adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...627L...6M (fulltext)
suited for tracing the very inner regions of a molecular disc ( Hirota et al. 2014 ; Ginsburg et al. 2018 ). To our sensitivity and resolution limit, the H 2 O emission appears devoid of any structure and does not extend beyond the outer radius of the disc as traced by the dust continuum emission (grey contours Fig. 2 b). The peak of emission is away from the centre of the dust disc in all velocity channels, while the H 2 O emission peaks between the enhanced continuum dust ring and the central location of G17.64 at the highest blue- and red-shifted velocities. The zero-moment contours in Fig. 2 a show that the H 2 O emission might appear like a disc with a central hole or like a single wide ring ranging from approximately half of the beam (∼30 au) to 120 au in radius (e.g. like the SO emission in Yen et al. 2014 ). The cut for the PV diagram was taken at a PA of 25.9° using a width of 5 pixels to encompass one synthesised beam (grey dashed line in Figs. 2 a,b). The PA was established by fitting a line through the centres of Gaussian fits to independent zero-moment maps of the blue- (3.2−21.4 km s −1 ) and red-shifted (22.7−44.8 km s −1 ) emission and the central position of G17.64 from the dust continuum. The PV diagram depicts the clear pattern of Keplerian rotation, particularly the red-shifted emission > 30 km s −1 , where the highest velocities peak closer towards the central source. The H 2 O undoubtedly traces a rotating disc. Fig. 2. Panel a : integrated intensity (zero-moment) map of the H 2 O 5 5, 0 −6 4, 3 ν 2 = 1 (232.68670 GHz) molecular line emission. The contours trace emission at the 3, 5, 7, 9, and 11 σ levels, where 1 σ = 7.13 mJy beam −1 km s −1 . Panel b : velocity-weighted integrated intensity (first-moment) map overlaid with the contours of the continuum emission from Fig. 1 a in grey. The outer extent of the H 2 O emission matches that of the dust continuum. Panel c : PV diagram for the H 2 O emission extracted from a 5 pixel wide slice along the dashed line at PA = 25.9° as indicated in (a) and (b). The contours of the data are plotted at the 20–80% levels in steps of 10%, where 10% corresponds to ∼1 σ (0.65 mJy beam −1 ). Panel d : same as (c), but the contours indicate the best representative model using inner and outer disc radii of 30 and 120 au, respectively, a stellar mass of 45 M ⊙ , and a disc inclination angle of 40°. The spatial (28 mas) and velocity resolution (1.3 km s −1 ) are indicated by the black cross at the bottom right. 4. Discussion 4.1. Stellar mass Based on its luminosity, G17.64 is firmly positioned as an O star (e.g. Vacca et al. 1996 ). However, previous estimates of the stellar mass, which are essential for understanding the disc stability, were not well constrained because the kinematics were poorly resolved ( Maud et al. 2018 ). Building from our previous representative models presented in Maud et al. (2018) , we matched the H 2 O PV diagram with only very minor changes. We used
Paper: 2020ApJ...903..127C (fulltext)
2. 2019 MMS Turbulence Campaign In the special MMS Solar Wind Turbulence Campaign, in 2019 February, the four MMS spacecraft were rearranged into a “beads on a string” configuration instead of the standard tetrahedron formation. The four spacecraft were positioned in a collinear configuration with log-spaced separation. Interspacecraft separations ranged from 30 to 200 km. Additionally, the spin axis was tilted to obtain improved electric field measurements. In this phase of the MMS orbital parameters, the formation spent a substantial period of time during each orbit in the solar wind, well beyond the bow shock. As such, the project was able to obtain a number of useful burst resolution measurements in the solar wind. The longest of those was a continuous interval of burst data lasting 5 hr, and which is analyzed in this paper, as well as in Bandyopadhyay et al. ( 2020 ). For orientation, the configuration of the four MMS spacecraft, while in the solar wind during the Turbulence Campaign, is illustrated in Figure 1 . Figure 1. Diagram of the configuration of the four MMS spacecraft near apogee, during the turbulence campaign. Note that close to apogee, when the spacecraft are in the pristine solar wind, they are aligned on the GSE Y – Z plane perpendicular to the solar wind flow. 3. MMS Data We employ 5 hr of magnetic field data from the Fluxgate Magnetometer (FGM; Russell et al. 2016 ) on board each each of the four MMS spacecraft. These are designated as MMS1, MMS2, MMS3, and MMS4. The magnetic field data are accumulated at a resolution of 128 Hz and synchronized across the formation. The orbital context is illustrated in the top panel of Figure 2 . The middle panel of Figure 2 shows the three Geocentric Solar Ecliptic (GSE) components of magnetic field recorded in this period by the MMS1 spacecraft. Figure 2. Top: location of the MMS spacecraft during this interval in bold red, along with the orbit in black. The magnetopause and bow shock are shown in light green and light blue, respectively, as estimated from OMNI data. Bottom: time series of magnetic field and PVI as defined in Equation ( 1 ), for the 5 hr interval in the solar wind. Several structures at MHD and kinetic scales (flux tubes, current sheets) can be seen. To visualize and quantify the roughness or intermittency of the magnetic field signal, we compute the partial variance of increments (PVI) of the magnetic field for this interval. The PVI, which can be computed either with two spacecraft or with a single spacecraft and the Taylor hypothesis (Chasapis et al. 2015 ; Greco et al. 2018 ) is defined aswhere here, for the multispacecraft case, the spatial lag , for the position of MMS spacecraft number I given by r I , so that that the vector increment is . The lower panel of Figure 2 shows the time series of the values of PVI computed in this way (Chhiber et al. 2018 ; Greco et al. 2018 ), namely, the two-point PVI value using spacecraft MMS2 and MMS3. The separation
