adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.470.3006C (fulltext)
( 2015 ) galaxies are compatible with our simulations] then the slope of the reddening law required to explain the data is much steeper than the SMC extinction law. For example, the position of the stack of undetected galaxies would require n −20, much steeper than any attenuation, or even extinction law, ever observed. Given this discrepancy, it is worth considering the potential systematics that could be affecting the Capak et al. ( 2015 ) and Bouwens et al. ( 2016 ) results. One possibility is an underestimation of the dust temperature, which is taken to be 35 K in both cases. It has been suggested that dust temperature may increase with redshift due to an increase in the intensity of the radiation field heating the dust (e.g. Béthermin et al. 2015 ), 1 and typically an upper limit of 45 K has been assumed (Schaerer et al. 2015 ). In this case, L IR will be underestimated by a factor ≈2 and the resulting IRX underestimated by ≈0.3 dex. The magnitude of this shift is illustrated by the arrow in the bottom right-hand corner of Fig. 16 . It would bring the Capak et al. ( 2015 ) and Bouwens et al. ( 2016 ) data into better agreement with our model, however it is not enough to fully account for the discrepancy. There are other potential systematic issues affecting the determination of the UV continuum slopes, and estimated upper limits on L IR , which we will discuss in detail in a future work (McLure et al. in preparation). 6.2.5 SMC-like attenuation at lower redshifts? In Fig. 16 , we have not included data at lower redshifts ( z ∼ 2–3) which also support steeper, SMC-like, attenuation laws. In particular, Reddy et al. ( 2010 ) find that galaxies with young mass-weighted ages and typically lower masses (age ≲ 100 Myr; M/M ⊙ 10 10 ) have a smaller IRX, at a given β, relative to older galaxies at the same redshift. Finally, similar conclusions have also been reached using small numbers of individual lensed galaxies (e.g. Baker et al. 2001 ; Siana et al. 2009 ). In Reddy et al. ( 2010 ), these young galaxies represented ≈13 per cent of their full star-forming galaxy sample, and in our simulated sample ≈6 per cent of galaxies have ages 100 Myr. However, since our method involves comparing to the observed LF, it can only be applied to the full galaxy population complete down to a given UV magnitude, and cannot be applied to populations split by age. Therefore, we cannot rule out the possibility that certain populations (e.g. the youngest galaxies), or any given individual galaxy, will follow a steeper attenuation curve. We also note that, since the absolute attenuations decrease towards fainter, lower mass galaxies, the uncertainties on the form of the attenuation law will correspondingly increase, as the difference in absolute attenuation between curves becomes more difficult to distinguish. Therefore, our conclusions are most robust at high masses. Nevertheless, our current analysis suggests that, on average, a Calzetti-like attenuation curve
Paper: 2019ApJ...872...66W (fulltext)
unexpected to condense from a gas cooling from high temperature (Grossman et al. 2012 ). The Fe content of these minerals is an increasing function of oxidation state. Even in an oxygen-rich condensing gas, these minerals are expected to be very Fe-poor, with fayalitic contents (=Fe/(Fe+Mg)) of no more than 4% (Grossman et al. 2012 ). Before the return of samples from comet Wild 2, it was thought that oxidation of crystalline silicates might have occurred within the parent bodies of meteorites, during aqueous alteration or thermal metamorphism. However, olivines returned from comet Wild 2 show a very broad and nearly flat distribution in fayalitic content from 0 to ∼40%, and a range of fayalitic content from 0% to 97% (Frank et al. 2014 ), so the oxidation appears to have taken place in a nebular environment, through an unknown mechanism. We note that all three collections of cometary materials have the potential to have suffered oxidation state change, or the appearance of oxidation state change through sample bias, between release from the parent body and analysis in the laboratory. Samples from comet Wild 2 were captured in the coma at 6.1 km s −1 , which may have led to reduction of Fe through smelting: the arrow in Figure 3 indicates that the original material may have been less reduced than observed in laboratory analyses. Cometary materials captured in the stratosphere may suffer from biases against metal-rich, high-density particles, because of their shorter residence time. This is consistent with the metal-poor composition of individual small CP-IDPs, in contrast to the relatively metal-rich composition of particles from giant cluster IDPs. However, none of these capture or sample bias effects go in a direction to reconcile cometary oxidation state with that of the ISM. 5.2. Summary A comparison of Fe L-edge absorption spectrum of continuum X-rays from Cygnus X-1 with extinction spectra derived from synchrotron X-ray microprobe analyses of mineral standards leads to the following conclusions: 1. Observations are consistent with the hypothesis that Fe is sequestered principally in metals, at least in this line of sight. The data are marginally consistent with Fe evenly divided between sulfide and metal, a composition that would be expected if Fe and S reside entirely in metal and sulfide, e.g., in a high-temperature condensation sequence. Less than 38.5% (2 σ ) of Fe resides in amorphous silicates. 