adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.468.3757W (fulltext)
for further analyses. Fig. 2 shows the final image after co-adding. It clearly shows a central galaxy surrounded by an extend blueish arc in the south-west (labelled as C and D) and two other blobs in the north (A) and south-east (B), respectively. Both the central galaxy and the extended arc are observed spectroscopically by the SDSS, and their spectra are shown in the right-hand panel of Fig.1. With the aid of the better imaging resolution provided by the CFHT/Megacam, the blueish arc is resolved into two blobs (labelled as C and D). Furthermore, another faint blob in the south-east (B) and a red, almost full Einstein ring, are clearly detected for the first time. This is certainly a strong lensing system of quadruple images, and the background galaxy contains a quasar which is very special because we can see not only the quasar but also its host galaxy. Figure 1. Left-hand panel: The SDSS image of the lens system. The fibre subtend an angle of 2 arcsec (white circle). Right-hand panels: The SDSS-measured spectra of the lens galaxy (top) and the lensed quasar (bottom). Figure 2. The co-added data of g band, r band and i band taken by CFHT. 3 LENS MODELLING STRATEGY The system is modelled by the lens modelling tool lfit_gui developed by Shu etal. ( 2016c ). Here we briefly describe the settings used in this work. 3.1 Foreground-light and source models We model the surface brightness distribution of the lens galaxy with elliptical Srsic profile defined as in which I e is the surface brightness at the effective radius r e that encloses half of the total light. The constant b ( n ) is defined in Ciotti Bertin ( 2012 ) and MacArthur etal. ( 2003 ) 4 . As the CFHT data clearly resolve the Einstein ring formed from the quasars host galaxy, we model the source surface brightness distribution with one elliptical Srsic model. We choose not to model the quasar itself because the Einstein ring already provides robust constraints on the lens model. 3.2 Lens model The projected surface mass distribution of lens galaxy is modelled by singular isothermal ellipsoid profile (hereafter SIE) (for details, see Kormann, Schneider Bartelmann 1994 ). Let denote the two-dimensional position of source on the source plane and the impact vector of light ray in the lens plane, both measured with respect to an optical axis. The gravitational lensing equation shows where D d , D s and D ds denote the angular diameter distance to the lens, the source and from the lens to the source, respectively. is the deflection angle, related to the surface mass density by Introducing angular coordinates by we can transform equation ( 2 ) to the interpretation of the lens in equation ( 5 ) is that a source with true position can be seen by an observer to be located at angular positions satisfying equation ( 5 ). So, the dimensionless lens equation is obtained as where , and length scale 0 are defined as and SIE is the lensing velocity dispersion. We can express the scaled deflection
Paper: 2019ApJ...881..119P (acknowledgments)
We thank the anonymous referee for useful comments which improved this paper. The authors also thank Sara Ellison and Mallory Thorp for many useful and enjoyable discussions regarding galaxy mergers and Gaoxiang Jin for discussions on merger stage classification. The work is supported by the Academia Sinica under the Career Development Award CDA-107-M03 and the Ministry of Science Technology of Taiwan under the grant MOST 107-2119-M-001-024-. P.B.T. acknowledges partial support from UNAB research grant 2019. M.B. acknowledges the FONDECYT regular grant 1170618. R.R. thanks FAPERGS, CNPq and CAPES. J.H.K. acknowledges financial support from the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No. 721463 to the SUNDIAL ITN network, from the Spanish Ministry of Economy and Competitiveness (MINECO) under grant No. AYA2016-76219-P, from the Fundación BBVA under its 2017 programme of assistance to scientific research groups, for the project “Using machine-learning techniques to drag galaxies from the noise in deep imaging,” and from the Leverhulme Trust through the award of a Visiting Professorship at LJMU. This project makes use of the MaNGA-Pipe3D dataproducts. We thank the IA-UNAM MaNGA team for creating this catalog, and the ConaCyt-180125 project for supporting them. We also thank the MPA/JHU teams for making their catalogs publicly available. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org . SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, the Korean Participation Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatório Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Paper: 2017MNRAS.472.1401A (fulltext)
