adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.501.5319A (fulltext)
Parkes and ASKAP DM histograms with corresponding modelled DM distributions, d R F /d DM , where R F is the total differential (fluence) event rate in the observer’s frame (d R F /d DM has units of events s −1 (pc cm −3 ) −1 sr −1 ). Table 2. Symbol definitions relevant to the ME 2018 model utilized herein with some minor notational changes and extensions. The DM distribution, for fluences above a minimum survey fluence limit, is dependent upon a number of factors including the underlying redshift distribution of the population (relating to the evolutionary history) and the mean DM gradient, , for an assumed homogeneous IGM. These are dependent upon the source energy distribution function, characterized by the source minimum and maximum energies E min and E max , respectively, the energy power-law index and the spectral index (Macquart Ekers 2018b ). Throughout this work we utilize a ΛCDM universe with cosmological parameters and equations consistent with the Planck Collaboration XVI ( 2014 ) 3 and Hogg ( 1999 ), and symbol definitions of the ME 2018 model with some minor notational changes and extensions as summarized in Table 2 . The FRB redshift and DM distributions, in a survey of limiting fluence, are respectively given by equations ( 2 ) ( 3 ) and where the minimum and maximum of the source energy is related, respectively, to the corresponding minimum and maximum fluence via . Here, ψ n ( z ) and represent redshift evolution (Madau Dickinson 2014 ), via equation ( 4 ), and the mean DM of an homogeneous IGM (Ioka 2003 ; Inoue 2004 ), via equation ( 5 ), respectively and Whilst the underlying redshift distribution of the FRB population is unknown, we follow Macquart Ekers ( 2018b ) and adopt the Madau Dickinson ( 2014 ) formalism for the cosmic star formation history of the Universe. Equation ( 4 ) accounts for the redshift evolution of the rate density for a progenitor population abundance, governed by stellar processes throughout cosmic history, via the relation ψ n ( z )∝Ψ n ( z ). Here, Ψ represents the cosmic star formation rate (CSFR) per comoving volume and the event rate per comoving volume, ψ n ( z ), is related via a power-law index, n (Macquart Ekers 2018b ). We specifically consider three cases: (i) ψ 0 ( z ) – no redshift evolution; (ii) ψ 1 ( z ) – redshift evolution being linearly proportional to the CSFR; and (iii) ψ 2 ( z ) – redshift evolution being quadratically proportional to the CSFR. The case of ψ 0 ( z ) = 1 represents a constant event rate per comoving volume and ψ 2 ( z ) represents a rapidly evolving population. Furthermore, throughout this work, we set the ionized fraction of Hydrogen and Helium to X e, H = 1 for z 8 and X e, He = 1 for z 2.5, respectively, and zero otherwise, and take α to refer to the spectral index of the burst fluence unless specifically noted otherwise. The dimensionless Hubble parameter, E ( z ), permits the Hubble parameter for an arbitrary redshift, H ( z ), to be determined given the
Paper: 2016A&A...586A.136P (fulltext)
Heyer et al. 2008 ; Chapman et al. 2011 ). Using the Chandrasekhar-Fermi method, Chapman et al. (2011) estimated a magnetic field strength of about 25 G in the cloud surrounding the B211 filament, concluding that the former is magnetically supported. Fig. 4 Same as Fig. 1 for part of the Taurus molecular cloud around the B211 (north-west) and the L1506 (south-east) filaments. The numbers and white lines on the total intensity map correspond to the positions of the different cuts used to derive the radial profiles shown in Figs. 5 and 6 . Fig. 5 Same as Fig. 2 for the Taurus B211 filament. Here corresponds to the southern side of the filament crest. The filament is clearly seen in and is located in the area of a steep variation of the emission. The dispersion of the emission is small along the length of the filament. Fig. 6 Same as Fig. 2 for L1506. Here corresponds to the south-eastern side of the filament crest. Figure 4 shows the Planck 353 GHz Stokes parameter maps of the TMC around the B211 and L1506 filaments. The B211 filament is well detected in the and maps, as a structure distinct from its surrounding. On the map, on the other hand, the filament is not seen as an elongated structure, but it is perpendicular to a large gradient that separates two regions of almost uniform . The emission is negative on the northern side of the filament while it is positive on the southern side. This indicates that the -field orientation varies in the cloud surrounding the B211 filament. This variation can be seen very clearly in the radial profiles perpendicular to the filament crest, shown in Fig. 5 . These profiles are derived as explained in the previous section (for the Musca filament). The four and profiles (shown in the middle and right-hand panels of Fig. 5 ) are derived by averaging the cuts within a distance along the filament crest of 3 times the Planck beam (3 4.8). The cloud intensity () increases from the southern to the northern side of the filament, while the emission is similar on both sides of the filament crest. The (negative) emission of B211 is very clearly seen in the radial profiles. The different profiles show the approximate invariance of the emission along the filament crest. The total and polarized emission components are remarkably constant along the length of B211. 