adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.459.3349R (fulltext)
in Fig. 7 . As can be seen, we now require 3 × 3 sets of MNn discs (i.e. 27 parameters) to obtain a fit at better than 10 per cent accuracy over 0.1 s ′ / r s 10. Figure 6. The twisted triaxial NFW density profile. The mass distribution is axisymmetric close to the centre and triaxial in the outskirts. Figure 7. MNn fit (dotted line) for a twisted triaxial NFW density profile (solid lines). We require 3 × 3 MNn discs for this more complex profile to get a good fit (better than 10 per cent accuracy over the range 0.1 s ′ / r s 10). 3.4 The potential and the force field So far, we have compared the original density profile with the one we obtain with our potential-density pair. It is interesting also to compare the potential generated by these density profiles and the force field as well. An important advantage of our potential-density pair is that the potential and the force field can be calculated analytically which is not the case, for instance, for the triaxial and twisted NFW profiles which have to be calculated numerically. We now compare the potential and the force field generated by a triaxial NFW density profile with those generated by our potential-density pair. We first calculate the potential generated by the original function, using the Chandrasekhar formula (Chandrasekhar Press 1969 ): This is an improper integral over the variable τ, where Once we have calculated the potential generated by the original density profile, now we calculate the potential generated by our superposition of MNn discs. This is just the sum of the potential generated by each single MNn disc that follows analytically from equation ( 3 ). We compare our MNn potential and force field with the true solution in Fig. 8 for the case of a triaxial NFW profile. Figure 8. Residuals of the potential and force field for a 3 × 2 MNn fit to the triaxial NFW density profile. 4 CONCLUSIONS We have presented a novel positive potential-density pair expansion for modelling galaxies, based on the MN disc. The key novelties of our method are: (i) we use three sets of such discs aligned along each symmetry axis, allowing us to model triaxial and twisted triaxial systems; (ii) we increase the efficiency of our expansion by allowing the scalelength parameter of each disc a to be negative; (iii) we introduce a constraint equation that ensures that the total density is everywhere positive; and (iv) we allow arbitrarily many discs in our expansion, allowing us to accurately model a wide range of density distributions relevant for modelling galaxies. We find that at better than 10 per cent accuracy in our density reconstruction, we can represent a radial and vertical exponential disc over 0.1–10 scalelengths/scaleheights with four MNn discs; an NFW profile over 0.1–10 scalelengths with four MNn discs; and a twisted triaxial NFW profile with three MNn discs per symmetry axis. Our potential-density pair is efficient, fully analytic, and well suited to reproducing the density
Paper: 2017ApJ...851...70M (fulltext)
Distribution parameters, namely , and δ , show significant positive correlations with the cycle strengths. These correlations are in general agreement with the previous findings of Solanki et al. ( 2008 ), although there are some differences in the values of the correlations, as the cycle lengths are computed differently in their study. In general, the observed correlations imply that stronger cycles begin sunspot eruptions at relatively higher latitudes, and they have wider extends of sunspot eruption latitudes. Another interesting result from this study is the hemispheric asymmetry in the distribution parameters, especially in the center values ( C ). We find that the parameter oscillates around the zero value, whereas the amplitude of this oscillation seems to decrease with the increase of the cycle number. To explore these features, we have applied a flux transport dynamo model in which polar field at the solar minimum is varied randomly. Although this model produces many features of the solar cycle correctly, the latitudinal distribution of sunspots is not in good agreement with observation because of the fewer sunspots near the equator. Despite of this dissimilarity, all correlations among the distribution parameters, as obtained in observation, are reproduced correctly. Our results are in broad agreement with that of Solanki et al. ( 2008 ), who reproduced similar correlations using a simplified thin shell dynamo model. While our model has a fixed single-cell (in each hemisphere) meridional flow, Solanki et al. ( 2008 ) showed that the model with and without meridional flow produce slightly different results. Our model also reproduces the detailed variation of these parameters within the cycle, as demonstrated by Cameron Schüssler ( 2016 ). Thus, our study supports a considerable variation in the polar field as the cause of the irregular solar cycle and variations in the latitude distributions. The variation in the polar field is directly and indirectly observed in the Sun (Muñoz-Jaramillo et al. 2013 ; Priyal et al. 2014 ). This variation is primarily caused by the the scatter in the tilt angles of active regions around Joy’s law (Cameron et al. 2013 ; Jiang et al. 2014 ; Hazra et al. 2017 ; Karak Miesch 2017 ), which is actually observed in the Sun (e.g., Dasi-Espuig et al. 2010 ; Stenflo Kosovichev 2012 ). While it is known that the variation in the meridional flow is expected to cause some variations in the solar cycle (e.g., Karak Choudhuri 2011 ; Belucz Dikpati 2013 ), due to difficulties in the measurements, we are uncertain about the amount of variation present in the deep meridional flow. However, the surface meridional flow is observed to change with the solar cycle and most of this variation is probably caused by the inflows around the active regions (Gizon et al. 2010 ). This meridional flow perturbation can change the polar field from cycle to cycle (Jiang et al. 2010 ; Hathaway Upton 2014 ; Shetye et al. 2015 ). In our
