adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...885...68B (fulltext)
on the age and wind velocity of the driving W-R star and also on the initial density of the surrounding molecular cloud. Figure 6 (b) shows the 13 CO spectrum of the G27 region. The molecular cloud hosting this particular region is located at a velocity range of 88–95 km s −1 (see Dewangan et al. 2016 ). The spectrum is constructed by combining all 13 CO emission within a 45′′ diameter (i.e., the beam size of the GRS data) centered on the W-R star. Two emission peaks are clearly visible in the spectrum. We estimated the kinematic distance corresponding to the velocity of the trough ( v LSR ∼ 92.0 km s −1 ; see Figure 6 (b)) using the “Kinematic Distance Calculation Tool” 10 10 http://www.treywenger.com/kd/index.php of Wenger et al. ( 2018 ), which evaluates a Monte Carlo kinematic distance adopting the solar Galactocentric distance of 8.31 ± 0.16 kpc (Reid et al. 2014 ). The calculation yields a near kinematic distance ( d near ) of 5.2 ± 0.6 kpc. The spectrophotometric distance of the W-R star associated with the reference region is 5.2 ± 0.5 kpc (Dewangan et al. 2016 , see also Table 1 ). A well-matched distance of a W-R star with the velocity of the trough in the 13 CO spectrum implies a physical association of the W-R star with this particular molecular cloud, and more precisely, with the trough at a v LSR of 92.0 km s −1 . The 13 CO spectrum (Figure 6 (b)) also exhibits two emission peaks corresponding to blueshifted (90.2 km s −1 ) and redshifted (93.2 km s −1 ) parts of the cloud, possibly dispersed by the energetics of the W-R star. Evidence of the influence of the W-R star was already found for this region by Dewangan et al. ( 2016 ). Hence, any region with a double-peaked 13 CO spectrum toward the position of a W-R star exhibiting a trough might be explored for any impact of W-R stars on their parent molecular clouds. The 13 CO spectra for our selected regions are constructed by integrating all emission within a 45′′ diameter, centered on the positions of the W-R stars (Figure 7 ) for velocity ranges where dust clumps are generally identified (see Section 2 ). All spectra show similar double-peaked spectral signatures as seen in the spectrum toward the reference region (see Figure 6 (b)) except for the G34 region. The spectrum for the G34 region constructed for a 45′′ diameter shows three peaks (see Figure 7 (c)). However, the highest velocity peak (at v LSR of ∼61 km s −1 ) dissolves when the spectrum is produced for a 240′′ diameter. This implies a small component of molecular gas along the sight line to the W-R star, which is peaking at a v LSR of 61 km s −1 . The emission peaks, troughs, and corresponding velocities are marked and labeled in Figure 7 . Figure 7. 13 CO spectra of our five selected regions (a) G15, (b) G24, (c) G34, (d) G35, and (e) G51. The spectra were constructed following the same procedure as that applied to the reference region, G27 (see the caption of Figure 6 ). Velocities corresponding to the blue- and
Paper: 2019MNRAS.490.4841L (acknowledgments)
ACKNOWLEDGEMENTS We gratefully acknowledge the anonymous referee for useful comments and suggestions. We also thank E. Bonnasieux, who carefully read the manuscript, helping us to improve it. FL and AB acknowledge financial support from the Italian Minister for Research and Education (MIUR), project FARE SMS, code R16RMPN87T. AB acknowledges financial support from the ERC-Stg DRANOEL, no 714245. IP acknowledges funding from the INAF PRIN-SKA 2017 project 1.05.01.88.04 (FORECaST). IP, FL and MM acknowledge support from INAF under the MAIN STREAM PRIN project ‘SAUROS’. The TRG computer cluster was funded by the Autonomous Region of Sardinia (RAS) using resources from the Regional Law 7 August 2007 n. 7 (year 2015) ‘Highly qualified human capital’, in the context of the research project CRP 18 ‘General relativity tests with the Sardinia Radio Telescope’ (P.I. of the project: Dr. Marta Burgay).
