adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...886...62L (acknowledgments)
We thank the referee for very helpful comments to improve the presentation of the paper. Y.P.L., H.L., and S.L. gratefully acknowledge the support by LANL/CSES and NASA/ATP. Simulations of this work were performed with LANL Institutional Computing resources. H.L. gratefully acknowledges useful discussions with S. Ida, R. Miranda, and R. Rafikov. Software: Astropy (Astropy Collaboration et al. 2013 ), LA-COMPASS (Li et al. 2005 , 2009a ), Matplotlib (Hunter 2007 ), Numpy (van der Walt et al. 2011 ), RADMC-3D (Dullemond et al. 2012 ).
Paper: 2019A&A...626A.130T (fulltext)
adopt κ 140 μ m = 13.9 cm 2 g −1 ( Draine 2003a ). We also calculate the optical depth of the silicate feature τ sil , following the method defined by Imanishi et al. (2007) where an absorption-free continuum is assumed to be the power-law function determined from the flux densities at 7.1 and 14.2 μ m. As a result, τ sil is estimated to be 2.3 ± 0.2 at 9.8 μ m, the wavelength of the bottom of the absorption feature. We also performed the SED fitting of the heavily obscured AGN, using the Bayesian SED fitting code CIGALE ( Burgarella et al. 2005 ; Noll et al. 2009 ; Boquien et al. 2019 ), which includes energy balance between the dust absorption of the stellar/AGN emission and the dust re-emission in the mid- to far-IR, similarly to the study by Ciesla et al. (2015) . As a result, M star and L IR are estimated to be 8 × 10 9 M ⊙ and 6 × 10 10 L ⊙ , respectively. Comparing the result in Table 3 , we find that M star increases while L IR does not change. The increase in M star , however, does not change our conclusion below. 4. Discussion 4.1. Comparison with other AGNs We detect a deep ( τ sil ∼ 2.3) absorption feature due to silicate grains at around 10 μ m. We compared the optical depth of the silicate feature in LEDA 1712304 with those in other AGNs in a wide range of IR luminosities (10 10 L ⊙ L IR 10 13 L ⊙ ). The spectra of the AGNs to be compared are taken from those of the IR galaxies observed by S p i t z e r /IRS ( Stierwalt et al. 2013 ; Imanishi et al. 2007 ; Imanishi 2009 ; Roussel et al. 2006 ) with the threshold that the equivalent width of the PAH 6.2 μ m feature is smaller than 0.27 μ m ( Stierwalt et al. 2013 ). In addition, we also take AGNs with low IR luminosities ( L IR 10 11 L ⊙ ) from Wu et al. (2010) . Figure 3 shows the relation between the IR luminosity and the optical depth of the silicate feature for LEDA 1712304 and the AGNs. We estimated the optical depths of the AGNs of Stierwalt et al. (2013) , Wu et al. (2010) , and Roussel et al. (2006) by ourselves in the same manner as was performed for LEDA 1712304 with the method defined by Imanishi et al. (2007) , the spectra of which were obtained from the NASA/IPAC IR Science Archive (IRSA). On the other hand, we adopted the values given in each reference paper for the other AGNs. The blue dotted lines in Fig. 3 show the differences between our estimates and those by Stierwalt et al. (2013) , which confirms that the differences between the different methods are not large enough to change a global relation. Figure 3 shows that galaxies with low IR luminosities ( L IR 10 11 L ⊙ ) show significantly shallow silicate absorption features, as already pointed out by Stierwalt et al. (2013) . Imanishi (2009) suggested that the number of heavily obscured AGNs that have deep silicate absorption features ( τ sil > 2) increases with the IR luminosities of the host galaxies. Therefore, LEDA 1712304 may be a rare galaxy from the aspect of having both deep absorption feature τ sil ∼
Paper: 2015MNRAS.448L..94S (acknowledgments)
This work was carried out as part of the ISIMA programme on Gravitational Dynamics held in 2014 July at CITA, Toronto. We acknowledge Pascale Garaud for the organization, the financial support and for providing a stimulating environment. MG thanks the Royal Society for financial support in the form of a University Research Fellowship (URF) and the European Research Council (ERC-StG-335936, CLUSTERS). RLS acknowledges the support of the Science and Technology Facilities Council (STFC) via the award of an STFC Studentship. RLS thanks Anna Lisa Varri for her support and guidance throughout this work. We thank Miklos Peuten, Antonio Sollima, Anna Lisa Varri and Alice Zocchi for discussions during the ‘Gaia Challenge’ meeting and Diederik Kruijssen,Vincent Hénault-Brunet, Jay Strader and Nate Bastian for comments on the manuscript. Finally, we thank the referee for constructive comments that helped to improve the paper.