Paper: 2016ApJ...822...81C (acknowledgments)
We are grateful to Eran Ofek for his help scheduling and carrying out the PTF observations, to Adrian Price-Whelan for his help extracting light curves from the PTF database, and to Aaron Dotter for providing a 125 Myr isochrone with colors computed in the specific filters used during the course of this analysis. We also thank John Stauffer, Luisa Rebull, Kristen Larson, and the anonymous referee for comments that improved our analysis and presentation of our results. K.R.C. thanks Hilary Schwandt and the WWU Faculty Research Writing Seminar for sage advice that accelerated the completion of this manuscript, and acknowledges support provided by the NSF through grant AST-1449476. M.A.A. acknowledges support provided by the NSF through grant AST-1255419. Observations were obtained with the Samuel Oschin Telescope as part of the Palomar Transient Factory project, a scientific collaboration between the California Institute of Technology, Columbia University, Las Cumbres Observatory, the Lawrence Berkeley National Laboratory, the National Energy Research Scientific Computing Center, the University of Oxford, and the Weizmann Institute of Science. The Two Micron All-Sky Survey was a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center (California Institute of Technology). The University of Massachusetts was responsible for the overall management of the project, the observing facilities and the data acquisition. The Infrared Processing and Analysis Center was responsible for data processing, data distribution and data archiving. This research has made use of NASA's Astrophysics Data System Bibliographic Services, the SIMBAD database, operated at CDS, Strasbourg, France, and the VizieR catalog access tool, CDS, Strasbourg, France (Ochsenbein et al. 2000 ). This research was made possible through the use of the AAVSO Photometric All-Sky Survey (APASS), funded by the Robert Martin Ayers Sciences Fund.
Paper: 2018ApJ...857...30M (fulltext)
expected line ratios of the prominent O i lines, when collisional excitation and Ly β fluorescence (referred to as photoexcitation by accidental resonance—PAR process—in Bhatia Kastner 1995 ) are the dominant excitation mechanisms. From a comparative analysis of the theoretical estimates with the observed emission strengths of O i λλ 7774, 8446, 11287, and 13165, Mathew et al. ( 2012b ) demonstrated that Ly β fluorescence is the dominant excitation mechanism for the production of O i λλ 8446, 11287 lines in CBe stars. CBe stars share similar spectral characteristics with HAeBe stars, such as emission lines of H α , O i , Fe ii , and Ca ii triplet. It is worth exploring whether both CBe and HAeBe stars share similar excitation mechanisms for the formation of O i lines. There could be considerable difference between the O i line forming regions in both the stellar systems. CBe stars are found to have a circumstellar gaseous decretion disk wherein the O i λ 8446 line is formed at a mean radial distance of ∼8 R ⋆ , considering Keplerian motion (Mathew et al. 2012b ). However, the location of the origin of the O i λ 8446 line in HAeBe is far from clear. Most of the accretion related emission lines in HAeBe stars (e.g., H α , Pa β , Br γ ) are thought to be formed in the magnetospheric accretion columns (Muzerolle et al. 2004 ). This work is an attempt to bring more clarity to our understanding of the formation mechanisms of O i emission lines in HAeBe stars. The paper is organized as follows. In Section 2 , we present the optical and near-infrared (near-IR) spectroscopic observations carried out over a period of 3 years and describe the data reduction techniques employed. We describe the methods and the python routines used for the spectral analysis and to estimate line flux in Section 3 . The dominant excitation mechanism for the formation of O i lines in HAeBe stars is evaluated in Section 4 . The main results of the paper are summarized in Section 5 . 2. Observations and Data Reduction The optical spectroscopic observations were carried out using the Himalayan Faint Object Spectrograph Camera mounted on the 2 m Himalayan Chandra Telescope (HCT). 6 6 http://www.iiap.res.in/iao/hfosc.html The spectroscopic observations were obtained with Grism 8 in combination with 167 l slit (192 wide and 11′ long), providing an effective resolving power of ∼1050. The spectral coverage is from 5500 to 9000 Å, which included the spectral lines relevant to this study, viz., H α , O i λ 7774, and O i λ 8446. After each on-source exposure, FeNe lamp spectra were obtained for wavelength calibration. We have followed the regular procedure of reducing the spectra after bias subtraction and flat-field correction using the standard tasks in Image Reduction and Analysis Facility (IRAF). 7 7 IRAF is distributed by the National Optical Astronomy Observatories, which are operated by the Association of Universities for Research in Astronomy, Inc., under cooperative agreement
Paper: 2018MNRAS.479..162S (acknowledgments)
ACKNOWLEDGEMENTS The authors thank Volker Springel for help with gadget 4 and helpful discussion about the convergence, Alexandre Barreira for providing the data for G 1 , and Titouan Lazeyras for data of the separate universe simulations and helpful discussions. AS is supported by DFG through SFB-Transregio TR33 ‘The Dark Universe’. FS acknowledges support from the Starting Grant (ERC-2015-STG 678652) ‘GrInflaGal’ from the European Research Council.