2. The observations are inconsistent with the hypothesis of Ishii et al. ( 2018b ) that interstellar silicates consist of GEMS or their subcomponents as found in chondritic-porous IDPs. This conclusion is robust even if some Fe is oxidized between residence in the ISM and observation in the laboratory. 3. Fine-grained cometary material, as represented by FGM returned by the Stardust mission from comet 81P/Wild 2, with a WD01 size distribution predicts an extinction spectrum that is inconsistent with Chandra observations. 4. Our conclusions
Paper: 2017MNRAS.464..274S (fulltext)
) within 3 arcsec of the new variables and (ii) manually check OGLE-II DIA light curves of nearby sources to make sure none of them show brightness trends mirroring the new variables. 3.6 Digitized photographic plates (66 Oph) Photographic images of the sky obtained in late 19th and 20th centuries contain a wealth of information about historical positions (e.g. Laycock et al. 2010 ; Vicente et al. 2010 ; Berezhnoi 2013 ; Robert et al. 2014 ) and brightness (e.g. Kolesnikova et al. 2008 ; Tang et al. 2013 ; Sokolovsky et al. 2014c ) of celestial objects. The efficient use of this information requires it to be converted to a digital form using purpose-built digitizing machines (Simcoe et al. 2006 ; De Cuyper et al. 2012 ) or a commercially available flatbed scanners capable of working with transparent materials (Vicente, Abad Garzón 2007 ; Simcoe 2009 ; Tuvikene et al. 2014 ). We use an Epson Expression 11000XL flatbed scanner operating at 2400 dpi resolution (1.4 arcsec pixel −1 , 16 bits per pixel colour depth) to digitize a 1.26 × 1.26 deg 2 area centred at α J2000 = 17:57:44.7 δ J2000 = +04:59:54 (66 Oph field; Kolesnikova et al. 2010 ) on 227 photographic plates obtained in 1976–1995 with the 40 cm astrograph. The digitized images were processed with vast following the procedure described by Sokolovsky et al. ( 2014b ). APASS B -band photometry of 1600 UCAC4 stars in the magnitude range B = 10–16 is used to calibrate the instrumental magnitude scale using the relation between aperture photographic and photoelectric magnitudes proposed by Bacher, Kimeswenger Teutsch ( 2005 ). We identify 23 variable stars including five not previously known (Table 4 , Fig. 2 ) by means of period search and visual inspection of light curves standing out in the magnitude versus σ w plot. 3.7 Light-curve filtering Often raw photometric data have to be pre-processed before computing the variability indices discussed in Section 2 . This may include (i) removing outliers from a light curve (possibly by applying iterative σ-clipping or median filtering); (ii) removing systematic effects from a set of light curves by applying local zero-point corrections (e.g. Nascimbeni et al. 2014 ) and/or the sysrem algorithm, decorrelating each light curve with external parameters such as airmass, seeing, object position on a CCD, detector temperature (e.g. Pál 2009 ; Bakos et al. 2010 ; López-Morales et al. 2010 ; Hartman et al. 2011 ; Burton et al. 2012 ; Guterman, Mazeh Faigler 2015 ; Baade et al. 2016 ) or de-trending the light curves if one is interested only in fast variability (e.g. Kovács, Bakos Noyes 2005 ; Weingrill 2015 ). A smaller-than-expected number of detections is an indirect indication of many photometry problems including the object being close to an image edge, a cosmetic defect, a bright star, a detection or saturation limit. Objects systematically affected by any of these problems can be removed from the analysis by discarding light curves having less than a
Paper: 2021ApJ...912...91T (fulltext)
