adsabs/FOCAL huggingface dataset:
An independent approach to constrain the strength of magnetic fields in accretion flow models in binary systems is by measuring how much angular momentum is lost from the system via magnetized outflows and estimating the corresponding orbital decay in the system due to magnetic braking. The orbital decay in A0620-00 is rapid, with orbital-period derivative $\dot{P} =-0.6 \, {\rm [ms \, yr^{-1}]}$ (González Hernández, Rebolo & Casares 2014), and it cannot be explained by the emission of gravitational waves alone. Magnetic braking of the system is a possible explanation for the measured $\dot{P}$, but other explanations, such as resonant interactions between the binary and the possible circumbinary disc, have been proposed (Chen & Podsiadlowski 2017). Here we can estimate the magnitude of the magnetic braking of the system using a first-principles approach. The orbital angular momentum of a binary system is $J_{\rm orb}= M_{\rm BH}M_*/(M_{\rm BH}+M_*) \sqrt{G(M_{\rm BH}+M_*) d}$, where d is the separation between the star and the black hole. In GRMHD simulations, the loss of total angular momentum through the outer boundary is defined as $\dot{J}(r_{\rm out},t)=\int _\theta \int _\phi T^r_\phi \,\mathrm{ d}A_{\theta \phi }$, where $T^r_\phi \equiv (\rho + \gamma u + b^2) u^r u_\phi -b^r b_\phi$ is the stress–energy tensor describing the radial flux of angular momentum. The quantity u is the internal energy of the gas, γ = 4/3 is the adiabatic index, uμ is the four-velocity of the gas and bμ is a four-vector that describes the magnetic field in a frame comoving with the gas. We integrate the above formula at rout = 50GM/c2 over θ ∈ (0, π) and ϕ ∈ (0, 2π). In our best-fitting model, the ratio of angular momentum flux through the outer boundary to the orbital angular momentum is extremely low: $\dot{J}/J_{\rm orb} \approx 10^{-22}$ [s−1], which could account for an orbital period derivative of $\dot{P} = 2 \times 10^{-8} {\rm [ms \, yr^{-1}]}$ only. Our calculations confirm that the inner highly magnetized, rotating accretion flow (with current $\dot{M}$) alone cannot be responsible for the rapid orbital decay observed in the system and this favours the idea of a circumbinary disc.
Citation: Chen & Podsiadlowski 2017
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The evolution of colour or the spectral index, α, (F(ν) ∝ ν−α where ν is the radiation frequency and F(ν) is the flux density provides an insight into the particle distribution giving rise to the observed flux density and its variability. In particular, at the synchrotron frequencies, within the simplest scenario of single-zone emission models with homogeneous magnetic field distributions, clear patterns between the spectral index and the total intensity are predicted, i.e. a ‘spectral hysteresis’, depending on the relative lengths of the radiative cooling time-scale and the escape time-scale of the accelerated particles from the emission zone (e.g. Kirk, Rieger & Mastichiadis 1998). A significant fraction of long-term multiband flux monitoring studies have revealed bluer-when-brighter trends for BL Lac objects but frequently redder-when-brighter trends for FSRQs (Gu et al. 2006; Osterman Meyer et al. 2009; Hao et al. 2010; Rani et al. 2010; Ikejiri et al. 2011; Bonning et al. 2012; Sandrinelli, Covino & Treves 2014; Li et al. 2018; Meng et al. 2018; Gupta et al. 2019) while ‘achromatic’ flux variability (no colour evolution, Stalin et al. 2006; Bonning et al. 2012; Gaur et al. 2019), and erratic patterns (Wierzcholska et al. 2015) have also been reported. It has been argued that particles accelerated to higher energies are injected at the emission zone before being cooled radiatively in BL Lac sources leading to their overall SEDs being bluer-when-brighter; however, the ‘redder’ and more variable jet-component can overwhelm the ‘bluer’ contribution from the accretion disc, leading to redder-when-brighter trends for FSRQ type sources (Gu et al. 2006). Achromatic variability is often ascribed to changes in the Doppler boosting factor (δ) as each frequency notes the same special relativistic multiplication of flux (Gaur et al. 2012). However, erratic colour trends together with the opposite behaviours, i.e. redder-when-brighter changes for BL Lacs (Gu & Ai 2011) and bluer-when-brighter trends for FSRQs (Wu et al. 2011), indicate that more complex scenarios, presumably involving the dominance of the relative contributions of the Doppler boosted jet emission component and the accretion disc component, respectively, are particularly relevant for blazars with peak synchrotron frequencies in the range of 1013–15 Hz (low-frequency peaked blazars; Isler et al. 2017; Gopal-Krishna, Britzen & Wiita 2019).
Citation: Bonning et al. 2012
