adsabs/AstroCLIMB huggingface dataset:
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Schematic illustrating how we choose DM windtunnel parameters from the LMC’s orbit. The left panel shows the LMC’s orbit up to the present day from the reference simulation (simulation 3 in GC19). The orbit (red line) is nearly polar, and it is shown projected onto the y–z plane in the rest frame of the MW’s center of mass (denoted by the black x). The blue and black points show the locations on the orbit that we use to select the wind parameters for the Fiducial and Infall orbit cases, respectively. The center column of panels shows the LMC’s distance from the MW center (r MW; top) and orbital speed (v; bottom) as a function of time during the reference simulation, while the right column shows the MW’s DM halo density (﹩\bar{\rho };﹩ top), and velocity dispersion (﹩\bar{\sigma };﹩ bottom) at the LMC’s location vs. time. Blue dotted and black dashed lines mark where we select the Fiducial and Infall wind parameters, respectively.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Schematic of the binning procedure to make wake profile plots, in which we compute a quantity of interest across the width of the wake. The filled contours show the typical location of a wake behind the LMC potential, whose center is shown by the x. We begin by selecting particles/cells from z ∈ [−60, 60], as in the projection plots (e.g., Figure 2). Then, as pictured, we take a 100 kpc-wide slice of the box of y ∈ [50, 150] (red), and select particles within 10 kpc-wide bins according to their x-position (black). In each x-bin, we calculate the quantity of interest, either density (as in Figures 4 and 6), or z-velocity dispersion (as in Figures 8 and 10). For each figure, this process is repeated for five snapshots spanning 100 Myr. The corresponding profile plots illustrate the average of the five snapshots.
Paper: Gravitational Wave Eigenfrequencies from Neutrino-driven Core-collapse Supernovae
Authors: Noah E. Wolfe, Carla Fröhlich, Jonah M. Miller, Alejandro Torres-Forné, Pablo Cerdá-Durán
Figure caption: Left column: histogram of the f-mode frequencies and the corresponding Gaussian mixture fit for models u25.8_BHBlp (top), s18.4_BHBlp (middle), and u24.0_DD2 (bottom). Right column: time evolution of the f-mode frequencies between 0.2 and 0.7 s post-bounce for models The discontinuity in f-mode frequencies is seen at ∼0.36 s post-bounce (top), at ∼0.6 s post-bounce (middle), and at ∼0.6 s post-bounce (bottom). Horizontal lines identify the μ 1 and μ 2 frequencies from the Gaussian mixture fit for each model.
Paper: Gravitational Wave Eigenfrequencies from Neutrino-driven Core-collapse Supernovae
Authors: Noah E. Wolfe, Carla Fröhlich, Jonah M. Miller, Alejandro Torres-Forné, Pablo Cerdá-Durán
Figure caption: Error (1σ confidence interval) in the location of the low-frequency Gaussian peak, μ 1, vs. the location of the high-frequency Gaussian peak, μ 2, from the Gaussian mixture fit procedure for all models. Points that lie outside the 2σ confidence region of the linear fit in Figure 6 are shown with fully opaque symbols, and all other models are shown with semi-transparent symbols. All models with large 1σ confidence intervals are associated with one of the three cases of misidentified Gaussian peaks (see text for details).
