adsabs/FOCAL huggingface dataset:
We also detect a Compton hump at around 15–30 keV during the 2016 NuSTAR observation. The inclusion of the 10–50 keV spectral data does not affect the spectral model parameters (disc inclination angle, spin, emissivity indices, and break radius) obtained from fitting of the 0.3–10 keV spectrum only. The best-fitting value of the disc electron density as derived from the modelling of the broad-band (0.3–50 keV) spectral data is $n_{\rm e}=5.2^{+5.2}_{-4.2}\times 10^{16}$ cm-3. Mrk 1044 is known to be a highly accreting AGN with the dimensionless mass accretion rate of $\dot{m}=\frac{\dot{M}c^{2}}{L_{\rm E}}=16.6^{+25.1}_{-10.1}$ (Du et al. 2015). At high accretion rate, the inner region of a standard α-disc (Shakura & Sunyaev 1973) is radiation pressure dominated and the electron density of the disc can be written as (Svensson & Zdziarski 1994)
(11)
\begin{eqnarray*}
n_\mathrm{ e}=\frac{1}{\sigma _{\rm T}R_{\rm S}} \frac{256\sqrt{2}}{27}\alpha ^{-1}r^{3/2}\dot{m}^{-2} [1-(3/r)]^{-1} (1-f)^{-3}.
\end{eqnarray*}
where σT = 6.64 × 10−25 cm2 is the Thomson scattering cross section, RS = 2GMBH/c2 is the Schwarzschild radius, MBH is the black hole mass, α = 0.1 is the disc viscosity parameter, r = R/RS, R is the characteristic disc radius, $\dot{m}=\frac{\dot{M}c^{2}}{L_{\rm E}}$ is the dimensionless mass accretion rate, and f is the fraction of the total power released by the disc into the corona. The variation of the disc electron density (ne) with the dimensionless mass accretion rate ($\dot{m}$) for α = 0.1, $M_{\rm BH}=3\times 10^{6}\,\mathrm{M}_{\odot }$, f = 0.9, and r = 10 is shown as the solid curve in Fig. 17. As evident from Fig. 17, the assumption of constant disc density (ne = 1015 cm-3) is not physically realistic for low-mass AGNs even when the mass accretion rate is very high. The observed best-fitting value for the disc electron density of the source and its 90 per cent confidence limits are shown as the dotted and dashed lines in Fig. 17, respectively. The corresponding dimensionless mass accretion rate of Mrk 1044 estimated using equation (11) is $\dot{m}\approx 10-32$, which is in agreement with that found by Du et al. (2015). We further verified the SMBH mass with the use of X-ray variability techniques as pioneered by Ponti et al. (2012). The relation between the SMBH mass (MBH, 7) in units of $10^{7}\,\mathrm{M}_{\odot }$ and normalized excess variance ($\sigma _{\rm NXS}^{2}$) in the 2–10 keV light curves of 10 ks segments and the bin size of 250 s, can be written as
(12)
\begin{eqnarray*}
\log (\sigma _{\rm NXS}^{2})=(-1.83\pm 0.1)+(-1.04\pm 0.09)\log (M_{\rm BH,7}).
\end{eqnarray*}
The SMBH mass of Mrk 1044 measured using equation (12) is $M_{\rm BH}=(4-5)\times 10^{6}\,\mathrm{M}_{\odot }$ which is close to that measured by Du et al. (2015).
