adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.459.3792C (fulltext)
n s is the number density of the s th species of the fluid. The mass density is given by Chattopadhyay ( 2008 ) and Chattopadhyay Ryu ( 2009 ), where , is the composition parameter and . The electron and proton masses are and , respectively. For single-temperature flow, the isotropic pressure is given by The EoS for multi-species flow is (Chattopadhyay 2008 ; Chattopadhyay Ryu 2009 ) The non-dimensional temperature is defined with respect to the electron rest mass energy, . Using equations ( 19 ) and ( 20 ), the expression of the energy density in equation ( 21 ) simplifies to where The expressions of the polytropic index, the adiabatic index and the sound speed are given as Integration of first law of thermodynamics (equation 13 ) by assuming adiabatic flow ( Q + = 0) and using the EoS (equation 22 ) gives us the adiabatic relation of multi-species relativistic flow (Chattopadhyay Kumar 2013 ; Kumar et al. 2013 ), where k 1 = 3(2 − ξ)/4, k 2 = 3ξ/4 and and is the constant of entropy. Equation ( 24 ) is the generalized version of . Combining equations ( 24 ) and ( 14 ), we get the expression of entropy accretion rate, Re-arranging equations ( 10 )–( 14 ) with the help of equations ( 9 ), ( 7 ), ( 19 ), ( 20 ) and ( 22 ) in geometric units, we present the spatial derivative of flow variables v , l and Θ, where Here, contains an extra term l 2 v /( r 2 γ 2 ) compared to the inviscid case (Chattopadhyay Chakrabarti 2011 ). There is γ φ term in the expression of disc height (equation 12 ). The radial derivative of equation ( 14 ) implies that the radial derivative of the specific angular momentum will be non-zero, which causes the extra term to appear. There are many height prescriptions (Lasota 1994 ; Riffert Herold 1995 ; Peitz Appl 1997 ), and choice of any one of them apart from the one used will not affect the result qualitatively. Then, Moreover, These differential equations are integrated by using fourth-order Runge–Kutta numerical method with the help of using critical point conditions and l′Hospital rule at critical point. 2.2.1 Sonic point equations Mathematical form of critical point equation is , which gives two equations as and Here, the subscript ‘c’ denotes the same physical quantities described in equations ( 26 )–( 28 ), but evaluated at the location of the critical point. The velocity gradient on the sonic point, i.e. (d v /d r ) c , is obtained by employing l′Hospital rule. 2.2.2 Relativistic shocks for viscous flow The relativistic shock conditions were first obtained by Taub ( 1948 ), which for viscous flow in the presence of mass-loss are where . We have solved four shock conditions ( 31 )–( 34 ) simultaneously, where viscous shear tensor () is continuous across the shock and we obtained the relation between pre-shock (suffix ‘−’) and post-shock (suffix ‘+’) flow variables, where . Here, , . From equation ( 11 ), viscous shear tensor can be written as . 2.3 Outflow equations The jet being tenuous, we idealize it to be
Paper: 2017MNRAS.468..946S (fulltext)
of protons that decay into pions, f sec 0.7 is the ratio of secondary and total cosmic ray e , m p and m e are the masses of protons and electrons, and c is the speed of light. The normalization of the proton injection spectrum Q p, 0 can be directly related to the supernova rate via where 0.1 is the fraction of the supernova energy E SN 10 51 erg that is converted into kinetic energy of cosmic rays (Dorfi 2000 ). The Lorentz factor p, 0 10 9 eV/( m p c 2 ) 1 marks the low-energy end of the cosmic ray proton spectrum. When travelling through the interstellar medium, cosmic rays lose energy continuously. The total number N e () of e can be described as where the energy losses are determined by the cooling time-scale e . At steady state, we find The different energy loss channels are: ionization (ion), bremsstrahlung (brems), inverse Compton scattering (IC), synchrotron emission (synch), and galactic outflows (wind). The individual time-scales can be summarized as and result in the total cooling time-scale Here, T 6.6510 25 cm 2 is the Thomson cross-section, u B B 2 /(8) is the energy density of the magnetic field B , u ISRF is the energy density of the interstellar radiation field (ISRF), n is a gas density, H is the galactic scaleheight, v wind the velocity of the galactic wind, and is the e Lorentz factor. 2.2 Synchrotron emission in a galactic magnetic field In the presence of a magnetic field, cosmic rays perform spiral motions and hence are constantly accelerated. A single electron with a Lorentz factor results in the spectral power where K 5/3 ( x ) is the modified Bessel function of second kind and (see e.g. the review by Blumenthal Gould 1970 ). To find the synchrotron emission L , synch produced from the full population of cosmic rays, one has to integrate over the cosmic ray distribution, i.e. The last integral over the pitch angle is roughly 8.9 for a cosmic ray spectrum with a slope of 2.2 and the lower limit of the integration is e, 0 10 7 eV/( m e c 2 ) 20. We note that the synchrotron luminosity L synch, is directly proportional to the supernova rate , which determines the total number of cosmic rays (see equation 3 ). As the supernova rate is correlated with the SFR, synchrotron emission can be used to estimate a galaxys SFR. 2.3 Freefree emission and absorption At low frequencies and high gas densities, the interstellar medium is optically thick and synchrotron emission, holding the information about the SFR, is absorbed. The optically thick regime is characterized by an optical depth ff larger than 1. The value of ff depends on the electron temperature T e , the emission measure EM , and the frequency : with The number density of the free electrons n e can be related to the gas density via the ionization degree f ion : n e f ion n . The filling factor f fill describes the clumping of the medium (Ehle Beck 1993 ; Berkhuijsen, Mitra Mueller 2006 ; Beck 2007 ). Hence, the critical frequency crit at which ff 1, determining
Paper: 2015MNRAS.448L...1R (acknowledgments)
We thank the anonymous referee for their various helpful comments and suggestions. IDR and LCP thank the National Science and Engineering Research Council of Canada for funding. We thank X. Yang et al. for making their SDSS DR7 group catalogue public, L. Simard et al. for the publication of their SDSS DR7 morphology catalogue, and the NYU-VAGC team for the publication of their SDSS DR7 catalogue. This research would not have been possible without these public catalogues. Funding for the SDSS 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 website is http://www.sdss.org/ .
Paper: 2015A&A...574A..20F (acknowledgments)
The authors would like to thank the referee, John Landstreet, for useful comments that improved the manuscript. L.F. acknowledges financial support from the Alexander von Humboldt Foundation. T.M. acknowledges financial support from Belspo for contract PRODEX GAIA-DPAC. N.L. and A.R. acknowledge financial support from the DFG-CONICYT International Collaboration Project (DFG-06). S.S.-D. acknowledges funding by the Spanish Ministry of Economy and Competitiviness under the grants AYA2010-21697-C05-05, AYA2012-39364-C02-01, and Severo Ochoa SEV-2011-0187, and by the Canary Islands Governments under grant PID2010119. L.F. is greatly in debt to Oleg Kochukhov for providing most of the tools used here for reducing and analysing the HARPSpol data. L.F. also thanks Gregg Wade, John Landstreet, and Coralie Neiner for fruitful discussions. The authors thank Andreas Irrgang, Ignacio Negueruela, Henk Spruit, Gautier Mathys, and Alexander Kholtygin for useful comments. S.H. and M.S. thank Thomas Szeifert for providing the pipeline for the FORS spectra extraction.
Paper: 2015ApJ...815..111S (acknowledgments)
A. Strugarek is a National Postdoctoral Fellow at the Canadian Institute of Theoretical Astrophysics, and acknowledges support from Canada’s Natural Sciences and Engineering Research Council. This work was supported by the ANR 2011 Blanc Toupies and the ERC project STARS2. We acknowledge access to supercomputers through GENCI (project 1623), Prace (8th call), and ComputeCanada infrastructures.
Paper: 2017ApJ...840...43A (acknowledgments)
The Fermi LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT, as well as scientific data analysis. These include the National Aeronautics and Space Administration and the Department of Energy in the United States; the Commissariat à l’Energie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France; the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy; the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the High Energy Accelerator Research Organization (KEK), and the Japan Aerospace Exploration Agency (JAXA) in Japan; and the K. A. Wallenberg Foundation, the Swedish Research Council, and the Swedish National Space Board in Sweden. Additional support for science analysis during the operations phase is gratefully acknowledged from the Istituto Nazionale di Astrofisica in Italy and the Centre National d’Études Spatiales in France. We made use of data products based on 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. This work was partially funded by NASA grants NNX14AQ37G and NNH13ZDA001N. Facilities: Fermi , Planck . Software: Astropy ( http://www.astropy.org , Astropy Collaboration et al. 2013 ), Fermipy ( http://fermipy.readthedocs.io/ ), GALPROP ( http://galprop.stanford.edu/ , Vladimirov et al. 2011 ), HEALPix ( http://healpix.jpl.nasa.gov/ , Górski et al. 2005 ), ROOT ( http://root.cern.ch/ , Brun Rademakers 1997 ), matplotlib (Hunter 2007 ).
Paper: 2015MNRAS.447..345Y (acknowledgments)
This work is supported by the national science foundation of China (grant nos. 11025316 and 11121062), National Basic Research Program of China (973 Program) under grant no. 2009CB24901, the CAS/SAFEA International Partnership Program for Creative Research Teams (KJCX2-YW-T23) and China Postdoctoral Science Foundation (2013M531231).
Paper: 2018ApJS..234...23A (fulltext)
Drake et al. 2009 ), retrieved from the Catalina Surveys Data Release 2. Between the WISE and NEOWISE-R epochs, the source dimmed by approximately 1 mag in both W1 and W2 , with further, lower amplitude variability observed between the epochs of each survey independently. In contrast, there is no strong optical variability observed by CRTS. This is consistent with both spectroscopic classifications, as in both an intermediate-type and a type 2 AGN, the optical emission is dominated by the host galaxy. The large drop in mid-IR fluxes suggests that the change in spectroscopic classification is most likely due to a decrease in accretion rate rather than a change in obscuration. Figure 10. Light curve of WISEA J142846.71+172353.1 in the optical from CRTS (green points) and in the WISE bands W1 (purple points) and W2 (red points). All magnitudes are shown in the AB system to limit the dynamic range of the vertical axis. For W1 and W2 , the small circles show the individual frame photometry in the AllWISE and NEOWISE-R surveys. The large circles show the median of each epoch. 5. Comparisons with Other Surveys As mentioned earlier, a number of AGN catalogs already exist in the literature that are similarly as large as ours. In this section, we compare with a number of them in order to characterize how they differ from our R90 and C75 catalogs as well as to assess what the relative completeness and biases are. We consider both mid-IR selected catalogs as well as catalogs selected in different wavelength ranges and, hence, affected by very different systematics. 5.1. Secrest et al. (<xref ref-type="bibr" rid="apjsaaa00abib79">2015</xref>) WISE AGN Catalog Recently, Secrest et al. ( 2015 ) presented an all-sky AGN catalog selected purely on their WISE colors, based on the selection criteria of Mateos et al. ( 2012 ). These criteria require a detection in the W3 band. A13 showed these criteria to be less reliable than the R90 criterion at faint W2 magnitudes, though they are equally reliable at brighter magnitudes. The trade-off is that the Mateos et al. ( 2012 ) selection criteria are more complete at faint W2 magnitudes than the R90 selection. As noted by Jarrett et al. ( 2011 ), requiring a detection in W3 can be particularly useful near the high survey coverage areas at the EPs, where W1 and W2 are confusion limited, and the lower source density W3 band provides robust photometry. However, the lower sensitivity of the W3 band restricts the AGN sample size created using the selection criteria of Mateos et al. ( 2012 ). Additionally, Secrest et al. ( 2015 ) required a 5 σ detection in all three WISE bands, creating a robust catalog at the cost of decreasing the number of sources selected. Indeed, the full catalog presented by Secrest et al. ( 2015 ) consists of 1,354,775 sources, and once we apply the same spatial filters as described in Section 3.1 , the catalog is reduced by 15% to 1,140,022 sources. This is roughly 25% of the sources in our R90
Paper: 2019MNRAS.487L..77V (fulltext)
the result of different masers flaring as the shock passes through the cluster, then we can estimate the distance between the flaring masers. If we assume the shock front is travelling at v ≈ 100 km s −1 = 10 7 cm s −1 and the time between flares is Δ T ≈ 1.5 × 10 7 s, we estimate the distance between the masers is Δ r ≈ 1.5 × 10 14 cm ≈ 11 AU. As already mentioned, the velocity extent of the water masers associated with W49N is ±200 km s −1 . Heckman Sullivan ( 1976 ) suggest that less than 10 per cent of the total integrated flux density across the entire spectrum is from these extreme-velocity masers. Also Goss et al. ( 1976 ) reported that the extreme-velocity maser features associated with W49N are weak relative to the low-velocity features. From the results of Very-long-baseline interferometry (VLBI) observations it has been inferred that the emission regions, where these extreme-velocity maser features exist, are more compact than those with intermediate (±20 km s −1 ) and velocities nearer to the local standard of rest velocity. The size of these maser clusters is less than 10 4 AU and they are located on the periphery of the intermediate-velocity maser features (Knowles et al. 1974 ). However, here we find that most of the water-maser flux density appears concentrated in the blue-shifted features and, in particular, the water maser at v = −81 km s −1 (Figs 2 and 3 ) is much stronger than the intermediate- and low-velocity masers. The extreme-velocity water masers associated with W49N have velocities that are more than 10 times faster than the isothermal speed of sound, ∼10 km s −1 , and concentrated in a region of only 0.1 pc in size. Larson ( 1973 ) showed that the accretion of matter onto a protostar has a velocity limited to a few times the speed of sound. Ergo, accretion cannot explain the appearance of extreme-velocity masers in W49N. Objects travelling at such extreme velocities would be dynamically unstable (Strel’nitskii Syunyaev 1972 ). Strel’nitskii Syunyaev ( 1972 ) proposed three possible acceleration mechanisms that may explain the extreme-velocity masers: acceleration by radiation pressure, stellar wind activity from hot massive stars, and by shock waves propagating through the maser space. Silk Burke ( 1974 ) suggested another acceleration mechanism: shock waves from supernova explosions. There is a supernova remnant near W49B; however, there is no non-thermal source in the vicinity of W49N. This may not eliminate it as a possible explanation, since supernovae less than 100 years old do not produce distinguishable radio emission (Spencer Burke 1973 ). Low-mass protostars in the T Tauri phase (Strom, Grasdalen Strom 1974 ) have extreme-velocity optical spectral features (∼100 km s −1 ) known as Herbig–Haro objects (Kuhi 1964 ). However, this model (Herbig 1974 ) cannot explain the asymmetrical nature of extreme-velocity water-maser features associated with W49N. Asymmetric models associated with the scattering of light
Paper: 2021ApJ...909..112D (fulltext)
along their main branch progenitor history (according to a given radius definition). In practice, applying this definition to merger trees created by almost any halo finder leads to erratic results due to spurious, temporary subhalo periods. In major mergers, for example, the host–subhalo relation can be ambiguous and switch between two halos, after which point the eventual host halo would be classified as a flyby halo. We follow the strategy of Mansfield Kravtsov ( 2020 ) to eliminate such cases: we do not count a halo as a flyby if its former host is no longer alive, if the former host is a subhalo of the halo in question, or if the former host’s mass is now smaller than that of the halo in question (all defined given the same radius and mass definition). In some cases, particularly for subhalo epochs at high redshift, the host may not be part of the merger trees because it never exceeded the necessary mass threshold. We also discard such events because the peak host mass was clearly smaller than the current mass of the halo in question. We emphasize that this definition of what constitutes a flyby halo is not unique. For example, we could consider future epochs to establish whether a flyby halo will eventually fall into its former host. Similarly, omitting any one of our exclusion criteria causes noticeable changes in the flyby fraction, raising the suspicion that f flyby is not a particularly well-defined quantity. Nevertheless, the relative differences in flyby fractions according to different radius definitions do remain similar, which is the focus of our work. Figure 4 shows the flyby fraction in the WMAP7 cosmology at z ≈ 0 and z = 2; the meaning of the lines is very similar to Figure 3 (see the Appendix for details). Regardless of the radius definition, mass variable, or redshift, f flyby asymptotes to zero at the highest masses and increases toward low masses. At the smallest masses we can probe, f flyby is still increasing so that we cannot put an upper bound on it. At fixed M 200m,bnd , f flyby varies between about 7% and 12% at the low-mass end. While this range sounds relatively modest, the relative fractions differ substantially between mass definitions, especially at intermediate masses. For instance, at M ≈ 10 12 h −1 M ⊙ , using R sp,90% leads to only 15% of the flyby halos found when using R 500c . When plotted as a function of peak mass, the fractions are shifted to higher values (middle column of Figure 4 ). In reverse, the shift means that flyby halos are more likely to have a high ratio of peak to current mass, meaning that they have lost mass at some point along their trajectory, which makes sense given that they had an encounter with a larger halo. Finally, the right column of Figure 4 shows the same results as a function of . In all cases, the relative differences between the radius definitions are similar. At higher redshift (bottom row), all curves are shifted to lower masses. Figure 4. Same as Figure 3 but for
Paper: 2018A&A...616A.165V (acknowledgments)
This paper is based on observations made in the Observatorios de Canarias del IAC with the TNG operated on the island of La Palma by the Fundación Galileo Galilei – INAF, Fundación Canaria in the Observatorio del Roque de los Muchachos . OV thanks to the Spanish TAC for the time allocation (programme number CAT_110) during semester 2010A. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. We used some data and images from Sloan Digital Sky Survey (SDSS, versions DR8 and DR14), which is a project funded by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. Thanks are due to the anonymous referee whose recommendations helped us to improve the paper.
Paper: 2017MNRAS.464.4721F (fulltext)
if using the relationships of the Ca ii triplet due to Paschen blending that may be causing the offset observed between HAeBes and CTTs. Another note of caution is advised when using the He i and O i lines, as their complex line profiles suggest various origins – which may or may not be associated with accretion. The complexities of their profiles are likely the cause of the low detection rate of these lines, as multiple absorption and emission components can contribute to the line equivalent width. An exploration of the line profiles will be presented in a future paper by the authors. 3.3 Emission lines as accretion diagnostics Since all of the emission lines appear to be correlated with the accretion luminosity, it is worth discussing which lines serve as the most sensitive tracers of accretion i.e. most readily detected (and therefore possibly associated directly with accretion). That way the best lines can be prioritized in future observations. In Table 3 , all 32 lines are presented along with details of the number of emission detections. The other columns of the table are split into different categories based on emission detection and UV-excess detection. HAeBe stars that are classed as having an accretion detection are those for which a UV excess was clearly detected in Paper I – 62 stars match this criterion. This includes the seven stars in which an accretion rate could not be determined within the context of MA; it would require an unrealistically high filling factor of shocked material to reproduce their UV excess. The measured Δ D B of these seven objects is likely associated with accretion due to the overall properties of the stars, although the exact mechanism is not known [an alternative could be boundary layer accretion for these hot objects Bertout, Basri Bouvier (see 1988 ); Blondel Djie (see 2006 )]. 2 Table 3. This table details all of the measured emission lines, along with their respective number of detections. The final four columns denote the four categories into which each star of the sample can belong to for a given line; full descriptions are provided in the text. ‘Emis’ denotes emission line, and Δ D B denotes the UV excess. The two categories of emission and accretion can be divided into two outcomes for each case, those with and those without. This gives a total of four possible categories for a star to be in e.g. a star may have a particular line in emission, but no Δ D B was detected in the star. Table 3 gives the number of stars split into each category, for each line, and is presented visually in Fig. 4 . What the four different categories represent are now detailed. The first category contains objects for which both emission lines and a UV excess are detected, represented by the green segments in the diagram. This category measures if the line is a good one to one tracer of accretion i.e. excess is present and so is the emission line. Examples of good tracers are Hα, Brγ and Paβ as their emission detections