stronger than the CF– L bol correlation. What causes this discrepancy? There are some possibilities, such as the difference in (i) redshift, (ii) sample selection (i.e., our sample is limited to type 1 quasars, while the sample in Ricci et al. 2017 is not), and (iii) definition of CF (i.e., is employed in this work, while a fraction of X-ray-obscured sources is employed in Ricci et al. 2017 ). It is hard to quantify, however, the influence of the difference in the sample selection and definition of the CF on the correlation strength, and it is beyond the scope of this work. We thus argue for the possibility that the difference in redshift could cause the discrepancy. Ricci et al. ( 2017 ) targeted nearby AGNs with a median redshift of z ∼ 0.037, whereas we focus on AGNs with much higher redshift up to z = 0.7. Figure 12 shows the redshift dependence of the CF– λ Edd relation. We find that the CF of AGNs with z 0.12 (which is similar to that of the sample in Ricci et al. 2017 ) tends to strongly depend on λ Edd , with a correlation coefficient of r = − 0.49 ± 0.32, while CF– λ Edd for AGNs at z > 0.2 shows weaker correlations than that at z 0.12. This indicates that the aforementioned discrepancy can be explained by the difference in redshift between the samples in Ricci et al. ( 2017 ) and this work. We also remind that our correlation analysis would be affected by the Malmquist bias, which could make the correlation strength weaker, as discussed in Section 4.2.2 . Figure 12. CF as a function of λ Edd . The blue, green, orange, and gray shaded regions represent quasars with z 0.12, 0.12 z 0.2, and z > 0.2 and the entire sample, respectively. This result also might suggest that the CF decreases with increasing redshift for at least z 0.7, a trend that is consistent with what is reported in Toba et al. ( 2014 ), although there is large uncertainty. On the other hand, the fraction of obscured X-ray AGNs increases with redshift for the same luminosity (e.g., Ueda et al. 2014 ). This discrepancy might be caused by the difference in sample selection and definition of CF. 4.6.2. BH Mass Dependence Recently, Kawakatu et al. ( 2020 ) constructed a model of a nuclear starburst disk supported by the turbulent pressure from Type II supernovae and investigated how the CF depends on AGN properties by taking account of anisotropic radiation pressure from AGNs. They found that the CF strongly depends on λ Edd for AGNs with M BH 10 8 M ⊙ , while the dependence of the CF on λ Edd is much weaker for AGNs with M BH > 10 9 −10 10 M ⊙ . Although they did not incorporate the AGN polar dust outflow, it is worth checking the dependence of the CF– λ Edd correlation on M BH . Figure 13 shows how the difference in M BH would affect the correlation strength of CF– λ Edd . The resultant r of CF– λ Edd for quasars with , , and are −0.56 ± 0.07, −0.50 ± 0.05, and −0.52 ± 0.22, respectively. This result indicates that the CF of quasars with 10 8 M ⊙ strongly depends on λ Edd .
Paper: 2019A&A...624A..32M (fulltext)
magnetic field modulus of HD 50169: 5.6 kG, derived from consideration of the differential broadening of spectral lines having Zeeman patterns of different widths. We retrieved the Julian Date of this observation from Adelman (1973) , as the same spectrum was used for detailed abundance analysis. The representative point of Preston’s field determination appears as a salmon-coloured filled square in Fig. 2 . The uncertainty of this determination is difficult to assess. The most meaningful comparison between the field values derived by Preston (1971) through application of his Eq. (3) and the ⟨ B ⟩ determinations of Mathys (2017) , based on the splitting of the Fe II λ 6149.2 doublet, is for those stars for which the mean field modulus only shows small, almost insignificant variations, such as HD 2453, HD 137949, and HD 192678. For these stars, the estimates of Preston (1971) and the measurements of Mathys (2017) are in excellent agreement, within ∼100 G of each other. The comparison is less straightforward when the field modulus shows definite variability with phase, but at least for some stars (e.g. HD 12288 or HD 188041), the agreement between the values that were derived from line broadening by Preston (1971) and those that Mathys (2017) obtained from consideration of the wavelength separation of split line components, seems somewhat poorer. The error bar on ⟨ B ⟩ that we adopted for the data point from Preston (1971) in Fig. 2 , ±200 G, is only tentative, and it may well underestimate the actual uncertainty of the field values reported in this reference. Even with this “optimistic” accuracy, however, the magnetic field strength estimated nearly 50 years ago by Preston (1971) is consistent with the value of the rotation period that we derived above. With P rot = 10 600 d, HD 50169 becomes the longest-period Ap star for which magnetic field measurements have been obtained over more than a full rotation cycle. At 29.04 yr, its period is considerably longer than the next longest one, 21.75 yr for HD 9996. Furthermore, with respect to the latter, HD 50169 presents the advantage of showing resolved magnetically split lines throughout its whole rotation cycle, so that its mean magnetic field modulus can be exactly determined even around the phase of its minimum and, as a consequence, the structure of the field can be better constrained. 4. Magnetic field characterisation The observed variations of both the mean longitudinal magnetic field and the mean magnetic field modulus of HD 50169 can be well represented by the superposition of a cosine wave and of its first harmonic. The best least-squares fit solutions for P rot = 10 600 d are where the field strengths are expressed in Gauss, φ = (HJD − HJD 0 )/ P rot (mod 1) and the adopted value of HJD 0 = 2441600.0 corresponds to a maximum of the mean magnetic field modulus; ν is the number of degrees of freedom, and χ 2 / ν , the reduced χ 2 of the fit. The fitted curves are shown in Figs. 1 and 2 ; the
