adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...910...17P (fulltext)
much shorter or much longer than the Galactic orbital timescales, the distributions of element abundances at any orbital phase should not be affected by these subtleties. We therefore expect this to be inconsequential. Finally, we have assumed all along that there are measurable gradients in stellar abundances with respect to kinematics in the Galaxy or stellar population of interest. In the Milky Way, these are readily observed with existing spectroscopic surveys, and our measurements of individual element abundances will only become more numerous and precise in the coming years. 7.4. Extensions of Orbital Torus Imaging The implementation of Orbital Torus Imaging used in this paper falls into the category of classical (frequentist) statistics. However, the concepts introduced here also lead naturally to probabilistic generative model (Bayesian) versions of Orbital Torus Imaging. Bayesian implementations of this method at first look very different: instead of trying to minimize the dependence of abundance-ratio statistics on the conjugate angles (as we do here), Bayesian formulations would instead involve building a flexible forward model for the element-abundance distribution as a function of the actions alone. A non-intuitive aspect of this is that the angles would never explicitly appear in the probabilistic model. The Bayesian implementations would therefore look conceptually like extended DFs (Sanders Binney 2015 ), but while also varying the mass models, or forward models of the DF (e.g., Magorrian 2014 ), where element abundances or stellar labels are treated as additional invariants. In the implementation presented here, we use an abundance deviation that is based on a nearest-neighbor interpolation in action space (see Section 6 and Appendix ). That is a blunt tool; in principle, our results would improve if we instead explicitly modeled the abundance distribution moments as functions of the actions. One option for this modeling would be to use a generative, physical model for the star formation history of the Galaxy (or sample), including the products of nucleosynthesis and effects of chemical enrichment and stellar migration (similar to what is done in Sanders Binney 2015 ). Another option would be to use a flexible machine-learning method, such as a Gaussian process. We have parameterized the Milky Way mass model with a very simple, few-component mass model. Another option would be to instead parameterize the torus foliation of phase space directly. The representation of this foliation could be very general: any foliation of phase space with closed 3-tori is, in principle, allowed by dynamics. A direct reconstruction of the foliation could then be interpreted in terms of the force law, and hence the potential or mass model. That would be a more data-driven approach—with far fewer assumptions—than what has been executed here. We used the means of a set of hand-chosen abundance-ratio deviations as our invariants for the inferences.
Paper: 2017ApJ...843...19S (acknowledgments)
This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the Pawsey Supercomputing Centre, which is supported by the Western Australian and Australian Governments. A.S. would like to thank Atul Mohan (graduate student at the National Centre for Radio Astrophysics—Tata Institute of Fundamental Research (NCRA-TIFR), Pune, India) for thought-provoking discussions and providing timely, constructive criticisms. A.S. would also like to thank NCRA-TIFR for the computation and infrastructure support, and the Kishore Vaigyanik Protsahan Yojana scheme under the Department of Science and Technology, Government of India for the financial support provided during the period of this work. Facility: MWA. Software: Python ( http://www.python.org ), NumPy (van der Walt et al. 2011 ), Scipy (van der Walt et al. 2011 ), Matplotlib (Hunter 2007 ), Scikit-Learn (Pedregosa et al. 2012 ) and Scikit-Image (van der Walt et al. 2014 ).
Paper: 2021MNRAS.503.2814C (fulltext)
in a close-up of the Bridge region, shown in the right-hand panel, and the range of [Fe/H] becomes wider, as indicated by the two near horizontal arrows for the younger stars in the Bridge region. 3.2 Chemo-kinematical analysis To connect the observed stellar chemical properties to kinematic properties, we compute the vertical action and the mean orbital radius for our sample of stars using galpy in the fixed MWPotential2014 configuration (version 1.5; Bovy 2015 ). Although MWPotential2014 is time-independent and axisymmetric and cannot capture disequilibrium effects present in the real disc, it is none the less a good and useful approximation for the purpose of this work. Fig. 7 shows the distribution of [α/Fe] and [Fe/H] as a function of age and vertical action J z . The general trend is that J z is decreasing with age, with the older population being significantly hotter than the younger population. As also inferred from Fig. 6 , Fig. 7 clearly shows that the Bridge region starts appearing at age 13 Gyr and it spreads to lower [α/Fe] and to a wider range of [Fe/H] with decreasing age as seen in the triangle features at [α/Fe] 0.12 dex in the panels of 10 τ 12 Gyr. The lower panels of age lower than 10 Gyr show the dominant population of the low-[α/Fe] and kinematically colder thin disc stars. The lower panels also reveal a small population of kinematically hot, young stars, and as high [α/Fe] as the thick disc population in Fig. 6 . To understand their origin, we compare this population of stars with the thick disc stars (high [α/Fe] and old), and we discuss the results in Section 3.3 . Figure 7. The distribution of [α/Fe] and [Fe/H] coloured by the square root of the vertical action, , for the samples of stars within eight different age bins. The top four panels show [Fe/H]–[α/Fe] relationship for the older stars (10 τ 16 Gyr), and the lower four panels present those for the younger stars (τ 10 Gyr). There is a kinematically hot population of young high-[α/Fe] stars seen in the lower panels. To further examine whether or not there is a clear distinction between the high and metal-rich, low-[α/Fe] sequence, we select the high-metallicity ridge shown in the left-hand panel of Fig. 8 . The ridge is considered to represent the most advanced chemical evolution path of the stars born in the inner disc, R 6 kpc (e.g. Schönrich Binney 2009 ), and in fact, as shown in the left-hand panel of Fig. 9 , their mean orbital radius is always smallest among the same [α/Fe] population. For the stars within this high-metallicity ridge, we divided the samples according to their [α/Fe]. We measure the scale height using a Markov chain Monte Carlo approach, where we fit the J z distribution in each [α/Fe] bin with an isothermal profile, i.e. (Binney 2010 ; Binney McMillan 2011 ; Ting Rix 2019 ). We compute the scale height, , and its uncertainty in 13 selected bins using [α/Fe]. The results, shown in the right-hand panel of Fig. 8 , reveal a smooth decrease
Paper: 2020ApJ...899...42Z (fulltext)
obliquity and angular velocity (Rubincam 2000 ; Vokrouhlický Capek 2002 ; Scheeres 2007 ), and even causing the tumbling motion (Vokrouhlický et al. 2007 ). Combined with the effect of internal energy dissipation, the YORP effect may lead to a new asymptotic tumbling state of asteroids (Breiter Murawiecka 2015 ). In Section 2 , the main equations and the shape model in the simulation are introduced. Highly elongated and disk-like irregular shapes are generated as pseudo ‘Oumuamua. In Section 3 , the description of the torque curve and statistic results are shown. The origin of tumbling motion of the interstellar asteroid ‘Oumuamua is discussed. A comparison is made between the ISM collision effect and the YORP effect for distant asteroids in the solar system. 2. Simulation 2.1. Equations In this study, the kinetic approach is used to analyze the collisions between the interstellar asteroid and gas, as first proposed by Hoang et al. ( 2018 ). For a surface element dS , The mass of particles colliding with the surface within a time interval dt isHere I set v as the translational velocity vector of the asteroid relative to surrounding interstellar gas. Although the asteroid is rotating, the rotational velocity is too small compared to the translational velocity. Here, n is the outward unit normal vector of the surface element dS . Several assumptions are made in the following derivation: (1) mirror reflection of particles and no sputtering on the surface, (2) the strongly supersonic regime of the body, and (3) the convexity of the surface. After colliding with the surface element, gas particles are reflected, with the velocity component perpendicular to the surface reversed. In this process, the momentum transferred from the particles to the asteroid is is the reflection coefficient, with γ r = 1 representing a mirror reflection. Plugging ( 6 ) into ( 7 ), we can obtain In this paper γ r is set to 1 because of the assumption (1). Note that the process of “sputtering” also produces a torque, which is calculated to be smaller by one order of magnitude than the ISM collision torque, which is shown in Appendix B . We should take caution in further research when neglecting this “sputtering” effect since the variation by just one order of magnitude could be removed by the uncertainty of the parameters. Assumption (2) discards the effect of the gas particles’ thermal motion. Let us consider interstellar gas in the warm phase with the temperature of 5000 K. Applying Maxwell’s velocity distribution, the averaged speed of the gas is , less than the assumed speed of the asteroid. The asteroid can be considered to be supersonic. In the hot region (e.g., above 20,000 K), the above equations are certainly invalid. Assumption (3) avoids the shadowing and secondary collisions of particles. But we could expect Equation ( 8 ) is still valid for moderately concave shapes. According to Golubov et al. ( 2016 ), for the YORP effect, the formulas proposed under the
Paper: 2017ApJ...846...66L (fulltext)
that the ejected metals are preferentially reincorporated back into galaxies from the circumgalactic medium (CGM). Li et al. ( 2010 ) essentially adopt this type of idea in their SAM by adopting a route to recycle most of the metals produced in newly formed stars through the hot phase. In their model, 95% of newly produced metals are deposited directly into the hot gas for galaxies with a dark-matter halo virial mass less than 5 × 10 10 M ⊙ . The metals then eventually fall back to the galaxy and fuel the next episode of star formation, resulting in a shallow metallicity−luminosity relation. Furthermore, the ejective feedback has other uncertainties that can affect predictions for low-mass galaxies. Henriques et al. ( 2013 , 2015 ) showed that when the outflowed mass has a long delay before being reincorporated into low-mass halos, the model can match the evolution of the field galaxy luminosity function remarkably well. Hou et al. ( 2016 ) demonstrated a model where the mass-loading factor decreases with time, as suggested by a recent hydrodynamic simulation (Muratov et al. 2015 ). Together with saturated outflows at the low-mass end and a strong local reionization, which are effectively similar to our extended model, the model (named “EvoFb-LR” in Hou et al. 2016 ) can achieve a reasonable fit to both the SMF and MZR of MW dwarf galaxies. While these prescriptions for outflow and reincorporation have been tested by recent hydrodynamical simulations, the actual behavior of the outflow is still a matter of active debate (e.g., Anglés-Alcázar et al. 2016 ; Christensen et al. 2016 ). We note the important caveat that tidal effects from the MW-mass stellar disk can reduce the number of surviving subhalos (e.g., Zolotov et al. 2012 ; Wetzel et al. 2016 ; Zhu et al. 2016 ), which can affect how the ejective model fits the stellar mass function. We stress, however, that the MZR is independent from the satellite abundance. Therefore, the requirement for weaker outflow implied by this relation is robust, even if a fraction of subhalos are destroyed by the MW disk. Still, it is clear that there is some degeneracy between the preventive feedback and the destruction of subhalos. To break this degeneracy, detailed modeling of these processes and quantitative inferences from dwarf galaxies both within and outside the MW will be essential. Although the model presented here with the addition of preventive feedback can match the data, the physical mechanisms that produce the prevention remain unclear. We have taken an extreme model where baryons are prevented from collapsing into low-mass halos, but the assumption has not been fully tested against observation. At the current stage, other alternative hypotheses, where baryons still make it into the galaxy but are prevented from forming stars by large turbulent pressure or photoheating (e.g., Forbes et al. 2016 ), can produce similar effects in galaxy formation models. The data constraints adopted in this
Paper: 2021ApJ...913...39S (fulltext)
