adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.490.1350B (fulltext)
of A for to match is then (taking ). This factor could include contributions from the turnaround time at each end of the corona or from other physical processes. magnetohydrodynamic calculations (Edwin Roberts 1983 ) show that magnetic fields affect the frequency of various modes of oscillation. Detailed calculations of expected values of A are beyond the scope of this work. Remillard McClintock ( 2006 ; fig. 10) show that QPO frequency correlates with disc flux in hard/steep power-law-intermediate states. The observations presented here have a weaker disc component, which is not unambiguously detected but may be clearer at soft energies, so this could be investigated, for example, with NICER . While the QPO is most dominant at high energies, it is also strongly detected in the 3–10 keV band, which is also covered by NICER . Owing to the more frequent coverage of MAXI J1820+070 by NICER , a more detailed analysis of the QPO progression can be made with these data. 4.3 Coronal temperature The corona also shows changes in its mean properties: the temperature anticorrelates with flux. This has been observed in other individual XRBs (Joinet, Kalemci Senziani 2008 ; Motta, Belloni Homan 2009 ) and AGN (Lubiński et al. 2010 ). This could happen because pair production from photon collisions is regulating the temperature (Svensson 1984 ; Zdziarski 1985 ; Fabian et al. 2015 ): at higher fluxes, there are more photons, which allows sufficient pairs to be produced at lower temperatures. The coronal temperatures observed here are allowed by the pair thermostat, being below the pair-production limit that is observed to limit accreting sources as a population (Fabian et al. 2015 ). The lower temperatures than the pair limit can be explained by a deviation from a pure thermal distribution, which is expected as the cooling time-scale is less than the collision time-scale (Fabian et al. 2017 ). The presence of a non-thermal tail to the particle distribution reduces the critical temperature (Fabian et al. 2017 ). These considerations do not take substructure in the corona into account; this remains a potential caveat when estimating the coronal compactness from its total luminosity and size. We consider this correlation in more detail by comparing the corona’s radiative compactness with its electron temperature. We take electron temperatures from our fits and calculate the coronal compactness following the methodology of Fabian et al. ( 2015 ). We take the luminosity from the total direct flux of our best-fittng models at a distance of 3.5 kpc. Calculation of the compactness also requires a radius; the coronal prescription used here (including contributions from two points) does not readily convert to an equivalent spherical size so we try several prescriptions. First, we consider a fiducial size for all observations. We also consider the effect of a shrinking radius, as could be implied by the reduction in illumination of the outer disc with time. We reduce
Paper: 2016MNRAS.456.1955D (acknowledgments)
This work was supported in Italy by ASI contract I/004/11/1. Facility: Swift
Paper: 2019A&A...631A.109W (fulltext)
the methodology of Rowan-Robinson et al. (2005 , 2008) and as in Wang Rowan-Robinson (2009) , with a combination of two cirrus templates, three starburst templates and an AGN dust torus template. The total IR luminosity L IR (integrated between 8 and 1000 μ m) is estimated based on the fitted templates. The methodology of Rowan-Robinson et al. (2008) is followed to calculate stellar masses and SFR. Briefly, the rest-frame 3.6 μ m luminosity is estimated and converted to stellar mass using the mass-to-light ratio derived from stellar synthesis models. To estimate SFR, the conversion recipes of Rowan-Robinson et al. (1997) and Rowan-Robinson (2001) are used where η is the fraction of UV light absorbed by dust, taken as 2/3. The SFRs are calculated for a Salpeter (1955) IMF between 0.1 and 100 M ⊙ . To convert to Kroupa (2001) IMF, we divide the values by 1.5. We can also estimate SFR based on the total IR luminosity L IR following the widely used recipe of Kennicutt (1998) after converting to Kroupa IMF, In principle, the formula of Eq. ( 2 ) is only suitable for dusty starburst galaxies in which all of the radiation from young stars is assumed to be absorbed by dust and subsequently re-emitted in the IR. In practice, Eq. ( 2 ) has been found to also apply to normal galaxies (e.g. Rosa-González et al. 2002 ; Charlot et al. 2002 ). The explanation is that there are two competing effects, which are overestimation in SFR caused by assuming all of the IR luminosity arises from recent star formation (as opposed to old stellar populations) and underestimation in SFR caused by neglecting the possibility that some of the young stellar radiation is not absorbed by dust. It is a coincidence that these two effects cancel out (e.g. Inoue 2002 ; Hirashita et al. 2003 ). For sources in the RIFSCz which have been cross-matched to SDSS DR 10, we also have SFR estimates based on the H α line luminosity, SFR Hα , corrected for dust attenuation and aperture effects provided in the MPA-JPU database ( Brinchmann et al. 2004 ). 