3.3. The Taurus L1506 filament The L1506 filament is located on the south-east side of B211 (see Fig. 4 ). Stellar polarization data are presented by Goodman et al. (1990) . The density structure and the dust emission properties have been studied by Stepnik et al. (2003) , and more recently by Ysard et al. (2013) using Herschel data. This filament has mean column densities comparable to those of the Musca filament. Star formation at both ends of L1506 has been observed with the detection of a few candidate prestellar cores ( Stepnik et al. 2003 ; Pagani et al. 2010 ). Figure 6 shows the radial profiles perpendicular to its crest. The colour profiles numbered from
Paper: 2018ApJ...860..136W (acknowledgments)
This research is supported by the National Natural Science Foundation of China (NSFC) under grant No. 11390371, and was accomplished (in part) by the Chinese Academy of Sciences (CAS), through a grant to the CAS South America Center for Astronomy (CASSACA) in Santiago, Chile. Y.Q.C. acknowledges support from NSFC grant No. 11625313. The Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences.
Paper: 2017ApJ...836...74J (fulltext)
magnitude is defined as QT = F814W 0 , where F555W − F814W) 0 for the ACS/WFC. We plotted QT magnitude versus (F555W–F814W) 0 diagrams of the resolved point sources in Figures 4 (e)–(h). Foreground reddenings are known to be very small: , 0.028, 0.035, and 0.007 for NGC 3021, NGC 3370, NGC 1309, and the HXDF, respectively (Schlafly Finkbeiner 2011 , NASA/IPAC Extragalactic Database). Internal reddenings of the selected outer regions are expected to be negligible so that these are assumed to be zero in this study. 3.2. Distance Estimation We determined the distances to the target galaxies using the TRGB method (Lee et al. 1993 ; Madore Freedman 1995 ). We selected resolved stars in the shaded regions of Figure 4 . The shaded regions for F814W – (F555W – F814W) CMDs were designed to sample blue RGB stars with near the TRGB level because the color (metallicity) dependence of the TRGB at the blue side is estimated to be small (Jang Lee 2017 ). In the cases of QT – (F555W – F814W) 0 CMDs, we set slightly wider shaded regions, sampling RGB stars with near the TRGB level. Figure 5 displays F814W (a)–(c) and QT (d)–(f) luminosity functions of the selected RGB stars in the CMDs. The luminosity functions for all three galaxies show abrupt increments at mag, which correspond to the TRGB. We applied an edge detection algorithm for the quantitative TRGB detection. We used a Sobel filter employing the zero-sum kernel of [−1, −2, −1, 0, +1, +2, +1] with a bin size of 0.05 mag. Output edge detection responses are shown as red lines in Figure 5 . Figure 5. (a)–(c) LFs (histograms) and edge detection responses (curved lines) of the RGB stars in NGC 3021 (a), NGC 3370 (b), and NGC 1309 (c). Estimated TRGB magnitudes are marked by vertical dashed lines in each panel. (d)–(f) Same as (a)–(c), except for the QT magnitude. Mean TRGB magnitudes and associated uncertainties were measured by running 10,000 simulations of bootstrap resampling as done for our previous studies (Lee Jang 2012 , 2013 ; Jang Lee 2015 ). In each simulation, we made a new sample of RGB stars by sampling a half size of stars randomly from the original RGB sample. We obtained TRGB magnitudes from the new samples as done for the original sample. We generated a histogram of the TRGB magnitudes obtained from the simulations and fitted a Gaussian function to the histogram, quoting the Gaussian mean as a mean TRGB magnitude and the Gaussian width as 1 σ uncertainty. Measured TRGB values are F814W TRGB = 28.179 ± 0.049, 28.252 ± 0.035, and 28.457 ± 0.044 mag for NGC 3021, NGC 3370, and NGC 1309, respectively. Similarly, QT RGB = 28.146 ± 0.033 mag for NGC 3021, 28.212 ± 0.041 mag for NGC 3370, and 28.398 ± 0.040 mag for NGC 1309 were obtained. Derived TRGB magnitudes from the QT luminosity functions are systematically brighter than those from F814W luminosity functions, because the QT system uses foreground extinction corrected F814W magnitude (F814W 0 ). We detected small photometric offsets from the
Paper: 2015MNRAS.453.3190B (acknowledgments)
Based on observations obtained at the SOAR telescope, which is a joint project of the Ministério da Ciência, Tecnologia, e Inovação (MCTI) da República Federativa do Brasil, the US National Optical Astronomy Observatory (NOAO), the University of North Carolina at Chapel Hill (UNC) and Michigan State University (MSU). We thank an anonymous referee for important comments and suggestions. Partial financial support for this research comes from CNPq and PRONEX-FAPERGS/CNPq (Brazil). BB and BD acknowledge partial financial support from FAPESP, CNPq, CAPES, and the LACEGAL project. LK, BB, and BD also thank CAPES/CNPq for their financial support with the PROCAD project number 552236/2011-0.