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: DF accelerations vs. time. The CDM simulations with and without self-gravity are shown by the solid red and dashed orange lines, respectively. The primary FDM mass (m  a  = 10−23 eV) is the blue dotted line. The black dashed line in each panel shows the analytic acceleration calculated from the classical Chandrasekhar formula as described in the text. In both orbit cases, the measured acceleration increases in strength with time as the wake forms. Left panel: the Fiducial orbit case. CDM produces a ∼10% stronger acceleration when self-gravity is turned on, consistent with the enhancement in the wake’s density and longevity when self-gravity is included. FDM produces a similar acceleration to the self-gravity-on CDM wake. The steady-state drag force is similar to the analytic approximation when ln (Λ) is calculated using C = 0.52. Right panel: the Infall orbit case. The reduced effect of DM self-gravity in the Infall scenario is apparent in the similar accelerations between the two CDM simulations. Again, the FDM traces the CDM acceleration well. The steady-state drag force matches the analytic approximation when ln (Λ) is calculated using C = 2.90. The drag force in the Fiducial case is much higher than the drag force in the Infall case, as the background density is roughly two orders of magnitude larger, while the wind velocity is only a factor of ∼3 larger. Overall, the FDM drag force matches the CDM drag force well, consistent with the predictions of Lancaster et al. (2020) with respect to DF on the LMC from an FDM wake.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Wake masses in our simulations, calculated as described in the text. In all panels, quantities are averaged over five snapshots spanning 100 Myr, and the points show the mean and standard deviation of the quantity of interest. The left column of panels shows the Fiducial wind simulations from 0.6–0.7 Gyr of evolution, and the right column shows the Infall wind simulations from 1.9–2 Gyr of evolution. The top row shows the wake masses. In the Iiducial case, the wake masses are roughly that of the LMC’s virial mass at infall, while in the Infall case, they are roughly an order of magnitude less. In both wind cases, the CDM wake without self-gravity is of the order of 10% less massive than the CDM wake with self-gravity. Both wakes with self-gravity have similar masses. The middle row of panels shows the DF acceleration on the LMC due to the wake during at the same times as we measure the wake mass. See Section 5.2 and Figure 23 for further discussion of the drag forces. The bottom row of panels shows the distance at which a point object of the mass in the upper row would produce the gravitational acceleration in the middle row, for each simulation. Thus, the Fiducial wake acts like an LMC-mass perturber trailing the LMC at a distance of ∼100 kpc, while the Infall wake acts as a perturber with ∼1/10 the mass of the LMC at a distance of ∼135 kpc.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Profile of the z-velocity dispersion across the wake (same as Figure 8) with the m  a  = 2.5 × 10−24 eV FDM simulation included as the purple dashed–dotted line. For clarity, we omit the CDM simulation without self-gravity. The increased granule strength at lower particle mass is also seen in the velocity dispersion. The four-fold decrease in particle mass manifests as a roughly four-fold increase in the amplitude of the oscillations in the low-mass FDM profile. On average, the two FDM profiles show a similar δ σ  z  ∼ 0.03 drop in the peak dispersion compared to CDM.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Comparison between the DM overdensity of GC19's Simulation 3 and our Fiducial CDM simulation with self-gravity. The right panel is a reproduction of the center panel of Figure 2. On the left, we compare this to the overdensity of MW DM particles in GC19's Simulation 3 when the LMC is at a Galactocentric distance of 70 kpc. We compute the density in the GC19 simulation within a 120 kpc-thick slab centered on the Galactic y–z plane such that the volume of the slab is identical to our Figure 2. We also draw a contour around the region with an overdensity greater than 0.38 as in the right panel. The size and strength of the wake agree between both panels, showing that the wakes formed in our windtunnels are a good approximation of the wake formed in a live-halo simulation.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Density of the stellar wake in the three simulations without CDM self-gravity with different LMC mass models (see Table 5), computed identically to Figure 13. From left to right, the panels show the simulation with the Light LMC, Fiducial LMC (i.e., this panel is identical to the left panel of Figure 13), and the Heavy LMC. Increasing the LMC mass increases the length of the response. However, raising the LMC’s mass from 1.8 → 2.5 × 1011 M ⊙ increases the wake’s length by only ∼25 kpc, compared to the ∼50 kpc increase caused by the addition of the DM wake’s gravity (see Figure 13).
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Density of the wakes in both FDM simulations with the Fiducial DM wind parameters (similar to Figure 2). The top panel shows the simulation with m  a  = 10−23 and is identical to the right panel of Figure 2. The bottom panel shows the simulation with m  a  = 2.5 × 10−24. Decreasing the FDM particle mass increases the de Broglie wavelength, granule size and strength, and acts to wash out the wake slightly.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Time-averaged median z-velocity dispersion enhancement of the stellar wake as a function of DM model and stellar wind dispersion (similar to Figure 16). The blue squares show the Fiducial simulations, while the black circles show the simulations with a lowered stellar velocity dispersion. The strength of the response increases by roughly a factor of 5 when the initial stellar velocity dispersion is lowered by a factor of 3. Meanwhile, the strength of granule heating in the FDM simulations is unaffected by the initial velocity dispersion of the stars, i.e., δ σ  z* is ∼0.01 higher in FDM compared to CDM in all cases.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption:  z-velocity dispersion of the stellar wake for the simulations with a low stellar velocity dispersion (﹩{\bar{\sigma }}_{* }=30﹩ km s−1), similar to Figure 15. The response is similar in both DM models. The black contours enclose the region in which the velocity response is greater than half its maximum in CDM, while the blue contours show the wake boundaries from Figure 15 for comparison (in which the initial stellar velocity dispersion is 90 km s−1). The wake is ∼60 kpc narrower in x when the stellar velocity dispersion is reduced, though the length of the wake does not change significantly.
