011)
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Citation: Masters et al. 2011
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We have used 3.0–75.0 keV NuSTAR data to probe any cyclotron line feature. To describe the continuum of 4U 1700–37, we have applied the NPEX model [cons*TBpcf*(powerlaw*npex+gaus+gaus)], following the previous work of Jaisawal & Naik (2015a). The NPEX model has been created by adding two cutoffpl models with their cutoff energies tied to each other and keeping the photon index of one to be frozen at –2.0. For the best fit, the χ2/d.o.f is found to be 577.64/475. The fit shows some residuals in the overall spectrum. We added a Gaussian absorption model around 39 keV (following the previous work of Jaisawal & Naik 2015a), but the best fit gives the line energy as $15.44^{+0.56}_{-0.53}$ keV with χ2/d.o.f = 487.32/472. The width and the depth of the line are found to be $5.47^{+0.90}_{-0.78}$ keV and $1.29^{+0.51}_{-0.39}$, respectively. The chance probability of the line has been computed using the ftest task in xspec. The F-test with this absorption line gives an F value  = 29.2 and a chance probability of 2.61 × 10−17(Table 3). If we add another Gaussian absorption line at 38.9 keV (Energy value frozen) the best-fitting χ2/d.o.f is found to be 486.8/470. This indicates that the second absorption line is not required for the fit. If we use only one Gaussian absorption line and freeze the line energy at 38.9 keV then the width and the depth of the line are found to be $4.54_{-0.87}^{+0.90}$ keV and $2.87_{-1.05}^{+1.24}$, respectively with a χ2/d.o.f  = 549.2/473. The ftest gives a chance probability of the 38.9 keV line to be 6.4 × 10−6. So, with NPEX model we find two valid model combinations of the data. One, the presence of a Gaussian absorption line at ∼15 keV, two, the presence of a Gaussian absorption line at 38.9 keV. But, the presence of both lines together is not supported by the data. The 10.0–70.0 keV flux of the source is found to be (2.26 ± 0.01) × 10−9 erg cm-2s-1, much lower than the value (5.6 ± 0.3) × 10−9 erg cm-2s-1, previously reported from SUZAKU data (Jaisawal & Naik 2015a).
Citation: Jaisawal & Naik 2015a
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In the main simulation, we modeled a Cartesian cube 2000 AU in size with 150 grid points in each direction. The cell size of 13.3 AU is comparable to the size of the star, which was represented by a point source. Clumps A to D were simulated as 3D structures of a Gaussian density profile. The choice of a Gaussian distribution was dictated only by the simplicity of the implementation. Whereas observations constrain the clump sizes and overall relative location in two spatial dimensions, the extent of a given feature along the line of sight was a free parameter. We included scattering in the radiative transfer calculations. It turned out early on in the simulations that the dusty medium is very optically thick for visual and ultraviolet (UV) photons making the calculations particularly time-consuming. To speed them up, we used the Modified Random Walk method implemented in RADMC-3D (Robitaille 2010). The number of photons used in the Monte Carlo simulations varied from 105 to 109. For each spatial configuration of dust distribution, first a thermal structure was calculated. Based on this calculation, regions where the dust temperature exceeded the sublimation temperature were removed from the simulation and the thermal structure was calculated once again. In this way we reconstructed images that were further processed in CASA for a direct comparison to ALMA continuum maps. Given a very high number of free parameters (over 40 in the basic version of the simulation), the best configurations were searched through iterative trial-and-error modifications of the models. We aimed to construct only a general 3D model of the dusty environment requiring a minimum dust mass possible. We attempted to reproduce the peak and integrated ALMA fluxes of each feature within the order of magnitude. This aim, however unambitious it may appear, was challenging without increasing further the number of free parameters. For example, better fits to total flux and its distribution would require considering density distributions other than Gaussian.
Citation: Robitaille 2010
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Analyzing the fraction of quenched galaxies for centrals and satellites as a function of stellar mass and environmental parameters has emerged as a fruitful way of gaining insights into the phenomenology and the physical processes of quenching. However, one of the difficulties in interpreting this fraction is that many of the environmental parameters are statistically correlated both with each other or with the stellar mass of the galaxy. For example, there is good evidence that halo mass correlates with the mass of the central galaxy (e.g., Yang etÂ al. 2009), that the local overdensity correlates with the group-centric distance (e.g., Peng etÂ al. 2012; Woo etÂ al. 2013), and that the local overdensity also correlates with halo mass (e.g., Haas etÂ al. 2012; Carollo etÂ al. 2013b). These correlations among the parameters can introduce spurious dependencies, i.e., dependencies which have no direct causal relation to quenching, if the quenched fraction is regarded as a function of certain parameters in isolation. Therefore, in principle one has to study the quenched fraction in the full parameter space and to vary only one parameter at a time, while keeping the others fixed (see, e.g., the discussion in Mo etÂ al. 2010; Section 15.5). However, this approach does also not necessarily guarantee that the measured dependence of the quenched fraction on a certain parameter is directly related to a physical quenching process, since the red fraction may also depends on the history of the galaxy, i.e., how the galaxy is moving through the parameter space. Therefore, interpreting trends of the quenched fraction even in the full parameter space has turned out to be quite difficult and has led to some confusing and apparently inconsistent statements in the literature. In this paper, we use the Sloan Digital Sky Survey (SDSS; York etÂ al. 2000) and the group catalog of Yang etÂ al. (2012) to take a new look at some of the issues that have been recently raised.
