adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.501.4917W (fulltext)
gradient. The negative radial gradient of is expected because is associated with the surface mass density (e.g. Freeman Bland-Hawthorn 2002 ; Binney Tremaine 2008 ), which decreases with increasing Galactocentric radius (e.g. Xiang et al. 2018 ). The positive vertical gradient mainly reflects the increase of surface mass density enclosed by increasing height above the disc. The dispersion of J Z , however, has less dependence with R . Again, this is likely to be because of the nature of J Z , as discussed above. The skewness of V Z is close to 0 and uniformly distributes across the R – Z plane, except for the outermost region ( R > 11 kpc), whereas the skewness in J Z shows strong patterns. These characteristics are similar to the case of skewness for V R and J R , and they share similar physical reasons (see Section 3.2.1 ). 4 ORBITAL PARAMETERS AND STELLAR MIGRATION In this section, we present the orbital parameters of the RGB stars across the age–metallicity plane and the disc R – Z plane. We discuss the impact of non-circular orbital motion on the stellar distribution by comparing the current position of the stars with the guiding-centre radii of their orbits. We further estimate the birth radii of the stars based on their age and metallicity in the same way as Chen et al. ( 2019b ), initially proposed by Minchev et al. ( 2018 ), and we study the effect of stellar migration (churning). Note that here we refer to the stellar migration as the churning process, in which stars change their orbital radii through angular momentum transfer via scattering or perturbation (e.g. Sellwood Binney 2002 ; Minchev et al. 2013 ). 4.1 Orbital parameters As mentioned in Section 2 , the orbital parameters of our sample stars are derived using positions and proper motions from Gaia DR2, distances from BJ18 and radial velocities from LAMOST DR4. The orbit calculation is implemented with galpy , assuming the MWpotential2014 (Bovy 2015 ). Fig. 10 plots the median, dispersion and skewness of the stellar eccentricity distributions in the age–[Fe/H] plane and the disc R – Z plane. The figure illustrates that the young or old but metal-rich thin disc stars have mean orbital eccentricities smaller than 0.2, and have small (0.1) dispersion among stars in each mono-age and mono-[Fe/H] bin, which is consistent with previous work (e.g. Kordopatis et al. 2015 ). The old, metal-poor thick disc stars have larger eccentricities and broader distribution, and their mean eccentricities vary from 0.2 to larger than 0.5. For the thin disc stars, the eccentricity steadily increases with stellar age and exhibits little change with [Fe/H] at a fixed age, possibly suggesting that the steady disc heating processes have played a decisive role in determining the orbital evolution of these stars (e.g. Ting Rix 2019 ). At a given age, the [Fe/H] of a star is an indicator of its Galactocentric radius because of the existence of radial [Fe/H] gradient for mono-age populations (e.g. Boeche
Paper: 2019ApJ...878..139T (fulltext)
et al. 2013 ). Thus, the rather small source size of R = 4.8 × 10 15 cm obtained from the SSC fitting for the core of Cen A suggests that the emission region is located very close to the nucleus. To verify that a higher magnetic field can be observed closer to the core, we plotted the obtained magnetic fields from the SED fits for various FR I AGNs as functions of the distance from the core, as shown in Figure 3 (no error is included). It should be noted that for the sake of simplicity, the distance of the emission region from the nucleus is defined as , where R is the source radius obtained from previous studies (Chiaberge et al. 2003 ; de Jong et al. 2015 ; Fraija et al. 2017 , 2018 ; Baghmanyan et al. 2018 ; Tanada et al. 2018 ) and θ op is the opening angle of the jet. The VLBI observations revealed that the opening angle is collimated to the range of 2°–8° at a distance of 1 pc from the core; therefore, we set the angle as 5° (Junor et al. 1999 ; Horiuchi et al. 2006 ). For the distances of the AX2 and BX2 knots, we used the directly measured values, as shown in Table 1 . As can be seen in Figure 3 , the magnetic field decays as a power-law function , where the Schwarzschild radius of Cen A is r s = 2 G M BH / c 2 ≃ 1.6 × 10 13 cm. The obtained fitting parameters are the initial magnetic field of B 0 = (1.5 ± 0.2) × 10 4 G at r s and α = 0.88 ± 0.14. The conservation of particle number in a freely expanding conical jet requires the particle number density of N ( d ) ∝ d −2 , which implies B ( d ) ∝ d −1 from equipartition (Hirotani 2005 ) and it is consistent with our results. Furthermore, the core-shift analysis with the Very Long Baseline Array observations of blazars shows that the attenuation of the magnetic