adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...863...28A (fulltext)
occupy distinct regions of NUV– r space, and all share some overlap with SPOGs, suggesting that SPOGs are a heterogeneous group comprised of galaxies with different mechanisms for UV emission. In FUV–NUV (bottom panel of Figure 6 ), we find a similar trend, with SPOGs displaying a larger range in FUV–NUV colors compared to the star-forming sample. Once again the E + A, AGN, and interacting samples all share a similar color distribution as SPOGs, but seem to occupy distinct subregions. These plots suggest that UV emission in SPOGs may be an amalgam of the mechanisms that the comparison samples represent in different proportions. Future multiwavelength and high-resolution morphological analyses will clarify which individual SPOGs exhibit similar UV emission mechanisms to each comparison group. Figure 6. Top: k -corrected NUV– r color vs. SDSS i -band absolute magnitude ( M i ) of SPOGS (gray contours) and all comparison samples. Bottom: k -corrected FUV–NUV color vs. SDSS i -band absolute magnitude ( M i ) of SPOGS (gray contours) and all comparison samples. In both plots, the black stars are SPOGs that fall outside the outer contour. While most of the comparison samples fall within the color–magnitude space occupied by SPOGs, no single sample fully covers the SPOGs parameter space, suggesting that SPOGs may be composed of a combination of these samples. All contours are in increments of 10 percentiles of the maximum density. Figure 7 shows the cumulative distribution of FUV–NUV colors for SPOGs compared to each of the comparison samples. We used AD tests to compare the shapes of these 1D UV color distributions. For samples in the full range of masses, all samples, except the AGN sample, have statistically distinct color distributions from SPOGs with greater than 99% significance. The AGN sample is distinct from SPOGs at the 78% significance level. When restricting the comparison to a specific range of masses (−23 M i −21), all samples, except the interacting sample, have statistically distinct color distributions from SPOGs with greater than 99% significance. The interacting sample is distinct from SPOGs at the 85% significance level. This test shows that the differences in UV colors are not attributable to differences in stellar mass distribution. Figure 7. Cumulative distributions of GALEX FUV–NUV color for SPOGs and each of our comparison samples. With Anderson–Darling tests, we show that SPOGs are a distinct distribution from all other comparison samples, except AGN (see Section 3.1 ). 3.2. Mixture Models To further test which of the comparison samples contribute to the FUV–NUV colors of SPOGs and in what fractions, we used the method of Gaussian mixture models (GMMs) 20 20 We provide an example of our code and method at https://github.com/f-ardila/SPOGs-GMMs . . GMM models some underlying distribution whose true probability density function is unknown as the sum multiple Gaussian distributions (Ivezic et al. 2014 ). Here, we first
Paper: 2016A&A...585A..73N (fulltext)
spectroscopic analysis. Results for 11 SB1 are shown as open circles. The atmospheric parameters we determined for 402 GK stars are presented in Table 1 (Cols. 69), where the numerical values of , , , and [Fe/H] are presented together with their intrinsic uncertainties. The values of resulting atmospheric parameters generally remain within the range of the TGVIT model grids. Figure 5 shows the relations between the atmospheric parameters presented in Table 1. We note that the results obtained for single-line spectroscopic binaries (SB1) agree very well with those for apparently single stars. The effective temperature lies between 4398 K and 6394 K with a median value of 5167 K. The distribution of , presented in Fig. 6 a, shows two maxima, one for giants (cooler) and one for subgiants (warmer), which are the two most numerous groups of stars in our sample. The intrinsic uncertainty distribution in is presented in Fig. 6 e. The gravitational accelerations for our sample range between 1.49 and 4.72 with a median of 3.65. The distribution of is presented in Fig. 6 b. We found that 5 stars have , 60 have , 277 have and 60 stars have . Most of our stars (194) have of which means that generally they are subgiants. The uncertainty distribution in is presented in Fig. 6 f. The microturbulence velocity, , reaches values from to and has a median at . Most of our stars have between and (Fig. 6 c). The uncertainty distribution in is presented in Fig. 6 g. The metallicity of the stars in our sample, [Fe/H], remains within the limit of to with a median value at . Most of our objects have [Fe/H] in the range of to . Figure 6 d presents the distribution, which clearly shows that our stars are mostly less metal abundant than the Sun. The uncertainty distribution in [Fe/H] is presented in Fig. 6 h. Fig. 6 Histograms of of , , , and [Fe/H] obtained for 402 stars with complete spectroscopic analysis. Results for 11 SB1 are shown in black. 