Paper: 2019MNRAS.489.3022S (fulltext)
form from that obtained for the fundamental frequencies of crustal torsional oscillations in the phases of spherical and cylindrical nuclei (Sotani et al. 2018 ). We also remark that the fundamental frequency in the tube and bubble phases can be smaller than that in the phases of spherical and cylindrical nuclei and that the fundamental frequencies with general values of l can also be well fitted in the same functional form: with a different set of the adjustable parameters , , and . Hereafter, we will thus attempt to identify the observed QPO frequencies using the fitting formula ( 10 ) for the tube–bubble layer, in addition to the formula given in Sotani et al. ( 2018 ) for the sphere–cylinder layer. Figure 2. (Colour online) Fundamental frequencies of the l = 2 torsional oscillations in the tube and bubble phases, as obtained for the neutron star models with M = 1.4M ⊙ and R = 12 km as well as with various sets of L and K 0 . Here, the left-hand and right-hand panels correspond to the results for the maximum and minimum enthalpies that participate in the oscillations, i.e. and 0, respectively (see the text for details). The solid line denotes the fitting given by equation ( 9 ). Note that the obtained fundamental frequencies in the tube–bubble layer are generally lower than those obtained in our earlier analysis by assuming that only the bubble phase is oscillating (Sotani et al. 2017a ). This tendency is more significant for larger values of L . This is partly because for larger L , the bubble phase is less likely to appear, as shown in Table 1 , and partly because the shear modulus and hence the shear velocity is relatively small in the tube phase, as shown in Fig. 1 . As we will see later, therefore the 26 Hz QPO observed from SGR 1806-20 is identified as the l = 4 fundamental torsional oscillation in the tube–bubble layer, in contrast to our earlier analysis (Sotani et al. 2017a ) in which it was identified as the l = 2 fundamental torsional oscillation. Up to now, we have already done many trials to identify the QPOs observed in SGR 1806-20 and SGR 1900+14 as the crustal torsional oscillations. As long as we adopt the QPO frequencies derived in the conventional non-Bayesian analyses of RXTE data for the X-ray afterglows of the giant flares and the recurrent X-ray outbursts, such identification has worked out relatively well (Sotani et al. 2012 , 2013a , b , 2016 , 2017a , 2018 ). In fact, the observed QPOs, except for the 26 Hz QPO in SGR 1806-20, can be identified as the torsional oscillations inside the phases of spherical and cylindrical nuclei in such a way that the QPOs of frequencies lower than ∼200 Hz correspond to the fundamental oscillations with various values of l, while the higher QPOs observed in SGR 1806-20 correspond to the overtones (Sotani et al. 2018 ). In this case, since it is still uncertain how much fraction of dripped neutrons in the phase of cylindrical nuclei would be locked to the motion of protons in the
Paper: 2020ApJ...888...97B (fulltext)
We thus expect an homogeneous envelope to become transparent after a timewhich is frustratingly well above the duration of the Eddington luminosity phase. Here we assume κ ≈ 0.1 cm 2 g −1 , which is a reasonable value for ordinary supernova material. At lower X-ray energies ( x ≲ 3 keV), photoionization may delay the time at which the envelope becomes transparent by an additional factor of . Despite this, the youngest neutron star we have uncovered has an age of about 340 yr (Fesen et al. 2006 ). In a few instances, a surviving binary companion has been detected in postexplosion deep optical imaging of extragalactic supernova (Maund Smartt 2009 ; Folatelli et al. 2014 ). In the case of 1993J, for example, the brightness of the transient dimmed sufficiently after about a decade so that its spectrum showed the features of a massive star superimposed on the supernova (Maund et al. 2004 ). It is perhaps a stinging fact that despite the expected manifestations of neo-neutron stars, one of the main issues in the field has been that most Galactic supernova remnants have no detectable central source. Figure 11 shows the current detections and upper limits of thermal emission in nearby neutron stars with model predictions. Observational selection effects are clearly at play when uncovering young objects yet there is the possibility that a sizable fraction of massive star collapses might produce black holes rather than neutron stars, with the clearest example being W49B (Lopez et al. 2013 ) and the other four