adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...887..222L (fulltext)
and Ultrascale The evaluation of the pathlength from the SDE steps in x and z directions, as given by Equation ( 7 ), proved unphysical. However, it does provide a possibility to solve the problem of determining the pathlength of a diffusing particle, given a suitable physical framework. Here, we employ field-line random walk as the framework for determining the pathlength of a diffusing particle. In the SDE method, the stepsize does not have a physical meaning, but in the physical world it does. Particle cross-field diffusion is believed to be dominated by their following the turbulent random-walk of the magnetic fieldlines (e.g., Fraschetti Jokipii 2011 ). The fieldlines do not, however, meander at infinitesimal scales, since physical processes damp small-scale fluctuations. Thus, a length scale that would describe the meandering of the fieldlines is a good candidate for evaluation of the pathlength of a particle in turbulent magnetic fields. Such a length scale can be derived from the definition of a fieldline diffusion coefficient and the concept of an ultrascale, . The fieldline diffusion coefficient for 2D-dominated turbulence is given bywhere B is the magnitude of the ambient background magnetic field, and is the turbulence variance (Matthaeus et al. 1995 ). For 2D turbulence spectrum S ( k ), the ultrascale is defined aswhere k is the wavenumber. Matthaeus et al. ( 1999 ) gives the ultrascale an interpretation as the representative scale size of turbulent closed magnetic 2D structures, “magnetic islands,” in the cross-field direction, x . The fieldlines in 2D-dominated turbulence can be thought to be either trapped in magnetic islands or meandering freely around these islands (Ruffolo et al. 2003 ; Chuychai et al. 2007 ). We can thus consider the ultrascale to be the relevant cross-field length scale for the meandering of the untrapped fieldlines around the islands that are of size . The distance Δ z FL along the mean field direction as the fieldline propagates a cross-field length can then be evaluated using the fieldline diffusion coefficient, Equation ( 13 ), using the general definition of a diffusion coefficient , where the distance along the z -axis takes the place of time in the denominator for fieldline diffusion. If we consider the mean square cross-field step given as the ultrascale, , we can writeEquation ( 15 ) gives a natural interpretation to the fieldline diffusion coefficient in Equation ( 13 ): the fieldline random walk across the mean field direction is described as random walk with stepsize , with the ratio between the steps along and across the field, , equal to B / δB ⊥ . Using the steplength as defined by the turbulent island size, given by Equation ( 15 ), we can solve the pathlength of the meandering fieldline as Gaussian random walk withThe pathlength of a particle following such a fieldline can then be estimated and integrated using equationwith the steps along and across the field given by Equations (
Paper: 2018ApJS..235...24F (fulltext)
we averaged spectra from 2004 October to 2016 December with the same criteria, using 57,000 spectra for S(0) and 86,000 spectra for S(1). Although these large averages were necessary to improve the signal-to-noise, the resulting atmospheric profiles (temperature and para-H 2 ) are averaged over a broad region of the planet, and in the case of Saturn, over different seasons from southern summer to northern spring. As shown in Section 4 and Figure 4 , these spectra reveal dimeric structure around S(0) on both planets, and tentatively around S(1) on Saturn. Figure 4. Spectral fits to the S(0) and S(1) lines detected on all four giant planets, using Cassini data for Jupiter and Saturn and Spitzer data for Uranus and Neptune. Uranus and Neptune. Spitzer /IRS acquired 7–36 μ m disk-integrated spectra of Uranus during Directors Discretionary Time on 2007 December 16–17, shortly after Uranus’ equinox (program 467; full details of the data reduction process are provided by Orton et al. 2014a ). Disk-integrated spectra of Neptune were acquired on 2005 November 19–20 during Spitzer ’s Cycle 2 (program 20500), and reduced using the same process as Orton et al. ( 2014a ). For both planets, the observations were designed to sample multiple longitudes during a complete rotation, but we averaged all longitudes to form a single spectrum. Here, we focus on high-resolution ( R ∼ 600) observations in the “Short-High” (SH) 9.95–19.30 μ m and “Long High” (LH) 19.27–35.97 μ m ranges, revealing the S(0) and S(1) lines in Figure 4 , respectively. Note that Orton et al. ( 2014a ) identified