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Comptonisation Monte Carlo code (MoCA; see Tamborra et al. 2018 for a detailed description of the code) is based on a single photon approach, working in a fully special relativistic scenario. MoCA allows for various and different physical and geometrical conditions of the accretion disc and of the Comptonising corona. In this paper, the corona is assumed to have either a spherical or a slab-like geometry, and to be as extended as the disc, whose radii have been set to be Rout = 500 rg and Rin = 6 rg, respectively. Even though arguments (e.g. variability, Uttley et al. 2014, and references therein, microlensing Chartas et al. 2009; Morgan et al. 2012 and timing Kara et al. 2016; De Marco et al. 2013) exist that favour a compact corona, we used extended coronae. In fact, as discussed by Marinucci et al. (2019), Comptonised spectra emerging from compact corona (Rout = 100 rg–Rin = 6) do not deviate significantly from those produced in more extended corona; see their Fig. 3. The adoption of even more compact coronae (Rout = 20 rg, Rin = 6) results only in the need for higher optical depths to recover the same spectral shape for a given temperature. However, in such cases, general relativity (GR) effects are not negligible (see Tamborra et al. 2018, for a detailed discussion on this topic), and the present version of MoCA does not include GR. For the slab-like geometry case, MoCA allows the user to set up the corona height above the accretion disc (set to 10 rg in our simulations). We use synthetic spectra computed assuming the source BH mass and accretion rate to be the same as those of Ark 120 (e.g. Marinucci et al. 2019, and references therein), namely MBH = 1.5 × 108 M⊙ and ṁ = Lbol/LEdd = 0.1. For both the slab and spherical hot electron configurations, we simulated the Comptonised spectra using a wide range of values for electron temperature and optical depth: 0.1   τ   7 and 20  kT  200 keV, and in Fig. 1 we show a sample of spectra obtained by MoCA. Moreover, spectra are computed from 0.01 keV up to 700 keV using 1000 logarithmic energy bins, and a Poissonian error accompanies each spectral point. The obtained spectra are averaged over the inclination angle and in Fig. 1 we show some exemplificative spectra normalised at 1 keV accounting for the two geometries considered in this work.
Citation: Chartas et al. 2009
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Citation: Uttley et al. 2014
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The detection of LSBGs is usually carried out by a sequence of tasks. The first step is a broad detection of LSBG candidates. Both the depth of the data and the efficiency of this detection in the low-surface-brightness regime will define the completeness of the sample, and so a high efficiency in the detection of diffuse sources in this first step is a key point in the process. Given the importance of this primary detection, it is common to use specialized software or procedures (e.g., Akhlaghi & Ichikawa 2015; Prole et al. 2018; Haigh et al. 2021). After a first detection of potential objects, a characterization of their structural and morphological properties is necessary in order to accurately define the sample, typically with certain criteria in surface brightness and radius. To obtain the structural parameters of the LSBG candidates, the detected sources are usually fitted with a Sérsic model (Sérsic 1968). Specific procedures such as GALFIT (Peng et al. 2010) or IMFIT (Erwin 2015) are used to obtain accurate morphological and structural parameters. However, the computational cost of these is very high, becoming a bottleneck in LSBG analysis pipelines. For this reason, it is advisable to minimize the number of LSBG candidates prior to their Sérsic fitting to save computational time. This is not trivial because structural parameters from automated segmentation catalogs are much less accurate than Sérsic fit modeling, making correct screening of the sample challenging. As a last step, a “supervision” of all the objects matching the criteria of the sample is necessary in order to discard the frequent presence of false positives. These are sources that, although meeting the imposed criteria, are clearly not LSBGs. Examples of false positives are clumped sources that have been considered as a single source, faint reflections due to the optics of the instrumentation, or artifacts present in the images. Depending on the data volume analyzed, the number of objects to be supervised can become very high. For this reason, it is increasingly common to use deep learning techniques to screen objects in order to eliminate these false-positive detections (e.g., Tuccillo et al. 2018; Burke et al. 2019; Tanoglidis et al. 2021a). However, unsupervised or automated detections can not yet reach the accuracy that human visual inspection is capable of obtaining, reaching almost 100%. Therefore, human supervision is desirable, when possible, in order to minimize the presence of false positives in samples of reasonably limited volume, such as that used here.