Paper: 2018ApJ...863..135C (acknowledgments)
The authors would like to thank the anonymous referee for providing detailed and constructive suggestions to improve this paper. J.L. acknowledges support from the Research Grants Council of Hong Kong through grant 17319316. J.L. also acknowledges a Seed Fund for Basic Research from the University of Hong Kong. T.B. was supported by a Visiting Research Professor Scheme from the University of Hong Kong, during which major parts of this work were conducted. J.M.D. acknowledges the support of projects AYA2015-64508-P (MINECO/FEDER, UE), AYA2012-39475-C02-01, and the consolider project CSD2010-00064 funded by the Ministerio de Economia y Competitividad. Y.O. is supported by the Ministry of Science and Technology ( MOST ) of Taiwan, MOST 106-2112-M-001-008.
Paper: 2020MNRAS.495.1340F (fulltext)
plot summarizing the relative marginal errors on parameters, , for all the parameters, the flux thresholds, the ELG samples and redshift bins, and the sky areas considered. In particular, each row refers to a specific parameter, namely from the top to bottom; each column refers to a specific flux threshold, i.e. from the left- to right-hand side; red, green, and blue lines, respectively, refers to refer to H α , O iii + H β , and O ii galaxies; and diamond, triangle, square, and circle markers refer to , respectively. Figure 7. Relative 1 σ marginal errors on redshift-dependent parameters from the clustering of galaxies detected through different line emission: H α in red, O iii + H β in green, and O ii in blue. Circles, squares, triangles, and diamonds refer to survey areas of 1, 5, 15, and 30 in , respectively, whereas the four columns illustrate the dependence of the constraints on the flux threshold, set to from the left- to right-hand side. The main conclusion one can draw from this plot is that – not counterintuitively – sensitivity is possibly more important than area for high-redshift observations. This is further demonstrated in Fig. 8 , where we focus on the extraction of RSDs in terms of constraints on the redshift-dependent quantity f σ 8 ( z ). We adopt the same colour code as before for the various ELG samples, and the two panels show forecast 1 σ marginal error bars on measurements of f σ 8 ( z i ) in each redshift bin, for a wide and shallow survey (left-hand panel) or a narrow and deep survey (right-hand panel). Clearly, measurements extracted from the original target, namely the H α -galaxy sample, are optimized for the former survey specifications, with error bars tighter than those obtained with the latter experimental configuration. It turns out that a large area and a relatively larger flux threshold is also better for RSD estimation from the O iii + H β sample, with error bars smaller than for a narrow and deep survey. On the other hand, when it comes to the extraction of cosmological information from redshift 3−5 O ii galaxies, it is better to observe as much as 30 times a smaller sky area, but with twice as deep a survey, which yields f σ 8 ( z ) measurements more constrained. Figure 8. Relative 1 σ marginal errors on f σ 8 ( z ) from the clustering of galaxies detected through different line emission: H α in red, O iii + Hβ in green, and O ii in blue, for two different surveys: on the left-hand side a lower sensitive but wide surveys (, ); on the right-hand side a narrow but more sensitive survey (, ). Finally, let us touch upon the importance of high-redshift oxygen-line galaxies to constrain cosmological parameters. Hitherto, we have focused on model-independent parameters related to the redshift evolution of the matter power spectrum, and on its modulation by BAOs, galaxy bias, and RSDs. The next stage will now be to translate constraints on those parameters to more fundamental cosmological parameters as, for
Paper: 2019ApJ...873...76R (acknowledgments)
S.R. thanks Chris Matzner, Eliot Quataert, and Yan-Fei Jiang for useful conversations. S.R. appreciates the referee for their comments and suggestions. This research was funded by the Gordon and Betty Moore Foundation through Grant GBMF5076.