offsets between low-resolution and high-resolution Uranus spectra related to flux losses from different slit sizes, and ultimately scaled the SH data to match the low-resolution modes, and abandoned the LH data from their analysis entirely. In the present work, where only narrow spectral ranges are considered to identify the dimer features, we do not find it necessary to perform such a scaling and present good fits to both SH and LH data. Voyager/IRIS: to supplement the high-resolution Cassini and Spitzer observations, we also reanalyze the low-resolution (4.3 cm −1 ) IRIS observations of all four giant planets. These 180–2500 cm −1 spectra were acquired by a Michelson interferometer in a series of north–south scans executed during the close flybys between 1979 and 1989. The selection criteria for the spectra and zonal averaging techniques have been previously described for Jupiter (Fletcher et al. 2017 ), Saturn (Fletcher et al. 2016 ), Uranus (Orton et al. 2015 ), and Neptune (Fletcher et al. 2014 ). Dimer features were previously identified only in the Jupiter and Saturn spectra (Frommhold et al. 1984 ), with the Uranus and Neptune data being too low in signal to reveal the dimer absorptions. The zonally averaged 300–550 cm −1 regions of these spectra are re-analyzed with the new opacity database in Section 4 . 3.2. Spectral Retrieval Model The NEMESIS spectral retrieval algorithm
Paper: 2019A&A...630A..94G (fulltext)
X-ray emitting corona. The energetic contribution to the SED of the former is observed to increase over the latter with increasing ṁ ( Vasudevan Fabian 2009 ; Jin et al. 2012 ). This is explained with the increased inner radial extent of the UV disk, at the expenses of the X-ray corona, at larger ṁ compared with lower ṁ . Assuming a constant radiative efficiency above ṁ ∼ 0.01, the relative UV/X-ray flux ratio will thus increase with increasing ṁ , moving from low-luminosity Seyfert galaxies toward higher luminosity-Seyfert galaxies and QSOs. A consequence of our scenario is therefore that the X-ray coronae in Seyfert galaxies will be extended up to a few tens R g , different than in QSOs where they will be very compact, only a few R g in size. Recent observations of distant microlensed QSOs revealed very compact X-ray coronae only ≲10 R g in size ( Chartas et al. 2009c ; Dai et al. 2010 ; MacLeod et al. 2015 ). In comparison, deep and broadband X-ray spectroscopic observations of the local Seyfert Ark 120 suggest an extended corona (∼10 − 40 R g , Matt et al. 2014 ), and a decrease of the corona size with increasing flux of the continuum source ( Porquet et al. 2019 ). Studies of larger samples of sources are clearly needed in order to assess the presence of a statistical difference in X-ray coronal size between Seyfert galaxies and QSOs. Radiation-driven disk winds driven by pressure on spectral lines are a physical component that can explain many observational features of AGN, but not all of them: for example, the BALs observed in the FR I radio galaxies ( Fanaroff Riley 1974 ), which are accreting at low ṁ , must be driven by magnetic pressure. In our scenario, the low and very low ṁ AGN appear as radio loud because they lack strong optical and UV emission, and would correspond to the FR I, or LEG (low excitation galaxies, Laing et al. 1994 ). At higher ṁ , radio-loud AGN appear as FR II (or HEG, high excitation galaxies) and do have an accretion disk (e.g., Ghisellini Celotti 2001 ; Grandi Palumbo 2004 ). The radio loudness associated to their powerful radio jets is probably driven by the efficient extraction of rotational energy from both the disk ( Blandford Payne 1982 ) and the black hole ( Blandford Znajek 1977 ); however, the exact physical mechanism behind the launching and acceleration of the most powerful radio jets is still under investigation (see Czerny You 2016 , for a recent review). We note how there are no apparent differences in observed properties such as black hole mass, Eddington ratio, and BLR size between radio-loud and radio-quiet BAL QSOs ( Bruni et al. 2014 ), suggesting that the physical difference between them might reside in different black hole spin values. The scarcity of powerful radio-loud FR II BAL QSOs is probably due to the combination of a large black hole mass needed to have a RL AGN ( Laor 2000 ), which is possibly related to the merger history of the host galaxies, where coherent mergers can increase
Paper: 2019MNRAS.483.4901B (acknowledgments)
ACKNOWLEDGEMENTS We thank the organizers of SPHERIC2018 for a useful and stimulating conference in Galway, Ireland. DP acknowledges funding from the Australian Research Council via FT130100034 and DP180104235.