Paper: 2017MNRAS.466.3834L (fulltext)
(e.g. Dalal et al. 2008 ; Desjacques Sheth 2010 ). Combination of the peaks approach plus N -body simulations led to further insights, including that haloes in dense regions that do not correspond to high peaks in the initial conditions accrete more slowly than haloes that do correspond to high peaks (Ludlow Porciani 2011 ). Faster supercomputers and better codes have led to improved N -body simulations, in which the haloes and subhaloes that host galaxies, groups and clusters are resolved. Such simulations have permitted more detailed analyses of the correlations of halo properties with each other and with the halo environments measured in various ways. One of the first papers to do this was Lemson Kauffmann ( 1999 ), which used Ω 0 = 1 τCDM and Ω 0 = 0.3 ΛCDM simulations and concluded that only the halo mass distribution varies as a function of environment, with more high-mass haloes in denser environments, in reasonable agreement with the analytic calculation by Mo White ( 1996 ) based on extended Press–Schechter theory and the spherical top-hat model. While subsequent N -body calculations confirmed this density effect on the halo mass function, they implied that environmental density also affects halo major merging rates (Gottlöber, Klypin Kravtsov 2001 ) and other halo properties. Bullock et al. ( 2001 ) found that haloes in dense environments tend to have higher concentrations than isolated haloes. Sheth Tormen ( 2004 ), using the same simulations as Lemson Kauffmann ( 1999 ), found evidence that low-mass haloes form somewhat earlier in dense environments. Avila-Reese et al. ( 1999 ) found that dark matter haloes that are isolated or in intermediate-density environments have outer density profiles r −β with β ≈ 2.8 ± 0.5 while haloes in denser regions have a wider range of β up to ∼5. Avila-Reese et al. ( 2005 ), again using ΛCDM simulations that were very small by modern standards, nevertheless found that haloes in dense regions had lower spin parameters and higher concentrations and were less prolate than haloes in lower density environments, with the differences arising mostly at low redshifts z 1 from phenomena such as tidal stripping in dense environments. In this paper, we confirm and expand on these results. Maulbetsch et al. ( 2007 ) used a 512 3 particle ΛCDM simulation in a (50 h −1 Mpc) 3 volume to study the mass accretion history of galaxy-mass haloes in different environments, and found that haloes of the same final mass accreted their mass earlier in denser environments, and also accreted a significantly higher fraction of their mass in major mergers. They suggested that this could help to explain the galaxy density–morphology relation and that early-type galaxies are more common in dense environments. They also found that ∼80 per cent of haloes in higher density environments are not accreting (i.e. have ), while this fraction is only ∼20 per cent in low-density environments. They defined the environmental density on a
Paper: 2018ApJ...854L..15M (fulltext)
indices of the thermal plasma/PUIs. The collisional thermal plasma stress tensor and PUI stress tensor in a 1D form may be expressed as , where η g is the thermal collisional viscosity coefficient and η I is the collisionless PUI viscosity coefficient associated with magnetic fluctuations (Jokipii Williams 1992 ; Zank et al. 2014 ). A 1D steady-state model in which all physical quantities depend on the x Cartesian coordinate is used and U = ( U x , 0, 0) and B = (0, 0, B z ). The 1D form of Equations ( 10 )–( 12 ) in normalized form is where is the thermal gas Mach number of the flow, , L ≡ K g / U 1 , is distance normalized to the diffusion length scale, and is the Schmidt number of the thermal plasma/PUI. The inverse compression ratio is y = U x / U x 1 and , , and . The inverse Alfvén Mach number squared of the flow far upstream is . Based on the thermal collisional term scales derived above from the expressions in Zank ( 2014 ), the dominant collisional terms in a collisional magnetized plasma can be estimated. The small mass of electrons compared to protons means they do not contribute significantly to either the thermal heat conduction or thermal