Paper: 2017A&A...599A..81M (fulltext)
spectra belong to the bright configuration, while lower panel spectra belong to the faint configuration. V total magnitude is in the bottom right inset. The overall S/N is 55, 39, and 15 for the bright spectra, and 9, 6, and 4 for the faint ones. 5. Redshifts We measured galaxy redshifts on the spliced spectra, that is, those extending over the entire wavelength range (3800–9000 Å), in order to gather information both from the blue part of the spectra (where at our redshifts, most optical absorption lines are present) and from the red part of the spectra (where the H + NII region is located). The night sky residuals, present in specific regions of the spectra, have been replaced by interpolated values before measuring the redshifts. On these cleaned spectra we measured both the redshift obtained by cross-correlating the spectrum with that of a template at zero velocity (using the IRAF/xcsao package) and the one obtained from the match of emission lines ( IRAF/emsao ). Each single spectrum was visually inspected by members of the team (A.M., B.P., D.B., J.F., M.G., A.P., A.C.) to ensure that the automatic estimation was correct, and to assign a flag that was then used to derive the final redshift determination. We used: the weighted average of the two determinations if both wereflagged as good; the emission line redshift if the cross-correlation was not convincing; and the cross-correlation redshift if spectra did not contain emission lines (and the cross correlation was reliable). Errors on the redshifts are the errors of the combined measurements of both absorption features and emission features if the two redshift estimates were averaged together, and are single errors on the measurements that do not have emission lines, or where we did not trust the absorption line cross-correlation. Among the original 18 995 spectra, we were able to measure 17 985 redshifts, with a median error of 50 km s and average completeness 95. Figure 4 shows the distribution in redshift of all the OmegaWINGS targets (black line). The filled histogram represents the redshift determinations obtained with the previous WINGS spectroscopic survey ( Cava et al. 2009 ), all in the inner cluster regions. The red histogram shows the new Omega WINGS redshifts. When a new redshift was present, we used this determination instead of the old WINGS one (the difference between the two determinations being peaked at , with a few cases of real mismatches in spectra with low S/N). The number of cluster members with a AAOmega redshift is 7497, that adds to the 2732 galaxies belonging to the clusters for which we had a previous WINGS measurement. This means that the total number of cluster members rises to 10 229 in total. 5.1. Completeness To ensure that we could properly derive global properties from the clusters under scrutiny, and from their galaxy populations, we calculated the spectroscopic completeness as a function of both magnitude and radial distance. The first term is
Paper: 2016MNRAS.462.1989A (acknowledgments)
AC acknowledges support from the National Aeronautics and Space Administration under Grant No. 12-ADAP12-0139 issued through the ADAP programme. EI acknowledges funding from CONICYT/FONDECYT post-doctoral project No.: 3130504. JWa is supported by a European Union COFUND/Durham Junior Research Fellowship under EU grant agreement number 267209, and acknowledges additional support from STFC (ST/L00075X/1). RJI acknowledges support from the European Research Council in the form of the Advanced Investigator Program, 321302, COSMICISM. SJO acknowledges support from the Science and Technology Facilities Council (grant number ST/L000652/1). SRS acknowledges support from NASA NESSF. The construction of MUSIC was supported by JPL RTD, NASA APRA, NSF ATI and NSF AAG. The Dark Cosmology Centre is funded by the Danish National Research Foundation. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00449.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan) and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. SPIRE has been developed by a consortium of institutes led by Cardiff Univ. (UK) and including Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy); MCINN (Spain); SNSB (Sweden); STFC (UK) and NASA (USA). HCSS/HSpot/HIPE are joint developments by the Herschel Science Ground Segment Consortium, consisting of ESA, the NASA Herschel Science Center and the HIFI, PACS and SPIRE consortia. This research has made use of data from the HerMES project ( http://hermes.sussex.ac.uk/ ). HerMES is a Herschel Key Programme utilizing Guaranteed Time from the SPIRE instrument team, ESAC scientists and a mission scientist. HerMES is described in Oliver et al. ( 2012 ). The data presented in this paper will be released through the HerMES Database in Marseille, HeDaM ( http://hedam.lam.fr/HerMES/ ).
Paper: 2016ApJ...831..204R (acknowledgments)
OHP and MR acknowledge support from NASA grant NNX10AH10G, NSF CDI-typeII grant CMMI1125285, and the Theoretical and Computational Astrophysics Network (TCAN) grant AST1333514. The authors acknowledge the University of Maryland supercomputing resources ( http://www.it.umd.edu/hpcc ) made available in conducting the research reported in this paper. This work also used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF grant number ACI-1053575. We thank the anonymous reviewer for helping improve the quality and clarity of this paper.