Paper: 2020A&A...642A.139P (fulltext)
the particle sampling noise introduced by the LPT. Since most of the particles cluster in high-density regions, voids are impacted by higher uncertainty in the LPT. The field-level approach of the PPT overcomes this problem, showing a lower standard deviation in the void profile. Fig. 9. Comparison of density profiles obtained with PPT and LPT. Upper panel : cluster density profile obtained by evolving the same initial conditions with PPT and LPT. Lower panel : test for a void profile. This demonstrates that PPT and LPT provide equivalent cosmic structures. The shaded regions indicate the uncertainty region of the profiles, corresponding to the standard deviation of 50 realisations. While the PPT and LPT show similar uncertainty regions for the cluster profile, the LPT has a larger standard deviation than the PPT in the void profile. This larger uncertainty is due to the poor sampling of the voids in the LPT model since most of the particles cluster in high-density regions. 6. Inference results In this section, we present the results of applying our algorithm to Ly- α forest data in redshift space. We show that our method infers unbiased density fields and corresponding power-spectra at all scales considered in this work. We also perform a posterior predictive test for quasar spectra, showing that the inferred quantities can explain the data within the noise uncertainty. 6.1. Inferred density fields As discussed above, our method uses a forward modelling approach, fitting a physical dynamical model to Ly- α forest data. This provides the full posterior distribution, from which we draw samples of the initial matter fluctuations and the non-linear spatial matter distribution at z = 2.5. In this section, the dynamical model is the PPT in redshift space, described in Sect. 2.4 . Since the optical depth is conserved under the mapping between physical and redshift space, our inference in redshift space focuses on the optical depth field, while the density field is obtained in real space. Figure 10 shows slices through the true fields, and the ensemble mean and variances of inferred three-dimensional fields, computed from 600 samples. A first visual comparison between ground truth and the inferred ensemble mean final density and optical depth fields shows that the algorithm correctly recovered the large-scale structure from Ly- α forest data. We note that the optical depth field is in redshift space while the final density field is in physical space. The lower right panel of Fig. 10 shows the corresponding standard deviations of the amplitudes, which is estimated from the samples in the Markov chain. The estimated density standard deviation correlates with the inferred density field. The same is true for the optical depth field. This is expected for a non-linear data model, which couples signal and noise. Higher uncertainty regions correspond to over-densities since the absorption saturates at high density, making the signal weaker. Once the light
Paper: 2020MNRAS.492.5218B (fulltext)
= PBH+CDM). We define the PBH+CDM mass fraction as where M PBH and M CDM are the total masses of PBHs and CDM particles. For our halo, we assume the Navarro–Frenk–White (NFW) form (Navarro et al. 1996 ) with scale density ρ 0 and scale length r s . The relaxation time is proportional to N /ln ( N ), where N is the number of particles. Assuming 100 M ⊙ PBHs and 10 7 M ⊙ halo, the particle relaxation will take between 1 and 12 Gyr depending on f m considered between 1 and 0.01. In comparison, the relaxation time for 10 8 (10 9 M ⊙ ) haloes are 10 (100) times longer than for 10 7 M ⊙ and longer than the age of the Universe, which is why we focus here on a 10 7 M ⊙ dwarf galaxy, starting at redshift z = 2 in the simulations. Given the halo mass and redshift, the halo concentration c 200 can be estimated from cosmological N -body simulations (Prada et al. 2012 ). Our halo is composed of DM and PBH particles with a total mass of 10 7 M ⊙ . To generate our NFW haloes, we use the initial-condition generator, magi . Adoption of a distribution-function-based method ensures that the final realization of the halo is in dynamical equilibrium (Miki Umemura 2018 ). We perform our simulations with the high performance collisionless N -body code, gothic (Miki Umemura 2017 ; Miki 2019 ). This gravitational octree code runs entirely on GPU and is accelerated by the use of hierarchical time steps in which a group of particles has the same time step. We have performed the simulations using NVIDIA GeForce GTX 1080 Ti (CUDA 9.0) on Intel Xeon Silver 4114 (GCC 4.8). We evolve different PBH+CDM haloes composed of 25, 50, 75, and 100 M ⊙ PBHs over 11 Gyr by adopting the softening length of ε 0 =1.331 pc and the accuracy control parameter of 2 −7 . All runs were made with CDM particles of 1 M ⊙ . We explored also PBH+CDM haloes with different mass fractions f m = [0.01, 0.1, 0.5]. In our simulations, we assume that the CDM and PBH components of PBH+CDM haloes initially follow NFW profiles with the same concentration. We tested two different scale lengths for the profile of the PBH component: and . The second corresponds to a scenario where the density of PBHs is enhanced in the central region. Indeed, we suppose that mass segregation of PBHs would occur, and this increases the density of PBHs at the centre. This scenario should also enhance the formation of cores due to dynamical heating of the CDM by PBHs. It will also accelerate the two-body relaxation. 3 RESULTS 3.1 Evidences for core formation from gravitational heating by PBHs First, we assess the impact on the PBH+CDM halo density profiles in our simulations of the two numerical parameters, which are the accuracy control parameter and the softening length ε. We tested 2 −6 , 2 −7 , 2 −8 , and 2 −9 for the accuracy control parameter in a PBH+CDM halo with f m = 0.1 and m PBH = 100 M ⊙ . The left-hand panel of the Fig. 1 shows that PBH+CDM density profiles hold the initial distribution for all the different values