Paper: 2019MNRAS.483.4678V (fulltext)
GARSTEC increase the grid size considerably. The advantage of one such giant grid is that we can reuse it for the newly observed stars unlike the grid constructed using the MESA. The disadvantage is that we need much larger disc space (current grid needs 4TBs of disc space to store the local properties of the models needed for the frequency computation). Furthermore, the models on a track have poorer temporal resolution. We obtained models for the individual stars from the above tracks following the same procedure as for the MESA, i.e. we scanned all the models in a track and located the age range where T eff , [Fe/H] s , and 〈Δν〉 were all within 4σ of the observed values, subsequently we fitted the surface-corrected model frequencies (Kjeldsen et al. 2008 ) to get a representative model for the star. Repeating this for all the 6000 tracks, we obtain several tens to few hundred models per star. 3.3 YREC models We used YREC with OPAL high-temperature opacities (Iglesias Rogers 1996 ) supplemented with low-temperature opacities of Ferguson et al. ( 2005 ). The metallicity mixture from Grevesse Sauval ( 1998 ) was used. We used the 2005 version of the OPAL equation of state (Rogers Nayfonov 2002 ). All the nuclear reaction rates were from Adelberger et al. ( 1998 ) except for the 14 N( p , γ) 15 O reaction, for which we used the updated rates of Formicola et al. ( 2004 ). A step overshoot (Maeder 1975 ) of α OV = 0.2 H p , where H p is the pressure scale height, was used for all the stars. The YREC models were used to isolate the impact of the atomic diffusion on Y s determination by using the models with and without diffusion. The adiabatic oscillation frequencies were calculated using a code described in Antia Basu ( 1994 ). The Yale Monte Carlo Method (Silva Aguirre et al. 2015 ) was used to get a set of representative models for each star. We started with using the average large frequency separation and frequency of maximum power along with the spectroscopically determined effective temperature to get an estimate of the mass and radius of the star using the Yale Birmingham Grid-Based modelling pipeline (Basu, Chaplin Elsworth 2010 ; Gai et al. 2011 ). Since each of the observables has the associated observational error, we created thousands of realizations of M, R, T eff , and [Fe/H] s . For each realization, we used the YREC in an iterative mode to obtain a model of given M and [Fe/H] s that had the required R and T eff . This was done in two different ways: in the first approach, we kept α MLT fixed at different values and iterated over Y i to get the model; and in the second approach, we kept Y i fixed at different values and varied α MLT to get the required model. Finally, a set of representative models of the star was obtained based on the merit function, where and are the reduced chi-squares for the oscillation frequencies and frequency ratios, r 02 and r 01 (for the definition of ratios, see Roxburgh Vorontsov 2003 ). The was computed
Paper: 2019MNRAS.490.1928Y (fulltext)
corresponding to the 1σ uncertainties of log A and γ at z ∼ 6 from the [C ii ] power spectrum in the fiducial survey. The L18, deL14d, deL14 z , O17, and V15 relations are assumed for generating the input power spectrum. In each panel, the regions with dark (light) colour refer to using the full power spectrum (shot noise only). We use δν 0 = 1.0 GHz to translate the [C ii ] luminosity into the flux per beam; see the top x -axis. We also mark with vertical lines the detection limits by HST ( M UV ∼ −16.5, dashed–dotted), ALMA (∼10 8 L ⊙ , dashed), and the 5σ level of the underlying IM survey (solid), respectively. Figure 10. The 1σ constraint contours of the log A– γ parameters from the full power spectrum for the five relations. The ‘ + ’ symbols mark the input values. Ideally, this is the key result that we can expect from our fiducial-like IM experiment combined with the existing upper limits on the LF coming from pointed observations. We warn that these results depend on the underlying assumption of a single power-law form for –SFR relation, , and shown in Fig. 2 . At present, this remains only an educated guess for high-redshift galaxies, as also hinted by the more complex shape found by state-of-the-art simulations in P15 , and more recently by Pallottini et al. ( 2019 ). ALMA surveys covering a larger area and/or reaching to fainter fluxes, plus a more accurate determination of the galaxy SFR, would be of fundamental importance for this issue. 4.2 Survey strategies We finally investigate how to optimize a survey for a [C ii ] IM experiment. Generally, given the observation time and bandwidth, when , , a narrow and deep field has larger S/N. However, when , , wider field has larger S/N. For some applications, though, a highest S/N is not necessarily the first priority indicator for optimizing surveys; instead, a wide survey is preferred in order to sample long-wavelength k -modes carrying cosmological information and break parameter degeneracy, even at the price of lowering the S/N. We discuss this issue in the context of recovering a fundamental physical quantity as the [C ii ] luminosity density, 10 the first moment of the LF We compute the expected relative variance of the recovered [C ii ] luminosity density, , as a function of t obs for four different surveys with different telescope diameter D , survey area Ω survey , or bandwidth. These cases allow us to compare the survey performances in terms of small and large diameters (S1 versus S3), narrow and wide field (S1 versus S2), low and high angular resolution (S1 versus S3), and number of bolometers and higher noise level (S4). We use the L18 relation to make the input signal. The details of each survey are listed in Table 3 . The results (in units of percentage) are plotted in Fig. 11 , where the relative variances obtained from the shot noise only (power spectrum at k max ) and from the full power spectrum are shown separately by thin and thick lines, respectively. Figure 11.
