adsabs/FOCAL huggingface dataset:
Virtually all formulas that are currently used to estimate the merger time, τmer, are based on the idealised Chandraseckhar (1943) description of the deceleration caused by dynamical friction on a point mass (representing the satellite) travelling through an infinite, uniform, and collisionless medium (representing the host). They are mostly prescriptions inferred from the analytic or semi-analytic modelling of mergers (e.g. Lacey & Cole 1993; van den Bosch et al. 1999; Taffoni et al. 2003; Gan et al. 2010; Petts et al. 2015; Silva et al. 2016) or parametric equations tuned by direct comparison with the outcome of numerical simulations of collisions of live galaxy pairs (e.g. Boylan-Kolchin et al. 2008; Jiang et al. 2008; Just et al. 2011; McCavana et al. 2012; Villalobos et al. 2013), or both (e.g. Colpi et al. 1999). However, although several decades of studies of galactic mergers have allowed reaching a general consensus on what probably are the most relevant parameters in any process of this type, the extent of their impact is still a matter of debate. Factors such as continued mass losses due to tidal interactions, the drag force exerted by tidal debris, the re-accretion of some of this material onto the colliding galaxies, or the mutual tidal distortion of their internal structures are elements that introduce nontrivial uncertainties in any attempt to calculate τmer using analytical expressions. To these difficulties, which are caused by physical complexity, one must add the lack of a 100% standard methodology regarding the way mergers are tracked. The starting point of this work is precisely the definition of a new metric for calculating τmer, which is an obligatory first step to compare the outcomes of different experiments under equal conditions and derive universally valid formulas, in a form that is suitable for major mergers. With this aim, we have run a suite of nearly 600 high-resolution N-body simulations of isolated major mergers, with which we further explored the dependence of the merger time on a range of orbital parameters and on the mass-ratios, spins, and morphologies of the progenitors that are representative of such systems.
Citation: Jiang et al. 2008
Citation function: Background
The numerical schemes in this work are essentially the same as those in Kuroda et al. (2016). Regarding the metric evolution, we evolve the standard BSSN variables (Shibata & Nakamura 1995; Baumgarte & Shapiro 1999; Marronetti et al. 2008) with a finite-difference scheme in space and with a Runge–Kutta method in time, both in fourth-order accuracy. The gauge is specified by the ‘1+log’ lapse and by the Gamma-driver-shift condition. Regarding the radiation-hydrodynamic evolution, the conservation equation $\Delta_\alpha T^{\alpha \beta }_{\rm (total)}=0$ is solved using the piecewise parabolic method (Colella & Woodward 1984; Hawke, Löffler & Nerozzi 2005). $T^{\alpha \beta }_{\rm (total)}$ is the total stress-energy tensor,
(1)\begin{equation*}T_{\rm (total)}^{\alpha \beta } = T_{\rm (fluid)}^{\alpha \beta } +\int \mathrm{ d}\varepsilon \sum _{\nu \in \nu _e, \bar{\nu }_e, \nu _x}T_{(\nu , \varepsilon )}^{\alpha \beta }, \end{equation*}
where $T_{\rm (fluid)}^{\alpha \beta }$ and $T_{(\nu , \varepsilon )}^{\alpha \beta }$ are the stress-energy tensor of the fluid and the neutrino radiation field, respectively. We consider three-flavour of neutrinos ($\nu \in \nu _e, \bar{\nu }_e, \nu _x$) with νx denoting heavy-lepton neutrinos (i.e. νμ, ντ, and their antiparticles). ϵ represents the neutrino energy measured in the comoving frame which logarithmically covers from 1 to 300 MeV with 12 energy bins. Employing an M1 analytical closure scheme (Shibata et al. 2011), we solve spectral neutrino transport of the radiation energy and momentum, based on the truncated moment formalism (e.g. Kuroda et al. 2016; Roberts et al. 2016; Ott et al. 2018). We include the gravitational red- and Doppler-shift terms to follow the neutrino radiation field in highly curved space–time around BH. Regarding neutrino opacities, the standard weak interaction set in Bruenn (1985) plus nucleon–nucleon bremsstrahlung (Hannestad & Raffelt 1998) is taken into account.