examples, from Kaplan et al. ( 2004 , 2006 ), all plotted in Figure 11 . Figure 11. Plot of luminosity as a function of the age of the remnant for nearby neutron stars together with the predictions from neutron star cooling models. The (green) dots are measurements and the (blue) squares are upper limits from detected neutron stars (data taken from Beznogov Yakovlev 2015 ) while the (red) dotted error bars are upper limits on the compact objects, black holes or neutron stars, expected to be presents in five core-collapse supernova remnants. These remnants are, in order of increasing age: G043.3–0.2 (a.k.a. W49B, marked by a diamond) from Lopez et al. ( 2013 ), and G127.1+0.5, G084.2+0.8, G074.0–8.5, and G065.3+5.7 from Kaplan et al. ( 2004 , 2006 ). Shaded areas show model predictions of Page et al. ( 2004 , 2009 ) for the minimal cooling of neutron stars that cover uncertainties on the chemical composition of the envelope and nucleon pairing at high densities. In contradistinction, the dashed-pentadotted curve, Z , exemplifies the effect of fast neutrino emission from the direct Urca process expected to occur in massive neutron stars (Boguta 1981 ; Lattimer et al. 1991 ) resulting in very cold stars (Page Applegate 1992 ). Also plotted are the three different models (A, C, D) shown in Figure 9 . 6.2. Neutron Star Mergers, Short Gamma-Ray Bursts, and LIGO Events The merger of binary neutron stars and the subsequent production of a beamed,
Paper: 2017ApJ...848...31H (acknowledgments)
We thank an anonymous referee for insightful comments. We are grateful to B. Burkhart, A. Lazarian and Z. Manchester for insightful comments on the manuscript. T.H. thanks B.T. Draine for stimulating discussions on spacecraft charging. This work was supported in part by a grant from the Breakthrough Prize Foundation.
Paper: 2017MNRAS.468..620Z (fulltext)
NLRs and in the NLRs, effects of the dusts on the observed values (marked by the suffix of obs) should be where D (BLRsextNLRs) , D (extNLRs) and D NLRs represent effects of dusts in all regions, effects of dusts in the regions from the BLRs to the observer and the effects of dusts in the NLRs, respectively. Effects of dust extinctions can be considered as the role factor to determine the scatters of the correlations shown in Fig. 5 . There are more similar dust extinctions on L (5100 ,obs) and on , but more different dust extinctions on the L (5100 ,obs) and on the . Therefore, there are tighter correlations between L (5100 ,obs) and than the correlation between L (5100 ,obs) and . Moreover, with considerations of dependence of Balmer decrements (which can be used to trace the dust extinctions) on [O iii ]luminosity, the steeper correlation of L (5100 ,obs) and can also be simply explained as follows. Based on the observed and the Balmer decrements from common NLRs BD obs, NLRs for our objects with both narrow H and narrow H, we can find that there is a moderately negative correlation shown in Fig. 7 , with Spearman rank correlation coefficient of about 0.32 with P null 510 8 . And the anticorrelation can be simply described by with narrow 0.045. Then, combining with the empirical equation to estimate colour excess with 3.1 as the intrinsic flux ratio of narrow H to narrow H, the intrinsic luminosity can be estimated by where k and E ( B V ) represent the reddening curve and colour excess, respectively, and narrow 0.8 k narrow . Meanwhile, similar equations to estimate L (5100 ,int)) and can be described by where ext 0.8 k ext and have probably different values of ext and 5100 from the value of narrow , because of different dust obscurations for observed continuum luminosity and for observed luminosity from the extended components of [O iii ]lines. And, the values of must be smaller than 1, otherwise an unreasonable result can be expected that intrinsic luminosity after considerations of dust extinctions could be smaller than the observed luminosity. Then, we have the following sub-equations Based on the third sub-equation in equation ( 10 ) combining with the shown results in the top panel of Fig. 6 , we can find that . Therefore, the correlation of L (5100 ,obs) versus is steeper, because of its larger slope of being about 1.2times larger than the slope of for the correlation of L (5100 ,obs) versus . Certainly, the results above are highly idealized, and seriously depend on physical properties of dusts in central regions of AGN; however, the results can be applied to find qualitative explanations to the results shown in Fig. 5 . Figure 7. Correlation between observed Balmer decrements(flux ratio of narrow H to narrow H) and the luminosities of the core components of the [O iii ]emission lines. Before the end of this section, we show our simple discussions on which component is well applied to trace central AGN intrinsic luminosity in type-2
Paper: 2016ApJ...825..154Z (acknowledgments)
We highly appreciate the referee for his/her valuable comments and suggestions that significantly improved our work. J.F.Z. thanks support from the National Natural Science Foundation of China (grant Nos. 11233006 and 11363003). A.L. acknowledges the NSF grant AST 1212096 and the Center for Magnetic Self Organization (CMSO). H.L. and J.C. acknowledge support by the National R D Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2064475).