Paper: 2018ApJ...855...75R (acknowledgments)
The authors thank Lou Boyd of Fairborn Observatory and Brian Skiff and Len Bright of Lowell Observatory for their years of crucial support to the APT and SSS programs. R.R.R. thanks the National Solar Observatory for support as an Emeritus Astronomer. G.W.L. thanks Lowell Observatory for informal emeritus status, access to facilities, and travel support in continuing post-retirement stellar research. G.W.H. acknowledges long-term support from NASA, NSF, Tennessee State University, and the State of Tennessee through its Centers of Excellence program. The referee’s careful reading and knowledgeable comments enabled us to improve the overall scholarship of our paper and expand our discussion of the results, and we appreciate that anonymous contribution. This research utilized data courtesy of NASA/ SORCE and the SIM and TIM science teams, and SOLIS data obtained by the NSO Integrated Synoptic Program (NISP), managed by the National Solar Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA), Inc. under a cooperative agreement with the National Science Foundation. This research has made use of the SIMBAD database and its VizieR catalog access tool, especially the catalog of MK spectral types (B/mk/mktypes) created and maintained by Brian Skiff of Lowell Observatory. SIMBAD and VizierR are operated at CDS, Strasbourg, France (Wenger et al. 2000 ). For the two lead authors, this paper will likely be our final word on this subject.
Paper: 2017MNRAS.470.2890B (fulltext)
independently confirms that the H 2 -bearing cloud is likely pressure confined. We note that while a high dust content reduces the H 2 formation time-scale, the number density can have major importance since t ∝ n −2 . It has two direct consequences: first, in low-metallicity environments, H 2 may trace only the densest regions in the patchy medium of DLAs. High number densities will obviously severely reduce the covering fraction of the H 2 -bearing gas. Secondly, the formation time-scale of H 2 can be shortened by supersonic turbulences that can create pockets of compressed gas, where H 2 is quickly formed, and consequently redistributed to the adjacent medium. In Section 4.2 , we showed that the H 2 -bearing gas in the DLA at z = 2.7865 has a kinetic temperature T ∼ 122 K, which is significantly higher than what is expected at such H 2 column density. We supposed that such high temperature was likely attributed to the low metallicity in the medium. The cooling time can be easily estimated from the cooling rate, l c , derived from C ii* , as ∼2 k B T / l c , where k B is the Boltzmann constant (the factor of 2 was chosen since the molecular fraction is not well constrained and the correct coefficients are 3/2 and 5/2 for monatomic and diatomic gas, respectively). This indicates a cooling time of about 0.1 Myr, which is at least an order shorter than the time-scales mentioned above. Hence, the cloud is likely in thermal equilibrium and the high kinetic temperature is indeed due to the low metallicity and not to the formation of H 2 in a shock-compressed medium. 5.3 CO content, <italic>X</italic><sub>CO</sub> and CO-dark gas In spite of extremely high H 2 column density, we did not detect CO. This is not surprising given the facts that we measured a low associated extinction and possibly an enhanced UV flux: CO is easily photodestructed by UV photons in the diffuse medium and therefore at least moderate level of extinction A V ≳ 1 is required for this molecule to survive. Models indicate that the abundance of CO scales with the metallicity, but there are only scarce measurements at low metallicities (e.g. Shi et al. 2015 ). Our upper limit on N (CO) is therefore very valuable since it provides a stringent limit on X CO at Z ∼ 0.03 Z ⊙ for the first time. Fig. 17 shows the dependence of the CO column density on that of H 2 measured in both the MW and high-redshift ISM. Values for high- z DLAs are from Srianand et al. ( 2008a ) and Noterdaeme et al. ( 2010 , 2017 ) for quasar absorption systems and Prochaska et al. ( 2009 ) for the GRB-DLA. 6 We also derived upper limits on the total CO column density in known strong H 2 absorption systems, using the covered A-X bands in the archival spectra and assuming that the population of CO rotational levels is dominated by the CMB excitation. We found N (CO) 12.55 in the DLA towards Q 1232 + 082 with log N (H 2 ) = 19.57 ± 0.10 and N (CO) 12.81 in the DLA towards Q 0812 + 3208 with log N (H 2 ) = 19.93
Paper: 2020MNRAS.493..106I (acknowledgments)
ACKNOWLEDGEMENTS This work was supported by United Kingdom’s Science and Technology Facilities Council. The authors would like to sincerely thank the anonymous reviewer for their careful and detailed review, and very helpful suggestions on how to improve the presentation of this paper.