Paper: 2021A&A...645A.126C (fulltext)
catalog of the whole area covered by Herschel (HELP, Shirley et al. 2019 ) can be taken into consideration for the analysis. Moreover, new submillimeter surveys like TolTEC ( DeNigris Wilson 2019 ) or the future mid/near-infrared James Webb Space Telescope (JWST, Gardner et al. 2006 ) will certainly increase the area and/or the density of the background sources. Appendix A: The ingredients of the model The dark matter transfer function has been computed using Eisenstein and Hu’s fitting formula ( Eisenstein Hu 1998 ), which takes baryonic effects into account in a ΛCDM model. Having chosen an analytical computation of the power spectrum over the traditional numerical one using CAMB ( Lewis et al. 2000 ) is mainly due to computation time. The galaxy-dark matter cross-power spectrum has been computed through Eqs. ( 7 ) and ( 8 ), where the linear dark matter power spectrum is evolved to redshift z via the linear growth factor approximation of Carroll et al. (1992) . The HMF has of course been parametrized according to ( 1 ) for the two different fits we have described in Sect. 2.1 . The deterministic bias associated with each model has been derived using the peak background split as in Sheth Tormen (1999) . Furthermore, we have expressed the mean number of galaxies in a halo of mass M as where ⟨ N c g ⟩ M and ⟨ N s g ⟩ M are the mean number of central and satellite galaxies in a halo of mass M , respectively, expressed in terms of three HOD parameters ( α , log M 1 , log M min ) as and following Zehavi et al. (2005) and Zheng et al. (2005) . In essence, M min is the minimum mean halo mass required to host a (central) galaxy and M 1 > M min is the mean halo mass at which exactly one satellite galaxy is hosted. The mean number density of galaxies at redshift z is then given by The halo density profile has been assumed to match a Navarro-Frenk-White (NFW) profile ( Navarro et al. 1997 ). The normalized Fourier transform of the dark matter distribution within a halo of mass M is then given by ( Cooray Sheth 2002 ) where and ρ s are a scale radius and density that parametrize the profile, concentration parameter of a halo of mass M at redshift z , which satisfies for an NFW profile. The virial radius R vir has been computed through the virial overdensity at redshift z , using the fit by Weinberg Kamionkowski (2003) . It should be noted that we have not defined halos at a certain redshift as overdense regions of a constant factor (say 200) times the background or critical density, but using the virial overdensity instead, which depends on redshift. In practice, for a halo of mass M , we have adopted the concentration parameter by Bullock et al. (2001) , computed r s through ( A.1 ) and, subsequently, calculated ρ s using ( A.2 ). Appendix B: Additional tables and figures Fig. B.1. One- and two-dimensional (contour) posterior distributions from run 5 (in red) and run 1 (in blue), that is, a three-parameter and a two-parameter ST fit with 0 p 1 0.5,
Paper: 2016ApJ...821...42H (fulltext)
Local Bubble. By analogy to the λ 5780.5 carrier, which appears to be a cation from the dependence of its EW on the ambient UV radiation field (Cami et al. 1997 ; Sonnentrucker et al. 1997 ; Vos et al. 2011 ), we suggest that the family of NIR DIBs also arises from cations. This idea is compatible with the DIB-PAH hypothesis, which proposes that the ionized PAHs are the carriers that have their transitions in NIR wavelength range (Mattioda et al. 2005 ; Ruiterkamp et al. 2005 ; Cox et al. 2014 ). Recently, in the first conclusive identification of DIB carriers, two DIBs at λ = 9577, 9632 Å were assigned to the absorption features of interstellar ionized buckminsterfullerene () (Foing Ehrenfreund 1994 ; Campbell et al. 2015 ). Ionized fullerenes and their derivatives are also expected to be the carrier sources of NIR DIBs. In Section 5.2 , we suggested that the λ 10504 carrier is destroyed by the ionizing photons in Cyg OB2, whereas those of the family DIBs can survive the Cyg OB2 environment. We also suggested that the λ 12623 carrier may be produced in Cyg OB2 (Section 5.2 ). The destruction of the λ 10504 carrier and possible production of the λ 12623 carrier are probably caused by the strong