Paper: 2020MNRAS.496..309H (fulltext)
) as well as the line and continuum fluxes using the normalization to the maximum continuum ( right ). The spectra cover ∼8 yr in the QSO rest frame. The shading of the markers indicates the progression of time. The first epoch available (SDSS, MJD 52200) is represented by the light blue marker in the centre of the plot. The initial rise in the continuum is to the epoch of maximum continuum (top right). The gradient of this response is approximately unity. In the next spectrum the continuum has collapsed, and the line has also almost disappeared (bottom left corner). When the continuum rises again, the gradient is considerably steeper. Figure 10. Left : The changing Mg ii line flux in J022556. The high cadence of observations for this object allows us to track the line flux as the continuum falls and rises over the course of ∼8 yr in the QSO rest frame. Right : The Mg ii line fluxes normalized to the maximum continuum. The epochs of the spectra are indicated with the colour, increasing chronologically from light to dark. This plot is based on the normalization of the fluxes to the epoch of maximum continuum. A 1:1 response is indicated with the dashed grey line. The initial increase in line flux is matched approximately 1:1 by the Mg ii line. Following a collapse in line and continuum, however, the gradient of the line responsivity is considerably steeper as the continuum recovers. The epoch-dependent responsivity visible in J022556 (and J002311) indicates that the scatter around the responsivity functions (in Figs 5 and 6 ) could be the result of sampling the varying objects at different stages during the BLR response to the continuum. The response of the line to the continuum does not follow a single gradient for the same object; therefore, we can expect considerable scatter around any linear response function fitted to a large ensemble. 5.3 The iBE A final measure for the responsivity is the iBE, which measures the change in EW compared to the change in continuum luminosity for individual objects. For the full population sample, the iBE is calculated as ΔEW/Δ L 2798 for each spectral pair. For the supervariable sample, the same is done for each chronologically sequential pair. The results for the full population sample are shown in Fig. 11 . Figure 11. A histogram of the intrinsic Baldwin effect for the full population sample. The number of spectral pairs, N , and the sample average, μ, are listed in the top right corner. The iBE is calculated as ΔEW/Δ L 2798 for each pair. Although there are some strong outliers, the distribution is clearly centred around −0.5. This is significantly lower than the slope of the ensemble BE of −0.16 calculated for the data from Shen et al. ( 2011 ). The distribution is largely symmetric and centred around a slope of −0.5. For the supervariable sample, the average slope is −0.54. Unfortunately, there are no literature values of the Mg ii iBE to compare these results to. For other UV lines such as C iv λ1549
Paper: 2019MNRAS.487.5070M (fulltext)
⊙ and 0.2 × 10 6 M ⊙ , respectively; the Plummer equivalent co-moving gravitational softening is 2.66 and 1.33 kpc, limited to a maximum physical softening of 0.7 and 0.35 kpc, respectively. The simulations assume the cosmological parameters of Planck Collaboration ( 2014 ), and the minor differences with the Planck Collaboration ( 2016 ) parameters adopted in that paper are unlikely to be important. To compare to the data presented earlier, we need to identify both DLAs and LAEs in eagle . Since neither neutral hydrogen nor Lyα radiative