adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.459.2701M (fulltext)
of a magnetohydrodynamic (MHD) fluid is that weak collisionality permits approximate conservation of particle adiabatic invariants and so when frozen-in magnetic field is locally increased or decreased by the plasma motions, perpendicular ( p ⊥ ) and parallel ( p ∥ ) pressures evolve differently (Chew, Goldberger Low 1956 ; Kulsrud 1983 ), giving rise to local pressure anisotropies. Pressure-anisotropic plasma is unstable when the relative pressure anisotropy is large enough compared to 1/β (otherwise Lorentz forces stabilize the plasma), namely, (the mirror threshold given here is approximate; see Hellinger 2007 ). These instability conditions are easily achievable in the ICM (see Schekochihin et al. 2005 and references therein). The instabilities are very fast in time and microscopic in space compared to any conceivable macroscopic dynamics because they happen essentially on ion Larmor scales. It is reasonable to expect that their effect will be to keep the plasma locally (at most) marginal with respect to the conditions ( 3 ) and ( 4 ); solar-wind measurements lend strong support to this expectation (Hellinger et al. 2006 ; Bale et al. 2009 ). In order to make that happen, the firehose and mirror fluctuations must impose an upper bound on the effective rate of change of the magnetic field (which gives rise to the pressure anisotropies) and/or increase the effective collisionality of the plasma (which relaxes the anisotropies) – either scenario can lead to macroscopic dynamics dramatically different from the conventional MHD evolution. This topic was discussed at length (if speculatively) by Mogavero Schekochihin ( 2014 ), with particular focus on the question of how, under pressure-anisotropic conditions, the magnetic field might be able to grow from a weak primordial seed (believed to be anywhere between 10 −21 and 10 −9 G, corresponding to β ∼ 10 32 − 10 8 ; see review by Durrer Neronov 2013 ) to its observed value ( B ∼ 10 −6 G, β ∼ 10 2 ), even as the rate at which it is allowed to change locally is constrained by the condition where ν eff is either the Coulomb collision frequency or some effective collisionality determined by particle scattering (if any) off microscopic firehose or mirror fluctuations (see further discussion in Section 4.2 ). This is the question of plasma dynamo – magnetogenesis in a weakly collisional turbulent plasma. Besides being of astrophysical (origin of observed fields) and fundamental (what happens?) physical interest, it is likely soon to be attacked not just theoretically, but also experimentally, with the advent of laboratory experiments aiming to reproduce the plasma dynamo process (Spence, Reuter Forest 2009 ; Meinecke et al. 2014 , 2015 ; Forest et al. 2015 ; Plihon et al. 2015 ). The first demonstration of this process in a 3D kinetic numerical simulation by Rincon et al. ( 2016 ) has indeed confirmed the ubiquitous appearance of mirror and firehose fluctuations, although a detailed investigation of
Paper: 2016ApJ...818..119I (acknowledgments)
I gratefully acknowledge the anonymous referee for useful comments that helped to clarify several aspects of the present work and I thank the Departamento de Ciencias Básicas, Facultad de Ingeniería Universidad Autonoma del Caribe. This work has been supported by the Vice-Rectoría de Investigación y Transferencia, Universidad Autonóma del Caribe through project PRYINT-049 2015.