Paper: 2017ApJ...837L..11C (fulltext)
of magnetohydrodynamic (MHD) waves with observations of solar atmospheric waves and oscillations gives information on the physical parameters of medium (e.g., Nakariakov et al. 2016 ), including sunspots (e.g., see Snow et al. 2015 ; Sych 2016 for recent results). Moreels van Doorsselaere ( 2013 ) designed a method for deriving the temperature of plasma confined in photospheric magnetic flux tubes by using the intensity, velocity, and magnetic field strength fluctuation amplitudes. Reznikova et al. ( 2012 ) estimated the inclination angle of umbral magnetic field, based on the interpretation of umbral oscillation as a slow magnetoacoustic gravity (MAG) wave in an inclined magnetic field. Yuan et al. ( 2014 ) established that the magnetic inclination angle derived seismologically was 30%–40% larger than that given by the magnetic field extrapolation. Roberts ( 2004 ) reviewed the analytical expressions for the cutoff frequency as a function of observable parameters for different solar atmospheric models. Roberts ( 2006 ) provided an analytical form of angular cutoff frequency of a slow magnetoacoustic wave in a non-isothermally stratified atmosphere with a uniform vertical magnetic field. The cutoff frequency not only prescribes the lowest frequency of a propagating wave, but also acts as a natural frequency of a medium (e.g., Roberts 2004 ). It suggests that the atmosphere should be observed to oscillate at the frequency equal or higher than the cutoff frequency in response to an external broadband excitation (Botha et al. 2011 ; Chae Goode 2015 ; Kwak et al. 2016 ). Thus, the cutoff frequency is always below the detected frequency (e.g., Tziotziou et al. 2006 ; Yuan et al. 2014 ). Thus, the cutoff frequency involves the physical parameter of the medium, independently of the specific excitation mechanism of atmospheric oscillations. In this work, we determine the cutoff frequency of umbral oscillations by using the weighted frequency, , where P ( f ) is the power spectrum (Takahashi et al. 2015 ), for 478 sunspots observed in the continuum with the Heliospheric Magnetic Imager (HMI; Scherrer et al. 2012 ; Schou et al. 2012 ) on board the Solar Dynamics Observatory ( SDO ; Pesnell et al. 2012 ). Using the theory given by Roberts ( 2006 ), we determine the Alfvén speed, plasma-beta ( β ), and mass density inside umbrae. In Section 2 , we describe the method used to obtain the weighted frequency and its link with the Alfvén speed, plasma- β , and mass density. In Section 3 , we provide maps of those quantities as well as their average behavior for the 478 analyzed sunspots. Finally, we summarize and discuss our results. 2. Method: Seismology We derive the physical parameters of an umbra by using the seismology, developing the approach designed by Yuan et al. ( 2014 ), who estimated acoustic cutoff frequencies of umbral oscillations at the heights of 1700, 1600, and 304 Å line formation. The authors found that the acoustic cutoff frequency is
Paper: 2019MNRAS.483.3673M (acknowledgments)
ACKNOWLEDGEMENTS VM and BWS acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 694745). The Parkes Observatory is part of the Australia Telescope which is funded by the Commonwealth of Australia for operation as a National Facility managed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). This work was supported by the Australian Research Council Centre for Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. The re-processing of the HTRU survey made extensive use of the GPU Supercomputer for Theoretical Astrophysics Research (gSTAR) hosted at Swinburne University, and funded by a grant obtained via Astronomy Australia Limited (AAL). We extend our gratitude to Craig Heinke for greatly helping the writing of the Swift proposal for the confirmation attempt of J1618–36, and for performing the subsequent data analysis. Finally, we thank the referee Scott Ransom for providing valuable comments.