Paper: 2016ApJ...826L...8M (fulltext)
show the rest of the sources detected; in the case of sources 32, 35, and 38 the K = 0, 1 lines were detected, but no valid solution was found for the simultaneous Gaussian fitting of all four components. Integrated line fluxes obtained from the Gaussian fitting are reported in Table 1 and were used to compute Boltzmann plots (i.e., the rotational diagram) relating the column density of the emitters normalized by the level degeneracy and the energy of the levels. Using standard procedures (e.g., Fontani et al. 2002 ), assuming LTE and optically thin conditions that are appropriate for this line and class of sources (e.g., Bergin et al. 1994 ) the temperature and total column density of CH 3 C 2 H can be derived from a linear fit to the rotational diagram (Goldsmith Langer 1999 ). Because our observations are single-pointed toward the 70 μ m peak of the sources, we could only derive beam-averaged column densities and we chose to focus on the analysis of the CH 3 C 2 H rotational temperatures (see Table 1 ). 3. RESULTS 3.1. CH<sub>3</sub>C<sub>2</sub>H Detection Statistics The sources listed in Table 1 are ordered according to their L/M value computed from the Herschel fluxes, augmented with ancillary data at mid-IR wavelengths (D. Elia et al. 2016, in preparation), and it can be immediately seen that the detection rate in CH 3 C 2 H( J = 12–11) is strongly dependent on the L/M values. We can distinguish three ranges of L/M : the rate is 0 (none out of 23 sources) for the sources with L/M 1, 58% (seven out of 12) for sources with 1 L/M 10 and 90% (16 out of 18 sources) for sources with L/M > 10. In Table 1, in sources # 32, 35, and 38, at least the K = 0 line is detected, but the four-component Gaussian fit to the four lines did not converge and so no further analysis could be carried out for these sources. This finding is preliminary confirmation of the working hypothesis that higher values of L/M should be related with a more advanced evolutionary stage. As CH 3 C 2 H is a temperature probe for gas with densities in excess of 10 5 cm −3 (Bergin et al. 1994 ), its values are influenced more by the inner clump regions and relatively less by the outer envelope. Looking at Figure 3 , the range of L/M ≲ 1 is characteristic of “pre-stellar” clumps (in which, contrary to the sources of the present study, 70 μ m emission is not detected); the internal input energy producing the detectable 70 μ m flux would not seem sufficient to drive up considerably the bolometric luminosity as well as the internal clump temperature, resulting in non-detections in CH 3 C 2 H( J = 12–11) for any of the sources with L/M 1. This suggests that in these clumps intermediate or high-mass star formation has not yet started. Above the L/M = 1 threshold, corresponding to the upper envelope of the “pre-stellar” clumps distribution in Figure 3 , the CH 3 C 2 H( J = 12–11) detection rate increases sharply, implying that the internal energy produced by star formation is rising to
Paper: 2017ApJ...850....1R (fulltext)
the power-law slope α , the dust temperature , and the spectral slope of the dust emissivity as fitting parameters, using the observed-frame 500 μ m flux density as a normalization factor. We also fit “general” models that allow for wavelength-dependent changes in optical depth, adding the wavelength , where the optical depth reaches unity as an additional fitting parameter. The optically thin fitting procedure yields statistical mean values of K, , and . 19 19 α is only poorly constrained by the data. The general fit yields mean values of μ m, K, , and . The fit also implies rest-frame infrared (8–1000 μ m) and FIR (42.5–122.5 μ m) luminosities of L ⊙ and L ⊙ , respectively. 