3.6. Uncertainy estimates of the atmospheric parameters The mean intrinsic uncertainties of our determinations, that is, those delivered by TGVIT, are K, , , and [Fe/H] . Figure 7 shows the intrinsic uncertainties of these three parameters as a function of . No correlation exists between the uncertainties and the obtained parameter values for any of the atmospheric parameters in the wide range of between 4500 K6000 K. The scatter of the uncertainties is uniformly distributed over the whole range of resulting parameters. We note, however, that for stars with below 4500 K or above 6000 K the uncertainties are slightly higher, especially in and [Fe/H]. To test the effect of the shift in EWs between DAOSPEC and ARES measurements presented in Sect. 3.4 , we calculated stellar atmospheric parameters for the same five stars using DAOSPEC and ARES EWs separately. We obtained agreement within 1 intrinsic in all parameters except for the microturbulence velocity, in which the results differ by 23 intrinsic . We therefore assume that
Paper: 2020MNRAS.494.1871W (fulltext)
in changing our results. We stop our pure infall models earlier than , as these runs require much more computing time than our standard runs. However, this should not greatly affect our analysis, as the behaviour of these runs is clear and is unlikely to change significantly in the period between t final and 1000 yr. In the pure infall realizations only one sink forms and grows rapidly via accretion, reaching a mass of over in only ∼100 yr. In contrast, all of the other runs show considerable fragmentation within the first 1000 yr, forming anywhere between 15 and ∼120 sinks. The total mass in sinks (M tot ) and the total number of sinks (N tot ) in each realization at the final output time is listed in Table 3, and time evolution of these quantities is shown in Figs 2 and 3 , respectively. In addition, Fig. 4 shows some illustrative examples of the evolution of some of our realizations. The difference in behaviour that we see between the pure infall runs and the other runs occurs because the pure infall runs are the only ones in which no disc forms over the course of the simulations. Some non-zero angular momentum arises during the gravitational collapse but is too small to trigger the formation of a disc. In all of the other runs 3 a dense, gravitationally unstable accretion disc forms which soon fragments. Figure 2. Total mass in sinks as a function of time after the first sink is created. Each panel shows the results for the five different realizations of each setup. Increasing the initial turbulent or rotational energy tends to systematically decrease the total mass in sinks at any given time. We also see that there is significant variation from simulation to simulation for all of the setups with non-zero initial turbulence, but not for the setups with non-initial turbulence. Figure 3. Total number of sinks as a function of the time elapsed after the formation of the first sink. The colour scheme is the same as in Fig. 2 . We see that the number of sinks that form varies substantially from simulation to simulation for all of the setups. A colour version is available online. Figure 4. Column density plots (two top rows: x – y plane of the simulation box; two bottom rows: x – z plane of the simulation box) of the evolution of realization β01 – 1, α005 – 5, and α025β001 – 3. In the purely rotational runs, the protostellar disc lies in the geometrical x – y plane of our simulation box during the whole of the simulation, as demonstrated here for run β01 – 1. In all runs including turbulence, however, the orientation of the disc varies strongly over time. The projections are centred on the centre of mass of both sink particles and gas within a 500 AU radius around the most massive sink particle at the time of the snapshot considered. The projection thickness is about half the size of our simulation box (), the distance between the position of the first sink and the edge of the simulation box. In our plots of the evolution of the total mass
Paper: 2019MNRAS.485L...6G (acknowledgments)
ACKNOWLEDGEMENTS This work is based on observations obtained with the Southern African Large Telescope (SALT), programmes 2010-1-RSA-OTH-001 and 2018-1-MLT-008, and supported by the Russian Foundation for Basic Research grant 19-02-00779. AYK acknowledges support from the National Research Foundation (NRF) of South Africa. LMO acknowledges partial support by the Russian Government Program of Competitive Growth of Kazan Federal University. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France.