low-Mg accreted population is Si-poor relative to the high-Mg population—but this trend starts to blur at metallicities below −1.5. In Figure 9 , we show the APOGEE-2 Jhelum giant and the GES candidates as well as both in situ and accreted GCs (Horta et al. 2020 ), along with all of the 1495 Jhelum pointing stars (light gray points). As seen in Figure 9 , the APOGEE-2 Jhelum giant star falls into the high-Si and high-Mg region that both accreted and in situ GCs occupy, as well as accreted dwarf satellite stars (e.g., Sculptor and Carina) and Milky Way field stars. Thus, based on currently available abundances, we cannot narrow down the origin of the Jhelum stream based on chemistry alone. Figure 9. The [Si/Fe] vs. [Mg/Fe] distributions for all Jhelum pointing stars (small gray points), the APOGEE-2 Jhelum RGB star (blue triangle), high- e metal-rich stars (cyan circles), high- e metal-rich stars with [Al/Fe] 0 (magenta circles), accreted MW GCs (orange circles) and in situ MW disk GCs (gray circles). The accreted GCs (orange) are associated with the GES merger and the H99 streams (see Horta et al. 2020 for details on the accreted/in situ classifications for the GCs). 5. Summary In this study, we looked in detail at stars in the direction of the Jhelum stellar stream, taken from the APOGEE-2 database. By isolating stars that have the proper-motion signal detected for Jhelum stream stars by Bonaca et al. ( 2019 ) and Shipp et al. ( 2019 ) and then fitting this proper-motion-selected sample to an isochrone for a 12 Gyr population with [Fe/H] = −1.4 and [ α /Fe] = 0.4, we identified one member of the Jhelum stellar stream. This Jhelum star is a bright red giant, near the tip of the RGB for the Jhelum population. By looking at the azimuthal and radial components of motion for stars in the six APOGEE-2 fields targeting the Jhelum stream, we identified a group of metal-rich ([Fe/H] > −1.7), high-eccentricity ( e > 0.8) stars that are likely to be part of the debris from the GES merger. We divided this GES-sample into high-Al and low-Al groups to help distinguish stars likely belonging to merger debris, as stars from accreted populations tend to have low [Al/Fe] values. While the errors for the distances to the Jhelum giants are large, and these propagate into derived quantities like energy and angular momentum, we compare the total energy as a function of angular momentum ( L z ) for the Jhelum giants to the GES giants and find that, while both populations have similar angular momenta (both are prograde), the Jhelum giants tend to have higher total energies than the GES giants. The orbit for the APOGEE-2 Jhelum giant and the APOGEE-2 GES giants were integrated forward for 5 Gyr and compared, and their respective orbits do not seem consistent with stars derived from same population. In particular, the orbits for the GES giants are highly radial and tend to stay more confined to the plane than does Jhelum. Unfortunately, at the low metallicities
Paper: 2021MNRAS.502.3769I (fulltext)
high L 2–10 and powerful outflows. Assuming the material scattering X-rays has an outflowing component, we can also calculate its maximum kinetic energy power using the n e estimates of the warm scattering region. We assume the same velocities v ≈ 3000 km s −1 seen in Alexandroff et al. ( 2018 ) launched at scale height h = 10 pc: which makes up a small fraction of the bolometric output. Thus, the phase of gas responsible for producing the scattered X-rays is not an energetically important component of the quasar ouput. 4.4 Comparing with other observations While there are X-ray studies of highly obscured quasars, most have been limited to low-redshift samples. Another class of reddened quasars are the HotDOGs, which have properties similar to those of ERQs in terms of spectral energy distribution, luminosity, and redshift (Eisenhardt et al. 2012 ; Assef et al. 2015 ). Despite these similarities, there are some interesting differences stemming from somewhat different selection methods. One such difference is that HotDOGs are well below the Stern ( 2015 ) and Chen et al. ( 2017 ) relationships (Ricci et al. 2017 ; Vito et al. 2018 ), while also showing Compton-thick obscurations (Piconcelli et al. 2015 ), in contrast to ERQs explored in Goulding et al. ( 2018 ) and in this paper. We compare their L 6 μm – L 2–10 properties in Fig. 5 . A recent NuSTAR observation of a z = 2.298 HotDOG (W1835+4355) showed Compton-thick obscuration, N H ≈ 0.9 × 10 24 cm −2 , and absorption-corrected luminosity of L 2–10 = 1.6 × 10 45 erg s −1 (Zappacosta