2.2. The LOFAR survey Exploiting the unique capabilities of LOFAR ( van Haarlem et al. 2013 ), LoTSS is an ongoing sensitive, high-resolution, low-frequency (120−168 MHz) radio survey of the northern sky and is described in Shimwell et al. (2017) . LoTSS provides the astrometric precision needed for accurate and robust identification of optical and NIR counterparts (e.g. McAlpine et al. 2012 ) and a sensitivity that, for typical radio sources, is superior to previous wide area surveys at higher frequencies such as the NRAO VLA Sky Survey (NVSS; Condon et al. 1998 ) and Faint Images of the Radio Sky at Twenty-Centimeters (FIRST; Becker et al. 1995 ) and is similar to forthcoming higher frequency surveys such as the Evolutionary Map of the Universe (EMU; Norris et al. 2011 ), and the APERture Tile In Focus survey (e.g. Röttgering et al. 2011 ). The primary observational objectives of LoTSS are to reach a sensitivity of less than 100 μ Jy
Paper: 2017ApJ...849...80C (acknowledgments)
S.C. is supported by the Netherlands Organization for Scientific Research (NWO; VIDI project 639.042.017) and by the European Research Council (ERC; project PALs 320620). This work was supported by the MOST grants MOST 106-2112-M-008-019-(Y.J.C.) and by the Spanish MINECO under projects AYA2011-29375, AYA2014-60585-P, and AYA2015-71975-REDT (R.M.-D. and G.M.M.C.). We acknowledge Dr. Wing-Fai Thi for useful feedback on the manuscript. We would like to thank the anonymous referee for valuable comments, which helped to improve the manuscript.
Paper: 2018MNRAS.476.2339B (acknowledgments)
ACKNOWLEDGEMENTS The SAMI Galaxy Survey is based on observations made at the Anglo-Australian Telescope. The Sydney-AAO Multi-object Integral field spectrograph (SAMI) was developed jointly by the University of Sydney and the Australian Astronomical Observatory. The SAMI input catalogue is based on data taken from the SDSS, the GAMA Survey and the VST ATLAS Survey. The SAMI Galaxy Survey is funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and other participating institutions. The SAMI Galaxy Survey website is http://sami-survey.org/ . GAMA is a joint European–Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the SDSS and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel -ATLAS, GMRT ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . Support for AMM is provided by NASA through Hubble Fellowship grant HST-HF2-51377 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. SMC acknowledges the support of an Australian Research Council Future Fellowship (FT100100457). MSO acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140100255). We thank Á. R. López-Sánchez for assisting in the proofreading of the paper.
Paper: 2019ApJ...883...16M (acknowledgments)
We appreciate Takashi Miyata, Takafumi Kamizuka, and Ryou Ohsawa for fruitful discussions regaring our study. This work is supported by JSPS KAKENHI Numbers 18K13590, 19H05088, 19K03932, 18H05441, 17H01103, 18K03709, 18K13595, and 17K14244. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2017.1.00124.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. Data analysis was partly conducted with the common use data analysis computer system at the Astronomy Data Center, ADC, of the National Astronomical Observatory of Japan. Facility: ALMA. Software: CASA v5.1.1; (McMullin et al. 2007 ).
Paper: 2019ApJ...880...24T (fulltext)
it is as follows: (a) the parameter h eff is sampled, marginalized over the velocity field; (b) the conditional probability of the parameter σ NL is drawn with the other parameters fixed; (c) a constrained realization of the density field is drawn assuming a ΛCDM power spectrum (Hoffman Ribak 1991 ); (d) a new set of distances is established from the sampled constrained realization with probabilities set by the current values of h eff , σ NL , and the velocity field, within priors on the distances. The process is carried through ∼10 3 MCMC steps until convergence. The procedure has been carried out on multiple constrained realizations and mock catalogs. With the current analysis, Graziani et al. ( 2019 ) find h eff = 1.02 ± 0.01 and σ NL = 280 ± 35 km s −1 . The resultant model makes predictions for the morphology and motions of structure locally within the ΛCDM framework and linear perturbations. Our present interest is in voids. It will be asked to what degree the overall model is in agreement with the excellent knowledge we have of the motions of very nearby galaxies. 2. Morphologies of Nearest Voids The Local Void does not have a simple shape. Moreover, as the void is followed to shallower levels it merges with adjacent voids, as part of a continuous network that extends beyond the volume that can currently be mapped. The three-dimensional interplay between complex high- and low-density structures is visually confusing. We should not have the ambition to get into great detail. As a prelude, previous efforts to identify nearby voids can be mentioned, derived from regions of emptiness in maps of the distribution of galaxies in redshift surveys. Among the earliest were the seminal studies of the Boötes Void by Kirshner et al. ( 1981 ) and the void in front of the “Great Wall” Coma and Abell 1367 clusters (Gregory Thompson 1978 ). On very large scales there is the pioneering work by Batuski Burns ( 1985 ) and Einasto et al. ( 1994 ) on the concentrations and absences of rich clusters. More nearby, and pertaining to the distribution of individual galaxies, of note is the work of Kauffmann Fairall ( 1991 ) and Fairall ( 1998 ). The latter gives a list of 33 void-like regions within 8500 km s −1 . Elyiv et al. ( 2013 ) have produced a more quantitatively rigorous catalog of 89 voids within 3000 km s −1 ; spherical regions with no known galaxies brighter than M K = −18.4. Typically these entities are modest in size, with radii ∼6 Mpc. It is well documented that voids network, and their dimensions as constrained by the exclusion of galaxies depend on the intrinsic properties of the galaxy samples. Sparse filaments of dwarf galaxies can snake through regions devoid of bright galaxies. This phenomenon has particularly been noted by Lindner et al. ( 1995 ) and Pustilnik et al. ( 2019 ) within the volume that attracts our attention. The linkage of the region under consideration including the Local Void to very extensive voids has been claimed by
Paper: 2017MNRAS.465.4611P (fulltext)
for the cold molecular phase. As this is the phase with the strongest magnetic fields as well as the phase in which star formation occurs, it is important to follow the evolution of the magnetized ISM together with its chemical evolution. In this study, which is part of the SILCC project (SImulating the Life-Cycle of molecular Clouds 1 ), we investigate the interplay between the chemical evolution of the SN-driven ISM and the magnetic field evolution. We use three-dimensional MHD simulations including a chemical network and shielding from an interstellar radiation field to explore how magnetic fields alter the formation of molecular gas in the ISM. We present the details of the simulations, the numerical methods and parameters, describing the chemistry network, the SN implementation, the list of simulations and the initial conditions in Section 2 . We discuss the morphological evolution of the simulations in Section 3 . The chemical evolution is described in Section 4 together with the magnetic field evolution and the influence of self-gravity. In this section we also evaluate the thermal, magnetic and kinetic energy density (Subsection 4.1 ). In Section 4.2 we show the magnetic field scaling and present our conclusions in Section 5 . 2 NUMERICAL METHOD, PARAMETERS AND SIMULATIONS 2.1 Numerical method We simulate the chemical, thermal and dynamical evolution of gas driven by SN feedback in a periodic volume using the Eulerian, adaptive mesh refinement (AMR), three-dimensional, magnetohydrodynamic code flash 4 (Fryxell etal. 2000 ; Dubey etal. 2008 ). To solve the ideal MHD equations, we use the directionally split three-wave MHD solver hll3r (Bouchut, Klingenberg Waagan 2007 , 2010 ; Waagan 2009 ; Waagan, Federrath Klingenberg 2011 ). Our simulations account for the effects of gas cooling and chemical evolution (Section 2.2 ) and SN feedback (Section 2.3 ) using an approach very similar to the one adopted by Gatto etal. ( 2015 ). Further details of our modifications to flash can also be found in Walch etal. ( 2015 ) and Girichidis etal. ( 2016 ). 2.2 Gas cooling and chemistry The simplified chemical network follows the evolution of seven chemical species (H , H, H 2 , C , O, CO and free electrons) and is based on the hydrogen chemistry of Glover Mac Low ( 2007a , b ) and the CO chemistry of Nelson Langer ( 1997 ). We model the effects of radiative cooling and diffuse heating by cosmic rays, X-rays and the interstellar radiation field (see Glover etal. 2010 ; Glover Clark 2012 ; Walch etal. 2015 ). Cooling by metals in hot ionized gas is modelled using rates from Gnat Ferland ( 2012 ), as outlined in Walch etal. ( 2015 ). Dust shielding and molecular self-shielding are included using the TreeCol algorithm developed by Clark, Glover Klessen ( 2012 ). Full details of the implementation of the chemical network and the coupling of TreeCol with flash are described by Walch etal. ( 2015 ) and Wnsch etal. (in preparation). In the simulations presented
Paper: 2019A&A...628A..89V (acknowledgments)
Acknowledgements The authors acknowledge support from the National Science Centre, Poland, through grant 2016/21/B/ST9/01455. This work was partially supported by Program 12 of the Russian Academy of Sciences. Partly based on observationswith the Effelsberg 100 m telescope of the Max-Planck-Institut für Radioastronomie in Effelsberg. We wish to thank the Maser Monitoring Organisation (M2O), a group of loosely organised radio telescopes that coordinates and cooperates for observations, for its prompt response for our request for confirmatory observations and increased monitoring of water masers associated with W49N.
Paper: 2018MNRAS.474..746B (fulltext)
distribution of central densities in SIDM haloes with cross-sections of this order of magnitude. This signature has not been studied in detail and in this section we present such an analysis based on our cluster-sized simulations. To get a simple measure of the mass assembly history of our halo sample, we trace back in time each halo from z = 0 through successive snapshots, picking the most likely progenitor based on proximity of mass and distance from the selected halo in consecutive snapshots. From this exercise, we define a measure of the ‘formation’ time of a halo as the redshift z f where it acquires a fraction f mass of the mass contained within the scale radius r −2 at z = 0, where r −2 is the radius at which the logarithmic slope of the density profile is −2: 5 This definition gives us a way to explore if the dispersion in the central densities at a fixed V max is related to the time when the mass in the inner regions of haloes was assembled. We note that using equation ( 10 ) for small values of f mass leads to a biased definition of formation time in the SIDM case since once within the region of the SIDM core, the enclosed mass is clearly smaller in the SIDM case than in the CDM case, leading to an artificially larger formation time in the former. In fact, SIDM and CDM haloes assemble at roughly the same time, since self-interactions start to be important later in the evolution of haloes. Equation ( 10 ) is nevertheless sufficient and simple enough for the purpose of this section. We have verified that using the formation time assigned to CDM haloes to the corresponding SIDM haloes leaves our results qualitatively unchanged. Fig. 9 shows the density at a fixed radius (4ε) as a function of V max (a proxy of halo mass) for our halo sample in the different cosmologies. We have chosen r = 4ε ≈ 22 kpc h −1 as the minimum radius we can safely trust in our density profiles. The size of each symbol is proportional to the formation redshift z f (defined with f mass = 1/2), with larger symbols corresponding to haloes formed earlier. It is well known that in CDM, at a fixed radius, more massive haloes (larger V max ) are denser than less massive ones, while at a fixed mass ( V max ), more concentrated haloes form earlier than less concentrated ones. Both of these features are present in Fig. 9 , although the trends are noisier due to the limited mass range we cover and the low number of haloes in our sample. Figure 9. Density at four times the softening (≈22 kpc h −1 ) versus the maximum velocity of the halo. The type of simulation is shown by the colour (blue for CDM, green for SIDM0.1 and red for SIDM1) and the symbol size indicates the square of the formation redshift [where the halo had obtained half its mass within M ( r −2 )], i.e. smaller (larger) symbols indicate haloes formed later (earlier). For CDM and SIDM0.1 we see a mild trend between the central density and the maximum velocity of a halo, which is absent negligible in the case of
Paper: 2020A&A...635A.114H (fulltext)
to depend on any model assumptions, so we will use the calibration based on V − V HB for these 19 clusters. For the remaining clusters, the absolute magnitude calibration would be the best choice, but we only have F606W photometry available for two additional clusters. So instead of presenting results from three different calibrations, the metallicities for all the remaining six clusters are derived from the luminosity approach. When necessary, these six clusters are marked in plots and tables with an asterisk. Using the set of average reduced equivalent widths as given in Table 3 , we now calibrate them with mean cluster metallicities from the literature (from Dias et al. 2016 ). The upper plot in Fig. 14 shows the metallicities from the literature as a function of reduced equivalent width. The three lines show a linear, quadratic, and cubic fit to the data, taking into account the errors on both axes (which are small), together with their corresponding root mean squares (rms). The coefficients for all relations are given in Table 4 for the following equation: Fig. 14. Upper panel : the metallicity from the literature ( Dias et al. 2016 ) is plotted over the mean reduced equivalent width of each cluster, derived from the quadratic relation on all RGB stars based on V − V HB . Three polynomials of different degree are fitted to the data and the rms for each is given in the legend. Lower panel : quadratic fits to three more calibrations using the same metallicity scale (only brighter than HB, and based on M and L ) are provided for comparison, together with all four on a different metallicity scale (C09, from Carretta et al. 2009a ). Table 4. Coefficients for CaT-metallicity calibration as given by Eq. ( 7 ) for three different polynomial degrees. Since both the Bayesian (BIC) and the Akaike (AIC) information criteria give the best results for the quadratic relation, we chose this for further analyses. Therefore, the relation between reduced equivalent width and metallicity is given by: The metallicities derived from this relation show two systematic errors: the mean metallicity of a cluster will be the value of the relation given above at the mean reduced EW of the cluster and, therefore, will have a small offset to the literature value for most clusters. Furthermore, the slope of the relation at any given point defines the metallicity spread. Following previous studies (see, e.g., Vásquez et al. 2018 ), the uncertainties are calculated as the quadratic sum of the uncertainty σ W ′ and the rms for the used relation as given in Fig. 14 . For the quadratic relation this yields: where rms is the root mean square for the used relation and σ r is the propagated uncertainty for σ W ′ : The derived metallicities and their uncertainties for all previously discussed calibrations are listed in Table 8. We already demonstrated that all previously discussed approaches yield a similar W ′. The lower panel in Fig. 14 shows that the metallicity calibrations