Citation: Colella & Woodward 1984
Citation function: Uses
The terrestrial magnetosheath, downstream of the bow shock generated by the interaction between the supersonic solar wind and the Earth’s magnetosphere, is representative of turbulence downstream of collisionless plasma shock in the universe. Usually, the downstream sheath regions have larger density, temperature, mean magnetic intensity, and compressibility, and higher plasma β, as compared to the upstream solar wind (Sahraoui et al. 2006; Alexandrova 2008; Hadid et al. 2015; Huang et al. 2016, 2017). Abundant unstable kinetic wave modes and multiscale coherent structures exist in the magnetosheath. For instance, mirror mode, ion cyclotron wave, kinetic Alfvén wave, and so forth are routinely observed (Sahraoui et al. 2006; Lucek et al. 2005; Alexandrova 2008; Zhao et al. 2018; Zhang et al. 2018). Hence, the turbulent energy in the kinetic range might not only consist of energy cascading from larger scales, but also comprise direct injection of energy due to local instability at these scales. This raises an interesting question: what are the differences and similarities of turbulence intermittency between solar wind and magnetosheath? This is one goal of our research. When going down to the ion scales, PSD(δB) steepens, while PSD(δE) is relatively flattened (Chen & Boldyrev 2017; Matteini et al. 2017). The coexistence of steep PSD(B) and flat PSD(E) is caused by the ion demagnetization, which gives rise to the Hall term and/or quasi-electrostatic field (Cramer 2001; Schekochihin et al. 2009). If monofractal scaling is a common phenomenon for magnetic fluctuations at kinetic scales both in the solar wind and in the magnetosheath, what would happen to the electric field intermittent fluctuations? is the electric field intermittency monofractal as well, or is it totally different from the magnetic one? Studying the above questions would help us better understand the characteristics and nature of turbulence at kinetic scales, which is more electrostatic than that at magnetohydrodynamic (MHD) scales.
Citation: Huang et al.; 2017
Citation function: Background
Here we take the gravitational waveform of the nonrotating model with an observer viewed from the equatorial direction as an example for our HHT analysis. The gravitational waveforms of other models with different parameters are shown in the appendix (Figures B10–B20). Figure 6 (a) shows the simulated strains, followed by the wavelet spectra (b)–(c) and the stacked HHT spectra (d)–(e). The waveform starts from the core collapse and reaches the core bounce at t = 0 s. As this model is nonrotating and the proto-neutron star convection happens after the core bounce, the gravitational strain shows a loud bounce signal at time zero, and then it is followed by a low-frequency peak PNS oscillation, which could be caused by the g-mode oscillation although the nature remains controversial (Müller et al. 2013; Pan et al. 2021). Multiple low-frequency oscillation signals dominate the first ∼0.15 s, and they can be well resolved with the HHT. In comparison, the limitation of the wavelet spectrum prevents us to identify them. As the frequency of the oscillation increases with time, another high-frequency mode oscillation signal appears with similar strength as the peak PNS mode. The low-frequency PNS oscillation vanishes at t ∼ 0.5 s. Then, interestingly, it followed by the quadruple radiation of the standing accretion shock instability (SASI Blondin et al. 2003) signal, which was suggested to be seen in the GW data (Kuroda et al. 2016; Pan et al. 2021). The detailed waveform of the SASI signal is model dependent, but its time-frequency property has been shown in a few analysis either with the spectrogram or the HHT (see, e.g., Andresen et al. 2017; Mezzacappa et al. 2020; Takeda et al. 2021). In our analysis, the SASI signal can be seen on both the wavelet and the HHT spectra. The wavelet and the HHT results for other simulated CCSN events are shown in the Appendix B. The Hilbert spectra of all the data sets show much better resolution than the wavelet spectra.
Citation: Pan et al. 2021; Pan et al. 2021
Citation function: Compare/Contrast; Similarities; Similarities
With the availability of such a wide range of photo-z codes and methods, comparisons of various implementations have been performed (Hildebrandt et al. 2010; Abdalla et al. 2011; Sanchez et al. 2014). No obvious best photo-z code was named since each code displays different strengths depending on the metrics used. The focus of recent photo-z analyses has turned to improving error estimation (Oyaizu et al. 2008; Hoyle et al. 2015; Wittman, Bhaskar & Tobin 2016), the use of new statistical techniques (Lima et al. 2008; Zitlau et al. 2016), improving existing algorithms (Cavuoti et al. 2015; Sadeh et al. 2016), and the addition of extra input information to get more precise and accurate photo-z’s. With regards to the inclusion of extra information, a recent example in template methods includes using surface brightness as a prior in spectral energy distribution templates (Kurtz et al. 2007; Stabenau, Connolly & Jain 2008), motivated by the knowledge of surface brightness dimming (1 + z)4. In empirical methods this application is more straightforward, since algorithms are constructed such that it is not difficult to add extra input parameters. For example, Collister & Lahav (2004) and Wadadekar (2005) demonstrated that by including the 50 and 90 per cent Petrosian flux radii (RP50, RP90), the photometric redshift root-mean-square errors improve by 3 and 15 per cent respectively for the SDSS main galaxy sample. Tagliaferri et al. (2003) used Petrosian fluxes and radii in their work on galaxies from the SDSS early data release and calculated robust errors decreasing as much as 24 per cent. Meanwhile, Vince & Csabai (2007) included the concentration of galaxy light profiles in their study and reported that the root-mean-square error of photo-z’s on SDSS galaxies improved by 3 per cent. Wray & Gunn (2008) included surface brightness and the Sérsic index, and found improvements in variance when compared to other template fitting methods applied to the SDSS main galaxy sample previously.