Citation: Shakura & Sunyaev 1973
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An et al. (1988) and Wu et al. (1990) investigated the effects of plasma injection on the formation of the Kippenhahn-Schlüter model of prominence in optimum conditions. These authors found that for high values of the plasma-β parameter (the ratio of plasma pressure to magnetic pressure) the magnetic arcade develops a magnetic dip at the centre of the structure that supports the prominence plasma. However, comparing with our study, in the low plasma-β regime (or under others injection conditions) they found that the dip is less deep and the system develops two additional plasma enhancements located at the lateral edges of the magnetic arcade. Recent works suggest that the deformation of the magnetic field lines is determined by the parameter δ (the ratio of the gravity to the magnetic pressure) (Zhou et al. 2018; Zhang et al. 2019). An et al. (1988) suggested that the steady lateral plasma accumulates because of both the injection process and because the field lines without dips do not geometrically contain the injected plasma, but Wu et al. (1990) proposed that the prominence mass is also supported by an increase in the pressure gradient. Since in this study we consider that magnetic field lines do not change owing to the presence of the dense thread we investigate in detail the results of An et al. (1988) and Wu et al. (1990) in part II of this paper. On the contrary, studies of the formation of 1D filament threads by chromospheric heating in the presence of non-adiabatic effects, such as radiative losses and thermal conduction, show that for magnetic loops without a dip, the plasma condenses but it streams along the magnetic field and disappears after falling to the footpoints (Antiochos et al. 2000; Karpen et al. 2006). Moreover, when the thread is initially in a thermal and force-balance equilibrium state but it is disturbed by a strong velocity perturbation, the prominence mass drains down to the chromosphere. Dense blobs of falling plasma have been habitually observed (Schrijver 2001; de Groof et al. 2005), therefore it seems that threads cannot be held static along vertical magnetic flux tubes in the corona. The coronal part of the tube can only slow down the falling blobs. Müller et al. (2004) proposed that the acceleration reduces because the pressure of the cooling plasma underneath the radiating blobs slows down the descent, and Oliver et al. (2014, 2016) and Martínez-Gómez et al. (2020) argue that pressure gradient is the main force that opposes the action of gravity. Our study proposes that the pressure gradient can cause the equilibrium of threads in quasi-vertical flux tubes without dips even though it has not been corroborated by observations. Besides, our model is relatively simple to study the stability of filament threads in the magnetic field without dips. Other processes such as radiation and heat conduction must be considered, which might change the stability results.
Citation: Zhang et al. 2019
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The other criteria that we can use to categorize the reconstruction methods is whether the reconstruction is performed using forward-modelling or uses a direct inversion from the data. Inverting non-linear problems from partial, noisy, observations is an ill-posed inverse problem, which makes forward-modelled Bayesian methods particularly suitable for the task of reconstruction of high-dimensional fields. Bayesian reconstruction methods have become increasingly popular in cosmology and have been applied in a range of different applications such as initial conditions reconstruction (Jasche & Wandelt 2013; Modi, Feng & Seljak 2018; Jasche & Lavaux 2019), weak lensing (Fiedorowicz et al. 2022; Porqueres et al. 2021, 2022; Boruah, Rozo & Fiedorowicz 2022), and CMB lensing (Millea et al. 2021; Millea, Anderes & Wandelt 2020). Such methods have also been used for the local velocity field reconstruction. The simplest of such methods uses a Wiener filtering technique (Zaroubi, Hoffman & Dekel 1999). This approach assumes that the density/velocity field is described as a Gaussian random field and the Wiener filtered reconstruction is the maximum-a-posteriori (MAP) solution for the problem. The Wiener filtering approach has been extended to account for uncertainties and biases in the reconstruction using a constrained realization approach (Hoffman & Ribak 1991; Hoffman, Courtois & Tully 2015; Hoffman et al. 2018; Lilow & Nusser 2021) An alternative way to account for the biases in the reconstruction in Wiener filtering is using the unbiased minimal variance approach (Zaroubi 2002). Another similar approach is the Bayesian hierarchical method, virbius (Lavaux 2016), which is based on the constrained realization approach but accounts for many different systematic effects in its analysis. This approach has been been applied to the Cosmicflows-3 (Tully, Courtois & Sorce 2016) data set by Graziani et al. (2019). A similar reconstruction code, hamlet, was introduced in Valade et al. (2022). However, these methods fail to account for the inhomogeneous Malmquist (IHM) bias which is an important source of systematic error in peculiar velocity analysis. The IHM bias arises from an incorrect assumption on the distribution of peculiar velocity tracers due to neglecting the line-of-sight inhomogeneities.
