adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.500.3743G (fulltext)
10 and 15 per cent, respectively. Figure 2. Distribution of the resultant distance uncertainties of the catalogued PGCCs. In Fig. 3 , we compare our newly derived extinction distances with the kinematic values deduced by Zhang et al. ( 2018 ). In general, the newly estimated extinction distances differ significantly from the kinematic ones. Amongst the sample objects, 10 clumps have similar kinematic distances ( d Z18 ∼ 0.46 kpc), as they all have similar radial velocities (8.0 or 3.0 km s −1 ). However, their directly measured extinction distances range from 0.21 to 0.51 kpc. It is quite possible that the peculiar and non-circular motions of the clumps may have significantly affected the calculation of their kinematic distances (Liszt Burton 1981 ; Gómez 2006 ; Reid et al. 2009 ). For some PGCCs, such as G176.17-02.10 and G177.14-01.21, the differences between the two sets of distances are very large. The kinematic distance probabilities of those clumps derived with the Bayesian Distance Calculator (see table 1 of Zhang et al. 2018 ) are very small (less than 0.5), indicating that those kinematic distances are poorly constrained. Figure 3. Comparison of our extinction distances with their kinematic values from Zhang et al. ( 2018 ). In the left-hand panel, the black dash line denoting complete equality is used to guide the eyes. 5 DISCUSSION The improved distance estimates allow us to revise some of the distance-dependent physical properties of the cores of the PGCCs, such as their radii, masses, and the virial parameters. We update those physical parameters by simply rescaling the results of Zhang et al. ( 2018 ). For example, effective radius R eff of a Planck core is proportional to distance d , R eff ∝ d . Thus the revised radius is evaluated as Similarly, the revised mass M ′ is given by We note that these updates also lead to changes of other parameters, such as the virial parameter, which is given by Fig. 4 plots the space distribution of these 61 PGCCs in the X – Y space with both the original kinematic distances and our updated distances, where the distance changes are indicated. According to the formula (3) and (4), we present the changes of these physical quantities in Fig. 5 . From the histograms in the left-hand panel of Fig. 5 , we find that on the whole the radii and masses calculated with the new extinction distances tend to be smaller than those of Zhang et al. ( 2018 ), by respectively ∼ 17.9 and 32.6 per cent. This leads us to conclude that to understand the local molecular structures, accurate distances are necessary. We use an MCMC algorithm to fit the mass–radius relation for both sets of distances. The new distances yield a slope of 1.95 ± 0.09, comparable to 1.93 ± 0.09 obtained with the kinematic distances. In spite of the significant changes, the slope is largely unaffected. The result is understandable, considering that a change in the distance measurement leads to changes in both mass and size estimates. Since the
Paper: 2019ApJ...877L..15K (fulltext)
of all bodies to zero. We also fixed the orbital period of the planet–moon barycenter to 287.378949 days (the best fit from TK18 ). We elected not to vary the period of the planet–moon barycenter because it is poorly constrained from a single transit observation. A longer period would cause a longer transit duration for the planet, but this could equivalently result from a smaller impact parameter, a larger star, or a massive moon that significantly perturbs the planetary orbit. We used the following priors: the stellar radius was normally distributed, R * ∼ N (1.81, 0.17) R ⊙ (see the next section). The planet radius was uniform from 8 to 14 R ⊕ . The planet transit time was uniform over the timespan of the observations, and inclination was uniform from 0° to 90°. The planet mass was log-normally distributed, M p ∼ 10 2.5±0.5 M ⊕ , based on the expectation for Jupiter-radius objects from Ning et al. ( 2018 ). For the moon model, we allowed the transit time to vary uniformly over the entire visit. The moon period was uniform between 1.6 and 260 days. These limits span the duration of the HST observations (so there is one possible moon occultation event), to the orbit at 0.5 the Hill radius, based on the stability limit for prograde moon orbits (Domingos et al. 2006 ). The Hill radius calculation assumed that the planet and stellar masses are 1 M Jup and 1.37 M ⊙ . The moon mass varied uniformly from 0 to 30 M ⊕ . The longitude of the ascending node was also uniform from 0° to 360°. The moon inclination was uniform from 0° to 90°, and was defined relative to the line of sight. We assigned zero prior probability to scenarios where the moon did not transit or experienced a grazing transit. We made this choice to put an upper limit on the radius of a fully transiting moon; for grazing transits or non-transits the moon radius could be arbitrarily large. 