Citation: Stabenau, Connolly & Jain 2008
Citation function: Background; Background; Motivation
Comparison of the DAXSS two temperature (2T) model fits to DEM estimates from other instruments could help validate the DAXSS simple modeling approach. Moore et al. (2018) shows that the X123 response is primarily over the temperature range of 1 MK to 10 MK, so this comparison needs to be done over a similar range, such as is accessible with using DEMs derived from extreme ultraviolet (EUV) emissions. One of the comparisons is with the DEM derived with SDO (Pesnell et al. 2012) EVE (Hock et al. 2012; Woods et al. 2012) solar EUV spectral irradiance data in the 6–37 nm range. The derivation of EVE-based DEM estimates is being developed for improving the X-ray ultraviolet Photometer System (XPS) data processing for the Solar Radiation and Climate Experiment (SORCE) (Woods et al. 2005a) and Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics (Woods et al. 2005b) missions. The primary DEMs for this analysis is the quiet Sun (QS) and active region (AR) DEMs as needed for estimating the daily variations of the solar XUV spectral irradiance for the XPS Level 4 product (Woods et al. 2008). The Fe viii to Fe xvi lines in the SDO EVE spectra were initially used to estimate the DEMs for the reference QS and AR spectra derived with EVE data between 2010 and 2013 using the technique described by Schonfeld et al. (2017). It was then found that fitting with DEM Gaussian profiles with logarithmic temperature (K, log(T)) peaks every 0.2 and with Gaussian width of 0.42 in log(T) (FWHM of 1.0) provided more robust solutions for the DEM (a similar technique was described by Warren et al. 2013). Furthermore, fitting just specific Fe lines was providing low irradiance estimates in the 6–15 nm range. Better spectral model values for this range was found when fitting the EVE spectra over the ranges of 10–14 nm and 26–30 nm. The DEM estimate using just the rocket EVE spectral data flown with DAXSS on 2018 June 18 and the DEM based on combining the QS DEM and the AR DEM with an AR scaling factor of 0.00806 are shown in Figure 12. This scaling factor for the AR EM was determined as the best fit for the DAXSS spectral irradiance. The conversion of the EVE-based DEM (





) to EM (cm−3) for comparison to DAXSS 2T model solution is the multiplication by the solar hemisphere area (





) and by the temperature bin size (0.23 * temperature in K).
Citation: Woods et al. 2008
Citation function: Uses
The intrinsic jet opening angles can be calculated as tan (αint/2) = tan (αapp/2) sin θ, where θ is the viewing angle to the jet axis. The latter, as well as the bulk Lorentz factor Γ, can be derived from apparent jet speed and Doppler factor using the following relations:

\begin{eqnarray*}
\theta = \arctan \frac{2\beta _{\rm app}}{\beta ^2_{\rm app}+\delta _{\rm var}^2-1}\,, \quad \Gamma = \frac{\beta _{\rm app}^2+\delta _{\rm var}^2+1}{2\delta _{\rm var}}\,.
\end{eqnarray*}
For βapp and δvar we used the fastest measured radial, non-accelerating apparent jet speed from the MOJAVE kinematic analysis (Lister et al. 2016) and the variability Doppler factor from the Metsähovi AGN monitoring programme (Hovatta et al. 2009), respectively. The corresponding overlap of the programmes comprises 55 sources, which are all members of the MOJAVE-1 sample. Variability Doppler-factors for 10 more MOJAVE-1 sources were measured within the F-GAMMA programme (Liodakis et al. 2017). The intrinsic opening angles calculated for the 65 sources range from 0$_{.}^{\circ}$1 to 9$_{.}^{\circ}$4, with a median of 1$_{.}^{\circ}$3, reflecting a very high degree of jet collimation. The intrinsic opening angles show an inverse dependence on Lorentz factor (Fig. 13), as predicted by hydrodynamical (Blandford & Königl 1979) and magnetic acceleration models (Komissarov et al. 2007) of relativistic jets. The median value of the product3 ρ = αintΓ is 0.35 rad, close to earlier estimates derived both from observations (Jorstad et al. 2005; Pushkarev et al. 2009) and from a statistical model approach (Clausen-Brown et al. 2013). The variability Doppler factors derived from variability can be underestimated due to a limited cadence of the observations. In this case, the intrinsic opening angle estimates would be smaller, while Lorentz factors would be higher if $\delta >(\beta _{\rm app}^2+1)^{1/2}$ and smaller otherwise, implying that if the variability Doppler factors are essentially underestimated, the majority of points in Fig. 13 would move downward and to the right.
Citation: Lister et al. 2016
Citation function: Uses
There have been rather strong claims of AGN QPOs in different bands of the electromagnetic spectrum, ranging from minutes through days through months and years (e.g. Gierliński et al. 2008; Lachowicz et al. 2009; Gupta, Srivastava & Wiita 2009; Gupta et al. 2018, 2019; King et al. 2013; Gupta 2014, 2018; Ackermann et al. 2015; Pan et al. 2016; Zhou et al. 2018; Bhatta 2019; and references therein). However, many of the claimed QPOs, particularly those made earlier, were marginal detections (Gupta 2014), lasting only a few cycles, and the originally quoted statistical significances are probably overestimates (Gupta 2014; Covino, Sandrinelli & Treves 2019). Among the better recent claims of QPOs in the gamma-ray band are of ∼34.5 d in the blazar PKS 2247–131 (Zhou et al. 2018) and of ∼71 d in the blazar B2 1520+31 (Gupta et al. 2019) found as part of a continuing analysis of blazar Fermi–LAT observations. A recent claim of a ∼44 d optical band QPO in the narrow-line Seyfert 1 galaxy KIC 9650712 from densely sampled Kepler data has been made by Smith et al. (2018); it was supported by an independent analysis of the same data, indicating a QPO contribution at 52 ± 2 d (Phillipson et al. 2020). Some possibly related QPOs of a few hundred days in two widely separated bands have been reported (Sandrinelli et al. 2016a; Sandrinelli, Covino & Treves 2016b; Sandrinelli et al. 2017). However, an analysis of the Fermi–LAT and aperture photometry light curves by Covino et al. (2019) argued that some multiwaveband QPOs, along with many earlier claims of gamma-ray QPOs, are not significant. Among the gamma-ray QPOs with month-like periods, none showed simultaneous oscillations in a different wavebands. Evidence for related QPOs in multiple wavebands was observed in PG 1553+113, where a QPO was detected in the 0.1–300 GeV and the optical waveband (Ackermann et al. 2015). The observed QPO had a dominant period of ∼754 d and the source showed strong inter-waveband cross-correlations.
Citation: Gupta et al. 2019
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Citation: Gupta et al.; 2019
Citation function: Background
In order to recover the intrinsic SED, we employ the values for interstellar extinction E(B − V) from Schlafly & Finkbeiner (2011), collected on NASA's Infrared Science Archive (IRSA) web pages, to deredden the spectra. We use the relations derived by Cardelli, Clayton & Mathis (1989) as implemented in iraf, and a standard value for the ratio of the total to the selective extinction R(V) = A(V)/E(B − V) = 3.1. The corresponding values are available as the average extinction within a 2 × 2 deg2 field. This represents a severe limitation as it does not take into account potential small-scale variations in the dust structure, nor the actual amount of absorbing material in the line of sight to the target. Since CVs are intrinsically rather faint (typically, MV > 4 mag; e.g. Patterson 1984), in general the correction for the interstellar extinction will represent an upper limit. An additional uncertainty regarding the intrinsic SED is introduced by obtaining the flux calibration on the basis of a single standard star that was observed on a different night than the targets. Furthermore, the observations were not conducted at a parallactic angle and thus might be affected by chromatic slit loss due to differential refraction, since Gemini-South is not equipped with an atmospheric dispersion corrector. As a measure for the SED, we derive the exponent α of a power law F ∝ λ−α that was fitted to the spectra, restricting the continuum to wavelengths 5000–7000 Å and masking strong emission and absorption lines. During that analysis we found that most of the post-novae required a two-component fit while previously this represented an exception (Paper I, Paper IV). Furthermore, in every case the two slopes differed in the same direction, with the red part of the spectrum requiring a steeper slope than the blue part, and the points that separated the two slopes were found to be within 5850–5900 Å which marks the region that is affected by the gap between two CCDs (see Section 2). All this suggests a systematic effect in the definition of the instrumental response function, and potentially a difference in the spectral efficiency of these two CCDs. We have thus further divided the fitting range into a blue (5000  λ  5870 Å) and a red (5870  λ  7000 Å) part that are fitted individually. The consequences for the interpretation of the SEDs are further discussed in Section 4.
Citation: Patterson 1984
Citation function: Uses; Uses
When the primary X-ray continuum is scattered by distant matter at several thousands of gravitational radii, the line is narrow (FWHM  10,000 km s−1) and is unlikely to carry any information about the conditions in the accretion disk and the strong-gravity regime near the SMBH. Narrow lines have been detected in the great majority of AGNs with luminosities L
X,2.0−10.0 keV  1045 erg s−1. The mean FWHM reported from Chandra High Energy Transmission Grating (HETG) spectra is ∼2000 km s−1 (Yaqoob & Padmanabhan 2004; Shu et al. 2010, 2011; see also Nandra 2006). Low equivalent width (EW) values from Suzaku observations (tens of eV; Fukazawa et al. 2011a) have also been interpreted as evidence of narrow lines originating in distant material (Ricci et al. 2014). In contrast, when the primary X-ray continuum is scattered close to the SMBH, Doppler and general relativistic effects combined may give rise to a significantly broader line (FWHM tens of thousands km s−1), reported in at least ∼36% of AGNs (de la Calle Pérez et al. 2010; see also, e.g., Porquet et al. 2004; Jiménez-Bailón et al. 2005; Guainazzi et al. 2006; Nandra et al.2007; Brenneman & Reynolds 2009; Patrick et al. 2012; Liu & Li 2015; Mantovani et al. 2016; Baronchelli et al. 2018). In this case, contributions to the line’s width become increasingly stronger as the primary continuum is scattered closer, and up to, the innermost stable circular orbit (ISCO), where the accretion disk’s inner edge is located. Since the ISCO location depends directly on black hole spin, the latter leaves an imprint on the broad line’s profile and can in principle be measured by means of relativistic modeling of the line via the X-ray reflection method of spin determination. As a result, there is a significant number of SMBH spin measurements (e.g., Brenneman 2013, and references therein). SMBH spin constraints have very significant implications for understanding both SMBHs and the way they affect their environment. Apart from potentially constituting a Kerr-metric-based test of general relativity in the strong-field regime, spin measurements can inform on jet-driving mechanisms, e.g., via extraction of SMBH rotational energy (Blandford & Znajek 1977), which in turn critically affect galactic environments and evolution.
Citation: Porquet et al. 2004
Citation function: Compare/Contrast; Compare/Contrast
Here, we present the GMC properties within the molecular disc of the closest giant elliptical galaxy, NGC 5128, which is the host of the radio-source Centaurus A (hereafter Cen A). Cen A is at a distance of only D ≃ 3.8 Mpc (Harris, Rejkuba & Harris 2010, 1 arcsec = 18 pc) and it is therefore by far the most adequate target in the class of giant elliptical galaxies as well as powerful radio galaxies for studies of their molecular gas with high resolution. Indeed, Cen A is a peculiar case of an elliptical galaxy whose gaseous component has been supplied a few 0.1 Gyr ago by the accretion of a HI rich galaxy (e.g. Struve et al. 2010). Along the dust lane of the elliptical galaxy, there is a molecular gas component of mass ∼109 M⊙ as probed by various molecular lines (e.g. Phillips et al. 1987; Eckart et al. 1990; Rydbeck et al. 1993; Liszt 2001; Espada et al. 2009; Espada 2013; McCoy et al. 2017), partially seen in the form of kpc scale spiral features (Espada et al. 2012). The dust lane is along the minor axis, different to other ellipticals where discs are usually along the major axis (Young 2002). The molecular gas is associated with other components of the ISM, such as ionized gas traced by the H α line (e.g. Nicholson, Bland-Hawthorn & Taylor 1992), near-infrared continuum (Quillen, Graham & Frogel 1993), submillimetre continuum (e.g. Hawarden et al. 1993; Leeuw et al. 2002), and mid-IR continuum emission (e.g. Mirabel et al. 1999; Quillen et al. 2006). In the inner hundreds of parsecs there is a circumnuclear disc (CND) of 400 pc total extent (∼24 arcsec) and a P.A. = 155°, perpendicular to the inner jet, at least as seen in projection (Espada et al. 2009). The total gas mass in this component has been estimated to be 9 × 10$^7\, \mathrm{M}_{\odot }$ (Israel et al. 2014, 2017). More detailed studies of the CND with higher resolutions of ∼5 pc in CO(3–2) and CO(6–5) have revealed the complexity of the molecular gas distribution and kinematics in that region, with multiple internal filaments and shocks (Espada et al. 2017).
Citation: McCoy et al. 2017
Citation function: Background