transfer is directly incorporated in eagle , we compute these quantities in post-processing as explained next. 4.1.1 Identifying DLAs in the <sc>eagle</sc> simulations The ionizing background in eagle is implemented in the optically thin limit. Self-shielding of gas, allowing for the appearance of DLAs, is computed in post-processing using the urchin radiative transfer code described by Altay Theuns ( 2013 ). Briefly, this algorithm allows each gas particle to estimate the local ionizing intensity it is subject to by sampling the radiation field in 12 directions, with neutral gas in neighbouring gas particles potentially decreasing the local photo-ionization rate below the Haardt Madau ( 2001 ) optically thin value. Assuming the neutral fraction of a particle is set by the balance between photo- and collisional ionization versus recombinations, a reduced ionization rate increases the particle’s neutral fraction, which in turn affects the ionization rate determined by the particle’s neighbours. The impact of a change in the neutral fraction on the photo-ionization rate and vice versa is iterated until the neutral fraction of each particle converges from one iteration to the next. This post-process step thus yields the neutral hydrogen fraction of each SPH particle. Further details on how radiative transfer affects the neutral fraction as a function of column density and physical processes (e.g. the relative importance of collisional ionisation and photoionsation) can be found in the literature (e.g. Fumagalli et al. 2011 ; Rahmati et al. 2013 ). The resulting H i volume density is then projected onto an 8192 2 grid along the coordinate z -axis of the simulation box, using the Gaussian smoothing described by Altay Theuns ( 2013 ). Applying a column density threshold of allows us to identify DLAs. 2 We also calculate the -weighted z -coordinate and velocity of particles along each DLA line of sight to obtain the 3D position of each DLA (two spatial coordinates x and y , and a redshift coordinate z ). The redshift of each DLA allows us to compare DLAs to galaxies in redshift space, although we note that observed DLA redshifts are derived from low-ionization metal lines rather than H i directly, yet these elements are believed to trace the same phase of the gas. To compare to the muse observations, we analyse the L0025N0752 eagle snapshot at z = 3.027, which is closest in redshift to the data. Similarly to the
Paper: 2018MNRAS.474.5413J (acknowledgments)
ACKNOWLEDGEMENTS The authors wish to thank the entire Kepler team, without whom these results would not be possible. Funding for this Discovery mission is provided by NASA’s Science Mission Directorate. Special thanks to the referee for pertinent comments on earlier versions of the manuscript that greatly improved the presentation. The research is supported by the European Community Programme ([FP7/2007-2013]) under grant agreement no. FP7-SPACE-2012-312844 (SPACEINN) and by Fundação para a Ciência e a Tecnologia (FCT) through national funds (UID/FIS/04434/2013) and by FEDER through COMPETE2020 (POCI-01-0145-FEDER-007672). MC also acknowledges support from FCT through Investigador FCT contractIF/00894/2012/CP0150/CT0004. This work was also supported in part by the ASTERISK project (ASTERoseismic Investigations with SONG and Kepler ) funded by the European Research Council (grant agreement no. 267864). Funding for the Stellar Astrophysics Centre is provided by the Danish National Research Foundation (grant agreement no. DNRF106). We also acknowledge supports by the NSFC grants (No. 11473053, 11233005, U1331201).