Paper: Structure, Kinematics, and Observability of the Large Magellanic Cloud’s Dynamical Friction Wake in Cold versus Fuzzy Dark Matter
Authors: Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo, Lachlan Lancaster, Martin Sparre, Emily C. Cunningham, Mark Vogelsberger, Facundo A. Gómez, Chervin F. P. Laporte
Figure caption: Time-averaged median z-velocity dispersion enhancement of the stellar wake (same as Figure 16) including the CDM simulation with self-gravity and both FDM simulations. The velocity dispersion of the stellar wake is increased more compared to CDM when the FDM particle mass is lower. This demonstrates that the strength of granule heating becomes increases as the FDM particle mass decreases.
Paper: DiskMINT: A Tool to Estimate Disk Masses with CO Isotopologues
Authors: Dingshan Deng, Maxime Ruaud, Uma Gorti, Ilaria Pascucci
Figure caption: Observational data that are used in this work. Top panel: SED. Photometry is presented with colored markers and Spitzer/IRS spectrum is shown with the magenta line. A combined BT-settl stellar photosphere spectrum (Allard et al. 2003, 2011) with 10,500 K blackbody radiation fitting the UV data points is shown as the black line. Lower panels: ALMA C18O (2–1) velocity profile (left) and radial profile (right); the data are from Huang et al. (2020), with uncertainties calculated by GoFish. Linear interpolation (the gray line) is adopted to recover the channel contaminated by a dark cloud (the dashed black line) in the line spectrum. SED references: photometry from the HST spectrum in France et al. (2014); optical photometry from Gras-Velázquez & Ray (2005), APASS (Henden et al. 2016), and Gaia (Gaia Collaboration et al. 2018); IR photometry from 2MASS (Skrutskie et al. 2006), WISE (Wright et al. 2010), and AKARI (Ishihara et al. 2010); and (sub)millimeter data from Ansdell et al. (2016), DSHARP (Andrews et al. 2018), and Lommen et al. (2007, 2009).
Paper: DiskMINT: A Tool to Estimate Disk Masses with CO Isotopologues
Authors: Dingshan Deng, Maxime Ruaud, Uma Gorti, Ilaria Pascucci
Figure caption: Flow chart summarizing our modeling approach. Free parameters are listed at the top of the chart, and stellar parameters are fixed. These free parameters include the disk's inner radius (﹩{r}_{{\rm{in}}}﹩), tapering-off radius (﹩{r}_{{\rm{tap}}}﹩), power-law index of the surface density distribution (﹩p﹩), dust opacity (﹩{\kappa }_{{\rm{dust}}}(a)﹩), dust disk mass (﹩{M}_{{\rm{dust}}}﹩), and the gas-to-dust mass ratio (﹩{{\rm{\Delta }}}_{{\rm{gd}}}﹩). In this model, we assume that the dust and gas are well-coupled. Therefore, the gas temperature (﹩{T}_{{\rm{gas}}}﹩) represents a cross-section weighted mean value of the dust grain temperature (﹩{T}_{{\rm{dust}}}﹩), adding the contribution from viscous heating (﹩{T}_{{\rm{visc}}}﹩). Similarly, the gas mass density (﹩{\rho }_{{\rm{gas}}}﹩) and number density (﹩{n}_{{\rm{gas}}}﹩) are derived from the dust density (﹩{\rho }_{{\rm{dust}}}﹩) by multiplying it with the factors of ﹩{{\rm{\Delta }}}_{{\rm{gd}}}﹩ and ﹩{{\rm{\Delta }}}_{{\rm{gd}}}/\mu ﹩, respectively, where μ represents the mean molecular mass.