UV flux in the Cyg OB2 environment. The different environmental dependence of these two DIBs and the family DIBs would be due to the difference of their carrier properties, such as the ionization state and the stability against the UV photon flux. The FWHM of λ 10504 is ∼1.2 cm −1 , which is one of the smallest widths among 17 DIBs investigated in this paper, whereas the FWHM of λ 12623 is ∼2.4 cm −1 , which is one of the largest. If the profiles of the DIBs are unresolved rotational contours, the widths are mainly determined by the rotational constants of the carriers (Cami et al. 2004 ; Oka et al. 2013 ). Assuming that the geometries of the molecules are similar, the band widths of the smaller molecules should be broader due to the larger rotational constants. Therefore, the λ 10504 carrier would be larger than the λ 12623 carrier. Then, we speculate that the photodissociation of the λ 10504 carrier results in the production of λ 12623 carrier. 6. SUMMARY We investigated the environmental dependence of DIBs toward Cyg OB2 using WINERED spectrograph data. We observed the seven brightest OB stars in the J band in Cyg OB2, and detected all known NIR DIBs with large EWs toward all observed stars in the wavelength coverage of WINERED. Our findings are summarized below: 1. Five of the DIBs ( λλ 10780, 10792, 11797, 12623, and 13175) are well correlated, suggesting similar physical and/or chemical properties of their carriers. 2. The EWs of DIB λ 10504, which were suggested to be well correlated with the family of NIR DIBs in Paper I , are almost constant toward Cyg OB2. In contrast, the EWs of the family of NIR DIBs vary by a factor of two. We attribute the different behaviors of the DIB λ 10504 and the family to the environmental sensitivity of the DIB λ
Paper: 2015MNRAS.453...53J (acknowledgments)
DIJ acknowledges support from STFC via grant number ST/H002359/1, travel support from NewCompStar (a COST-funded Research Networking Programme), and useful feedback from members of the Continuous Wave group of the LIGO Scientific Collaboration and the Virgo Scientific Collaboration, including many discussions with Matt Pitkin and Graham Woan.
Paper: 2016MNRAS.455.3735A (acknowledgments)
We thank Ivan Ramírez for providing his abundance differences and interpolation routines, which were used in Table 6 and Fig. 10 , and Karin Lind for helping with uploading the results on to the online non-LTE data base INSPECT. AMA and MA are supported by the Australian Research Council (ARC) grant FL110100012. RC acknowledges support from the ARC through DECRA grant DE120102940. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.
Paper: 2019MNRAS.487.4409K (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to thank Sean Fillingham, Michael Cooper, Alex Drlica-Wagner, Denis Erkal, and Josh Simon for useful discussions. TK and JSB were supported by NSF AST-1518291, HST-AR-14282, and HST-AR-13888. MBK acknowledges support from NSF grant AST-1517226 and CAREER grant AST-1752913 and from NASA grants NNX17AG29G and HST-AR-13888, HST-AR-13896, HST-AR-14282, HST-AR-14554, HST-AR-15006, HST-GO-12914, and HST-GO-14191 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. Support for SGK was provided by NASA through the Einstein Postdoctoral Fellowship grant number PF5-160136 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060. MSP acknowledges that support for this work was provided by NASA through Hubble Fellowship grant #HST-HF2-51379.001-A 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. AGS was supported by an AGEP-GRS supplement to NSF grant AST-1009973. Numerical business was taken care of in a flash using computational resources of the Texas Advanced Computing Center (TACC; http://www.tacc.utexas.edu ), the NASA Advanced Supercomputing (NAS) Division and the NASA Center for Climate Simulation (NCCS), and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. This work also made use of Astropy, 6 a community-developed core python package for Astronomy (Astropy Collaboration 2013 , 2018 ), matplotlib (Hunter 2007 ), numpy (van der Walt, Colbert Varoquaux 2011 ), scipy (Jones et al. 2001 ), ipython (Perez Granger 2007 ), pandas (McKinney 2010 ), mayavi (Ramachandran Varoquaux 2011 ), and the NASA Astrophysics Data System.