Citation: Sérsic 1968
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The first SMGs were detected using SCUBA at 850 μm (Smail et al. 1997; Barger et al. 1998; Hughes et al. 1998), which remains one of the prime wavelengths to detect these galaxies (e.g., Geach et al. 2017), thanks to a combination of available instruments, spectral window, and the negative k-correction at that wavelength. Other single-dish samples of SMGs have also been obtained at 1.1–1.3 mm using MAMBO (e.g., Eales et al. 2003; Bertoldi et al. 2007; Greve et al. 2008) and AzTEC (e.g., Aretxaga et al. 2011; Yun et al. 2012), at 1.4 mm/2 mm with the SPT (Vieira et al. 2010), and at 2 mm with GISMO (Staguhn et al. 2014; Magnelli et al. 2019). Selecting SMGs from observations at longer wavelengths is thought to favor galaxies at higher redshifts (e.g., Smolčić et al. 2012; Vieira et al. 2013; Staguhn et al. 2014; Magnelli et al. 2019; Hodge & da Cunha 2020), although it is difficult to compare the redshift distributions in an unbiased way (see, e.g., Zavala et al. 2014 for a discussion), and account for intrinsic variations of galaxy far-IR spectral energy distributions (SEDs). Nevertheless, the 2 mm band has been put forth as a potential candidate to detect high-redshift (z > 3) galaxies (e.g., Casey et al. 2018a, 2018b, 2019; Zavala et al. 2021). The negative k-correction is stronger at 2 mm than at 850 μm; thus, for a fixed SED, the 2 mm band should pick up more high-redshift galaxies than at 870 μm. In addition, better atmospheric transmission and larger fields of view can be achieved at 2 mm (but corresponding poorer resolution). Such an effort is currently ongoing (see Zavala et al. 2021 for first results). To understand the relationship between the populations detected at 850 μm and at 2 mm, we require a detailed characterization of the (sub)millimeter SEDs of these sources. Multiwavelength submillimeter observations are still rare, with most observations focusing on a single wavelength. Only a handful of sources observed at 2 mm have complementary shorter-wavelength detections (Staguhn et al. 2014; Magnelli et al. 2019). Thus, a more systematic multiwavelength dust continuum investigation is warranted in order to reveal the dust properties of (sub)millimeter-detected sources.
Citation: Staguhn et al. 2014
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Citation: Staguhn et al. 2014
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The PN IC 5148 (PN G002.7-52.4) is a nebula which is to date not well investigated in detail. First listed in the Second Index Catalogue of Nebulae and Clusters of Stars (Dreyer 1910) with two independent entries as numbers IC 5148 and IC 5150 discovered by Swift (1899) and Gale (1897) independently, it was finally discovered to be the same object by Hoffmeister (1961). Morphologically it is declared as a round nebula in all catalogs and Chu et al. (1987) classified it as a multiple shell planetary nebula (MSPN) due to a small step in the H α image with a radius ratio of only 1:1.2 between the two structures. In addition, the intensity decrease was found smaller than that for typical MSPNe. A few years later the authors searched with a larger field systematically around many nebulae for extended emission features without detecting the very low surface brightness halo we investigate here (Hua et al. 1998). While earlier spectroscopic studies took only a small fraction of one or two spectral lines to obtain radial velocity and expansion of the nebula (e.g. Meatheringham et al. 1988), to our knowledge up to now the only, more extended spectroscopic analysis were performed by Kaler et al. (1990) and by Kingsburgh & Barlow (1994) within two surveys of 75 and 80 southern PNe, respectively. Both studies used pre-CCD electronic spectral scanning devices, taking a very small aperture region of the nebula and detected only a hand full of lines. They end up coinciding in the result that the nebula has about galactic disk abundance or only slight underabundance despite its large galactic latitude (b approximately −52°). Further, thesurvey spectra used in the PN catalog of Acker et al. (1992) gives intensities of only four lines. The inspection of the original data file of this survey, provided now at the Hong Kong/AAO/Strasbourg PN data base (thereafter HASH2; Parker et al. 2016; Bojičić et al. 2017), does not recover more usable lines above the noise level.
Citation: Hua et al. 1998
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In this Letter, we directly test the relationship between proton kinetic instabilities and plasma turbulence in the solar wind using a hybrid expanding box model that allows us to study self-consistently physical processes at ion scales. In the hybrid expanding box model, a constant solar wind radial velocity vsw is assumed. The radial distance R is then 