Paper: 2016A&A...592A..66W (fulltext)
the 615460 Å doublet and the set of stellar parameters derived according to Carretta et al. (2006) prescriptions with those derived by Carretta et al. (2006) . The right panel shows the good agreement between the spectrum-synthesis-based abundances and the (615460 Å doublet) EW-based values, based on our own set of stellar parameters. Here only LTE abundances are considered. Overall, we can then conclude that the different methods explored so far for the derivation of stellar parameters and/or abundances lead to small systematic differences (within the associated errors in our case), while the errors associated with the EWs/line-fitting measurements are mostly responsible for the dispersions around these values. To complete our diagnostic tests, we have also evaluated the effects of deriving the stellar parameters spectroscopically on our final Na. The results of this test are summarised in the Appendix. 4. Observed Na distribution along the RGB and AGB in NGC 2808 4.1. [Na/H] versus [Na/Fe] Fig. 7 Abundance distributions of our complete (AGB RGB) sample. Left : [Na/H]; middle : [Na/Fe i ]; right : [Na/Fe ii ]. Symbols are the same as in Fig. 2 . The horizontal dash-dotted lines mark the critical values distinguishing the 1P and 2P stars following Carretta et al. (2009b) criteria (see the text). The typical error bars are shown at the right-bottom corner of each panel (considering the similarity of the errors of the GIRAFFE and UVES samples and the clarity of the figure, we only show the error bars for the GIRAFFE sample which is the largest sample here). By comparing the errors in our derived metallicities () and the star-to-star dispersions (), we find the intrinsic spread in [Fe/H] of NGC 2808 to be within , when considering only the GIRAFFE sample (representing the large majority of our dataset). The stellar population in NGC 2808 can thus be considered homogeneous in its Fe content within a few hundredths of , as also pointed out by Carretta et al. (2006) . Figure 7 shows these effects on the observed abundance patterns, where we present our final Na abundance distributions for AGB and RGB stars in NGC 2808 as [Na/H], [Na/Fe i ], and [Na/Fe ii ] versus . Overall, the differences among the three panels are minimal. However, because of the extra uncertainties associated with Fe i (due to the NLTE corrections) and Fe ii (due to the paucity of lines), we chose to use the [Na/H] ratio to discuss our derived Na abundance distributions along the RGB and AGB. 4.2. [Na/H] distribution A quick inspection of Fig. 7 (left panel) shows that the AGB and RGB samples in NGC 2808 have similar Na abundance ranges. A two-sided Kolmogorov-Smirnov (K-S) test confirms this: the K-S statistic D (0.268) and the -value (0.137) derived for the [Na/H] distribution both indicate that there is only weak evidence to reject the null hypothesis, i.e. the two samples have the same distribution. Examining in more detail the dispersions () and the interquartile range (IQR)
Paper: 2016MNRAS.456.3671A (acknowledgments)
We thank Diego Muñoz and Smadar Naoz for useful discussions, and especially Cristobal Petrovich for useful discussion and comments on the manuscript. This work has been supported in part by NSF grant AST-1211061, and NASA grants NNX14AG94G and NNX14AP31G. KRA is supported by the NSF Graduate Research Fellowship Program under Grant No. DGE-1144153.