Paper: 2021MNRAS.500..310D (fulltext)
for the sample of velocity-selected clusters. As in Fig. 5 , θ is the acute angle between the projected major axis of each galaxy and that of the cluster in which it resides. Cluster position angles were determined from moments of inertia of the projected galaxy distribution. The Kuiper V, Rao, and binomial statistical tests all indicate a probability that the observed distribution of angles is consistent with random BCG orientations. Figure 9. BCG alignments as a function of peculiar velocity. Here, Δ V BCG /σ is the absolute difference between the BCG’s velocity and the mean cluster velocity, and σ is the cluster velocity dispersion. 4 DISCUSSION The main findings of this paper are: A significant fraction of BCGs have spatial offsets of a few tens of kpc or more from the centroid of the X-ray light distribution that presumably traces the centre of the DM halo and reflects the cluster’s dynamical state. Compared with theoretical models, these offsets are much larger than the expected ‘wobble’ around the centre of a standard CDM halo (2 kpc versus a few tens of kpc) as in the simulations of Kim, Peter Wittman 2017 ; Harvey et al. 2017 ). However, the X-ray gas may not trace the DM halo distribution (e.g. by gas sloshing, etc.) A significant number of BCGs also have significant line-of-sight peculiar velocities relative to the cluster. These observations suggest that many BCGs are not currently at rest in the potential well of their host cluster. Despite the prevalence of BCG displacements, these galaxies still show a strong tendency to share the same orientation as the cluster in which they reside (cf. Tempel, Stoica Saar 2013 , a remarkably robust coherence of structures over scales from tens to thousands of kpc, as suggested by the simulations of Rhee et al. 2017 ). 4.1 Comparison of observations with simulations The computation of the cluster and BCG elongation axes is done as in Ragone-Figueroa et al. ( 2020 ). They are obtained from the principal axes of the ellipsoids that best describe the corresponding distribution of matter. For the purposes of this work cluster principal axes and centre of mass are obtained using DM particles within r 200 . While for obtaining BCG principal axes we use star particles inside 0.1 r 500 . Fig. 10 shows the distributions of BCG offsets in position and velocity as well as their correlations with the BCG–cluster alignment angle, in full 3D space (four left-hand panels) and in projection and radial velocity (four right-hand panels). We find in simulations galaxies with significant offsets, in projected space the mean(median) offset is ∼53 kpc(78 kpc), which is larger that the mean found in the observational data (∼15 kpc). Conversely, the mean(median) normalized 1D BCG velocity | V BCG /σ| (with respect to the cluster mean) is ∼0.09(0.07), which is lesser than in the data (0.26). The maximum Δ V /σ in these simulations is lower than that observed because of the smaller number of simulated cluster samples.