Paper: 2021MNRAS.501.3088G (fulltext)
has not taken place yet). Future observations of the disc may provide additional constraints, considering there is indirect evidence for a much larger binary population within it. For example, binaries could account for the observed radial trend in disc membership among young stars in the Galactic Centre (Naoz et al. 2018 ). The remainder of this paper is organized as follows. In Section 2 , we review the effects of mass ratio on binary disruptions. In Section 3 , we describe the approximate method we use to model disruptions. In Section 4 , we describe how the probability of not producing massive S-stars varies with binary parameters. In Section 5 , we discuss the effects of stellar disruptions. Finally, we summarize our conclusions in Section 6 . 2 MASS RATIO AND THE HILLS MECHANISM When an unequal mass binary is disrupted, the primary star’s final orbit will be closer in energy to that of the original binary than the secondary star’s. This is because the primary star is closer to the binary’s centre of mass, and experiences a smaller tidal force. Analogously, when a single star is disrupted the most tightly bound debris comes from its surface (Rees 1988 ). Stars can either gain or lose energy in the disruption, depending on the binary’s phase. The final (specific) energy of star i will be (see e.g. Kobayashi et al. 2012 ) where ε o is the initial energy of the centre-of-mass orbit, M is the mass of the SMBH, r t is the tidal radius (approximately ( M / m bin ) 1/3 a bin , where m bin and a bin are the binary mass and semimajor axis, respectively). The δ i factor is the characteristic distance between the star and the centre of mass. This is approximately qa bin /(1 + q ) for the primary and a bin /(1 + q ) for the secondary, where q is the ratio of the secondary mass to the primary mass. Thus, the post-disruption (specific) energies of the primary (star 1) and the secondary (star 2), are where k is a factor of order unity that depends on the binary’s eccentricity, phase, and orientation (see GM20). In the parabolic limit, the corresponding semimajor axes are The absolute value of the primary’s semimajor axis is larger by 1/ q . 3 MONTE CARLO EXPERIMENTS We explore how the mass and semimajor axis distribution of the bound stars depend on the properties of the progenitor binary population. In particular, we construct mock binary samples from the distributions in Table 1 . For each binary, we calculate post-disruption energies of its member stars according to equation ( 2 ), assuming the initial semimajor axis of the binary’s centre-of-mass orbit is 0.05 pc. The scatter from the binary’s phase and orientation is encapsulated by k . We use the suite of ∼500 000 archain binary disruption simulations from GM20 to construct an empirical distribution for this parameter. Although the final energies of the stars depend on the binary mass, semimajor axis, and its centre-of-mass orbit, the k -distribution will not depend on these properties. Thus,
Paper: 2016MNRAS.462S.376B (acknowledgments)
The authors of this paper want to thank Dr J. Koppenhoefer for his support in the provision of the Pan-STARRS1 catalogue data and Dr K. Beisser for making available the Monte-Carlo simulation software for the dust coma and tail of comets. We also wish to acknowledge the important contributions for this work resulting from the Pan-STARRS1 Survey catalogue data. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen's University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under Grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under Grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE). This research was supported by the cluster of excellence ‘Origin and structure of the universe’ of the Deutsche Forschungsgemeinschaft DFG.
Paper: 2017MNRAS.466.3928H (fulltext)
galaxy. Some arcs are longer than others, some may wind in the opposite direction to the majority and some ‘arcs’ are actually just noise mistaken for an arc. We found that the most reliable measure of the winding direction of the galaxy was a length-weighted vote of the winding direction of all the detected spiral arcs. Figure 3. Steps sparcfire (Davis Hayes 2014 ) takes in describing a spiral galaxy image. (a) The centred and de-projected image. (b) Contrast-enhanced image. (c) Orientation field (at reduced resolution for display purposes). (d) Initial arm segments found via hierarchical agglomerative clustering (HAC) of nearby pixels with similar orientations and consistent logarithmic spiral shape, overlaid with the associated logarithmic spiral arcs fitted to these clusters. (e) Final pixel clusters (and associated arcs) found by merging compatible arcs. (f) Final arcs superimposed on image (a). Red arcs wind S-wise, cyan arcs wind Z-wise. To demonstrate that this measure is unbiased with regard to spin direction, we refer to Fig. 4 , which shows a scatter plot of the left-to-right mirrored versus unmirrored value of the galaxy's pitch angle as measured by sparcfire ; in a perfect reversal, the two should be negatives of each other. We find that in 29 094 out of 29 250 cases (99.47% of cases), the two are negatives of each other to within 10 −4 deg. Even more relevant to this paper, we find that in