Citation: Zaroubi, Hoffman & Dekel 1999
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Citation: Boruah, Rozo & Fiedorowicz 2022
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Before 1500 A.D., when the magnetic latitude of China was higher than that at present, most of the observed aurorae were caused by CIRs and moderate CMEs. After 1500 A.D., the GNP moved to Canada. This caused the geomagnetic field latitude of China to decrease considerably. Meanwhile, the field intensity decreased to its minimum value in the last 2000 yr. Under these conditions, it would have been very rare to observe the aurora in most places in China after the 16th century, just like the present case (Wu et al. 2016a, 2016b; Hayakawa et al. 2020b, 2020c). However, this provides the best opportunity to investigate the existence of great-storm CMEs, which generate a wider auroral belt and equatorward shift of the EBAO and the EBAV (Boudouridis et al. 2003; Shue et al. 2009; Sigernes et al. 2011). If auroras were observed in China at lower magnetic latitudes than the EBAV related to the great storms, this indicates that the strong CMEs occurred. To quantify our results, we applied the classification of magnetic storms at the great level having Dst  −350 nT (Loewe & Prölss 1997). According to the EBAV of the great level shown as the red dashed line in Figure 2(b), the auroras caused by great-storm CMEs could be identified, which were below the red dashed line. Surprisingly, there were many great-storm CMEs between 1500 and 1900, when Spörer minimum (1390–1550), Maunder minimum (1645–1715), and Dalton minimum (1797–1827) were inside (Lean et al. 1995; Solanki & Fligge 2000; Usoskin et al. 2015; Cliver & Herbst 2018; Hayakawa et al. 2020e). There were auroral sighting records in the same year, such as 1620 and 1646, from different places in China. As previously reported by Hayakawa et al. (2018b) there were simultaneous auroral records on the same day from different places in China in 1730. There are quite a few studies about the aurorae in September 1770 (Willis et al. 1996; Ebihara et al. 2017; Hayakawa et al. 2017a). Apart from the case analyses for extreme space weather events (Hattori et al. 2019; Isobe et al. 2019), our results indicate that there are more great-storm CMEs in the past 400 yr. Some of the great-storm CMEs happened in the solar maximum years, and some happened in the solar minimum years, as shown in Figure 7.
Citation: Hayakawa et al. 2020b
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There are several different methods used in the literature for creating mock catalogues and light-cones. In purely empirical methods such as the JAdes extraGalactic Ultradeep Artificial Realizations (jaguar) models used to create mock catalogues in support of the JADES survey (Williams et al. 2018), observed galaxy properties are interpolated or extrapolated, and there is no underlying physics model nor setting within a Λ cold dark matter (ΛCDM) context. In what are sometimes called ‘semi-empirical’ methods [also called subhalo abundance matching (SHAM) or halo occupation distribution (HOD) models; see Wechsler & Tinker 2018], galaxy properties are mapped on to the properties of dark matter haloes such that a set of observational quantities is reproduced (Behroozi, Conroy & Wechsler 2010; Moster, Naab & White 2013, 2018; Behroozi et al. 2019; Wechsler et al. 2022). Both of these methods have the advantage that they are computationally efficient, are not dependent on a specific model for galaxy formation, and are guaranteed to match the observations that were used to calibrate them. However, they have the disadvantage that using them for forecasts for new observations is highly uncertain, and they are of limited use for interpretation. Semi-empirical models are typically calibrated using derived physical properties such as stellar masses and star formation rates (SFRs), which are highly uncertain at high redshifts, leading to models that are nominally calibrated on the same observations, but which have very different predictions for the link between galaxy and dark matter halo properties (see e.g. Yung et al. 2019b). We note that this is only a general overview for the semi-empirical modelling approach. These models are designed with different purposes in mind and adopt different calibration criteria. For example, UniverseMachine used high-redshift ultraviolet (UV) luminosity functions (LFs) to calibrate galaxy growth at the highest redshifts (Behroozi et al. 2019, 2020) and the semi-empirical model presented in Behroozi & Silk (2015) allows forecasts under the assumption that galaxy–halo growth relationships are given by power laws.