Paper: 2017A&A...606A.142C (acknowledgments)
The authors thank the anonymous referee for the careful reading and useful comments, Nicolas Billot for his support and help in the data reduction process, the IRAM 30 m staff, and Jonathan C. Tan and Silvia Spezzano for their useful suggestions. A.C.T., P.C., and J.E.P. acknowledge the financial support of the European Research Council (ERC; project PALs 320620).
Paper: 2021MNRAS.500.1261C (fulltext)
(measured either by stellar light absorption A V or gas absorption H α/H β) and H 2 is due to the correlation between dust and H 2 . This method is convenient since spatially resolved extinction maps are available for large samples of galaxies thanks to optical integral-field spectroscopy surveys. However, unlike 12 μ m, extinction as measured by the Balmer decrement is only valid over a range that is limited by the signal-to-noise ratio of the H β emission line. With extreme levels of extinction, e.g. in local ultraluminous infrared galaxies, the H β line becomes invisible, so this method cannot be used. It is not yet known whether the correlation between 12 μ m and CO holds at subgalaxy scales, or how it compares with the resolved SFR–H 2 and A V –H 2 correlations. Comparing these correlations at resolved scales may give insight into the factors driving the 12 μ m–CO correlation. The WISE 12 μ m beam full width at half-maximum (FWHM) is 6.6 arcsec (Wright et al. 2010 ), which corresponds to ≤1 kpc resolution for galaxies closer than 31 Mpc. This resolution and distance range is well matched to the Extragalactic Data base for Galaxy Evolution survey (EDGE; Bolatto et al. 2017 ). EDGE is a survey of CO(1–0) in 126 nearby galaxies with 4.5 arcsec spatial resolution using the Combined Array for Research in Millimeter-wave Astronomy (CARMA). One of the main goals of EDGE was to allow studies of resolved molecular gas and optical integral-field spectroscopy data in a large sample of nearby galaxies. In this study, we use the EDGE CO and WISE data to measure the 12 μ m and CO(1–0) correlation within individual galaxies. We find that the best-fitting parameters describing this relation vary significantly among galaxies. We perform multivariate linear regression using a combination of global galaxy measurements and quantities derived from spatially resolved optical spectroscopy from the Calar Alto Legacy Integral Field Area Survey (CALIFA; Sánchez et al. 2012 , 2016 ; Walcher et al. 2014 ). This yields a set of linear functions with log Σ(H 2 ) as the dependent variable that can be used as spatially resolved estimators of H 2 surface density. These estimators can predict H 2 surface density with a root-mean-square (RMS) accuracy of ≃0.2 dex in galaxies for which 12 μ m data are available. 2 DATA AND DATA PROCESSING 2.1 Sample selection The sample is selected from the EDGE survey (Bolatto et al. 2017 , hereafter B17 ). The typical angular resolution of EDGE CO maps is 4.5 arcsec, and the typical H 2 surface density sensitivity before deprojecting galaxy inclination is 11 M ⊙ pc −2 ( B17 ). Every EDGE galaxy has optical integral field unit (IFU) data from CALIFA, allowing joint studies of the content and kinematics of cold gas (H 2 ), ionized gas, and stellar populations, all with ∼kpc spatial resolution. We processed the CO data for all 126 EDGE galaxies, and as a starting point we selected the 95 galaxies which had at least one detected pixel
Paper: 2016A&A...588A..99L (fulltext)
14 for components A, B, C, and D, respectively. In light of the large core radii of most of the four components (21, 28, 21, and 5 for A, B, C, and D, respectively); however, these offsets are not necessarily significant. Specifically, our analysis does not support the hypothesis advanced by Diego et al. (2015b) that component A is significantly offset from the closest light peak. These authors argued that this offset is probably due to the lack of lensing constraints in that area. Since three of the four components have core radii larger than 15, i.e., larger than 100 kpc at the redshift of MACS J0717, their superposition leads to a relatively flat mass distribution. In the following, this model is referred to as the cored mass model. Various mechanisms that could lead to such large cores have been proposed (see also Diego et al. 2015b , a , for a thorough discussion). Large cores in massive central galaxies are thought to be caused by massive black holes that clear the centre of galaxies of stars (see, e.g. López-Cruz et al. 2014 ). This may affect dark matter as well, but the scales on which this mechanism can be expected to be effective (less than 10 kpc) are smaller than the scale we are interested in here (100 kpc). Actif galactic nucleus feedback can also contribute to the flattening of the central region ( Martizzi et al. 2012 ), but once again this mechanism is efficient only on small scales (up to 10–15 kpc). On scales of 100 kpc, large cores may result from violent interactions related to merging events in this actively evolving cluster, although