Citation: Peng etÂ al. 2012
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In our investigation we were looking for alignment of galaxy clusters in a sample of 1056 low redshift ACO clusters with known Bautz–Morgan type. We found a statistically significant effect for structures with separation distance 30  R ≤ 45 h−1 Mpc. This allowed us to conclude that, for the analyzed clusters, the effect has a range to about 45 h−1 Mpc. We found a stronger alignment of elongated clusters of BM type I (an excess of small values of the Δθ angles is observed), having a range to about 45 h−1 Mpc. The alignment for BM I is probably connected with the origin of a supergiant galaxy. One should note that during studies of the isolated Abell groups (Flin & Olowin 1991; Trevese et al. 1992; Kim 2001; Niederste-Ostholt et al. 2010), only a rudimentary alignment was found and related only to the brightest cluster members. Gonzalez-Sanchez & Teodoro (2010) interpreted the alignment of just the brightest galaxies within a cluster as the effect of gravitational tidal forces. Correlation between the orientation of the brightest galaxy within a cluster and the cluster’s large axis was also found by Sastry (1968), Carter & Metcalfe (1980), Binggeli (1982), Struble & Peebles (1985), Rhee & Katgert (1987), West (1989, 1994), van Kampen & Rhee (1990), Plionis (1994), Fuller et al. (1999), and Kim et al. (2002). The role of a much more massive central galaxy is related to the problem of galaxy mergers in a cluster. This, in turn, is due to the fact of (generally speaking, random) interaction between cluster members and the dynamic evolution of structures near to specific cluster members. The problem of dynamic evolution can be studied by combined studies of the Binggeli effect, i.e., the relation between the positions of major axes in groups or clusters of galaxies and the directions toward their neighbors, and of the mutual orientation of the brightest galaxy (and other bright galaxies) in a structure relative to the position of cluster major axes or even examination of structure ellipticity redshift dependence, especially in the enlarged observational samples.
Citation: Kim 2001
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However, using the well-assessed new post-AGB evolutionary models, we confined the main-sequence ages of our halo sample to be mostly ∼2–5 Gyr, with the oldest being ∼6–8 Gyr, while the outer-disk sample are mostly ≲1–4 Gyr. We thus conjecture that our targets probably formed prior to the encounter with M33. Obviously, our sample represents the population that is different from the underlying, smooth, extended (and mostly metal-poor) halo component of M31 (Ibata et al. 2007, 2014), which was formed through the repeated accretion of smaller galaxies in the distant past. These bright PNe seem to resemble the younger, metal-rich population in the outer stream of M31, as revealed by HST pencil-beam pointings on the Giant Stream (Brown et al. 2006a; Bernard et al. 2015b). The metallicity of the stream fields was enriched continuously from [Fe/H] ∼ −1.5 to at least solar level about 5 Gyr ago (Bernard et al. 2015b). This timeline of metal enrichment is generally consistent with the stellar ages of our metal-rich sample. N-body simulations suggested that the Giant Stream and other stream-like features in the halo are debris of a massive (≳109–





) progenitor that was recently disrupted during the course of a merger (e.g., Ibata et al. 2004; Fardal et al. 2006, 2007, 2008, 2013; Font et al. 2006; Geehan et al. 2006; Mori & Rich 2008; Sadoun et al. 2014). The extended star formation history and the broad range of metallicity (−1.5 ≲ [Fe/H] ≲ 0.2) discovered in the stream fields can be explained by a disk galaxy progenitor (Brown et al. 2006a, 2006b; Bernard et al. 2015b). If the stellar streams in M31's halo indeed have a common origin, our sample of halo PNe then probably formed through extended star formation in this possibly massive, disk-like progenitor. Moreover, some simulations predict that the remnant of the disrupted satellite resides in the NE Shelf (e.g., Fardal et al. 2008, 2013; Sadoun et al. 2014); PN17 in our sample is located in this region and might be associated with this substructure (see Section 4.4).
Citation: Bernard et al. 2015b
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Citation: Bernard et al. 2015b
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