Paper: XMM-Newton Observations of Two Archival X-Ray Weak Type 1 Quasars: Obscuration Induced X-Ray Weakness and Variability
Authors: Zijian Zhang, Bin Luo, W. N. Brandt, Pu Du, Chen Hu, Jian Huang, Xingting Pu, Jian-Min Wang, Weimin Yi
Figure caption: IR-to-X-ray SEDs of (a) SDSS J0831+3213 and (b) SDSS J1423+0420. For SDSS J0831+3213, we also show the XMM-Newton OM measurements. The XMM-Newton 2 and 10 keV luminosities were derived from the best-fit models (Section 3.1). The X-ray spectral slopes and their uncertainties are indicated by the blue lines and the green-shaded areas. The Chandra 2 keV luminosities were adopted from Pu et al. (2020). The gray dashed curve in each panel shows the mean quasar SED from Krawczyk et al. (2013) normalized to the 2500 Å luminosity; the X-ray component is a Γ = 2 power-law continuum with 2 keV luminosity determined from the Steffen et al. (2006) α OX–L 2500 Å relation (f weak = 0).
Paper: XMM-Newton Observations of Two Archival X-Ray Weak Type 1 Quasars: Obscuration Induced X-Ray Weakness and Variability
Authors: Zijian Zhang, Bin Luo, W. N. Brandt, Pu Du, Chen Hu, Jian Huang, Xingting Pu, Jian-Min Wang, Weimin Yi
Figure caption:  α OX vs. 2500 Å monochromatic luminosity for the two quasars, displaying their significant X-ray weakness in the archival Chandra observations and nominal levels of X-ray emission in the XMM-Newton observations. The Steffen et al. (2006) α OX–L 2500 Å relation is shown as the solid line, with the dotted lines representing the 1σ scatter of the relation at the luminosities of the two quasars. The dashed line indicates f weak = 10 with respect to the α OX–L 2500 Å relation. The small gray dots and downward arrows represent the α OX values and upper limits of the Steffen et al. (2006) quasar sample, respectively.
Paper: XMM-Newton Observations of Two Archival X-Ray Weak Type 1 Quasars: Obscuration Induced X-Ray Weakness and Variability
Authors: Zijian Zhang, Bin Luo, W. N. Brandt, Pu Du, Chen Hu, Jian Huang, Xingting Pu, Jian-Min Wang, Weimin Yi
Figure caption: (a)–(b) ZTF g-, r-, and i-band light curves of SDSS J0831+3213 and SDSS J1423+0410. (c)–(d) Pan-STARRS g-, r-, i-, z-, and y-band light curves. (e)–(f) NEOWISE W1- and W2-band light curves. All the data points have been corrected for the Galactic extinction. In the ZTF light curves, we added corresponding measurements from the Lijiang spectra, which are broadly consistent with the ZTF data. The vertical dotted lines in the Pan-STARRS light curves of SDSS J1423+0410 represent the date of its Chandra observation. For the NEOWISE light curves, we grouped any intra-day measurements. These light curves indicate that the two quasars do not show any substantial long-term variability in the optical/UV and IR bands.
Paper: XMM-Newton Observations of Two Archival X-Ray Weak Type 1 Quasars: Obscuration Induced X-Ray Weakness and Variability
Authors: Zijian Zhang, Bin Luo, W. N. Brandt, Pu Du, Chen Hu, Jian Huang, Xingting Pu, Jian-Min Wang, Weimin Yi
Figure caption: (a) The Lijiang and SDSS spectra of SDSS J0831+3213. (b) The Lijiang, HET, and SDSS spectra of SDSS J1423+0420. The spectra were smoothed by a Gaussian kernel with a standard deviation of 5 pixels. Some of the major UV emission lines are marked. Two main telluric absorption windows (observed-frame 6864–6945 Å and 7586–7658 Å) of the Lijiang spectra are masked out. Overall, the rest-frame UV spectra of these two quasars did not vary greatly over the years.(The data used to create this figure are available).
Paper: XMM-Newton Observations of Two Archival X-Ray Weak Type 1 Quasars: Obscuration Induced X-Ray Weakness and Variability
Authors: Zijian Zhang, Bin Luo, W. N. Brandt, Pu Du, Chen Hu, Jian Huang, Xingting Pu, Jian-Min Wang, Weimin Yi
Figure caption: The XMM-Newton spectra of (a) SDSS J0831+3213 and (b) SDSS J1423+0420 overlaid with the best-fit simple power-law models. The bottom panels show the ratios of the spectral data to the best-fit models. The EPIC pn (black), MOS1 (red), and MOS2 (green) spectra were jointly fitted.
