adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.496L..33Z (fulltext)
of a disc and also contributed to the increase of the central BH mass. The analysis of the formation histories of eagle galaxies and its connection to the scatter of the MZR will be the subject of a forthcoming article (Zenocratti et al., in preparation). Sharma Theuns ( 2019 ) propose a model of self-regulated galaxy formation, in which the SFR, stellar mass, gas mass, and metallicity depend on halo mass, cosmological accretion rate, and redshift, but regulated by feedback. In their ‘Iκεα’ model, the main astrophysical parameter ε is a dimensionless measure of the efficiency of stellar feedback: a large value of ε implies efficient feedback and low SFR, while feedback is inefficient and the SFR is high for a small ε. This leads to the following scaling for Z and f g with M ⋆ and efficiency ε: where n ≈ 1.4 is the slope of the Kennicutt–Schmidt star formation law. These relations show that when feedback is efficient (meaning ε is large, ε ≈ 1), Z is low and f g is high, whereas, inefficient feedback (ε ≪ 1) implies Z is high and f g low. We can connect this to the morphology of the galaxy by speculating that feedback in a thin disc – corresponding to a galaxy with high κ co – is more efficient than when κ co is low (a more spheroidal gas distribution). High-resolution simulations that resolve individual supernova energy injections in gas columns suggest this type of relation between feedback efficiency and disc morphology. For example, the simulations by Creasey, Theuns Bower ( 2013 ) show that supernova explosions that occur above or below the disc are more efficient at driving winds, because cooling loses are suppressed when the explosion occurs at the lower densities that prevail there (see also Girichidis et al. 2016 ). Thus, in eagle , accretion of metal-poor gas seems to trigger the formation of a disc but, once the disc has already been formed, feedback effects would play a role in regulating the subsequent metallicity evolution. We leave the analysis of feedback effects on eagle disky galaxies for a future work. 5 CONCLUSIONS We analysed the MZR as a function of morpho-kinematical parameters in the eagle cosmological hydrodynamical simulations. At z = 0, we found new secondary dependences of metallicity on the internal kinematics and morphology of galaxies. At low masses (), higher metallicities are found for more spheroidal and lower rotation-supported galaxies. A preliminary analysis of the star formation histories of low-mass galaxies indicates that late accretion of metal-poor gas dilutes the metal content of galaxies, triggers their star formation activity, and contributes to the formation of the galaxy disc. When the gas is in a thin, rotationally supported disc, feedback may be more efficient, which results in lower O/H and a higher gas fraction (Sharma Theuns 2019 ). The trends in more massive galaxies are generally less strong, with lower metallicities found in more prolate galaxies with lower levels of rotational
Paper: 2016A&A...585A...4C (fulltext)
of hydrogen was switched on at s and for the first published snapshot, at s. The non-equilibrium hydrogen ionization leads to much higher electron density in the cool pockets at 0.5–2 Mm and also higher electron densities up to 3.5 Mm height. There is not much change with time of this probability density function after s. 3.6. Velocity field Spectral lines are normally observed to be broader than what the thermal broadening of the opacity profile would give. An extra free parameter, called microturbulence, is often introduced in 1D semi-empirical models to account for this broadening. The “micro” in the name comes from the fact that this parameter is introduced as an extra broadening of the opacity profile, acting in exactly the same way as thermal broadening. This would be a physically correct description in the limit of zero length-scale for the velocity field. It is also often necessary to introduce a second free parameter to account for the observed lineshape. This is called macroturbulence and is equivalent to a Gaussian convolution of the emergent intensity profile (rather than a convolution of the opacity profile as is the case for microturbulence). Realistic 3D radiation hydrodynamic simulations of the solar photosphere give line profiles that are close to the observed profiles without the addition of extra free parameters – the non-thermal broadening comes from Doppler shifts arising from the convective flows and oscillations (e.g., Asplund et al. 2000 ). Also spectral lines formed in the outer atmosphere are broader than what thermal broadening alone predicts. The nature of this non-thermal broadening in the outer atmosphere is still unclear, but the presence of strong shocks ( Carlsson Stein 1992 , 1997 ; De Pontieu et al. 2015 ), torsional motions ( De Pontieu et al. 2014a ), and Alfvén wave turbulence ( van Ballegooijen et al. 2011 ) are some of the candidates. Fig. 14 Average non-thermal velocity calculated over 1 dex in (column mass) (red, left scale) and average temperature (blue, right scale) as function of logarithmic column mass for the simulation snapshot at s. There is no simple way to characterize the macroscopic velocities in the simulation that give rise to non-thermal broadening. The effect of a given velocity field on the spectral line width depends on whether the spectral line is optically thick or optically thin, where in the atmosphere the line is formed and the width of the contribution function to intensity. One possible way of quantifying the velocity field is the standard deviation of the vertical velocity over a given height range as function of height and horizontal position. At each column of the simulation box at s we calculate the column mass scale (which is more closely related to line formation quantities like optical depth than a geometric height) and take the standard deviation of the vertical velocity over a range of 1 dex in (column mass). We multiply the standard deviation by the square root
Paper: 2017ApJ...846L..20B (fulltext)
tend to have the steepest radio spectra (), pointing to another possible bias against finding fast-spinning MSPs in ongoing surveys, which focus on central observing frequencies around 350 MHz (Deneva et al. 2013 ; Stovall et al. 2014 ) and 1.4 GHz (Cordes et al. 2006 ; Keith et al. 2010 ; Barr et al. 2013 ). As a result, radio pulsation searches at frequencies below 300 MHz have the potential of opening up a so far largely unexplored parameter space, in the cases where IISM scattering is low and eclipsing does not hinder detection either. Here, we present the discovery of PSR J0952−0607, a very-steep-spectrum MSP, which is now the fastest-spinning neutron star known in the Galactic field (outside of a globular cluster). PSR J0952−0607 was found in a radio pulsation survey using the Low-Frequency Array (LOFAR; Stappers et al. 2011 ; van Haarlem et al. 2013 ) to target unassociated Fermi γ -ray sources. This Fermi -targeted approach has been successful in finding many new MSPs (Ray et al. 2012 ), but our survey is the first to use LOFAR to survey at observing frequencies of 135 MHz (see also Pleunis et al. 2017 ). To enable this survey, a combination of coherent and incoherent dedispersion has been employed to limit the effects of dispersive smearing (Bassa et al. 2017 ). In Section 2 , we will highlight the discovery and multi-wavelength follow-up, with the results being presented in Section 3 . We discuss the broader implications of PSR J0952−0607's discovery and conclude this manuscript in Section 4 . 2. Observations and Analysis 2.1. Radio PSR J0952−0607 was discovered as part of an ongoing LOFAR survey at 135 MHz, continuing on the pilot survey by Pleunis et al. ( 2017 ). Unassociated γ -ray sources were selected from an all-sky source list based on seven years of Fermi -Large Area Telescope (LAT; Atwood et al. 2009 ) Pass 8 data. That list resulted from a preliminary version of the procedure that will be used to produce the next public release LAT source catalog. Among these is a new γ -ray source that has a test statistic of and a relatively small error radius of . The source’s γ -ray spectrum is strongly curved, peaking at 1.4 GeV, and no significant γ -ray emission is detected above 10 GeV. As the source lies well out of the plane (), these characteristics made it a prime millisecond pulsar candidate. The γ -ray source was observed for 20 minutes on 2016 December 25 with LOFAR. The high-band antennas (HBAs) from the innermost 21 LOFAR core stations (longest baseline of 2.3 km) were used to form seven tied-array beams ( FWHM), each covering a 39 MHz wide band centered at 135 MHz. This setup is identical to that of Pleunis et al. ( 2017 ). The complex voltage output for each tied-array beam—in the form of 200 Nyquist-sampled, dual-polarization subbands of 195 kHz each—was processed using GPU-accelerated software to perform coherent dedispersion and channelization with cdmt (Bassa et al. 2017 ) at steps of 1 pc cm −3 between dispersion
Paper: 2019A&A...631A..68K (fulltext)
imposed in each layer and the wide and position required for the transition region inside the domain. In this simulation we take a 0 = 4.9 × 10 5 K, a 1 = 2 × 10 8 cm, a 2 = 0.5 × 10 8 cm, and a 3 = 5. × 10 5 K. In Fig. 4 the temperature profile obtained for these values and the consequent hydrostatic pressure distribution are shown, for ρ 0 = 10 −11 g cm −3 at the base domain, constant gravity g = 2.74 × 10 4 cm s 2 , specific heat ratio γ = 5/3, and particular gas constant R = 16.6289196 × 10 7 erg (K mol) −1 (totally ionized hydrogen). The numerical results of the hydrostatic condition shown in Fig. 4 are presented in Fig. 5 , where the convergence curves for both the linear and the local hydrostatic reconstruction MUSCL-Hancock schemes are plotted. As expected, the results of the scheme with linear reconstruction exhibit the aforementioned third order superconvergence but they present strong errors in the hydrostatic equilibrium preservation when low resolution grids are used. On the other hand, the results corresponding to the local hydrostatic reconstruction technique show a similar convergence rate for large numbers of cells ( M ≳ 100), but with smaller errors than the standard scheme, which can be even several orders of magnitude smaller when coarse grids are used. This behavior shows the potential of the local hydrostatic reconstruction scheme, since it allows for improvement of the results for very stratified atmospheres with large temperature gradients, as found in many solar astrophysical applications. Fig. 4. Temperature distribution and hydrostatic pressure profile in the solar atmosphere model by Vernazza et al. (1981) given in Eq. ( 63 ). Fig. 5. Convergence curves for the numerical results of the hydrostatic equilibrium atmosphere shown in Fig. 4 . The blue line with circles shows the L 1 -norm of the pressure error for the standard second order MUSCL-Hancock scheme (linear reconstruction), and the red line with crosses corresponds to the MUSCL-Hancock scheme with local hydrostatic reconstruction. 6.2. Diffusion analysis In this analysis we simply study the ability of the local hydrostatic reconstruction scheme to capture a shock wave traveling in a gravitationally stratified medium. The aim of the analysis is to verify that this scheme does not introduce an excessive numerical diffusion compared to the scheme with linear reconstruction to model the shock wave. In addition, we check the behavior of the hydrostatic scheme in the default AMR module of FLASH, which uses the PARAMESH 2 package ( MacNeice et al. 2000 ). This toolkit builds a hierarchy of sub-grids blocks with identical logical structure that cover the physical domain with different resolution levels according to the gradient magnitude of the set refinement variables. Each block is treated as an independent uniform grid, so that the local hydrostatic reconstruction is directly applied on adaptive meshes without the need to modify any subroutine. For this simulation
Paper: 2017MNRAS.464..635M (acknowledgments)
We thank the anonymous referee for comments that have greatly improved this manuscript. The simulations were performed at the National Energy Research Scientific Computing centre (NERSC), Lawrence Berkeley National Laboratory , and at NASA Advanced Supercomputing (NAS) at NASA Ames Research centre. The analysis was performed on the Astric cluster at HU. This work was supported by ISF grant 24/12 , by BSF grant 2014-273 , by GIF grant G-1052-104.7/2009 , by the I-CORE Program of the PBC , ISF grant 1829/12 , by CANDELS grant HST-GO-12060.12-A , and by NSF grants AST-1010033 and AST-1405962 . DC has been partly funded by the ERC Advanced Grant, STARLIGHT: Formation of the First Stars (project number 339177).
Paper: 2019MNRAS.486.1377D (fulltext)
We then investigated the possibility that these objects could form a cluster in some bi-dimensional projections of the parameter space. We tried all the possible magnitudes, colours, and redshifts combinations without finding any obvious clustering (some of these combinations are shown in Appendix C ). We also checked whether the group could correlate with a specific (high) error measure associated with any of the used features, but no any evident correlations were found. The nature of the objects in the overdensity region is still under further investigation. Figure 6. The scatter plot in the top left corner (a) shows the distribution of outliers in the SFRs spectroscopic versus SFRs photometric space with a superimposed density map, while the diagrams int the top right (b) and bottom left (c) corners show highlighted in orange all the objects with a density, respectively, six and eight times higher than the average point density. The histogram in the bottom right corner (d) shows the outliers density distribution. 4.7 Comparison with a recent work In order to compare our regression models with the k-NN used by Stensbo-Smidt et al. ( 2017 ) and their feature selection, we performed an experiment using the full training set and the set of 8 features found by Stensbo-Smidt et al. ( 2017 ). In Table 12 , we present the statistical results, which show a comparable performance among the three methods, although with a lower RMSE obtained by RF and MLPQNA. Using the features found by ΦLAB, the RF and MLPQNA can achieve even better performance, as shown in Table 6 . This is not surprising as k-NN is much more sensitive to the dimensionality of the parameter space (the so-called curse of dimensionality) than other two models. These latter can, therefore, take advantage of the information carried by a larger number of features than a k-NN model. Table 12. Comparison between our RF and MLPQNA against (Stensbo-Smidt et al. 2017 ) k-NN using the full train set and the best eight features found by Stensbo–Smidt. 5 DISCUSSION AND CONCLUSIONS In this work, based on our preliminary analysis of the problem presented at the ESANN-2018 conference (Delli Veneri et al. 2018 ), we estimated star formation rates for a large subset of the SDSS-DR7 and produced a catalogue of SFRs derived using photometric features only (magnitudes and colours) and the MLPQNA ML model (see Appendix B ) trained on a knowledge base of spectroscopically determined SFRs. By looking at Fig. 3 and the statistics in Table 6 , the regression results appear very promising. This is particularly true, considering that the dynamical range of SFR is between −12 and −7, and also that we have ∼5000 outliers out of the objects of the blind test set and, finally, taking into account the low percentage of outliers (∼2 per cent). However, from the results obtained by varying the size of the model training set (Tables 7 and 8 ), we think that a larger knowledge base of SFRs would further improve the
Paper: 2018ApJ...868..120M (fulltext)
perpendicular HTS and find that the PUIs and thermal gas are more heated and energized at the parallel than the perpendicular shock. We also model a quasi-parallel HTS and a quasi-perpendicular HTS to consider the case of an oblique HTS. In the last section, we summarize our results about the structure of collisionless oblique shock waves when mediated by energetic particles. 2. Model In this section, we present a brief review of the formulation of the PUI or SEP-mediated plasma model presented by Zank et al. ( 2014 , 2016 ), Zank ( 2016 ), and Mostafavi et al. ( 2017b ). The heliosphere comprises thermal solar wind, suprathermal energetic particles such as PUIs or SEPs, and neutral atoms (beyond an ionization cavity of about 6–10 au). The charge exchange mean free path in the IHS is approximately 2500 au and greater than 200 au in the supersonic solar wind beyond 10 au (Zank 2015 ). Thus, the charge exchange mean free paths are very large, and we can ignore the coupling between neutral atoms and solar wind protons when studying the structure of shocks. Using appropriate parameters for the supersonic solar wind beyond 10 au, the IHS, and the VLISM, Zank et al. ( 2014 ) estimated the equilibration timescales of PUIs and thermal plasma in these regions. They showed that the PUIs are not equilibrated with the background thermal plasma. Zank et al. ( 2016 ) showed that SEPs with energies greater than 5 keV cannot equilibrate in the inner heliosphere. Therefore, energetic particles such as PUIs, SEPs, and ACRs are a distinct component in the plasma system. The plasma flow is described by a multi-component model comprising thermal solar wind ions and electrons with Maxwellian distributions, and an energetic ion component (PUIs, SEPs, or ACRs). The multi-component model is too complicated for most problems, but it can be reduced to a single-fluid-like model by making some assumptions (see details in Zank et al. 2014 ). The collisionless diffusion or heat flux tensor and pressure tensor for the energetic particles may be expressed, respectively, as where η ≃ P p τ s is the viscosity coefficient and τ s is PUI-scattering timescale. P p is the PUI scalar pressure and Π p a stress tensor. The assumption is that the magnetic field is along the x -axis. K ∥ and K ⊥ are diffusion coefficients parallel and perpendicular to the magnetic field. The reduced single-fluid-like model equations, together with the Maxwell equations, are given by (Zank et al. 2014 , 2016 ) where μ 0 is the permeability of free space and J is the current density. B and E denote the magnetic and electric fields, P p the PUI pressure, P g the thermal gas (sum of electrons and protons) pressure, U the bulk flow velocity, and ρ the total density. We denote all quantities pertaining to PUIs and thermal gas with the subscripts p and g , respectively. γ p/g denotes the adiabatic index for PUIs/thermal gas. The viscosity and heat flux dissipation terms introduce distinct length scales into
Paper: 2020ApJ...897L..33J (fulltext)
along an almost perfect line in the northwest–southeast direction. The centroids, however, depart from this line at high velocities, especially for the blueshifted gas (≤−8 km s −1 ), although some of the associated positional errors are large. This suggests the presence of a second kinematic component. As shown in Section 4 , this configuration is consistent with the presence of an almost edge-on ionized disk, and a disk wind. 4. Modeling of the H21<italic>α</italic> RRL Similar configurations to the RRL centroid map obtained for MonR2-IRS2 have been observed for MWC349A (Martín-Pintado et al. 2011 ; Báez-Rubio et al. 2013 ; Zhang et al. 2017 ) and MWC922 (Sánchez Contreras et al. 2019 ). This configuration has been modeled assuming that the massive star is surrounded by a (partly) ionized Keplerian disk and an ionized wind. We have thus adopted a similar physical structure to model the H21 α centroid map derived for MonR2-IRS2. The set of input parameters that best fit our ALMA observations is described below (see also Table 1 ). Table 1 Input Parameters of the Best Fit of the MonR2-IRS2 H21 α Data Note. a r is in units of au and angles are measured relative to the polar axis; θ and θ a , are in degrees. In our model, the ionized wind lies inside a double cone with a semi-opening angle of 57° (the neutral disk), while the ionized disk layer is contained within an opening angle of 65 on the surface of the double cone (Figure 4 ). The ionized wind expands radially at a constant velocity, v 0 , while the circumstellar disk rotates following a Keplerian law with the velocity rotation component being added to the expansion velocity of the wind (Martín-Pintado et al. 2011 ; Báez-Rubio et al. 2013 ). The inclination angle is measured from the direction perpendicular to the disk and is −25°. Figure 4. Sketch of the double-cone geometry used for the modeling of MonR2-IRS2 (see also Martín-Pintado et al. 2011 ; Báez-Rubio et al. 2013 ). We have used the non-LTE 3D radiative transfer code MORELI (MOdel for REcombination LInes; Báez-Rubio et al. 2013 ) and the departure coefficients of hydrogen, b n and β n , calculated by Walmsley ( 1990 ). The velocity separation between the H21 α emission peaks (blueshifted and redshifted; Figure 2 ) constrains the central mass of the MonR2-IRS2 source to 15 M ⊙ . This mass corresponds to a B0-type star on the zero-age main sequence, which is consistent with the luminosity of MonR2-IRS2 (∼0.5–1 × 10 4 L ⊙ ; Howard et al. 1994 ). The radial density distribution of electrons within the ionized double cone, N e , follows a r −2 law with an inner radius of 13 au (Table 1 ). We choose a r −2 law because it fits nicely the 1.3, 0.85 and 0.4 mm free–free continuum fluxes of MonR2-IRS2, and it allows us to simultaneously model the disk and the expanding wind in a simple way. The predicted mass-loss rate for MonR2-IRS2 is 2.8 × 10 −7 M ⊙ yr −1 , and the mass of the ionized disk is 2.4 × 10 −5 M ⊙ , a factor ≥10 lower
Paper: 2016ApJ...826..223B (acknowledgments)
The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. We acknowledge the exellent commentary by two referees and the ApJ statistician on the paper’s manuscripts. This work was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. C.M.T. is supported by an Australian Research Council DECRA Fellowship through project number DE140100316.
Paper: 2019A&A...623A..40I (acknowledgments)
Y.I.I. and N.G.G. thank the hospitality of the Max-Planck Institute for Radioastronomy, Bonn, Germany. They acknowledge support from the Program of Fundamental Research of the Department of Physics and Astronomy (Project No. 0117U000240) of the National Academy of Sciences of Ukraine. Funding for the Sloan Digital Sky Survey (SDSS) has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the U.S. Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society. The SDSS Web site is http://www.sdss.org/ . The SDSS is managed by the Astrophysical Research Consortium (ARC) for the Participating Institutions. The Participating Institutions are The University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, The Johns Hopkins University, Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, University of Pittsburgh, Princeton University, the United States Naval Observatory, and the University of Washington.
Paper: 2018ApJ...853...93R (acknowledgments)
We thank Prateek Agrawal, Imre Bartos, Luke Kelley, Frans Pretorius, Jakub Scholtz, and Kip Thorne for extremely useful conversations on several aspects of our paper. L.R. is supported by NSF grants PHY-0855591 and PHY-1216270. Z.-Z.X. is supported in part by the Center of Mathematical Sciences and Applications, Harvard University.
Paper: 2017MNRAS.469.4306P (acknowledgments)
Acknowledgments SJP is supported by a Royal Society University Research Fellowship.