, where R0 is the initial position and 





 is the initial value of the characteristic expansion time 





 Transverse scales (with respect to the radial direction) of a small portion of plasma, comoving with the solar wind velocity, increase ∝ R. The expanding box uses these comoving coordinates, approximating the spherical coordinates by the Cartesian ones (Liewer et al. 2001; Hellinger & Trávníček 2005). The model uses the hybrid approximation where electrons are considered as a massless, charge-neutralizing fluid and ions are described by a particle-in-cell model (Matthews 1994). Here, we use the two-dimensional (2D) version of the code, fields and moments are defined on a 2D x–y grid 2048 × 2048, and periodic boundary conditions are assumed. The spatial resolution is Δx = Δy = 0.25dp0, where 





 is the initial proton inertial length (vA0: the initial Alfvén velocity, Ωp0: the initial proton gyrofrequency). There are 1024 macroparticles per cell for protons that are advanced with a time step 





, while the magnetic field is advanced with a smaller time step 





 The initial ambient magnetic field is directed along the radial z-direction, perpendicular to the simulation plane 





, and we impose a continuous expansion in the x- and y-directions. Due to the expansion, the ambient density and the magnitude of the ambient magnetic field decrease as 





 (the proton inertial length dp increases ∝ R; the ratio between the transverse sizes and dp remains constant; the proton gyrofrequency Ωp decreases as ∝R−2). A small resistivity η is used to avoid accumulation of cascading energy at grid scales; initially, we set 





 (μ0 being the magnetic permittivity of vacuum) and η is assumed to be 





 The simulation is initialized with an isotropic 2D spectrum of modes with random phases, linear Alfvén polarization (





), and vanishing correlation between magnetic and velocity fluctuation. These modes are in the range 0.02 ≤ kdp ≤ 0.2 and have a flat one-dimensional (1D) power spectrum with rms fluctuations = 0.24 B0. For noninteracting zero-frequency Alfvén waves, the linear approximation predicts 