20 20 The measured agrees to within ∼2% with independent estimates based on integrating a normalized MAGPHYS-based SED template based on the z = 6.34 starburst HFLS3 (Cooray et al. 2014 ), showing that the adopted power-law approximation of the short wavelength emission has a minor impact on the measured quantities. Assuming a dust absorption coefficient of m 2 kg −1 at 125 μ m (e.g., Dunne et al. 2003 ), we also find a dust mass of M ⊙ . 21 21 Given the limited photometry, the uncertainties may be somewhat underestimated. Assuming a Chabrier ( 2003 ) stellar initial mass function, these parameters suggest a total SFR of ∼2400 M ⊙ yr −1 . Given the limited SED constraints in the rest-frame optical, we obtain an estimate for the stellar mass M ⋆ of ADFS-27 by normalizing the MAGPHYS-based SED template of HFLS3 in Figure 4 to the observed-frame 4.5 μ m limit. This yields M ⋆ 1.2 × 10 11 M ⊙ . 4.2. Molecular Gas Mass, Gas-to-dust Ratio, and Gas Depletion Time The value of ADFS-27 (based on the adopted r 51 = 0.39) implies a total molecular gas mass of M ⊙ . 22 22 We here adopt a conversion factor of M ⊙ (K km s −1 pc 2 ) −1 for nearby ultra-luminous infrared galaxies and SMGs (e.g., Downes Solomon 1998 ; Tacconi et al. 2008 ). Taken at face value, this yields a gas-to-dust ratio of /, which is comparable to that in the z = 6.34 starburst HFLS3 and within the range of values found for nearby infrared-luminous galaxies (Wilson et al. 2008 ; Riechers et al. 2013 ), but ∼4× lower than that for the z = 5.30 starburst AzTEC-3 (Riechers et al. 2014 ). At its current SFR, this implies a gas depletion time of /SFR ≃ 100 Myr, consistent with the general SMG population (e.g., Carilli Walter 2013 ). 4.3. SFR and Gas Surface Densities, Gas Dynamics, and Conversion Factor The apparent 870 μ m continuum sizes of ADFS-27N (mal) and ADFS-27S (yong) imply physical sizes of (1.83 ± 0.18) × (1.28 ± 0.16) and (2.05 ± 0.18) × (0.87 ± 0.15) kpc 2 at z = 5.655, which are comparable to the ∼2.5 kpc diameters found for other z > 4 dusty starbursts like AzTEC-3, HFLS3, and SGP-38326 at similar wavelengths (Riechers et al. 2013 , 2014 ; Oteo et al. 2016 ). Assuming that their flux ratios at 870 μ m are representative at the peak of the SED, this implies surface densities of and 7.5 × 10 12 L ⊙ kpc −2
Paper: 2018A&A...612A..27K (acknowledgments)
Acknowledgements The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. Swift /BAT transient monitor results are provided by the Swift /BAT team. This research has made use of MAXI data provided by RIKEN, JAXA, and the MAXI team. TSA acknowledges support through the Russian Government Program of Competitive Growth of Kazan Federal University. We thank Sebastian Heinz and Chris Fragile for useful discussions.
Paper: 2019ApJ...882..119Z (acknowledgments)
This work required the use and integration of a number of Python software packages for science, including AstroPy (Astropy Collaboration et al. 2013 ), 5 5 http://www.astropy.org Matplotlib (Hunter 2007 ), 6 6 http://matplotlib.org NumPy, 7 7 http://www.numpy.org SciPy, 8 8 http://www.scipy.org/scipylib/ and yt (Turk et al. 2011 ). 9 9 http://yt-project.org We are thankful to the developers of these packages. We also thank Volker Springel for the use of the AREPO code. J.A.Z. thanks David Barnes, Rahul Kannan, Federico Marinacci, and Hui Li for useful discussions. He also thanks Grant Tremblay for assistance with a last-minute fix to a figure. J.A.Z. acknowledges support through Chandra Award Number G04-15088X issued by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of NASA under contract NAS8-03060. J.Z. acknowledges support by a Grant of Excellence from the Icelandic Research Fund (grant No. 173929−051). The numerical simulations were performed using the computational resources of the Advanced Supercomputing Division at NASA/Ames Research Center.