several studies find that DM profiles are not strongly altered by a collision ( Ricker Sarazin 2001 ; Dekel et al. 2003 ; Molnar et al. 2010 ). Finally, heating of massive galaxies by dynamical friction against the diffuse dark matter distribution of the cluster can flatten the slope of the DM density profile, and sometimes even dominate over adiabatic contraction (see, e.g. El-Zant et al. 2001 , 2004 ). Another explanation might be self-interacting dark matter particles ( Natarajan et al. 2008 ; Rocha et al. 2013 ), known to flatten the cusps of cluster scale haloes on scales up to 100 kpc, or the multi-coupled Dark Energy scenario ( Garaldi et al. 2016 ). Given the implications of a flat mass profile for MACS J0717, we investigate its mass distribution further by testing whether large-core components are really required by the data. To this end, we explore in the next subsection a peaky, non-cored mass model. Table 2 Same as Table 1 for the non-cored mass model. Table 3 NFW parameters inferred for the four large-scale dark matter clumps (A, B, C, D) and the dPIE parameters for the galaxy-scale component (core radius fixed to 0.3 kpc). Notes. These parameters correspond to the NFW mass model. Coordinates are given in arcseconds with respect to . The ellipticity is for the mass distribution. Error bars correspond to confidence level. Concerning the scaling relations, the zero point is set
Paper: 2016ApJ...832..102M (fulltext)
material absorbs the particle and then shortly after reemits the absorbed energy isotropically. Numerically, the time step is too large for the absorption and emission processes to be resolved separately, such that they appear as one scattering process ( as ). The simulation is initially populated with Monte Carlo particles according to Equation ( 66 ). During each time step, the code creates new Monte Carlo particles to simulate the process of emission. The number of particles created depends on how many particles are already in the simulation: if there are relatively few existing particles, then many are created, but if the number of existing particles is close to the maximum allowed value then few are created. We typically set the maximum to 10 6 particles; we want enough particles to populate every cell and frequency group (Section 3.1 ), but not so many that our simulation runs slowly. For each particle, the details of its creation (location, direction, etc.) are randomly chosen. The cell of creation is randomly chosen with weighting , the amount of energy the cell emits in the time step. Using Equations ( 10 ), ( 33 ), and ( 77 ), we havewhere is the volume of the cell and is the time step for the cell (see below). For simplicity, the radius within the cell and the time within the time step are randomly chosen with uniform weighting; from Equation ( 10 ), the direction is weighted uniformly (isotropically) while the frequency is weighted by . The particle also has an energy weight, equal to the total energy of the photon packet. This is not randomly chosen; instead, all of the particles “emitted” in a time step have the same energy weight, given bywith being the number of Monte Carlo particles emitted in that step. At the end of the time step, the cell temperature is adjusted due to emission using(see below). Particles are also created at the inner boundary of the simulation, the base of the atmosphere. Here the physical process for the creation of the particles is not emission, but the escape of photons from the hot layers beneath the atmosphere. As before, the details of creation are chosen randomly; the time within the time step is chosen with uniform weighting, while from Equation ( 35 ), the direction is weighted by μ and the frequency is weighted by B ν . The energy weight for these particles iswith being the number of particles created at the base in the time step and being the energy entering the simulation from the base of the atmosphere. From Equations ( 35 ) and ( 53 ), we havewhere is the area of the base and is the time step at the base (see Equation ( 67 )). The created particles (photon packets) are transported according to the left-hand side of Equation ( 5 ). If general relativistic effects are ignored, the particles travel in straight lines. For a particle traveling a distance s from the position and moving in the direction initially defined by , where , the new radius of the particle is given by the law of cosines:The
Paper: 2017MNRAS.466.1213K (acknowledgments)
We thank the referee for helpful comments. MRK acknowledges support from an Australian Research Council Discovery Project (DP160100695). JMDK gratefully acknowledges financial support in the form of a Gliese Fellowship and an Emmy Noether Research Group from the Deutsche Forschungsgemeinschaft (DFG), grant number KR4801/1-1. RMC is the recipient of an Australian Research Council Future Fellowship (FT110100108). MRK and JMDK thank the Aspen Center for Physics, which is supported by NSF Grant PHY-1066293, for its hospitality during the early phases of this work.