Paper: 2020A&A...633A.109N (acknowledgments)
We thank Rick Bogart for useful discussions. The German Data Center for SDO (GDC-SDO), funded by the German Aerospace Center (DLR), provided the IT infrastructure to process the data. The HMI data used are courtesy of NASA/SDO and the HMI science teams. BWH acknowledges NASA support through grants 80NSSC17K0008, 80NSSC18K1125, and 80NSSC19K0267. We acknowledge partial support from ERC Synergy grant WHOLE SUN 810218.
Paper: 2017MNRAS.466..921C (acknowledgments)
Acknowledgments The authors acknowledge the comments and suggestions made by Sam Lindsay, assistant editor of MNRAS, and the comments of an anonymous referee, which helped us in improving the quality of our analysis and presentation. This study was possible thanks to DAIP-Ugto (1006/2016). RAO-M acknowledges support from CONACYT (Mexico) through PhD fellowship 322095, and CAPES (Brazil) through a PDJ fellowship from project 88881.030413/2013-01, programme CSF-PVE. The funding for SDSS has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the US Department of Energy Office of Science. The full acknowledgement can be found here: www.sdss3.org . This research has made use of the r language and environment for statistical computing, available at www.r-project.org . This paper also made use of the VizieR catalogue access tool, CDS, Strasbourg (Ochsenbein etal. 2002 ) and of the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration.
Paper: 2017MNRAS.467.3620Z (acknowledgments)
Acknowledgments We thank Dmitri Uzdensky and Mitch Begelman for fruitful discussions and for comments on the manuscript. VZ acknowledges support from NSF grant AST-1411879. JW is supported by the Wisconsin Alumni Research Foundation at the University of Wisconsin-Madison. SB is partly supported by NSF grant AGS-1261659, and by the Vilas Associates Award from the University of Wisconsin-Madison. Simulations were performed at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin under the NSF-Teragrid Project TG-PHY110016.
Paper: 2019ApJ...877....1D (fulltext)
emission is integrated over a velocity range of [−12, 6] km s −1 . The axes are offsets with respect to a central position (i.e., R.A. (2000) = 5 h 52 m 129; decl. (2000) = 26°59′33′′). The positions of IRAS 05490+2658, IRAS 05488+2657, and IRAS 05483+2728 are denoted by stars (in red). We have also presented integrated intensity (moment-0) maps of 13 CO(1–0), C 18 O(1–0), and CS(2–1) emission in Figures 3 (a)–(c), respectively. The 13 CO moment-0 map is shown here only for comparison (see also Figure 2 ). The C 18 O line is a tracer of total molecular gas column density, while the CS line is known to depict denser molecular gas compared to the 13 CO data. Hence, the denser parts in the filament can be spatially examined in Figures 3 (b) and (c). The dense gas traced in the C 18 O(1–0) and CS(2–1) maps is found toward both the filament ends (i.e., the S242 H ii region and all the PGCCs). Figures 3 (d)–(f) show moment-1 (velocity field) maps of the 13 CO(1–0), C 18 O(1–0), and CS(2–1), respectively. With the help of moment-1 map, some signatures of velocity gradients are seen at both the ends of the filament (see Figure 3 (d)). Figure 3. (a) 13 CO(1–0) integrated intensity map in the direction of the site S242 (i.e., Moment-0 map). (b) Moment-0 map of C 18 O(1–0). (c) Moment-0 map of CS(2–1). (d) Moment-1 map of 13 CO. e) Moment-1 map of C 18 O. (d) Moment-1 map of CS(2–1). In all the moment-0 maps, the vertical bar shows the color-coded intensity in K km s −1 . In each moment-1 map, the vertical bar shows the color-coded velocity in km s −1 . Figures 4 and 5 display the 13 CO and CS(2–1) velocity channel maps from −1 to 5 km s −1 , respectively. Each channel map is obtained by integrating the emission over 1 km s −1 velocity intervals. These channel maps help us to examine the gas distribution in the filament for a given velocity interval. In this paper, the molecular gas in the filament is mainly studied in the velocity interval of [−1, 5] km s −1 . However, we find an additional velocity component at [−12, −6] km s −1 toward the northern part of the filament using the 13 CO line data (not shown in this