et al. 2018 ) similar to those of SDSSJ1652 presented here. Compared to other HotDOGs, this target also exhibited higher X-ray luminosity, nearly consistent with the Stern ( 2015 ) and Chen et al. ( 2017 ) relations, shown in Fig. 5 . In contrast to SDSSJ1652, their target was modelled with θ i = 70° and a larger model-dependent . This suggest remarkable similarities between ERQs and HotDOGs with the only differences in the obscuration properties: θ i and f sc . This indicates a variety in HotDOG properties with some that have overlapping L 6 μm – L 2–10 properties to ERQs. 5 CONCLUSION We report on deep ∼ 130-ks XMM–Newton and ∼ 218-ks NuSTAR observations of SDSSJ1652, an obscured, extremely red quasar at z = 2.94. We characterize the intrinsic X-ray properties and obscuration geometry using XMM–Newton , which is sensitive to the absorbing column, and NuSTAR , which probes the intrinsically unobscured X-rays. We determine the accretion luminosity and the obscuration properties of SDSSJ1652 to provide verification for severely photon-limited studies of the high-redshift, obscured quasar population. We extracted spectra from the XMM–Newton MOS and PN and the NuSTAR FPMA detectors. Unfortunately, we could only place a 3 σ upper limit for the NuSTAR FPMB. We fit 0.5–10 keV XMM–Newton and 6–79 keV NuSTAR spectra with a physically motivated MYTorus model that models the zeroth-order continuum reprocessing due to the
Paper: 2020A&A...634A..30M (acknowledgments)
The authors thank Natalya Lyskova and Florian Sarron for sharing data and providing useful insight during the preparation of this manuscript, as well as Andrea Biviano, Clotilde Laigle, and Miguel A. Aragón-Calvo for fruitful discussions. This research has been supported by the funding for the ByoPiC project from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement ERC-2015-AdG 695561. This work makes use of observations obtained with Planck ( http://www.esa.int/Planck ), an ESA science mission with instruments and contributions directly funded by ESA Member States, NASA, and Canada. It also made use of the SZ-Cluster Database ( http://szcluster-db.ias.u-psud.fr ) operated by the Integrated Data and Operation Centre (IDOC) at the Institut de Astrophysique Spatiale (IAS) under contract with CNES and CNRS. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS Web Site is http://www.sdss.org/ . The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.
Paper: 2017A&A...606A..24A (acknowledgments)
S.A. dedicates this work to the memory of his father Duilio. We thank Adam Mantz for discussion on his papers.
Paper: 2016ApJ...832...69O (acknowledgments)
The SAMI Galaxy Survey is based on observations made at the Anglo-Australian Telescope. The Sydney-AAO Multi-object Integral field spectrograph (SAMI) was developed jointly by the University of Sydney and the Australian Astronomical Observatory. The SAMI input catalog is based on data taken from the Sloan Digital Sky Survey, the GAMA Survey and the VST ATLAS Survey. The SAMI Galaxy Survey is funded by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020, and other participating institutions. The SAMI Galaxy Survey website is http://sami-survey.org/ . S.O. thanks D.W.L. for his continued support. SKY acknowledges support from the Korean National Research Foundation (NRF-2014R1A2A1A01003730). S.M.C. acknowledges the support of an Australian Research Council Future Fellowship (FT100100457). J.T.A. acknowledges the award of a SIEF John Stocker Fellowship. M.S.O. acknowledges the funding support from the Australian Research Council through a Future Fellowship Fellowship (FT140100255). JvdS is funded under Bland-Hawthorn’s ARC Laureate Fellowship (FL140100278). S.B. acknowledges the funding support from the Australian Research Council through a Future Fellowship (FT140101166). Mahajan acknowledges the funding support from the INSPIRE Faculty award (DST/INSPIRE/04/2015/002311) and the Fast track fellowship (SB/FTP/PS-054/2013) from the Department of Science and Technology (DST) and the Science Education and Research Board (SERB), respectively. NS acknowledges support of a University of Sydney Postdoctoral Research Fellowship. H.J. acknowledges support from the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2013R1A6A3A04064993). Support for A.M.M. is provided by NASA through Hubble Fellowship grant # HST -HF2-51377 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. This work has been supported by the Yonsei University Future-leading Research Initiative of 2015 (RMS2 2015-22-0064).