at the highest stellar masses ( M * > 10 10.5 M ⊙ ). The dashed vertical lines denote the 95 per cent completeness limits, determined at the upper redshift range ( z = 2). Representative error bounds for individual galaxies are shown on the left-hand panels, based on the median errors on the PSB sample. For the CANDELS sizes, data are taken from van der Wel et al. ( 2012 ), which include estimates for random and systematic errors from galfit . For the ground-based individual errors, we add in quadrature the scatter in δ R / R determined by the comparison with CANDELS (Section 3 ). The representative uncertainty on stellar masses is based on our Bayesian mass-fitting analysis described in Wild et al. ( 2016 ), as briefly outlined in Section 2.3 . The size–mass relations show the expected trends for star-forming and passive galaxies, consistent with previous studies (e.g. Daddi et al. 2005 ; Trujillo et al. 2006 ; van Dokkum et al. 2008 ; McLure et al. 2013 ; van der Wel et al. 2014 ). On average, passive galaxies appear significantly more compact than star-forming galaxies of equivalent stellar mass, but show a steeper size–mass relation, leading to convergence at the highest masses ( M * ∼ 10 11.5 M ⊙ ). Intriguingly, we find that PSB galaxies at this epoch are also extremely compact; they are comparable in size to the established passive galaxies, with evidence that they are smaller on average at high mass ( M * > 10 10.5 M ⊙ ). These trends are apparent with the large ground-based sample and with the smaller CANDELS sample. We performed a bootstrap analysis as a simple test of significance, randomly sampling the (ground-based) populations within each mass bin, with replacement. For the four bins above 10 10.5 M ⊙ , the fraction of the resampled populations in which the passive galaxies show mean sizes equal to (or smaller than) the mean of the PSB population are 0.07, 5 × 10 −5 , 1.2 × 10 −4 and 4 × 10 −5 (low-to-high mass, respectively). We note that the final bin contains 86 passive galaxies but only three PSBs, so the bootstrap comparison for this bin may be unreliable. Overall, assuming no systematic errors, we find evidence that massive PSB galaxies at z > 1 are significantly more compact, on average, than passive galaxies of comparable mass. Repeating the analysis using the median sizes produced very similar trends. The median analysis and further tests of robustness are presented in Appendix A . Our results are consistent with the findings of Yano et al. ( 2016 ), who also found evidence that high-redshift PSB galaxies are very compact. As an additional comparison, in Fig. 5 we display the distribution of for the three galaxy populations, measured for the redshift range 1 z 2 and stellar masses M * > 10 10.5 M ⊙ . Following Barro et al. ( 2013 ), we use this parameter to effectively remove the slope in the galaxy size/mass relation. Fitting a function of the form to the passive population, we find a best fit with α = 1.55, in very
Paper: 2017ApJ...843....3A (acknowledgments)
This paper is based upon work supported by NASA under award Nos. NNX12AC68G and NNX16AF42G. R.N. acknowledges support by FONDECYT grant No. 3140436. We would like to thank Moshe Elitzur for productive discussions that resulted in improvements to the manuscript and Ying Zu for assistance in the use of his code JAVELIN. We also thank the anonymous referee for useful comments that improved the clarity of the paper.
Paper: 2020MNRAS.496.4593K (fulltext)
= 'envelope_mass_limit' # kappa_file_prefix = 'gs98' change_initial_net = .true. new_net_name = 'o18_and_ne22.net' new_surface_rotation_v = 2 ! solar (km sec^1) set_near_zams_surface_rotation_v_steps = 10 change_D_omega_flag = .true. new_D_omega_flag = .true. set_initial_cumulative_energy_error = .true. new_cumulative_energy_error = 0d0 set_initial_age = .true. initial_age = 0 set_initial_model_number = .true. initial_model_number = 0 pgstar_flag = .true. / ! end of star_job namelist controls max_years_for_timestep = 5d7 initial_mass = 0.7 # use_gold_tolerances = .true. # use_eosELM = .true. # use_eosDT2 = .true. use_eps_mdot = .true. use_dedt_form_of_energy_eqn = .true. min_cell_energy_fraction_for_dedt_form = 0 use_eps_correction_for_KE_plus_PE_in_dLdm_eqn = .true. num_trace_history_values = 2 trace_history_value_name(1) = 'rel_E_err' trace_history_value_name(2) = 'log_rel_run_E_err' max_number_backups = 10 max_number_retries = 160 max_model_number = 6500 backup_hold = 3 # initial_z = 0.02d0 use_Type2_opacities = .true. Zbase = 0.02d0 am_nu_visc_factor = 0 am_D_mix_factor = 0.0333333333333333d0 # D_DSI_factor = 0 D_SH_factor = 1 D_SSI_factor = 1 D_ES_factor = 1 D_GSF_factor = 1 D_ST_factor = 1 varcontrol_target = 1d-3 delta_lgL_He_limit = 0.01d0 envelope_mass_limit = 1d-2 ! Msun cool_wind_full_on_T = 9.99d9 hot_wind_full_on_T = 1d10 cool_wind_RGB_scheme = 'Reimers' cool_wind_AGB_scheme = 'Blocker' # RGB_to_AGB_wind_switch = 1d-4 Reimers_scaling_factor = 0.8d0 Blocker_scaling_factor = 0.7d0 photo_interval = 50 write_model_with_profile = .true. write_pulse_data_with_profile = .true. pulse_data_format = 'GYRE' profile_interval = 10 model_data_prefix = 'model' model_data_suffix = '.mod' max_num_profile_models = 1500 history_interval = 10 terminal_interval = 10 write_header_frequency = 10 / ! end of controls namelist A2 GYRE inlist Name lists that were not used (e.g ’nad_output’ ) are not listed here. The frequency interval to be scanned by GYRE, set by the freq_min and freq_max keys, is determined as [ν max − 10 · Δν, ν max + 10 · Δν], with a minimum value for freq_min of 0.001 μ Hz . Here, ν max is calculated as described in Section 3 and the large frequency separation Δν is calculated for each model via the scaling relation of Kjeldsen Bedding ( 1995 ): with Δν ⊙ = 135 μHz. model model_type = 'EVOL' file = 'path_to_MESA_gyre_file' file_format = 'MESA' / mode l = 0 / osc outer_bound = 'JCD' inertia_norm = 'RADIAL' / num diff_scheme = 'COLLOC_GL4' / scan grid_type = 'LINEAR' freq_min = freq_min_value freq_max = freq_max_value freq_min_units = 'UHZ' freq_max_units = 'UHZ' n_freq = 500 / grid alpha_osc = 20 alpha_exp = 4 n_inner = 10 / ad_output summary_file = 'path_to_summary_file' summary_file_format = 'TXT' summary_item_list = 'M_star,R_star,l,n_pg , freq,freq_units,E,E_p,E_g,E_norm' mode_template = 'path_of_output_file' mode_file_format = 'TXT' mode_item_list = 'l,n_pg,freq, freq_units,E ,
Paper: 2016MNRAS.459.2701M (fulltext)
as local instances of a decreasing mean field. By way of illustration of the key features of the moderate- and ultra-high-beta regimes, we refer the reader to Fig. 13 . This shows two numerical experiments, one with β Ω/ S , and the other with β > Ω/ S , in which the sign of the shear is alternated several times, so the system goes through multiple successive periods of decaying (firehose-unstable) and growing (mirror-unstable) mean field (this is analogous to successive instances of the experiments described in Sections 3.1 and 3.2 ). The key point is that at ultra-high β, periods of firehose growth and decay alternate, the system spends most of its evolution close to the firehose threshold, the mirror instability is almost completely suppressed, while the mean magnitude of the magnetic field barely changes at all; in contrast, at moderate β, the periods of decaying/growing mean field feature (relatively) lively firehose/mirror fluctuations which, however, do not survive long past the point when they cease to be driven by the external shear. In the context of the emergence of the threshold value of β, we have also carried out a β scan of the driven firehose turbulence, to complement a previous study by Kunz et al. ( 2014a ), where only one value of β was used. It has turned out (Section 2.1 ) that the saturation level of firehose fluctuations depends on β, viz. , , so when β ≳ Ω/ S , we have . The latter saturation regime takes the system on the order of one shear time to achieve and follows after a long phase of secular growth, during which the pressure anisotropy is pinned at marginal stability purely by the increase in the fluctuation energy, with no appreciable scattering of particles. This is broadly in line with the earlier theoretical argument of Schekochihin et al. ( 2008 ) and Rosin et al. ( 2011 ), although unlike in their study, the fluctuations are oblique, rather than parallel. We do not as yet have a complete theoretical understanding of this regime, or of the β and S dependence of the saturated firehose amplitude. Figure 13. Time evolution of the magnitude of the mean field (solid black), mean magnitude of the field 〈 B 〉 (bold red), firehose fluctuation level (solid blue) and mirror fluctuation level (dashed blue) in two runs where the sign of shear was alternated several times: (a) the case of β Ω/| S |, (b) the case of β > Ω/| S |. Note the much-delayed onset (amounting to near-complete suppression) of mirror instability and near constancy of 〈 B 〉 in the ultra-high-β case. These plots should be compared to Figs 11 and 12 (a,c). 4.2 Implications for cosmic magnetogenesis As large-scale dynamical motions that drive pressure anisotropies are invariably associated with changing the macroscopic magnetic field and since the resulting instabilities give rise to microscopic magnetic fluctuations, the implications of all this for the long-standing problem of the origin (and maintenance) of cosmic magnetism are the obviously
Paper: 2019A&A...628A..28H (fulltext)