Paper: 2017MNRAS.467.1033B (acknowledgments)
Acknowledgments We thank the reviewer for their valuable and constructive feedback which greatly contributed to the quality of this paper. We thank Greg Snyder for useful discussions and input. PT acknowledges support for Program number HST-HF2-51384.001-A that was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. This research made use of a University of Victoria computing facility funded by grants from the Canadian Foundation for Innovation and the British Columbia Knowledge and Development Fund. We thank the system administrators of this facility for their gracious support. Funding for the SDSS IV has been provided by the Alfred P. Sloan Foundation, the US Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS web site is www.sdss.org . SDSS-IV 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 Astrofsica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut fr Astrophysik Potsdam (AIP), Max-Planck-Institut fr Astronomie (MPIA Heidelberg), Max-Planck-Institut fr Astrophysik (MPA Garching), Max-Planck-Institut fr Extraterrestrische Physik (MPE), National Astronomical Observatory of China, New Mexico State University, New York University, University of Notre Dame, Observatrio Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autnoma de Mxico, 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: 2019ApJ...876...36M (acknowledgments)
We acknowledge our referee, Dr. James Binney, for the detailed review and for the helpful suggestions, which allowed us to significantly improve the manuscript. This work was supported by the Brazilian CNPq, FAPESP, and CAPES. A.P.V. acknowledges FAPESP for the postdoctoral fellowship No. 2017/15893-1 and the DGAPA-PAPIIT grant IG100319. This work has made use of data from the European Space Agency (ESA) mission Gaia ( www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPACl; https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work has made use of the facilities of the Laboratory of Astroinformatics (IAG/USP, NAT/Unicsul), funded by FAPESP (grant 2009/54006-4) and INCT-A.
Paper: 2020ApJ...892..125H (fulltext)
constructs mock catalogs including both photometry and morphologies. Similar to JAGUAR, EGG uses empirical prescriptions, starting with a derivation of the evolution of the stellar mass function from deep observations. In this section, we compare the recovered TPD, completeness, and accuracy for the EGG catalog to what we found using the JAGUAR catalog. A few of the primary differences between JAGUAR and EGG that will influence the present analysis are the evolution of the stellar mass function, the treatment of galaxy morphologies and dust obscuration, and the inclusion of self-consistent nebular continuum and line emission. The EGG team started with a framework for the evolution of the star-forming and quiescent galaxy mass function at z = 0.3–4.5 based on observations from CANDELS (Grogin 2011 ; Koekemoer 2011 ), where they computed photometric redshifts using EAZY (Brammer et al. 2008 ) and galaxy stellar masses using FAST (Kriek et al. 2009 ). At z = 4.5–7.5, the authors rely on the stellar mass functions from Grazian et al. ( 2015 ). The resulting mass function evolution has a steeper low-mass slope than the prescription that underpins the JAGUAR catalog at z > 1.5, and the discrepancy is larger at higher redshifts. In addition, the evolution of the EGG mass function predicts fewer high-mass galaxies at z > 4 than JAGUAR. Both of these differences are likely a consequence of the necessary extrapolation that was done for each catalog due to lack of observational data. The SEDs in EGG were generated by first assigning a U − V and V − J color to each mock galaxy based on the observed evolution of these colors for star-forming and quiescent galaxies. At this point, each mock galaxy was given an SED based on the average SED for observed CANDELS galaxies with those UVJ colors (from the FAST fits, using the Bruzual Charlot 2003 stellar library). As the morphology of each EGG mock galaxy is defined to be a combination of a bulge and disk component, each component was assigned a separate SED. This process differs significantly from the SED creation in JAGUAR, which uses BEAGLE fits to 3D-HST objects to calculate the SEDs for each object. In the version of the EGG catalog generation tool we used in this analysis, v1.4.0 ( egg-gencat ), the authors included a simple prescription for emission lines, where the strength of each line is estimated using each mock galaxy’s SFR, metallicity, total infrared luminosity, and gas mass, 15 15 See: https://github.com/cschreib/egg/blob/master/CHANGELOG . which we include to better compare to JAGUAR. We used egg-gencat to create two catalogs, one with 100 square arcminutes and one with 10.8 square arcminutes, with a minimum stellar mass of 10 6 M , at z = 0.2–15. We then constructed 500 noisy catalogs with each area in the exact manner as was done in Section 2.2 for the JAGUAR catalogs, although we modified this process to account for the combination of the disk and bulge components in each EGG mock galaxy. From
Paper: 2015MNRAS.446..622Q (acknowledgments)
We thank M.M. Romanova for valuable discussions and an anonymous referee for valuable comments. This work was supported in part by NASA grants NNX11AF33G and NSF grant AST-1211318.