field at the vicinity of the core is described by B ( d ) ∝ d −1 appropriately (O’Sullivan Gabuzda 2009 ). According to the theory of the magnetically powered jet (Komissarov et al. 2007 ; O’Sullivan Gabuzda 2010 ), the initial magnetic field strength for a black hole launched jet can be calculated by G, where L rad ≃ 1.5 × 10 43 erg s −1 is the total jet luminosity of Cen A obtained from the SSC fitting, which is about one-fourth of the fitted value. Although there is a discrepancy between the theory and fitted value, which might be caused by the rough estimations such as the distance of the emission zones, these results support our suggestion that the SSC emission region of Cen A is located near the nucleus. Figure 3. Magnetic fields obtained from the SED fits for various FR I AGNs as functions of the distance from the core. The blue filled circle, square, and triangle represent the magnetic field strength of the Cen A core, AX2, and BX2, respectively. The blue open marks indicate the magnetic field strength of the core of the other FR I AGNs. The distances of AX2 and BX2 are directly measured using the Chandra analysis (Kraft et al. 2002 ). Except for these knots, the distance is calculated with the emission region size
Paper: 2019MNRAS.488.3222S (fulltext)
× 28.9 arcsec beam), which has the characteristics of a rotating disc with a moderate rotation velocity and a signature of a warp in the SE. The black contours at the centre of this panel are the two highest H i column density contours from the medium resolution total intensity H i map. These H i contours coincide with the kinematic centre as well as the optical centre of the galaxy. The velocity field of UGC 2162 shows no significant sign of recent major interaction. The position velocity (PV) diagram for a slice taken along the H i major axis, position angle (PA) = 307 ° , is shown in the middle panel of Fig. 6 and confirms a rotating disc. A small mass of low density extra-planar H i is seen ∼15–20 arcsec (∼1 kpc) SE of the kinematic centre in the PV diagram. The lower panel of Fig. 6 shows the rotation curve extracted from bbarolo (Di Teodoro Fraternali 2015 ) fit to the medium-resolution cube. The figure shows a slowly rising rotation curve that is a common feature for dwarf galaxies. The bbarolo best fit gives an inclination of 55.5 ° , with V rot at the outermost fitted ring, r = 67.5 arcsec (4.05 kpc) ∼ 25 km s −1 . The M dyn enclosed within each ring fitted with bbarolo is used in Section 4.1 to estimate the galaxy’s virial mass ( M vir ). We also estimated the V rot using the H i W 20 (adjusted for the inclination from the bbarolo model fit) where V rot = 0.5 Δ V [km s −1 ]/sin(i) with Δ V = W 20 (50 km s −1 ) from the GBT spectrum and i = 55.5 ° /180. This second method gives V rot ∼ 30.8 km s −1 at the outer edge of the detected H i disc. These values are also used in the Section 4.1 analysis. Figure 6. UGC 2162 H i kinematics: Top: GMRT velocity field from the medium-resolution cube. The two highest column density contours from the medium-resolution H i integrated map are shown in black. The PV diagram (panel below) slice PA = 307 ° is shown with a grey line. Middle: PV diagram from a PA = 307 ° slice. Positive positional offsets are to the NW. The blue contours are from the data and the red are from the bbarolo best-fitting model. Bottom: Rotation curve derived from the bbarolo five ring model fit. In Fig. 7 , we compare the V rot from for UGC 2162 with the V rot derived from model fits to the VLA H i for the LITTLE THINGS dwarf galaxies by Oh et al. ( 2015 ). The plot shows that the UGC 2162 V rot = 30.8 km s −1 , derived from the GBT W 20 , falls within the range of V rot for LITTLE THINGS with similar M H i and below the LITTLE THINGS median V rot of 39.5 km s −1 . Fig. 8 shows the baryonic and stellar Tully-Fisher relations. Figure 7. V rot versus M gas : UGC 2162 (Black square) and LITTLE THINGS dwarf galaxies (red crosses) from Oh et al. ( 2015 ). The UGC 2162, V rot = 30.8 km s −1 is derived from the GBT W 20 in Section 3.2 . For both UGC 2162 and the LITTLE THINGs galaxies, M gas = 1.4 × M H i with the additional factor to account for molecular gas and He. Figure 8. UGC 2162 Tully–Fisher relations: The position of UGC
Paper: 2018ApJ...864L..12L (fulltext)