Paper: 2017A&A...606A.113G (fulltext)
( UVJ colour selected) at = 1.6. They found a mean age of 1.1 Gyr. As stated by the authors, this value is in excellent agreement with the age of local counterparts, if high- massive quiescent galaxies evolve passively. We verified that more than 80 of their sample is composed of massive passive galaxies with pc, thus we can reasonably compare their results with ours. If we assume passive evolution between and , the mean age of their massive quiescent galaxies rises to 3.1 Gyr. This is in fair agreement with the mean age of high- MPGs we find at ( Gyr) (see also Whitaker et al. 2013 ). Different conclusions were reached by F16. Using stacked spectra, the authors constrained the stellar population ages for dense and less dense passive galaxies in the zCOSMOS sample from to . They found that the age of dense massive quiescent galaxies increases less than would be expected from passive evolution alone. Moreover, they found no correlation between the age of stellar population and the dimension of the source. The present analysis differs from the F16 analysis in a number of ways: the selection of passive galaxies (no emission lines plus no MIPS in F16 vs. NUV colour in our work); the selection of the dense sub-population (cut at fixed or along the SMR vs. cut at fixed ); and the procedure adopted to constrain the stellar population age. Unfortunately, we cannot exactly reproduce their analysis with our data set. In Appendix B, we checked how our results change if we adopt the same criteria used by F16 to select dense and less dense galaxies. We found that the mean age of dense MPGs is consistent with passive evolution and that less dense MPGs are younger than dense MPGs, independently of the criteria used to divide the sub-populations. However, as stated above, we stress that the previous checks rely on a sample of MPGs selected in a different way and adopt different techniques to constrain the stellar population age. In fact, we cannot repeat the same analysis with our data set, so we cannot fully account for the effect of these two factors on the results. Fig. 9 Evolution of the number density of MPGs (filled red circles) and of star forming massive galaxies (MSFGs, blue filled stars). Open circles show the expected growth in the abundance of MPGs below , assuming that this is fully due to the observed decline of MSFGs. Solid and open circles have been shifted for visualisation purposes. 6. Where do new large MPGs come from? The evidence that the new MPGs are systematically younger than high- MPGs contrasts with the hypothesis that all massive galaxies are assembled mostly through dry mergers (e.g. Hopkins et al. 2009 ; Cappellari et al. 2012 ). In fact, dry mergers should dilute any trend between stellar population age and time of appearance by mixing up the stellar population of pre-existing systems. In fact, more recently new pieces of evidence have come to light supporting a scenario in which PGs are mostly the final evolutionary stage of
Paper: 2016MNRAS.459..841M (fulltext)
of H 2 in the interstellar medium (Habart et al. 2005 ), several studies have attempted to gain insight into the conditions in PNe where this species is found. The correlation between the bipolar morphology of PNe and the H 2 detection is well documented (Kastner et al. 1996 ), suggesting that the H 2 -bright waists of bipolar PNe are the remnants of dense molecule-rich circumstellar discs. Alternatively, it has been claimed that the preferential detection of H 2 emission in bipolar PNe may be an effect of the typically higher effective temperature of their central stars (Phillips 2006 ; Aleman Gruenwald 2004 , 2011 ). The consensus is that the brightest H 2 emitters tend to be bipolar PNe, but deep near-IR imaging of the 2.122 μm H 2 emission line shows that this emission is not exclusive to this morphology (Marquez-Lugo et al. 2013 ). Besides the excellent near-IR spectroscopic survey of PNe and study of upper vibrational levels carried out by Hora, Latter Deutsch ( 1999 ), an actual investigation of the excitation mechanism of H 2 in PNe and calculations of its excitation temperature have been made in the context of other detailed studies (see e.g. Hora Latter 1994 , 1996 ; Shupe et al. 1998 ; Rudy et al. 2001 ; Lumsden, Puxley Hoare 2001 ; García-Hernández et al. 2002 ; Davis et al. 2003 ; Likkel et al. 2006 ; Ramos-Larios, Guerrero Miranda 2008 ; Aleman et al. 2011 , and references therein). Mid-IR spectroscopy is a powerful tool for the study of the thermal and molecular emission from PNe, since such observations are much less affected by dust extinction. Mid-IR studies of PNe have focused on imaging surveys (e.g. Hora et al. 2004 ; Phillips Ramos-Larios 2008 , 2010 ; Zhang Kwok 2009 ; Zhang, Hsia Kwok 2012 ), but mid-IR spectroscopy of PNe has only been carried out for individual objects for which chemical abundances and H 2 excitation temperatures were obtained (Cox et al. 1998 ; Matsuura Zijlstra 2005 ; Hora et al. 2006 ; Phillips, Ramos-Larios Guerrero 2011 ). Currently, mid-IR studies of the excitation temperature of H 2 in PNe are scarce. Using archival Spitzer mid-IR spectroscopy, we have conducted a spectroscopic survey of PNe to assess the presence of ionic species, polycyclic aromatic hydrocarbons (PAHs), and molecular hydrogen. The H 2 excitation temperature has been derived using its mid-IR emission lines. These temperatures shed light on the purely rotational component of the excitation temperatures to complement the rotational–vibrational studies obtained by near-IR observations. Section 2 details the criteria for target selection and data processing. Section 3 describes the mid-IR spectroscopic properties of each PN in our sample. A review of the theory behind the data analysis is given in Section 4 and the final discussion is presented in Section 5 . 2 ARCHIVAL DATA: SELECTION AND REDUCTION 2.1 The <italic>Spitzer</italic> archive This study made use of the Spitzer Space Telescope (Werner et al. 2004 ) data base. We