Paper: A New Photoionization Model of the Narrow-line Region in Active Galactic Nuclei
Authors: Peixin Zhu, Lisa J. Kewley, Ralph S. Sutherland
Figure caption: Optical diagnostic diagrams for AGN regions and H II regions. Dusty, isobaric AGN models with different N/O–O/H scaling relations (“NHlow” in solid lines and “NHhigh” in dashed lines), different radiation fields (from top to bottom panels: “Cagn,” ﹩\mathrm{log}{E}_{\mathrm{peak}}=-1.5,-1.25,-1.0﹩) are presented in black grids that consist of constant-metallicity lines (from left to right: ﹩12+\mathrm{log}({\rm{O}}/{\rm{H}})=8.00,8.28,8.43,8.70,8.89,9.02,9.26﹩) and constant-ionization parameter lines (from bottom to top: ﹩\mathrm{log}(U)=-3.4,-3.0,-2.6,-2.2﹩) at gas pressure ﹩\mathrm{log}(P/k)=7.4﹩. The black arrow in the upper right corner represents the average effect of gas pressure when it changes from ﹩\mathrm{log}(P/k)=7.4﹩ to ﹩\mathrm{log}(P/k)=8.2﹩. The H II model is shown with a red grid that consists of constant-metallicity lines (from left to right: ﹩12+\mathrm{log}({\rm{O}}/{\rm{H}})=7.75,8.15,8.43,8.63,8.76,8.85,8.94,9.00,9.10﹩) and constant-ionization parameter lines (from bottom to top: ﹩\mathrm{log}(U)=-3.75,-3.5,-3.25,-3.0,-2.75,-2.5﹩) at gas pressure ﹩\mathrm{log}(P/k)=5.4﹩. The red arrow in the upper right corner represents the average effect of gas pressure when it changes from ﹩\mathrm{log}(P/k)=5.4﹩ to ﹩\mathrm{log}(P/k)=7.4﹩. The optical spectra for galaxies with S/N ≥5 from SDSS DR16 are presented with colors reflecting their classifications: red dots stand for H II galaxies, black dots stand for Seyfert galaxies, and blue dots stand for composite galaxies. The mean error bars for all galaxies are shown in the lower right corner.
Paper: A New Photoionization Model of the Narrow-line Region in Active Galactic Nuclei
Authors: Peixin Zhu, Lisa J. Kewley, Ralph S. Sutherland
Figure caption: UV diagnostic diagrams for AGN regions and H II regions. Dusty, isobaric AGN models with different N/O–O/H scaling relations (“NHlow” in solid lines and “NHhigh” in dashed lines), different radiation fields (from top to bottom panels: “Cagn,” ﹩\mathrm{log}{E}_{\mathrm{peak}}=-1.5,-1.25,-1.0﹩) are presented in black grids that consist of constant-metallicity lines (from left to right: ﹩12+\mathrm{log}({\rm{O}}/{\rm{H}})=8.00,8.28,8.43,8.70,8.89,9.02,9.26﹩) and constant-ionization-parameter lines (from top to bottom: ﹩\mathrm{log}(U)=-3.8,-3.4,-3.0,-2.6,-2.2,-1.0﹩) at gas pressure ﹩\mathrm{log}(P/k)=7.4﹩. The black arrow in the upper right corner represents the average effect of gas pressure when it changes from ﹩\mathrm{log}(P/k)=7.4﹩ to  ﹩\mathrm{log}(P/k)=8.2﹩. The H II model is shown with a red grid that consists of constant-metallicity lines (from left to right: ﹩12+\mathrm{log}({\rm{O}}/{\rm{H}})=7.75,8.15,8.43,8.63,8.76,8.85,8.94,9.00,9.10﹩) and constant-ionization-parameter lines (from top to bottom: ﹩\mathrm{log}(U)=-3.5,-3.25,-3.0,-2.75,-2.5﹩) at gas pressure ﹩\mathrm{log}(P/k)=5.4﹩. The red arrow in the upper right corner represents the average effect of gas pressure when it changes from ﹩\mathrm{log}(P/k)=5.4﹩ to ﹩\mathrm{log}(P/k)=7.4﹩. UV spectra for local Seyfert 2 galaxies, Type 2 quasars, and high-z radio galaxies are collected from Dors et al. (2019) and presented with hollow blue squares, solid gray dots, and solid gray squares. The blue arrows at the upper left corner of the top three diagrams indicate the A  V  = 5.0  mag dust extinction correction for the UV observations.