visible. 3. Physical and Observational Constraints of the Allowed Parameter Space Region that Enables the Production of FRBs The physical conditions in the region where the masing takes place can be constrained using the physics of the maser and constraints from observations, allowing us to place limits on the magnetic fields and number densities where the synchrotron maser can plausibly be the emission mechanism for FRBs. We consider a cataclysmic FRB progenitor. However, as the repeating burst FRB 121102 is the only one to have a known redshift, it is the only source that provides observational constraints for quantities such as the burst energy and the DM of the host galaxy. Therefore, we use the values it provides as representative limits for our calculations. The data enable us to obtain constraints linking the size and number density of the masing region to the magnetic field of the neutron star. These constraints are obtained from: (i) the energetics of the burst and size of the masing region, (ii) the efficiency of the maser mechanism, (iii) the dispersion measure of the burst, and (iv) the frequency of the signal. We consider that masing takes place in a spherical shell of thickness d , located a distance R from the central object. The maser will be activated by the formation of a population inversion in the shell. The magnetic fields and short timescale (≲ d/c Γ) required suggest that this object is a neutron star, though the timescale for maser emission is given by the duration of the maser itself (Ghisellini 2017 ). Assuming that the blast wave is relativistic, the shocked plasma has a width ∼ R /Γ (Blandford McKee 1976 ), where Γ is the Lorentz factor of the blast wave. This typical width provides the first constraint, on the thickness of the shell:The minimum thickness of the shell depends on the number of particles that contribute to the masing, , and their number density, n e . Here, E is the energy of the bursts (in the range 10 38 –10 40 erg for the repeater (Law et al. 2017 ; Tendulkar et al. 2017 )), η is the fraction of the electrons’ energy that contributes to the maser, and is the average energy of the masing electrons. These shocked electrons have a thermal energy of γ ≈ Γ. Constraint (ii) originates from the efficiency of the maser. For the maser to be a viable emission mechanism, the growth rate of the signal must be large enough to extract a fraction η of the particle energy. The maser will be quenched when the maser reaches saturation. The efficiency of the maser mechanism in simulations of relativistic shocks was shown to be η ≲ 10 −1 (e.g., Gallant et al. 1992 ; Sironi Spitkovsky 2009 ). In the case of AKR, the efficiency is in the range η ∼ 10 −1 –10 −3 (Wu 1985 ). The exact value, however, depends on the form of the particle distribution, which is uncertain. We therefore examine values of η in the range 10 −3 ≲ η ≲ 10 −1 in this Letter. Lu Kumar ( 2018 ) gave upper limits to the efficiency of η ≲ 10 −5 ,
Paper: 2019ApJ...883...19A (fulltext)
decrease by more than 25% with respect to an older adjacent bin. The choice of just five coarse age bins is motivated by the low number of stars in the CMD, the large photometric errors, and the reached magnitude limit of just ∼1.5 mag below the RGB tip. Indeed, from the existence of an RGB we know that stars older than 1–2 Gyr are present in S1, but we are not able to infer in great detail the SFH from the age of the universe to ∼2 Gyr ago. The best-fit solution from SFERA is presented in Figure 9 ; in the top panels, we show the empirical and the model CMDs in the form of Hess diagrams, i.e., density plots, and the residuals in units of Poisson uncertainties ((data−model)/). As visible from the residuals, the model reproduces quite well the observed CMD above F814W ∼ 28. The TP-AGB star region at F814W ∼ 26, F606W−F814W ≳ 1.2 is better populated than in the previous SSP-fit case, although a shortage of counts is still present in the synthetic CMD. However, we do not consider this partial discrepancy as a problem here, as it is known that this evolutionary phase is very difficult to model and still lacks proper calibration over the whole range of metallicity regimes. Figure 9. Results of the SFERA procedure for the SFH recovery in S1. Top panels: Hess diagrams of the observed CMD, of the best-fit model, and of the residuals. Middle panel: derived metallicity behavior as a function of look-back time. Bottom panel: derived star formation rate as a function of look-back time. The total formed stellar mass is indicated. The middle and bottom panels of Figure 9 show the resulting metallicity and star formation behaviors as a function of look-back time. The solution recovered by SFERA implies that the vast majority of the SF activity occurred at epochs earlier than ∼500 Myr ago; the peak of the activity is predicted to have occurred in the 2–6 Gyr bin, while the adjacent 500 Myr–2 Gyr and 6–13.7 Gyr intervals were characterized by a lower SF. As already expected from the CMD appearance in Figure 7 , the SF rate in S1 was compatible with zero at epochs more recent than ∼0.5 Gyr ago. At earlier epochs we are not able to infer the details of the SFH with the data in our hands; however, the total formed stellar mass of (1.27 ± 0.41) × 10 6 M ⊙ can be considered a quite robust result and is in reasonable agreement with the estimates from both the SSP-fitting and the integrated light in Annibali et al. ( 2016 ). Concerning metallicity, the best-fit solution implies a continuous increase in Z starting from ∼0.0006 at the oldest look-back times and reaching up to Z ∼ 0.006 (∼40% solar) at recent epochs. This value is significantly higher than the Z ∼ 0.0004 (∼2%) solar metallicity measured in DDO 68's H ii regions, and also much higher than expected for a ∼10 6 M ⊙ stellar mass galaxy. Admittedly, given the strong age–metallicity degeneracy in the RGB phase combined with the relatively large photometric errors of our data, we can not claim a robust