paper). In this velocity range, there is no C 18 O(1–0) and CS(2–1) emission detected toward the filament. It implies that the gas at [−12, −6] km s −1 has low density. In the position–velocity (pv) plot, we find that the velocity component at [−12, −6] km s −1 is kinematically unrelated to the one at the [−1, 5] km s −1 (not shown in this paper). Furthermore, the emission at [−12, −6] km s −1 appears to be more extended to the north of the filament, which is not covered in this observational work. In order to make any further conclusion on the kinematical structure at [−12, −6] km s −1 , one needs to obtain further observations for a wide-scale area containing the north part of the filament. Figure 4. 13 CO(1–0) integrated velocity channel maps (starting from −1 km s −1 at intervals of 1 km s −1 ) in the direction of the site S242. In
Paper: 2016A&A...587A..36P (acknowledgments)
We wish to thank Dr. Andrea Giuliani for useful discussions. The research that led to these results has received funding from the European Union Seventh Framework Programme (FP7-SPACE-2013-1), under grant agreement n. 607452, “Exploring the X-ray Transient and variable Sky – EXTraS”.
Paper: 2016A&A...588A..97B (fulltext)
to examine several SNR candidates proposed in the MAGPIS survey. Fig. 21 Spectral index of the SNR G018.8+00.3 (THOR source G18.761+0.287). The black contours represent the continuum emission in steps of 10, 20, 30, and 40 mJy beam. MAGPIS ( Helfand et al. 2006 ) provides 49 new SNR candidates, which are only partly included in the SNR catalog by Green (2014) . Their criteria for a SNR candidate is that they have: 1) a high ratio between 20 cm continuum and 21 m flux density; 2) a counterpart at 90 cm continuum emission; and 3) a distinctive SNR morphology, e.g., shell-type or filled-center ( Dubner Giacani 2015 ). Followup observations for these candidates to determine the distance using H i absorption are presented in Johanson Kerton (2009) . Since MAGPIS has higher spatial resolution, it is more sensitive at detecting the mentioned SNR morphology. However, the THOR survey can help to characterize the radiation and distinguish between thermal and non-thermal radiation. Within the THOR region, we find 33 MAGPIS SNR candidates, which are listed in Table 4 . Only five of them are included in the SNR catalog presented by Green (2014) . In contrast to this, 24 of these MAGPIS SNR candidates have at least one counterpart in the WISE H ii region catalog presented by Anderson et al. (2014) . However, thanks to the high density of H ii regions within the Galactic plane, these can be chance alignments along the line of sight. Table 4 MAGPIS SNR candidates in comparison with THOR sources, Green SNR and WISE H ii regions. Notes. The SNR candidates are given in Helfand et al. (2006) . Since the SNR candidates can be clumpy, several THOR sources can be found within one MAGPIS SNR candidate so we list all corresponding THOR sources. The associated SNR names taken from Green (2014) are given, as well as associated WISE H ii regions given in Anderson et al. (2014) . The given H ii regions can be associated with the SNR, but they can also be foreground or background objects. Figure 22 shows the size for each H ii region. The diameter of the MAGPIS SNR candidates is taken from Helfand et al. (2006) , and the distance is determined via H i absorption and taken from Johanson Kerton (2009) . The spectral index is measured for the peak position of the THOR source (see Sect. 3.7 ) and can vary significantly within the source (see Fig. 22 ). (a) Spectral index map is shown in Fig. 22 . (b) Multiple H ii regions can be found within the region. (c) Determination of the spectral index is uncertain, since the source is not detected in all spectral windows. Fig. 22 Spectral index maps of extended MAGPIS SNR candidates. The color scale represents the spectral index from 1.5 to 0.5, the black contours show the continuum emission in steps of 5, 10, 15, 20, 40, and 100 mJy beam. The red and white circles indicate the sizes of the SNR candidates given in Helfand et al. (2006) and the sizes of the H ii regions given in Anderson et al. (2014) , respectively. In each panel the