all but five cases (99.983% of cases), the chirality of the mirrored image is correctly flipped compared to the unmirrored case. Thus, the chirality determination of sparcfire is unbiased, with respect to flipped images, to better than two parts in 10 4 . (It is instructive to discuss why a deterministic algorithm like sparcfire is not 100% unbiased to image flips; we discuss some of the issues in the appendix.) Figure 4. Galaxy-level pitch angles reported by sparcfire using unmirrored and left-to-right mirrored input images across 29 250 ‘clear’ spiral galaxies (see text for definition). These galaxy-level pitch angles are calculated as the arc-length-weighted average of all arcs agreeing with the dominant winding direction (as determined by an arc-length-weighted vote). We see that for almost all galaxies the measured pitch angle almost exactly negative, as it should be. The diagonal line gives y = − x ; cases on this line are visually underrepresented due to overlap. More importantly, only five cases out of 29 250 disagree on chirality, showing that sparcfire is chirality unbiased to a level of almost one part in 10 000. We then ran sparcfire on the entire Galaxy Zoo sample of galaxies, in order to determine the chirality of galaxies in an unbiased manner. However, we still used the human GZ1 determination of P S + P Z to select which galaxies actually display spiral structure. The second quarter of Table 2 details the statistical significance of the winding direction bias, as a function of the human confidence in observed spiral structure,
Paper: 2016MNRAS.457.3409A (fulltext)
2013 ), due to a likely very late thermal pulse (born-again scenario). Moreover, while most HE-PNe show N/O ratio larger than 1 (N rich), SuWt 2 has an N/O = 0.8 which makes it somewhat different. Most of the HE-PNe in our group (Tables 3 and 4 ) lie in the low-left corner of the PNe regime on the Hα/[N ii ] versus Hα/[S ii ] diagnostic diagram (Sabbadin, Minello Bianchini 1977 ; Gonçalves et al. 2003 ) of Galactic PNe (Fig. 9 ). Akras Gonçalves ( 2016 ) have recently demonstrated that in this regime also lie shock-excited regions, e.g. low-ionization structures such as knots and jets. Since these PNe do not exhibit high velocities, the scenario of shocks in such large and HE-PNe seems unlikely. Interestingly, some HE-PNe exhibit uncommonly bright [N i ] λ5200 and [O i ] λ6300 emission lines, which are strong indicators of shock activity. Recently, Ali et al. ( 2015 ) showed that a reverse shock of only 25 km s −1 in the PN G342.0–01.7, due to its motion through the ISM, can successfully explain the strong neutral emission lines. The low photoionization rate of their central stars, (very low L ) and large nebular size, would make the contribution of shocks easily perceptible even at small velocities (Akras Gonçalves 2016 ), or produce emission line spectra that resemble those of shock-excited regions (Raga et al. 2008 ). Figure 9. A diagnostic diagram of Galactic PNe by Sabbadin et al. ( 1977 ) with the density ellipses introduced by Riesgo López ( 2006 ) and for the HE-PNe in Tables 3 and 4 . Table 3. Chemical abundances † and dereddened emission lines of HE-PNe. † Chemical abundances are given in the notation of 12+log[ n (X)/ n (H)], †† These values have been derived from photoionization models. a Relative to Hβ= 100, b Average value from seven slit positions, c This value is a lower limit and corresponds only to N + . d This ratio was calculated based on the assumption of N/O = N + /O + , e The night sky emission line at 6300 Å may have not been properly subtracted. f They have been dereddened using R = 3.1 Table 4. Stellar and physical parameters of HE-PNe. † The stellar parameters have been derived from photoionization models. a These kinematical ages have been calculated by us, assuming a nominal expansion velocity of 40 km s −1 . b A possible born-again PN. 3 out of 11 HE-PNe are seen to exist outside the normal region in Fig. 9 , namely SBW 1, ESO 166–PN 21, and WeBo 1. SBW 1 and WeBo 1 are the youngest members in this group, and their central stars are such luminous that their high photoionization rate overcomes the shock excitation. Unfortunately, the stellar parameters of these HE-PNe are still unknown. Regarding ESO 166–PN 21, the [N ii ]/Hα and [S ii ]/Hα line ratios correspond to an average value from seven slit positions (for more details see in Peña et al. 1997 ). Studying the spectrum of each slit position separately, we deduce that the most distant regions (A1 and A5 in Peña et al. 1997 ) exhibit stronger [N ii ] and [S ii ]
Paper: 2018A&A...610A..89I (acknowledgments)
Acknowledgments We thank the Referee for providing a constructivereport that has improved the presentation of the results. We thank P. Nulsen for providing the software required to produce the SB fit reported in Fig. 4 and M. Markevitch for useful discussion. AI thanks A. Botteon and G. Lanzuisi for useful discussions. MG, GB acknowledge partial support from PRIN-INAF 2014. EOS was supported in part by NASA grants GO5-16123X and GO6-17121X. CLS was supported in part by NASA Chandra grants GO4-15123X, GO5-16131X, and GO5-16146X. KWW was partially supported by Chandra grants GO5-16125X and GO6-17108X, and NASA ADAP grant NNH16CP10C.
Paper: 2016A&A...592A..54A (fulltext)
may have been found in the form of braided velocity-coherent substructure in the Taurus B211–3 filament ( Hacar et al. 2013 ). Another major result from Herschel in nearby clouds is that most (75%) low-mass prestellar cores and protostars are found in dense, supercritical filaments for which the mass per unit length exceeds the critical line mass of nearly isothermal long cylinders (e.g. Inutsuka Miyama 1997 ), /pc, where km s is the isothermal sound speed for molecular gas at K (e.g. Könyves et al. 2015 ). These Herschel findings support a scenario for low-mass star formation in two main steps (cf. André et al. 2014 ): First, large-scale compression of interstellar material in supersonic MHD flows generates a cobweb of filaments 0.1 pc in width in the ISM; second, the densest filaments fragment into prestellar cores (and subsequently protostars) by gravitational instability above , while simultaneously growing in mass through accretion of background cloud material. In addition to the relatively modest filaments found in non-star-forming and low-mass star-forming clouds, where rarely exceeds ten times the thermal value of , significantly denser and more massive filamentary structures have also been observed in the most active giant molecular clouds (GMCs) of the Galaxy and may be the progenitors of young massive star clusters. The DR21 main filament or ridge is probably the most emblematic case of such a massive elongated structure with about inside a 4.5 pc long structure (i.e., /pc; Motte et al. 2007 ; Schneider et al. 2010 ; Hennemann et al. 2012 ). Other well-known ridges include Orion A ( Hartmann Burkert 2007 ), Vela-C ( Hill et al. 2011 , 2012 ), IRDC G035.39–00.33 ( Nguyen Luong et al. 2011 ), and W43-MM1 ( Nguyen-Luong et al. 2013 ; Louvet et al. 2014 ). These ridges, which exceed the critical line mass of an isothermal filament by up to two orders of magnitude, are believed to be in a state of global collapse, to be fed by very high accretion rates on large scales ( Schneider et al. 2010 ; Peretto et al. 2007 , 2013 ), and to continuously form stars and clusters. The formation of these ridges is not yet well understood but may result from the large-scale collapse of a significant portion of a GMC ( Hartmann Burkert 2007 ; Schneider et al. 2010 ). Whether the low-mass star formation scenario summarized above – or an extension of it – also applies to regions dominated by hyper-massive clumps and ridge-like structures ( Motte et al. 2016 ) is not yet known. In particular, further work is needed to confirm that the inner width of interstellar filaments remains close to 0.1 pc in regions of massive star formation beyond the Gould Belt, where the moderate angular resolution of Herschel (–36 at –500 m) is insufficient to resolve this characteristic scale. At a distance of 1.7 kpc, NGC 6334 is a very active complex of massive star formation ( Persi Tapia 2008 ; Russeil et al. 2013 ) with about 150 associated luminous stars of O- to
Paper: 2017MNRAS.470.2687C (acknowledgments)
Acknowledgments The authors are very grateful to the reviewer of this article for the helpful comments and suggestions that led to the improve of the quality of this work. The authors wish to thank the São Paulo State Science Foundation (FAPESP, grants 13/15357-1 and 14/06762-2) and CNPq (grants 150360/2015-0 and 312313/2014-4) for their generous support of this work.
Paper: 2015MNRAS.454.2804G (acknowledgments)
We thank Simone Aiole, Seshadri Nadathur, Mark Neyrinck, and István Szapudi for discussing this work with us. BRG and AJH acknowledge support of the European Research Council through the Darklight ERC Advanced Research Grant (# 291521). AK was supported by the Spanish Ministerio de Economía y Competitividad (MINECO) under projects FPA2012-39684, and Centro de Excelencia Severo Ochoa SEV-2012-0234. The results were derived with cosmopy ( www.ifa.hawaii.edu/cosmopy ) and healpix with healpy (healpix.jpl.nasa.gov, code.google.com/p/healpy). Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the US Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/ . SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington and Yale University.
Paper: 2017MNRAS.469.3722R (acknowledgments)
Acknowledgments This research is supported by the Canada Research Chair programme and Natural Science and Engineering Research Concil of Canada. DW is supported by European Research Commission grant ERC-StG-6771177 DUST-IN-THE-WIND.
Paper: 2020MNRAS.494...84N (fulltext)
were done using the software flagcal , developed for automatic flagging and calibration for the GMRT data (Prasad Chengalur 2012 ). The calibrated data were imaged using AIPS task imagr . The flux density and errors are obtained from Gaussian fit to the SN position using the task jmfit . The details of GMRT observations and the flux densities of the SN are presented in Table 1 . The GMRT radio light curves at 1.39, 0.61, and 0.33 GHz spanning ∼10–1136 d post-explosion are shown in Fig. 1 . Figure 1. GMRT light curves of ASASSN-16fp at frequencies 0.325, 0.610, and 1.40 GHz. The days since explosion are calculated assuming the date of explosion as 2016 May 25.9 ( ut ). The inverted triangle denotes 3 σ upper limits. Majority of the error bars in the figure (see Table 1) are smaller than the marker size. Table 1. Details of GMRT observations of ASASSN-16fp. a The age is calculated assuming 2016 May 25.9 ( ut ) as the date of explosion (see Section 1 ). b The errors in the flux density are from the task jmfit . 3.2 JVLA observations We observed ASASSN-16fp with the Karl G. Jansky Very Large Array (JVLA) on 2017 February 17.65, 17.67, and 25.8 (project code 17A-167) spanning a frequency range 2.2–9.7 GHz. The observations were done in the standard continuum mode with a bandwidth of 2 GHz split into 16 spectral windows. We used 3C 286 and 3C 48 as the flux density calibrators, and J2139+143 as the phase calibrator. We also analysed the publicly available archival JVLA data of ASASSN-16fp at five epochs from 2016 June 03.44 ( ut ) to 2016 September 07.15 ( ut ), spanning a frequency range 2–25 GHz. The JVLA observations at each frequency were carried out with a bandwidth of ≈2 GHz split into 16 spectral windows. 3C 286 and 3C 48 were observed for the flux density calibration, and J2139+143 was observed as the phase calibrator. The data analysis was done using standard packages within the Common Astronomy Software Applications package ( casa ; McMullin et al. 2007 ). We split the data into four sub-bands, each of ∼0.5-GHz bandwidth during the data reduction. The details of VLA observations and the flux densities of the SN at various epochs are summarized in Table 2 . We plot the full VLA data set in Fig. 2 . Figure 2. JVLA spectra of ASASSN-16fp on day 8 to 276 d post-explosion spanning a frequency range frequencies ∼2–24 GHz. The days since explosion are calculated assuming the date of explosion as 2016 May 25.9 ( ut ). The error bars in the figure (see Table 2) are smaller than the marker size. Table 2. Details of JVLA observations of ASASSN-16fp. a The age is calculated assuming 2016 May 25.9 ( ut ) as the date of explosion (see Section 1 ). b The errors in the flux density are from the task jmfit . 4 A RADIO MODEL Radio emission from core-collapse SNe is synchrotron in origin (Chevalier 1982a , b ), produced due to the interaction of SN ejecta with the CSM created by the stellar wind of the progenitor star. According to the standard
Paper: 2017ApJ...835...29Y (acknowledgments)
We thank Bill Coles for help with the definition of the goodness-of-fit statistic, for suggesting the adopted method of estimating parameter uncertainties, and for useful discussions on the effects of interstellar scattering, and M. Krishnakumar for providing us with tables of scattering data for Galactic pulsars. We also thank Matthew Bailes, Simon Johnston, J.P. Marquart, and Ryan Shannon for useful discussions, and George Hobbs and Hailong Zhang for help with the YMW16 web interface. Finally, we thank the referee for helpful comments. This work was supported by National Basic Research Program of China (973 Program 2015CB857100 and 2012CB821801), the Strategic Priority Research Programme (B) of the Chinese Academy of Science (No. XDB09000000), the National Natural Science Foundation of China (No. 11373006), and West Light Foundation of CAS (No. ZD201302). We acknowledge extensive use of the SAO/NASA Astrophysics Data System ( http://www.adsabs.harvard.edu/ ) in the preparation of the work presented here.