Paper: 2020ApJ...905...68A (fulltext)
and along with the dust distribution, it sets the thermal profile of the disk. Likewise, UV and X-ray photons dominate the chemistry in the upper layers through molecular ionization and photodissociation. Given that photodissociating photons play a considerable role in the disk chemistry, we use a large number of photon packages for the X-rays and the UV frequencies to minimize the photon noise in the Monte Carlo simulation. We run the simulations using 5 × 10 6 photon packages for the optical and longer wavelengths, but we include a refinement factor of 10 3 for Ly α photons, 5 × 10 3 for UV photons, and 10 6 for X-rays. By using the refinement factor, we increase the number of photon packages in the respective spectral window by the corresponding refinement value. Even using a refinement for the UV photons, the dust opacity does not let the UV photons reach the midplane. Nevertheless, the UV flux reaching the midplane and its associated photodissociation rate are negligible compared to the optical irradiation and the gas-phase chemistry, so it does not change our final results. Further, Agúndez et al. ( 2018 ) show that FUV photodissociation rates for T Tauri disks are negligible for z / r 0.3 at 100 au. In our basic or benchmark model, the surface density, Σ basic , follows the self-similar solution from Lynden-Bell Pringle ( 1974 ), i.e.,where r e is the radius of the exponential cutoff, r 0 is the characteristic radius for the disk, and Σ 0 is the surface density of the disk at r = r 0 . In our basic model, the gas and dust components have the same profiles, where Σ 0 is the only changing value. For the disk with the dust substructure, we add the ring and the gap as modulations of the self-similar solution. Those modulations are further discussed in Section 2.1.1 . We aim to include how the gas and the distinct dust populations alter the chemistry and physical conditions in the disk. So, we set different vertical distributions for them, but with the same functional form. Both gas and dust have vertical Gaussian distributions (Armitage 2010 ):where and h is the scale height, which we vary for each population. Particularly for the gas, the aspect ratio is set to be h / r = 0.08 at r = 100 au. The scale height also has a radial dependence through a power law with a flaring index ψ :The flaring index is a common value for the gas and dust populations. We set it with a value of ψ = 1.22. Besides the gas, we include two main dust populations differentiated by the grain size they represent and an additional third component only within the inner 3.5 au. The two main populations are composed of a mixture of 80% silicates and 20% graphite. The optical opacities where taken from Draine Lee ( 1984 ), while the X-rays opacities come from the prescription in Bethell Bergin ( 2011 ). Both dust populations follow a grain size distribution with a standard power law, n ( a ) ∝ a −3.5 , which is taken from Mathis et al. ( 1977 ). We also set a
Paper: 2018ApJS..235...17M (fulltext)
97% reliability threshold and matched to their input counterparts are plotted as blue circles. The red dashed line is the 1:1 relation, while the yellow contours indicate where most of the points lie. The Eddington bias flattening is clearly visible for input fluxes erg cm −2 s −1 . Bottom: ratio between the output and input flux as a function of the input flux for the simulations performed. The yellow filled circles are averages computed in bins of input flux, while the red horizontal line marks the 1:1 relation. 4. Source Detection We repeat the same procedure of source detection on the data mosaics. After deblending the list of potential sources, we detect 43 unique sources above the threshold of 99% reliability in at least one of the “canonical” F, S, and H bands. When considering the 97% reliability threshold, we detect 67 sources. We will refer to these catalogs as UDS99 and UDS97, respectively, and the detailed numbers for each band are reported in the last two rows of Table 5 . We note that the numbers of detections agree very well (within 1 σ of the distributions; see Figure 7 ) with the simulation expectations. To maximize the statistics, we will focus on UDS97 henceforth, keeping in mind that the spurious fraction of this catalog is 3%. 