 (Dong et al. 2014). Protons initially have the parallel proton beta 





 and the parallel temperature anisotropy 





 as typical proton parameters in the solar wind in the vicinity of 1 AU (Hellinger et al. 2006; Marsch et al. 2006). Electrons are assumed to be isotropic and isothermal with βe = 0.5 at t = 0.
Citation: Liewer et al. 2001
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Citation: Matthews 1994
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One of our main long-term goals is to work towards a full forward modelling pipeline for multiwavelength galaxy surveys. Over the next decade, wide area surveys from DESI, VRO, Euclid, the Nancy Grace Roman Space Telescope, 4MOST, and other facilities will be carried out. We can use the legacy observations from surveys such as CANDELS, to build a foundation for interpreting these new surveys. What we have shown here is that the current generation of SAMs produce decent broad agreement with key properties of galaxy evolution as represented by CANDELS over the redshift range 0.5 ≲ z ≲ 3. It has been shown elsewhere that these models produce similar results to those of numerical cosmological simulations and other SAMs (Somerville & Davé 2015), and that they are also in agreement with higher redshift observations of galaxy populations (Yung et al. 2019a, b), the reionization history (Yung et al. 2020), and observational probes of the cold gas phase in galaxies (Popping, Somerville & Trager 2014; Popping et al. 2019). While there are certainly remaining tensions with observations, as seen here and also in, e.g. Popping et al. (2019), there is promising ongoing work to continue to improve the realism of the treatment of physical processes in SAMs (e.g. Pandya et al. 2020). In work in progress, we are using this framework to create similar mock observations for future planned surveys with the James Webb Space Telescope and the Nancy Grace Roman Space Telescope (L.Y.A., Yung et al., in preparation). SAMs coupled with light-cones extracted from large volume N-body simulations have recently been used to create a 2 deg2 light-cone from 0  z  10 (Yang et al. 2020; Yung et al., in preparation). In order to create mock surveys for even larger areas – tens to hundreds of square degrees – that will be probed by the projects mentioned above, it is likely that new, even more computationally efficient techniques will need to be developed, perhaps enabled by machine-learning-based tools.
Citation: Yung et al. 2020
Citation function: Similarities; Similarities; Similarities
Given this correlation between dust luminosity and SFR, it is reasonable to expect the dust mass to scale with SFR as well. In the top panel of Figure 10 we show this relation for our data with the additional parameter of the dust temperature indicated by color. This dust temperature is a weighted average of the temperatures of the two components of the dust model, namely the ISM and birth clouds. Using MAGPHYS, da Cunha et al. (2010) found a tight correlation between dust mass and SFR in a local sample derived from the Sloan Digital Sky Survey, which we show as a black line. The da Cunha et al. (2010) sample is selected to be star-forming by emission line diagnostics and lies at z ≤ 0.2. It is also important to note that the SFRs for their sample lie mostly below ∼20 M⊙ yr−1. The solid portion of the line represents the extent of their data. We also show the Spitzer Infrared Nearby Galaxy Sample (Kennicutt et al. 2003) analyzed in da Cunha et al. (2008) as purple stars for comparison. At first, our data do not appear to follow any trend, with only our highest measured dust masses falling near the da Cunha et al. (2010) relation. Given that MAGPHYS allows a range of dust temperatures when fitting for the dust mass, we investigate the effects of dust temperature in this diagram. When we examine the temperature of the dust (which MAGPHYS allows to vary from 20 to 80 K) as indicated by color in the figure, we see that sources with lower dust temperature actually lie on the published relation. If we consider bands of constant dust temperature in the diagram we see that the slope of the Mdust–SFR relation for our sources actually closely matches the slope of the da Cunha et al. (2010) relation. As shown in Figures 6 and 7, we observe a slight increase in average dust temperature with redshift for our sample, so it may be tempting to interpret the variation in this diagram as a redshift dependence. We checked the relation between dust temperature and redshift for our sample and found the scatter in temperature at a given redshift to be much larger than the evolution between redshift bins. This suggests that the local calibration between dust mass and SFR can be extended to higher redshift samples through the incorporation of the additional parameter of dust temperature. Given our particular sample selection, the application of these results to a more general star-forming population would have to be done with caution.
Citation: Kennicutt et al. 2003
Citation function: Uses; Uses