Citation: Wechsler et al. 2022
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Observing with instruments such as the Atacama Large Millimeter/submillimeter Array (ALMA) is crucial to our understanding of planet-formation mechanisms, as we can observe at wavelengths that trace continuum emission from the cold midplane (e.g., Testi et al. 2014), where we expect planets to be forming or have already formed. In the case of midplane spiral structures, their origin may be linked to the presence of a companion—stellar, fly-by, or planetary (Pohl et al. 2015; Bae & Zhu 2018a; Dong et al. 2018; Forgan et al. 2018b; Cuello et al. 2019; Keppler et al. 2020). Spirals may also be excited if the system is gravitationally unstable. Gravitationally instability is expected in cool and massive disks, where the disk-to-star mass ratio is larger than 0.1 (Bell et al. 1997; Gammie 2001; Lodato & Rice 2004; Cossins et al. 2009; Hall et al. 2016; Kratter & Lodato 2016; Rice 2016; Hall et al. 2019; Zhang & Zhu 2020). To date, not many spirals in dust continuum emission have a clear origin, except for those in multiple systems where the presence of spirals has been linked to stellar interactions (Kurtovic et al. 2018; Rosotti et al. 2020). On the other hand, there are disks where spirals have been reported at millimeter wavelengths and where no companion to which the spiral origin may be linked has been detected yet (to date these are Elias 27, IM Lup, WaOph 6, and MWC 758; Pérez et al. 2016; Dong et al. 2018; Huang et al. 2018c). If no companion is detected and the disk is massive compared to the host star mass, the gravitational instability (GI) scenario arises as a possible explanation for the origin of the observed spirals. Studying disks undergoing GI is important, as population synthesis models show that GI primarily ends up forming brown dwarf mass objects (Hall et al. 2017; Forgan et al. 2018a). It seems that giant-planet formation through GI is rare (Rice et al. 2015), but it may still be the formation mechanism for important systems like HR 8799 (Vigan et al. 2017).
Citation: Forgan et al. 2018b
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In a quasi-isothermal jet, Uj is (17)\begin{equation} \label{eq:U_j_quasi} U_{\rm j} = \zeta n_0 m_{\rm p} c^2\left(\frac{\gamma_{\rm j}\beta_{\rm j}}{\gamma_0\beta_0}\right)^{-\Gamma}\left(\frac{z}{z_0}\right)^{-2} \cdot \end{equation}Uj=ζn0mpc2γjβjγ0β0−Γzz0-2·Substituting Eqs. (17) and (13) into Eq. (10), and assuming the jet is launched with an initial γ0β0 equal to the sound speed (Eq. (16)), the 1D Euler equation that results is \begin{eqnarray} \label{eq:AGNJET_Corrected} &amp;&amp;\left\{\gamma_{\rm j}\beta_{\rm j}\frac{\Gamma+\xi}{\Gamma-1}-\Gamma\gamma_{\rm j}\beta_{\rm j}-\frac{\Gamma}{\gamma_{\rm j}\beta_{\rm j}}\right\}\frac{\partial \gamma_{\rm j}\beta_{\rm j}}{\partial z} = \frac{2}{z}; \\ &amp;&amp;\xi = \frac{1}{\zeta}\left(\frac{\gamma_{\rm j}\beta_{\rm j}}{\gamma_0\beta_0}\right)^{\Gamma-1}; \qquad \gamma_0\beta_0=\sqrt{\frac{\zeta\Gamma(\Gamma-1)}{1+2\zeta\Gamma-\zeta\Gamma^2}} \cdot \end{eqnarray}γjβjΓ+ξΓ−1−Γγjβj−Γγjβj∂γjβj∂z=2z;ξ=1ζγjβjγ0β0Γ−1; γ0β0=ζΓ(Γ−1)1+2ζΓ−ζΓ2·The above equation should reduce to the jet Lorentz factor profile used in Falcke (1996), Markoff et al. (2005) when ζ = 1. However, it differs from Eq. (2) in