Paper: 2018MNRAS.479.4526L (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to thank Jim Hinton for useful discussions and help during the production of the paper. The authors would also like to thank the HAWC collaboration for useful discussions. The research of GG was supported by a Grant from the GIF, the German-Israeli Foundation for Scientific Research and Development.
Paper: 2016A&A...588A..76S (fulltext)
Again we note that has a small constant component, , in the direction of the body frame and a big one of modulus describing a circle in the plane of the body frame. The angular velocity in the body frame is given by It follows from Eqs. ( 3 )and ( 5 )that . Since the main axis, , of the ellipsoid is much greater than the other axes, and , we find that . Moreover, for low values of , , and therefore is also small. Finally let us note that when , then and both, angular momentum and the angular velocity are aligned on the axis. Since the angular momentum, , is preserved in an inertial reference frame, we take the inertial system so that the axis is aligned with . The major axis of the bar has to keep an angle with respect to , and then also measures the angle between the main axis of the bar and the plane of the inertial reference system. For this reason, from now on, it will be referred to as the tilt angle of the motion of the bar (see bottom panel of Fig. 2 ). In the inertial frame the major axis of the bar describes a cone about the axis, while and are slightly misaligned since the bar is also rotating about its major axis. When , the major axis of the bar rotates inside the plane, there is no rotation of the bar about its major axis, and and are again aligned. We define now a new non-inertial reference frame henceforth called the precessing reference system. In this reference frame, the axis is aligned with the major axis of the bar, but the bar is not fixed as in the body frame but rotating around the axis. In the body reference system, the angular momentum and angular velocity vectors rotate around the main axis of the bar ( axis in the body frame) with angular speed . The precessing reference frame rotates with respect to the body frame about the axis they share. This is, these frames are related by means of a time-dependent rotation of axis and angular velocity , in such a way that and are constant in the precessing reference system, with values of We are interested in the equations of motion of our galactic model in the precessing reference frame. To compute them, we need the angular velocity of the precessing frame with respect to the inertial one. This angular velocity, , is the sum of the angular velocity of the body, , and the angular velocity of the body axes in the precessing frame. Therefore, and taking the value of into account, we obtain where (see Chapter 2 of Sánchez-Martín 2015 for more details about the rotations). Finally, as a complement, we briefly discuss the motion of the bar in the inertial frame. According to Poinsot’s theorem (see Arnold 1989 ), the bar, which is described by an ellipsoid, rolls without slipping on a fixed plane normal to the angular momentum . If the ellipsoid has axial symmetry, as in our case, this motion is the superposition of a rotation of the ellipsoid along its symmetry axis with constant angular velocity, , and a precession with constant pattern speed around the axis of the angular momentum.
Paper: 2019ApJ...881..104M (acknowledgments)
C.S. was partially supported by NSF grant AST-1616040, and by the Rex G. Baker, Jr. Endowment at the University of Texas. C.S. also thanks the Dipartimento di Fisica—Università di Roma Tor Vergata for a Visiting Scholar grant, and INAF—Osservatorio Astronomico di Roma for his support during his stay. We thank the reviewer for the precise notes and the useful suggestions to improve our work. This research has made use of the services of the ESO Science Archive Facility. This work has made use of the VALD database ( http://vald.astro.univie.ac.at/vald3/php/vald.php ), operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
Paper: 2015ApJ...813...82R (acknowledgments)
It is our pleasure to thank Rich Plotkin, Eric Bell, Kayhan Gültekin, and Jenny Greene for very helpful discussions. We also thank the anonymous referee for useful comments and suggestions. Support for AER was provided by NASA through Hubble Fellowship grant HST-HF2-51347.001-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 NAS 5-26555. M.V. acknowledges funding from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013 grant agreement No. 614199, project “BLACK”).