3.1.1. Stellar Parameters For both the moon and no-moon scenarios, we used a quadratic stellar limb-darkening law and fixed the coefficients to the prediction for a 5700 K, solar metallicity PHOENIX model from Espinoza Jordán ( 2015 ); u 1 , u 2 = [0.216, 0.183]. We estimated the host star parameters using the Gaia second data release (DR2) parallax (Gaia Collaboration et al. 2016 , 2018 ) along with UBV photometry from Everett et al. ( 2012 ) and JHK photometry from the Two Micron All-Sky Survey (Skrutskie et al. 2006 ). We employed the isochrone python package (Morton 2015 ) with the Dartmouth isochrone grid (Dotter et al. 2008 ) to obtain posterior constraints on the stellar parameters. The resulting parameters indicate that Kepler-1625 has stellar mass M ⊙ , radius R ⊙ , and age Gyr. In our analysis, we fixed the stellar mass to the best-fit value (1.37 M ⊙ ), and used a Gaussian prior on the radius, R * ∼ N (1.81, 0.17). 3.2. Instrument Systematics Model There are two systematic trends in the data. One is the orbit-long ramp, attributed to charge traps in the detector filling up over the orbit
Paper: 2018A&A...620A..53G (fulltext)
results, the most important one being that due to the photoelectric charging. This is further illustrated below. Two typical runs, though limited to 100 grains for each a value and with integration times of only 0.1 Myr for clarity, are illustrated in Figs. 4 and 5 . The spread of the orbits into broom-like structures is entirely due to the turbulence; if v turb is set equal to 0 the orbits collapse to a single vertical line. It is obvious from these figures that the choice of initial conditions for the velocity is important for the orbits, although the fraction of grains falling into the star as a function of grain radius is not as dependent on these conditions. Fig. 4. The orbits of grains over 10 5 yr, from a point indicated by an “ x ” in black along the z axis at a distance of 10 4 AU from the star (which is at the origin of the coordinate system), and with different grain radii a as indicated by the different colours. The dust grains were given an initial velocity identical to the gas free-fall velocity. The turbulent mean speed was set to 0.1 km s −1 . We note that the scales of the x and y axes are different from that of the z axis. Fig. 5. The orbits of grains over 10 5 yr, from a point indicated by an “ x ” in white along the z axis at a distance of 10 4 AU from the star (which is at the origin of the coordinate system), and with different grain radii a as indicated by the different colours. The initial velocity of the dust grains was set to zero. The turbulent mean speed was set to 0.1 km s −1 . We note that the scales of the x and y axes are different from that of the z axis. The main results of these numerical experiments are displayed in Fig. 6 . Here we have plotted a quantity called “the defect”, D ( a ), of the run, as a function of a for different choices of the defining parameters. D ( a ) measures the fraction of grains that leave the proto-solar cloud as a result of the radiative cleansing. It is defined as where N l is the number of dust grains lost inside 100 AU, and N 0 is the corresponding number lost in a run with identical parameters with the exception that the stellar luminosity L is set equal to zero. In this way, we try to correct for the fact that even at zero luminosity some of the gas may depart at high turbulence speeds from the stellar neighbourhood. It is seen from the figure that the variation of D with a is smoothed when v turb increases, from a very steep change when a passes the limiting value in Eq. ( 9 ) for small turbulence, to a less steep variation. We also note that an increased inflow of gas decreases the deficit at a given a , as does a change of the initial condition from v ( r 0 )=0 to setting v ( r 0 ) equal to the free fall velocity of the gas flow. Also, the coulomb forces diminish D but not drastically so. None of these effects is unexpected. Obviously, these variations of the deficit with the different parameters are not very dramatic: the conclusions concerning the cleansing effects in
Paper: 2019MNRAS.485.5733Q (fulltext)
does not depend on stellar mass The mass–IRX relation has been widely examined over a wide redshift range (Reddy et al. 2010 ; Heinis et al. 2014 ; Whitaker et al. 2014 ; Álvarez-Márquez et al. 2016 ), finding that it evolves little out to z = 3 at the low-mass end of M * 10 in contrast to a rapid evolution at the high-mass end (Whitaker et al. 2014 ; Leslie et al. 2018 ). Some authors concluded from this that galaxy stellar mass is the most fundamental parameter determining the dust obscuration in SFGs (Garn Best 2010 ; Zahid et al. 2014b ). Generally speaking, more massive SFGs have a higher SFR and a larger amount of dust in the ISM formed in the past and tend to have a higher IRX. However, we find that four free parameters L IR , R e , Z , and b / a are sufficient to fit IRX, and residuals from the universal relation show no dependence on stellar mass. The inclusion of stellar mass as a free parameter in our fitting gives , suggestive of no correlation within the uncertainties. Studying the Balmer decrement in a sample of ∼1000 nearby SFGs with integral-field spectroscopy, Li et al. ( 2019 ) come to the same conclusion that trends of (spatially resolved) extinction with various local and global galaxy quantities can be reproduced by a model that lacks an intrinsic dependence on galaxy stellar mass. Moreover, as shown in Fig. 6 , the high- z SFGs of different stellar masses all follow the universal IRX relation derived from the nearby SFG population, further confirming that exclusion of stellar mass in our fitting works for distant SFGs as well. The bolometric luminosity (UV+IR) of a star-forming galaxy is vastly dominated by young stars and can thus be seen as a good SFR indicator (Kennicutt Evans 2012 ). The dust obscuration tracer IRX is intimately related to the star formation process as well. Dust in the birth clouds around young stars and in the surrounding ISM absorbs the UV radiation and re-emits into the IR. The overall dust obscuration of the galaxy, in general, depends on the dust column density averaged over the galactic scale. The latter relies on the gas density and gas-phase metallicity. The gas density on its turn is what sets the star formation surface density (i.e. the K–S law). While none of these quantities are directly linked with old stellar populations, SFR, metallicity, and galaxy size are all globally correlated with stellar mass. The observed mass–IRX relation can hence be considered the natural product of the universal relation of dust obscuration together with the scaling relations of stellar mass. We thus conclude that stellar mass is not a causal parameter that directly controls the dust obscuration in an SFG. 6.3 The term <inline-formula><tex-math id="TM0041" notation="LaTeX"></tex-math></inline-formula> and implications on the dust-star geometry One can wonder why IRX depends more strongly on rather than on Σ IR (= ). The latter has been shown to tightly correlate with dust temperature among SFGs that are
Paper: 2017ApJ...842...12W (fulltext)
of the blended line intensities of Fe xiii and Fe xii in the analysis of EIS spectra, we applied the same method to the Fe xvii line complex around λ 204.6 Å in the LHD spectra to unblend the Fe xiii and xii line intensities. The results are summarized in Figure 5 . The time evolution of the electron density for low-temperature LHD peripheral plasmas is plotted in Figure 5 (a), where the electron density ( n e ) is estimated to be log n e (cm −3 ) ∼ 10.5–11.8 after the NBI controls, slightly higher in the later phases of discharge. The estimated electron densities are somewhat lower than those measured by Thomson scattering at t = 4.3 s and t = 4.9 s (see Figure 2 of Murakami et al. 2014 ), but these line intensity ratios of Fe xii and xiii are located in the range that is close to the high-density limit, and the apparent large difference in the absolute n e values derived from Fe xii and Fe xiii does not affect the estimation of the blended Fe xiii/xii line intensities much, and therefore is negliglible in the following analysis results. Figure 5. (a) Electron densities for the LHD plasma plottted against time since the discharge of shot 107,802. The line types are the same as in Figure 4 (b). (b) Reduced Fe xvii line ratios by unblending the contributions of the Fe xii and xiii lines. The horizontal thin dotted line indicates the line intensity ratio averaged at the eight epochs in discharge 107,802. Error bars show photon noise ranges only. We take this contribution of the Fe xiii emissions to the emission line intensity at 204.6 Å into account and plot the Fe xvii λ 204.6/ λ 254.8 ratios against time after the start of discharge in Figure 5 (b). No lines are assumed to be blended in the Fe xvii λ 254.8 line, and a common electron density is assumed for the Fe xii/xiii emitting plasmas. The measured ratio is reduced to 20%–30% as a result of subtracting the Fe xiii line contribution, and the average value (∼1.12) is very close to the theoretically predicted constant vlaue of ∼1.1, although the temporal variation, henceforth, the temperature dependence of the line ratios, could not be completely eliminated in this analysis. The average value at the four epochs ( t = 4.8–5.4 s) is rather close to ∼1.3, when the line complexes at 204.6 Å are the strongest and the electron temperatures are the lowest during the discharge. 4. Discussion and Summary The Fe xvii line intensity ratios obtained from the solar active region in Section 2.3 and the LHD experiment are plotted against electron temperature in Figure 6 , in which the revised sensitivity proposed by Del Zanna ( 2013 ) is adopted for the EIS radiometric calibration. The LHD experimental results clearly show that the Fe xvii line branching ratio of λ 204.6/ λ 254.8 is close to the theoretical value of ∼1.1, which means that the revised in-orbit EIS sensitivity calibration (Del Zanna 2013 ; Warren et al. 2014 ) makes the solar data consistent with the laboratory data. The apparent
Paper: 2020ApJ...903...24M (fulltext)
with the yellow shades. The light-gray shades represent the 3 σ noise levels. The dark-gray shades present the wavelength range of bright sky lines and bad pixels. Left: HETDEX Fiber positions atop the broadband images taken with CFHT/MegaCam or HST/WFC3. The red, blue, green, and yellow circles present the positions and the sky coverage of the fibers used for the measurements of the LAE spectra. Figure 17. Same as Figure 11 , but for the H i radial profile around LAE1–4 in the the extreme QSO overdensity EGS–QO1 (red circles). The blue and black circles are the H i radial profiles of LAEs in the blank fields: the blue circles are the remaining 22 EGS LAEs that reside outside of the EGS–QO1 (i.e., EGS outside), and the black circles are the COSMOS LAEs. A significant H i transmission underdensity is identified around LAE1–4, which is similar to the one around the QSOs in EGS–QO1 (Figure 15 ). We find that one of the four LAEs, LAE4, shows triple-continuum components over the scale of a 10 pkpc radius circle on the HST F814W/F160W images of the EGS, indicative of a triple merging system (middle/bottom panels of Figure 18 ). LAE4 is located at (R.A., decl.) = (14:19:13.02, +52:49:11.2) near the center of the ionized bubble of EGS–QO1 (bottom panel of Figure 13 ). The average Ly α redshift of LAE4 over the HETDEX fibers is , and the total Ly α luminosity over the fibers is erg s −1 . The top panel of Figure 18 presents the spectrum of LAE4, summed over the fibers, which is the same as the one in Figure 16 . The spectrum of LAE4 has a broad Ly α emission line whose FWHM is ∼1100 km s −1 (about 3 times broader than the instrumental resolution), possibly suggesting a type I AGN (e.g., Kakuma et al. in preparation). 24 24 Other AGN features of high-ionization emission lines, C iv and He ii , are not detected in the LAE4 spectrum shown in Figure 16 . This is probably because the LAE4 is optically faint (∼25 mag) and its metal lines are too faint to be identified. The bottom panel of Figure 18 presents the positions and the sky coverage of the fibers used for the measurements of the LAE4 Ly α redshift and total luminosity and indicates that three fibers cover the triple-continuum components with the blue, green, and yellow circles. The spectra of these three fibers are shown on the right-hand side in Figure 18 . All of the three fiber spectra of the blue, green, and yellow circles have Ly α emission lines at , suggesting that the three objects probably reside at the same redshift. Moreover, these three fiber spectra have broad Ly α emission lines with an FWHM of ∼1000 km s −1 . Although each of these fibers does not separately cover one of the triple-continuum components, whose blending due to the HET image quality and large fiber diameter may produce apparent broad lines, these broad Ly α emission lines found in the different positions over the triple-continuum components imply that the LAE4 system could be a merging type I AGN doublet or
Paper: 2021ApJ...910...50P (fulltext)
leads to a strong back-reaction of the Lorentz force, which is expressed asThis force triggers latitudinal flows, and in the end, the wind speed is compressed to negative flows because of it, which leads to accretion at the poles. Appendix D Computations for Helicity D.1. Computation of <italic>A</italic><sub><italic>p</italic></sub> with Various Boundary Conditions D.1.1. General Principle The relative magnetic helicity, which is gauge-invariant, is defined aswhere B p = ∇ × A p , but also B p is potential (∇ × B p = 0) and a reference component from B ( B p · n ∣ S = B · n ∣ S ). Thus, we want to find a potential magnetic field B p derived from a scalar magnetic potential φ ( B p = −∇ φ ) that we can project on spherical harmonics: Because it is potential, B p is also poloidal; thus, we can introduce a quantity C p , such as Thus, to obtain B p and A p , we only need to compute C p , which can be linked to φ using the following relation: Under the assumption of axisymmetry, we have, in the end, The computation of and depends on the boundary conditions of the domain. D.1.2. Potential Components with Perfect Conductor and Potential Field For the dynamo, we have a perfect conductor boundary condition at the bottom, which leads to At the top, we have a potential field boundary condition, which implieswith . We combine these two equations to obtain, in the end, D.1.3. Potential Components with Potential Field and Outflow Boundaries For the wind, we have a potential field boundary at the bottom, which leads towith . At the top, we have an outflow boundary condition, which is expressed using a modified outflow boundary condition with ∂ r ( B r r 2 ) = 0 instead of ∂ r B r = 0, which leads to With Equation ( D2 ), we obtain We combine this condition with Equation ( D10 ) to obtain, in the end, D.2. Computation of <bold><italic>A</italic></bold> We have a magnetic field B that derives from a vector potential A such that B = ∇ × A . Knowing B , we want to recover A in 2.5D. To do so, we decompose the magnetic field B in a poloidal and a toroidal term in spherical coordinates: To obtain C , we can use direct integration, given that To obtain A , we use two different methods: direct integration for outside the star and projection on spherical harmonics for inside the star. With direct integration, we have We can project A onto spherical harmonics, which leads to Now, if we take the r component of the projection of the current on spherical harmonics, we obtain We can then link A and C to A using the following relation: D.3. Balance for Helicity Fluxes To make sure that all of the helicity computations were carried out correctly, we have checked the balance described by Equation ( 21 ), which we rewrite here in the formwhere Γ d is the diffusion term due to the electric currentsand F H is the helicity flux The results are presented in Figure 19 . Case DW1 is on the left, and case DW2 is on the right. The top panel is the budget for the dynamo domain,
Paper: 2017MNRAS.464.2840A (fulltext)
Figure 8. Galaxy stellar mass function at z = 0 for simulations with different black hole feedback parameters, including (i) no feedback (grey), (ii) v out = 10 3 km s −1 and (blue; fiducial simulation), (iii) v out = 10 4 km s −1 and (green), and (iv) v out = 10 3 km s −1 and (red). Error bars show cosmic variance estimates computed as the standard deviation over the eight sub-octants of the simulated volume. Black points connected by a dashed line show the observed stellar mass function from Baldry et al. ( 2012 ). Quasar mode feedback alone is not sufficient to reproduce the observed galaxy mass function, either at low or high masses. 4.4 Mass dependence of black hole feedback effects Fig. 9 shows in more detail how the effects of black hole feedback depend on galaxy stellar mass. We cross-match galaxies between simulations based on the unique ID of the particles they contain at z = 0. Each galaxy is matched to the galaxy that contains the largest number of common star particles, which we use to compute the stellar mass of galaxies in simulations with black hole feedback relative to the no-feedback simulation. Figure 9. Mass-dependent effects of galaxy-scale black hole feedback. Left: ratio of the stellar mass of galaxies at z = 0 in simulations including black hole feedback to the stellar mass of the corresponding galaxies in the no-feedback simulation as a function of stellar mass. Different colours indicate simulations with different outflow velocity and momentum flux. Solid lines and shaded regions indicate median and 10 per cent–90 per cent percentile ranges. Right: same as left for black hole masses. Black hole feedback appears to be more effective in galaxies with mass M ⋆ ∼ 10 10 M ⊙ for outflows with velocity v out = 10 3 km s −1 , while the mass suppression efficiency increases to higher mass galaxies for simulations with v out = 10 4 km s −1 . The overall effect of feedback on the growth of black holes and galaxies is qualitatively similar. Black hole feedback in our simulations with v out = 10 3 km s −1 is more effective at suppressing star formation and black hole growth in galaxies in the mass range M ⋆ ≈ 10 9 –10 11 M ⊙ at z = 0. This trend with M ⋆ , already present at z = 2, suggests that black holes in lower mass galaxies have not coupled enough energy to affect their host galaxies from the time of seeding down to z = 0 (which may be sensitive to our seed model), while outflows with v out = 10 3 km s −1 may not be sufficient to unbind gas in galaxies at the highest masses. With the same momentum flux and v out = 10 4 km s −1 , the results are similar at low M * but the mass suppression efficiency keeps increasing towards high masses, giving a ∼60 per cent reduction of the stellar mass of galaxies with M ⋆ ≳ 10 11.5 M ⊙ by z = 0. As expected, our simulation with v out = 10 3 km s −1 and yields the strongest suppression of stellar growth at all masses, but the efficiency follows a trend with M ⋆ similar to that of the