Falcke (1996): (20)\begin{equation} \label{eq:Heino96} \left\{\gamma_{\rm j}\beta_{\rm j}\frac{\Gamma+\xi}{\Gamma-1}-\frac{\Gamma}{\gamma_{\rm j}\beta_{\rm j}}\right\}\frac{\partial \gamma_{\rm j}\beta_{\rm j}}{\partial z} = \frac{2}{z}; \end{equation}γjβjΓ+ξΓ−1−Γγjβj∂γjβj∂z=2z;(21)\begin{equation} \xi = \left(\gamma_{\rm j}\beta_{\rm j}\frac{\Gamma+1}{\Gamma(\Gamma-1)}\right)^{1-\Gamma} \cdot \end{equation}ξ=γjβjΓ+1Γ(Γ−1)1−Γ·The difference between our equation and the equation in Falcke (1996) can be accounted for as follows: the − Γγjβj term in Eq. (18) results from a neglected \hbox{$\frac{\partial}{\partial z}(U_{\rm j}/n)$}∂∂z(Uj/n) term, the difference in the exponent in ξ results from an arithmetic error, and finally the difference in the inside of the parenthesis of ξ terms is from setting \hbox{$\gamma_0\beta_0 = \beta_{\rm s0}^{2}$}γ0β0=βs02 instead of using the proper value given in Eq. (16). The difference between the solutions of Eqs. (18) and (20) are small and shown in Fig. 1. In Fig. 1, we also include solutions to the 1D Euler equations when the jet is isothermal (Tj = const., i.e., Eq. 20 with ξ = 1) and adiabatic (Tj ∝ (γjβj)1 − Γz2 − 2Γ, see Eq. (25)).
Citation: Falcke (1996)
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Citation: Falcke (1996)
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The uncertainties in number counts were derived from Poisson statistics, which apply when event rates are calculated from small numbers of observed events (Gehrels 1986). Our results are given in Table 6 and plotted in Figure 7. For comparison, we also show the integral number counts from the lensing cluster surveys of Knudsen et al. (2008) and Johansson et al. (2011), from the SCUBA Half-Degree Extragalactic Survey (SHADES; Coppin et al. 2006), from LESS (Weiß et al. 2009), from SCUBA-2 (Hsu et al. 2016), and from the high-resolution ALMA follow-up of LESS (Karim et al. 2013). We find that, for the intrinsic flux density range covered by our survey, results are consistent within uncertainties with previous single-dish surveys conducted in blank fields (e.g., Coppin et al. 2006), toward lensing clusters (Knudsen et al. 2008; Johansson et al. 2011), and combining both cluster and blank fields (Hsu et al. 2016). The exception is the data point at Sint = 6.3 mJy, which is fainter than the 4σ detection threshold for all clusters and therefore comprises sources that are necessarily magnified. The discrepancy is possibly due to the uncertainties in our analytical lens models, which may generally underestimate magnification factors relative to those derived from strong-lensing models, as suggested by the comparison presented in Figure 5. If magnification factors are minimally increased across the field, intrinsic flux densities and binned number counts do not vary significantly, but the detectable area Asource where μ > 4σ/Sint is increased, thus affecting the number counts per unit area. To test this hypothesis, we repeated our calculations but slightly scaled our analytical magnification maps by a factor of ∼1. We find that a satisfactory match between our resulting number counts at Sint = 6.3 mJy and previous surveys can be reached if all magnification estimates are varied by only ∼5%, which is within the uncertainties obtained for μ and listed in Table 5.