26 26 Details about this catalog can be found in the Appendix . For homogeneity with the other NuSTAR surveys, and given the few detections in the H1, H2, and VH bands, we will also consider only the 67 sources detected in the canonical bands (of which two are expected to be spurious), while discussing the results in the new bands elsewhere (A. Masini et al. 2018, in preparation). Table 6 shows how these 67 sources are distributed within the F, S, and H bands. Figure 7. Distribution of detections within the simulations. The six upper panels, in red, refer to the six bands at the 97% reliability threshold. The six lower panels, in green, refer to six bands at the 99% reliability threshold. From top left to bottom right, the bands are F, S, H, H1, H2, and VH. In each panel, the dashed lines mark the ±1 σ interval from the average, while the black solid line marks the position of the detections in the data mosaic, which are always within ±1 σ from the mean of the simulations, shown by the solid red or green line. Numerical values for the average number of detections in the simulations in each band and the number of detections in the data mosaics are explicitly addressed in the last four rows of Table 5 . Table 6 Summary of Detection, UDS97 Note. Capital letters are for sources above the threshold, while lowercase letters refer to sources detected but below the threshold. 4.1. Catalog Creation We create a catalog of the 67 sources detected in the three canonical bands of UDS97. Following the same strategy adopted for the simulations, we use dmextract to get total and background counts from circles of 20′′ radius, from the data and background mosaics, respectively. Similarly, we extract the exposures from
Paper: 2021MNRAS.504.3711L (fulltext)
that in the inertial frame only 2l + 1 out of (2l + 1) 2 peaks dominate, consistent with the matrix remaining approximately diagonal even at substantial obliquities (see Fig. 9 ). Closer inspection of how the shape of this dominant sub-multiplet changes with obliquity reveals that while this may be asymmetric in general, it is still possible for this to appear nearly symmetric for some values of β and have a spacing close to the value expected of pure rotation (Fig. 12 , lower left-hand panel). However, the centroid of this sub-multiplet is offset compared to the pure rotational multiplet. At larger field strengths (see Fig. 14 ), there may be many peaks of significant amplitude in the inertial frame. Notably, due to the heavy mixing, it would be possible to find symmetric sub-multiplets among these (e.g. bottom row, middle panel). Figure 12. Splitting diagram for a p-dominated mode of Model C, with n = −16 and l = 1 (i.e. that used in Fig. 9 ), under the default rotation and field strength listed in Table 1 , for six different values of the obliquity angle. Short, thick, horizontal black lines joined by thin dotted lines illustrate the splitting process, from the unperturbed mode (leftmost line at ω 1 = 0) into a multiplet of 2l + 1 modes in the corotating frame (middle group), and then further into (2l + 1) 2 modes in the inertial frame (rightmost group). In the rightmost group, the various modes have been horizontally offset for clarity. The different grey-scale shades correspond to the absolute value of the associated a m coefficient, with black being 1 (maximum amplitude) and white being 0. For comparison, horizontal coloured dashed lines mark the position of the non-magnetic rotationally split multiplet in the inertial frame. Figure 13. As for Fig. 12 , but a mode with n = −28 and l = 2 (i.e. that used in Fig. 10 ). Figure 14. As for Fig. 12 , but with a field strength three times higher. Frequency splittings for all l = 1 modes of Models C and D are summarized in Fig. 15 , where points are coloured according to the associated value of | a m |. The increased m -mixing with larger obliquity and field strength can clearly be seen across all modes, both p- and g-dominated. The main difference between p- and g-dominated mixed modes appears to lie in the centroid frequency of the multiplet: this is located towards systematically higher values compared to the unperturbed value for g-dominated multiplets, a feature not present in the case of pure rotation (as seen in Figs 7 and 8 ), where the centroid offset is zero for all multiplets. This offset between p- and g-dominated multiplets increases towards lower frequencies, where magnetic effects become more significant. Figure 15. Inertial-frame frequency shifts versus unperturbed frequency, for all l = 1 modes of Models C (left) and D (right). Values of the field strength and obliquity are shown in the panel headers. Within each panel, the different colours correspond to the corotating-frame