viscosity. Proton–electron collisions are negligible since the mean free path is very large compared to that of proton–proton collisions. The dominant collisional terms derive from proton–proton collisions. The diffusion coefficient associated with proton–proton collisions, K pp , upstream of the observed VLISM shock is about 10 15 m 2 s −1 , and the thermal viscosity is 2.5 × 10 −8 kg m −1 s −1 , giving a thermal Schmidt number of about 0.25. Energetic PUIs outside the heliosphere are generated by secondary charge exchange, and their number density is very small in the VLISM ( n p ≈ 5 × 10 −5 cm −3 ; Zirnstein et al. 2014 ), therefore they contribute a very small pressure compared to the thermal plasma ( P I1 / P g 1 ≈ 0.05). A characteristic value for the PUI heat conduction, K I , in the VLISM is ∼10 14 m 2 s −1 . The pitch-angle scattering timescale in the VLISM is taken to be , giving 5.17 × 10 2 s, which gives a value for the PUI viscosity, η I , as ∼3.57 × 10 −13 kg m −1 s −1 , making the PUI Schmidt number about 10 −3 . The PUI Schmidt number is much smaller than that of the thermal gas (), thus PUIs and the dissipation associated with them cannot mediate the VLISM shock, as shown by Mostafavi et al. ( 2017a , 2017b ). Consequently, we can neglect PUI dissipation terms in favor of the corresponding thermal gas terms. Unlike the HTS, PUIs will therefore behave adiabatically, is constant through a broad VLISM shock, and the thermal gas will be heated preferentially. Recall that PUIs were predicted (Zank et al. 1996 ) and observed (Richardson et al. 2008 ) to be heated preferentially at the HTS, with the thermal plasma component remaining relatively unheated. The physical processes determining the structure of the HTS and the VLISM shocks are therefore different. We use the 1D form of the
Paper: 2021ApJ...913...93C (fulltext)
Blandford 1990 ), and M 8 is the black hole mass in units of 10 8 M ⊙ . A was defined by Daly et al. ( 2018 ), A = α /( α + b ), a and b are the coefficients of the fundamental plane of black hole activity, where A = 0.43 (Merloni et al. 2003 ). L bol is the bolometric luminosity (Netzer 1990 ), L bol = 10 L BLR , and L Edd is the Eddington luminosity, L Edd = 1.3 × 10 38 ( M BH / M ⊙ ). The spin of the black hole can be estimated using the following formula (Daly 2019 ),where L j is the beam power, and g i = 0.1 and g bol = 1 are used (Daly 2019 ). The black hole spins obtained using this method can be compared with those obtained using other methods, such as the X-ray reflection method (Reynolds 2014 ). The spin values of GX 339-4 are 0.94 ± 0.02 (Miller et al. 2009 ) and (Garcĺa et al. 2009 ) using the X-ray reflection method, and the spin obtained using the method of Daly ( 2019 ) is 0.92 ± 0.06. The spin obtained using the method of Daly is consistent with those obtained with the X-ray reflection method. Because of the limitation of the observations, it is very difficult to directly measure the spin of large samples using the X-ray reflection method. Therefore, we use the method of Daly to measure the spin for our samples. The accretion disk magnetic field strength values of ∼10 4 G are also reported using other methods (Mikhailov et al. 2015 ). Thus, the accretion disk magnetic field strengths obtained here are similar to the values obtained using other methods. We use Equations ( 2 ) and ( 3 ) to estimate the magnetic field strength of the accretion disk and the spin of black hole, respectively. The values of the magnetic field strength and the spin of black hole are listed in columns (9) and (10) of Table 1 . 3.3. The Intrinsic <italic>γ</italic>-Ray Luminosity We estimate the intrinsic γ -ray luminosity using the formula (Nemmen et al. 2012 )where is the observational γ -ray luminosity. f b is the beaming factor, and f b is estimated as 1-cos(1/Γ), where Γ is the bulk Lorentz factor. The bulk Lorentz factor comes from Ghisellini et al. ( 2014 ). is the observed γ -ray luminosity from the catalog of LAT (Abdo et al. 2010 ; Ackermann et al. 2011 ). The values of and f b are listed in columns (7) and (11) of Table 1 , respectively. The values of the radiation jet power and the jet kinetic power come from Ghisellini et al. ( 2014 ). Ghisellini et al. ( 2014 ) used a simple one-zone leptonic model to get the radiation jet power and the jet kinetic power. They are listed in columns (12) and (13) of Table 1 , respectively. 4. Results and Discussions 4.1. The Distribution of Physical Parameters The density distributions of the spin of SMBHs and the magnetic field strength of the accretion disks are shown in Figure 1 . The average spins of SMBHs for FSRQs and BL Lacs are 〈 j FSRQs 〉 = 0.50 ± 0.23 and 〈 j BL Lacs 〉 = 0.44 ± 0.27, respectively. The average values of the magnetic field strength of the accretion disk for FSRQs and BL Lacs are and ,