Citation: Karim et al. 2013
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Our assumption of a constant f
dg does not account for vertical settling and depletion of dust in the disk atmosphere, which could in principle reduce the height of the different τ = 1 surfaces shown in Figure 6. Even though the Stokes number remains smaller than 1 for all considered particle sizes well above the τ = 1 surfaces for irradiation, the actual dust distribution in the atmosphere depends on the balance between turbulent stirring and vertical settling. The dust distribution away from the midplane is better captured by hydrodynamical simulations than by simple prescriptions relying on a parameterization of the turbulence strength (see, e.g., Dubrulle et al. 1995; Dullemond & Dominik 2004b), since it depends on the particular source of turbulence. An example of this is the anisotropic turbulence produced by the VSI, whose vertically elongated modes significantly increase the vertical stirring of dust with respect to models of homogeneous isotropic turbulence (Stoll & Kley 2016; Stoll et al. 2017). We note that these authors obtain a vertical Gaussian dust distribution of micron-sized particles with the same scale height as the gas in the entirety of a domain reaching z = 5H at 5 au, which supports the validity of our prescription. On the other hand, dust sedimentation of small grains in outer regions can even be affected by nonideal magnetohydrodynamical effects and magnetized winds (Riols & Lesur 2018; Booth & Clarke 2021; Hutchison & Clarke 2021). In principle, we do not expect dust settling effects to be relevant for our work as long as they occur in optically thin regions where radiative transport prevents any shadowing from occurring. Future disk models could test this hypothesis by studying the stability of disks in which the opacity is computed as a function of a dynamically evolving small dust distribution. We have also assumed instantaneous thermal equilibrium of gas and dust particles, which is not verified at high altitudes, as collisions between dust and gas particles become less frequent for smaller densities (see, e.g., Malygin et al. 2017; Pfeil & Klahr 2021). We do not expect this effect to alter our results, since at most it could increase the time it takes to form temperature perturbations in such regions.
Citation: Dubrulle et al. 1995
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The ARGO-YBJ detector, hosted in a building at the YangBaJing Cosmic Ray Observatory (Tibet, China, 90°31′50″E, 30°06′38″N), 4300 m above sea level, has been designed for very high-energy (VHE) gamma-ray astronomy and cosmic-ray observations. It is made up of a single layer of resistive plate chambers (RPCs) operated in streamer mode, 2.850 m × 1.225 m each, organized in a modular configuration to cover a surface of about 5600 m2 with an active area of about 93%. The RPCs detect the charged particles in air showers with an efficiency ≥98%. To improve the shower reconstruction, other chambers are deployed around the central carpet for a total instrumented area of 100 m × 110 m. A highly segmented readout is performed by means of 55.6 cm × 61.8 cm external electrodes, called “pads,” whose fast signals are used for triggering and timing purposes. These pads provide the digital readout of the detector up to 22 particles m−2, allowing the count of the air shower charged particles without any significant saturation up to primary cosmic-ray energies of about 200 TeV (Bartoli et al. 2012a). In order to extend the dynamical range to PeV energies each RPC is also equipped with two large size pads (139 cm × 123 cm), allowing the collection of the total charge developed by the particles hitting the detector (Aielli et al. 2012). The digital output of each pad is splitted in two signals sent to the logic chain that builds the trigger and to the 18,360 multi-hit time-to-digital converters, which are routinely calibrated with 0.4 ns accuracy by means of an off-line method using cosmic-ray showers (He et al. 2007; Aielli et al. 2009a). More details about the detector and the RPC performance can be found in Aielli et al. (2006, 2009b). The detector is connected to two independent acquisition systems corresponding to two different operation modes, referred to as the shower mode and the scaler mode (Aielli et al. 2008). The data used in this paper were recorded by the digital readout in shower mode. This mode is implemented by means of an inclusive trigger based on the time correlation between the pad signals, depending on their relative distance. In this way the data acquisition is triggered when at least 20 pads in the central carpet are fired in a time window of 420 ns. By means of this trigger the energy threshold for gamma-induced showers can go down to 300 GeV with an effective area depending on the zenith angle (see Figure 1 in Bartoli et al. 2013).
Citation: Bartoli et al. 2012a
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