of J H K photometry of the entire 3C 294 system. Notes. To derive the magnitudes from Steinbring et al. (2002) , the fluxes from components a, b, and c have been summed. Photometry of individual components have been derived by Quirrenbach et al. (2001) and Steinbring et al. (2002) . A comparison to our data is shown in Table 5 . The difference between the Quirrenbach et al. (2001) and Steinbring et al. (2002) data is large, approximately two magnitudes, while our measurements are somewhere in between. Again, the size of the aperture used is not given for either the Keck-data or CFHT-data. Table 5. Comparison of HK photometry of components a, b, and c of the 3C 294 system. Notes. The K images have been taken through different filters (see Table 4 ) but the differences are small and did not affect the large differences seen between the individual measurements. We also created J − H , H − K s , and J − K s color maps for 3C 294 to search for any evidence of spatially-dependent extinction in the three components. No obvious feature was detected. 3.2. Optical spectra of the <ext-link ext-link-type="aoi">3C 294</ext-link> system The one-dimensional and two-dimensional spectra of the 3C 294 system are shown in Fig. 4 . Despite our 2 h integration time, we do not detect any obvious continuum. When rebinning the spectra in spectral and spatial direction by a factor of five, a hint of continuum can be glimpsed in the red channel. However, with this rebinning, the potential continuum of components a, b, and c would overlap, preventing us from distinguishing between the two components (see below). We detected the Ly α λ 1215 Å, C IV λ 1550 Å, He II λ 1640 Å, and C III] λ 1907/1909 Å lines in the blue channel. The C III] line is blue-shifted with respect to the other lines. This is not unusual in AGN (e.g., Barrows et al. 2012 ) and may be a result of the increased intensity of the transition at λ 1907 Å relative to the one at λ 1909 Å at low densities ( Ferland 1981 ). Only one emission line at λ 6749.4 Å can be detected in the red channel. It is faintly present in the individual spectra from both MODS instruments and is not an artifact of the data reduction. Under the assumption that it originates from the same source as the emission lines in the blue channel, we identify this line with the Ne [IV] doublet λ 2424/2426 Å at z = 1.783. Similarly to our C III] λ 1907/1909 Å line in the blue channel we would not be able to separate the double lines individually given an instrumental resolution of ∼5 Å. If the identification is correct, we may even associate the very faint emission at λ ∼ 6485 Å with C II] λ 2326 Å at z ∼ 1.788. The position of this line coincides with a forest of night-sky emission lines, however, so this detection is tentative. To our surprise, we do not detect Mg II λ 2798 Å in our spectra, which we would expect at λ ∼ 7790 Å. However, Mg II is relatively weak in radio galaxies ( McCarthy 1993 ) and line ratios can vary strongly in
Paper: 2017MNRAS.472.1324G (acknowledgments)
Acknowledgements This work was supported by the Victorian Life Sciences Computation Initiative (VLSCI), grant reference UOM0005, on its Peak Computing Facility hosted at The University of Melbourne, an initiative of the Victorian Government. Part of this work was performed on the gSTAR national facility at Swinburne University of Technology. gSTAR is funded by Swinburne University of Technology and the Australian Governments Education Investment Fund. AM acknowledges support from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 638809 AIDA). This research was funded by the Australian Research Council through the ARC Laureate Fellowship FL110100072 awarded to JSBW. Parts of this research was conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020 (PMG). Finally, we would like to thank the anonymous referees for their constructive comments which helped to improve this paper.
Paper: 2019MNRAS.489.3739R (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for useful comments. This work was partially supported by the Italian Space Agency (ASI) through the grant Attivit di Studio per la comunit scientifica di astrofisica delle alte energie e fisica astroparticellare (Accordo Attuativo ASI-INAF n.2017-14-H.0). TAD acknowledges support from a Science and Technology Facilities Council UK Ernest Rutherford Fellowship. IP acknowledges support from INAF under the PRIN SKA project FORECaST. This paper made use of the following ALMA data: ADS/JAO.ALMA#[2015.1.01572.S] and ADS/JAO.ALMA#[2015.1.00878.7]. 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. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This paper has also made use of the NASA/IPAC Extragalactic Database, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency.
Paper: 2017ApJ...850...95S (acknowledgments)
The authors would like to thank the referee for the constructive comments. This project has received funding from the Science and Technology Facilities Council (UK) through the consolidated grant ST/N000609/1. This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Thales Investing in Knowledge Society through the European Social Fund. The authors acknowledge support by the Royal Society. This work was supported by computational time granted from the Greek Research Technology Network (GRNET) in the National HPC facility—ARIS. This work used the DIRAC 1, UKMHD Consortium machine at the University of St Andrews and the DiRAC Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( http://www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, and STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure.