adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.494.1180G (fulltext)
the density and dynamic range of the frequency sampling may be computationally attainable via offloading this part of the workload to multicore architectures within the MHD code in the future, even though the attainable speedup may in fact be limited by restrictions in the memory bandwidth. Even without these technological advancements, we have demonstrated that the spectral TF method of Brandenburg et al. ( 2008 ) and its time-dependent counterpart introduced by Hubbard Brandenburg ( 2009 ) are highly useful tools and indeed produce interesting results for advanced applications, such as our investigation of realistic high-Mach-number turbulence driven by SNe in the multiphase ISM, where mean-field effects occur naturally as an outcome of the combined action of gravity and the Coriolis force. Notwithstanding the challenges of understanding the quenching in the strong-field limit (Gressel, Bendre Elstner 2013a ) and covering the parameter space required by genuinely global models (Gressel et al. 2013b ), combining these approaches (as pioneered by Rheinhardt Brandenburg 2012 ) will allow to derive the most comprehensive mean-field closures achievable. APPENDIX A: BENCHMARK USING HELICAL FORCING IN THE KINEMATIC LIMIT We verify our implementation of the spectral TF method for the simple test case of helically forced (mildly compressible) hydrodynamic turbulence, that is, in the strict kinematic limit of the mean-field dynamo, where the back reaction via the Lorentz force is ignored. To this end, we perform a hydrodynamic DNS in a cubic domain, solving along with the TF induction equation ( 11 ), and using as the equation of state. We closely follow Brandenburg ( 2001 ) and adopt a helical forcing function , with the normalization with respect to the numerical time-step, δ t , and f 0 ≲ 1 a dimensionless forcing amplitude. The forcing obtains its helical character via with an isotropically sampled arbitrary unit vector required for constructing a vector . Note that , and – being an eigenfunction of the curl operator – we have , such that the helicity of the forcing, , is positive definite. The wave vector is chosen from a list of 66 (158) pre-computed discrete wave vectors obeying the periodicity of the box, and that have , with k f = 3 ( k f = 5), and Δ k = 1/4. Note that both and the random phase φ( t ) are updated after each time-step, δ t , of the DNS. A1 Non-locality in space In Fig. A1 , we show the spectral dependence of and , adopting units of and . Both curves are very well approximated by a Lorentzian with a characteristic length scale . This is in contrast to the results of Brandenburg et al. ( 2008 ), who find k c = k f for , and for , respectively, for a very similar test case with k f = 5 and Rm = 10. While the qualitative trend is the same, the difference is much less pronounced in our case. Figure A1. Spectral amplitudes for the α effect (top panel) and turbulent diffusion, η T (lower panel) for helically forced isothermal
Paper: 2020ApJ...890..169P (fulltext)
spacetime (Mattingly 2005 ; Amelino-Camelia 2013 ). Any possible violation of Lorentz invariance would have far-reaching consequences for our understanding of the nature, i.e., the pillars of modern physics will be shocked and new physics is needed (Cao et al. 2018a ). Therefore, the pursuit of testing the possible LIV at much higher precision has continued in recent decades, concerning various astrophysical and cosmological observations. Formulating and quantitatively interpreting the test of LIV is another question: an interesting proposal, in this respect, has been formulated in the frameworks of many QG: at small spatial scales, a foamy structure of spacetime predicted by QG theory will interact only with the high-energy photons (Amelino-Camelia et al. 1998 ; Amelino-Camelia Piran 2001 ). In this case, the speed of light is varying at a different energy range in vacuum, i.e., the high and low-energy photons will not reach us at the same time. More specifically, the deformed velocity of light usually takes the form , where E QG is effective QG energy scale, s ± , is a dimensionless parameter depending on the particular QG model, and c is the limiting speed of light on low-energy scales, respectively. On low-energy scales, E ≪ E QG , the effect of LIV will be more obvious and the high-energy photons propagate slower than low-energy photons. Although the QG effect is expected to be very weak (since E QG is typically close to the Planck energy scale), some effects of LIV are expected to increase with energy and over very long distances due to cumulative processes in photon propagation. Therefore, astrophysical searches provide sensitive probes of LIV and its potential signatures, such as the energy-dependent time delay and many other phenomena. Gamma-ray bursts (GRBs), the most luminous astrophysical events observable proposed as distance indicators at high redshift (Pan et al. 2013 ; Cao Zhu 2014 ), create such an opportunity to test the LIV, through the precise measurement of unprecedented very-high-energy photons. Compared with commonly used supernova Ia, the advantages of GRBs lies in their high-energy photons in the gamma-ray band (from KeV to GeV), short spectral lags, and the propagation distance at cosmological scales. Such a natural laboratory provides a possibility of testing LIV, through the well-measured time delays between light curves in different energy bands caused by the LIV. Recently, some advances have been made concerning the limits on LIV using different samples of GRBs (Ellis et al. 2003 , 2006 ; Rodriguez Martínez Piran 2006 ; Jacob Piran 2007 ; Pan et al. 2015 ; Zhang Ma 2015 ; Wei et al. 2017 ; Zhang et al. 2018b ; Zou et al. 2018 ). The original idea of such studies of possible LIV constraints can be traced back to the papers of Ellis et al. ( 2003 ), which developed a method to analyze samples of GRBs with different redshifts and energy bands, by extracting time-dependent features from the GRB signals. It is worth
Paper: 2016ApJ...818..122A (acknowledgments)
This investigation is based on observations obtained with XMM-Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. This research made use of data obtained from the High Energy Astrophysics Science Archive Research Center (HEASARC), provided by NASA’s Goddard Space Flight Center. We acknowledge support from NASA ADAP grant NNX15AE63G. We thank Eric Gotthelf for valuable discussions and the anonymous referee for several helpful comments.
Paper: 2018MNRAS.474.4507L (fulltext)
ways. They facilitate the comparative analysis of measurements in a pairwise fashion. One may use them with n measurements of a particular variable to generate n ( n −1)/2 comparisons for each pair of data. The benefits are twofold: (1) one can test how well each pair of data fits the models, and (2) assess how closely the published error bars fit a normal distribution, thereby providing some indication of possible contamination by correlated systematic uncertainties. Zheng et al. ( 2016 ) recently used such an approach to conclude that the stated errors in cosmic chronometer data are strongly non-Gaussian, suggesting that the quoted measurement uncertainties are almost certainly not based exclusively on statistical randomness (see also Leaf Melia 2017 ). As we shall see, the diagnostic Δμ( z i , z j ) is expected to be zero if the model being tested is the correct cosmology. To allow for possible non-Gaussianity in the published errors, we shall use both weighted-mean and median statistics to determine the degree to which each model's distribution of Δμ( z i , z j ) values is consistent with this null result. So while Wei, Wu Melia ( 2016 ) optimized the overall ΛCDM and R h = ct parametric fits to the H ii galaxy Hubble diagram, here we will test the consistency of each fit with individual pairs of data. We will begin with a brief description of the data in Section 2 , and then define and apply the diagnostic Δμ( z i , z j ) in Section 3 . The outcome of our analysis will be discussed in Section 4 , followed by our conclusions in Section 5 . 2 OBSERVATIONAL DATA AND METHODOLOGY We base our analysis on the methodology described in Chávez et al. ( 2012 , 2014 ) and Terlevich et al. ( 2015 ), using their total sample of 156 sources, including 107 local H ii galaxies, 24 GEHRs, and 25 high- z H ii galaxies. The correlation between the emission-line luminosity and the ionized gas velocity dispersion may be written as (Chávez et al. 2012 ; Chávez et al. 2014 ; Terlevich et al. 2015 ) where α is the slope and the constant κ represents the logarithmic luminosity at log σ(Hβ) = 0. As noted, previous applications of this relation have produced a very small scatter in the correlation for L (Hβ), making it a viable luminosity indicator for cosmology. But one cannot completely avoid its cosmology dependence because the Hβ luminosity is calculated using the expression where D L is the model-dependent luminosity distance at redshift z and F (Hβ) is the reddening corrected Hβ flux. From equation (1), we may then obtain the distance modulus of an H ii galaxy according to with an associated error in terms of σ log σ and σ log F , these being the 1σ uncertainties in log σ(Hβ) and log F (Hβ), respectively. This is to be compared with the theoretical distance modulus as a function of the cosmology-dependent luminosity distance D L . In ΛCDM, the luminosity distance may be written where p de = w de ρ de is the dark-energy equation of state; radiation is
Paper: 2019ApJ...886..121B (acknowledgments)
We are grateful to the referee for constructive and useful comments that improved this paper.
Paper: 2020ApJ...888...69B (fulltext)
transformation back to the lab frame then gives an electric field and an associated electrostatic potential in the lab frame. We assume here that the jet velocity greatly exceeds the difference J / ne between electron and ion drift velocities, so the jet velocity is nearly the same as the Harris-frame velocity. This means that in the jet frame the electric field and associated electrostatic potential are zero. The nonrelativistic Lorentz transformation relates the jet-frame electric field E ′ to the lab-frame electric field E byBecause the Harris construction in the jet frame gives E ′ = 0, and assuming a jet velocity Equation ( 20 ) gives the lab-frame radial electric field asUsing Equation ( 14 ), the lab-frame radial electric field can then be expressed asSince a gauge with has been assumed, the radial electric field reduces toCombining Equations ( 22 ) and ( 23 ) and integrating gives the lab-frame electrostatic potential asso using Equation ( 16 ) we have 5. Construction of the Electric and Magnetic Field Vectors We now have sufficient information to construct the complete lab-frame electric field. Because of axisymmetry, the electrostatic potential V is independent of φ , so the toroidal electric field isthis electric field component is missing in the static models used by Mestel ( 1961 ), Blandford Payne ( 1982 ), Lynden-Bell ( 2003 ), and Pudritz et al. ( 2012 ); it also differs from the singular E φ proposed in Contopoulos ( 1994 ). The axial electric field in the jet region iswhere Equation ( 24 ) has been used and the last line comes from Equation ( 17 ). The radial electric field using A r = 0 is given by Equation ( 21 ) asThe lab-frame magnetic field is There is no lab-frame electric field parallel to the magnetic field sincewhere Equation ( 9 ) has been used. 6. Demonstration That the Time-dependent Induction Equation Is Satisfied Because ψ is time dependent, both the electric and magnetic lab-frame fields are time dependent. This contrasts with the previous analytic models (e.g., Mestel 1961 ; Sauty 1994 ; Beskin 2010 ; Contopoulos 1994 ) where the jet fields were assumed to be independent of time and the lengthening of the jet was not taken into account. These previous analytic models assumed that the magnetic field is independent of time, so the induction equationis reduced to , which forces U × B to be the gradient of a potential. In contrast, time dependence of the magnetic field is retained here, so U × B is not the gradient of a potential. Using , we see thatsoDirect calculation of the time derivative of Equation ( 29 ) giveswhich on invoking Equation ( 9 ) becomesComparison of Equations ( 33 ) and ( 35 ) shows that the time-dependent induction equation, Equation ( 31 ), is exactly satisfied. Various points of view have been expressed on how the detailed flow moves with respect to the magnetic field. According to one point of view (e.g., Blandford Payne 1982 ), the predominant flow is along magnetic field lines (beads on a
Paper: 2018MNRAS.479..536D (acknowledgments)
ACKNOWLEDGEMENTS We thank Matt Smith for kindly providing the M31 β map. TED wishes to thank Prof. Chung-Ming Ko at National Central University for his support of this project. We also want to thank Jesús Toalá, whose insights in the data encouraged us to pursue this project. This research has been supported under grant MOST104-2628-M-001-004-MY3 from the Ministry of Science and Technology of Taiwan. AZ acknowledges support from the UK Science and Technology Facility Council under grant ST/L000768/1. JC is supported by an NSERC Discovery Grant. The JCMT is operated by the East Asian Observatory on behalf of The National Astronomical Observatory of Japan; Academia Sinica Institute of Astronomy and Astrophysics; the Korea Astronomy and Space Science Institute; the Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments, budgeted from the Ministry of Finance of China and administrated by the Chinese Academy of Sciences, as well as the National Key RD Programme of China (No. 2017YFA0402700). Additional funding support is provided by the Science and Technology Facilities Council of the United Kingdom and participating universities in the United Kingdom and Canada. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA. This research used the facilities of the CADC operated by the National Research Council of Canada with the support of the Canadian Space Agency. In addition to software cited above, this research made use of the scipy (Jones et al. 2001 ) and astropy (The Astropy Collaboration et al. 2018 ) python packages. This research has made use of the SVO Filter Profile Service (http://svo2.cab.inta-csic.es/theory/fps/) supported from the Spanish MINECO through grant AyA2014-55216. We thank the anonymous referee for refereeing this paper in a timely manner and for their suggestions which lead to the improvement in its clarity.
Paper: 2015A&A...573A..94T (acknowledgments)
This work was supported by the São Paulo State Science Foundation, FAPESP, and the Brazilian National Research Council, CNPq. This work has made use of the facilities of the Computation Center of the University of São Paulo (LCCA-USP) and of the Laboratory of Astroinformatics (IAG/USP, NAT/Unicsul), whose purchase was made possible by the Brazilian agency FAPESP (grant 2009/54006-4) and the INCT-A. The authors are grateful to the anonymous referee for numerous suggestions and corrections to this paper.
Paper: 2017ApJ...850..205K (fulltext)
field, S , exists near the pole. This result is due to spatial symmetry. Figure 4 shows the time-averaged toroidal flux S in . The toroidal magnetic field S decreases gradually by the radius from the star. The maximum value of S falls inside the light cylinder radius. The maximum value does not match the radius of the light cylinder. The concentrated poloidal current area is the light cylinder interior. Figure 4. Time-averaged toroidal magnetic flux function S for the center of the star. . The toroidal magnetic field, S , increases due to large electrical conductivity . In our model, the value increases with the increase of r Equation ( 49 ). The toroidal magnetic field increase proportionally to electrical conductivity. Figure 3 (c) shows the electromagnetic field for , five times bigger than the case. In addition, this field area spreads out. This is in contrast to the poloidal magnetic field G , which is not changed by the electrical conductivity. 4.1.3. Electrostatic Potential Φ Figure 5 (a) shows the electrostatic potential contour of . From to 0.08, contours are equally spaced for each 0.01. In the numerical calculation, the electrostatic potential on the star’s surface does not change with time because of the boundary conditions Equation ( 36 ). Blue lines in Figure 5 (a) spread out from the surface boundary and extend in the radial direction. Blue lines are connected vertically toward the equatorial plane because the field’s boundary condition has only an R component in this plane. Figure 5 (b) shows the electrostatic potential of the spatial region from Z = 0 to 30. Here, the contour line is drawn in increments of 0.005 from . Figure 5. Electrical potential Φ for . The red line is the light cylinder radius R = 5. The black line is the equal value of the poloidal-magnetic flux function G . The black dotted line shows a dipole-magnetic flux equal to the value of the function. The bottom panels show zoomed-out views. The densities of the equipotential lines are proportional to the strength of the electric field, which is strong on the star surface and decreases as one goes radially outward. No electric field dominant () area exist in . The magnetic field in the obtained results is always stronger than the electric field. Figure 5 (c) is the drawn contours of the electrostatic potential cases. Electrical conductivity increases along with the electrostatic potential of the equatorial plane. However, the difference of electrostatic potential of the equatorial plane between the and is about 10%. When the electrical conductivity is different from this case, the overall structure of the solution remains similar. For example, electrical conductivity is increased in a toroidal magnetic field, but little shape change was observed about the potential. 4.1.4. Poloidal Current j<sub>p</sub> Current density is divided into poloidal and toroidal components. The poloidal component of current density j p consists of j r and : Figure 6 (a) plots
Paper: 2019MNRAS.484.1756M (acknowledgments)
ACKNOWLEDGEMENTS ADM holds an Australian Research Council (ARC) Future Fellowship (FT160100206), and acknowledges support from ARC Discovery Project DP150103294. We would like to thank the anonymous referee for their thorough reading of this manuscript and for their helpful suggestions for improvement. This paper is based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada–France–Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institute National des Sciences de l’Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii. This paper is also based, in part, on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), and Ministério da Ciência, Tecnologia e Inovação (Brazil). These observations were obtained via programmes GN-2014B-Q-26 and GN-2015B-Q-17.
Paper: 2018A&A...615A..49C (fulltext)
those integrated orbits. Both these quantities correlate with age, with OCs younger than 300 Myr being all contained within 180 pc of the plane, while half the older clusters have orbits that extend beyond this limit. The oldest OC in our sample is NGC 2682, one of the oldest known open clusters, and its orbit strays more than 400 pc from the Galactic plane. We also computed the eccentricity of each orbit ( where r p and r a are the perigalacticon and apogalacticon of the orbit), and found no apparent correlation of eccentricity with age (bottom panel of Fig. 12 ). The orbital parameters of the integrated orbits are listed in Table 3 . 6 Discussion The two-million-star TGAS sample allows us to identify members and derive mean parallaxes in clusters located further than 1000 pc from us, thus enabling studies of the Galactic disk on a larger scale than the works based on the results of the H IPPARCOS catalogue (e.g. Robichon et al. 1999 ; Bouy Alves 2015 ), which is severely incomplete beyond 100 pc and for which the relative parallax error reaches 50% at 350 pc. The distribution of OCs identified in this paper is shown in Fig. 13 , along with the schematic location of the spiral arms of the Milky Way in the model of Reid et al. (2014) . We remark that almost all OCs in this study are located in the inter-arm region. In order to trace the spiral structure of the thin disk, our sample would require a significant number of objects younger than 20 Myr (log t 7.3), as clusters drift away from their birthplace over this timescale (see e.g. Dias Lépine 2005 ). These young clusters and associations are often sparsely populated and/or embedded in their progenitor molecular cloud (thus requiring the use of infrared photometry). Due to the magnitude limits of the TGAS solution, even nearby clusters such as Chamaeleon I (2 Myr old, 160 pc from us, Whittet et al. 1997 ) are not present in the catalogue. Hints of objects such as IC 2395 (6 Myr, 800 pc Clariá et al. 2003 ) can be seen, but not with a sufficient number of stars for our method to establish secure membership. Another common tracer of the spiral structure of the disk are the so-called OB associations, which are even sparser groups of bright, short-lived blue stars, whose identification requires the use of multi-band photometry (and ideally spectroscopy). Such a search is well beyond the scope of this paper, aimed at identifying stellar clusters from astrometric data. Fig. 13 Position of the OCs studied in this paper in Galactic rectangular XY Z coordinates. Black dots: OCs from this study with relative parallax errors under 50% (considering an additional 0.3 mas calibration error for all OCs). Crosses: OCs from this study with relative parallax error over 50%. Open symbols: OCs from Gaia Collaboration (2017) . The spiral arms are traced according to the model of Reid et al. (2014) . Out of the 128 OCs for which we derived mean astrometric parameters, 67 have mean proper motions obtained from