adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...836..204N (fulltext)
based as closely as possible on standard physical principles. It includes winds and heating from young massive stars as well as feedback from SN explosions, the latter following the known physical processes that govern the different phases of SN remnants. We use the smoothed particle hydrodynamics (SPH) code SPHGal (Hu et al. 2014 ), an implementation that incorporates several improvements into the Gadget code (Springel 2005 ) to address some known SPH problems. Our approach is most similar to the stellar feedback in Agertz et al. ( 2013 ) and Agertz Kravtsov ( 2016 ), as well as the Feedback in Realistic Environment ( Fire ) project, presented in Hopkins et al. ( 2014 ) and used by Hopkins et al. ( 2012 , 2013 ), Muratov et al. ( 2015 ), and Su et al. ( 2016 ). We apply our stellar feedback to an isolated Milky Way-like galaxy within an isolated dark halo. The tools developed here are in use by the cosmological code in Choi et al. ( 2016 ). We break our approach into three parts: feedback from young, pre-SN stars, feedback from dying low-mass stars, and feedback from SN events. (1) We include the effects of stellar winds from young stars by transferring momentum to neighboring gas particles for the first few Myr. We also heat gas particles within the Strömgren sphere of young star particles, mimicking the effects of ionizing radiation from massive stars (see e.g., Renaud et al. 2013 ). (2) We transfer momentum from old, low-mass stars to the surrounding gas throughout several Gyr after a type II SN has occurred to imitate the slow winds from asymptotic giant branch (AGB) stars. (3) In our SN feedback model, we transfer SN energy to the closest gas particles to the SN event, each one receiving mass and energy according to a computed high-resolution evolution of SN remnants (SNR). Detailed very high resolution hydrodynamic simulations of SNR evolution (see Li et al. 2015 ), for details and a review) show that they pass through three successive phases: free expansion (FE), energy-conserving Sedov–Taylor (ST), and momentum-conserving snowplow (SP) (see also Kim Ostriker 2015 ; Martizzi et al. 2015 ; Walch Naab 2015 ). In our model, which we call the SP model, each neighboring gas particle receives energy following the physics governing the SNR phase in which the gas particles lie. If the gas particle lies within the radius of the ejecta-dominated FE phase, SN energy is transferred by conserving the ejecta momentum. If the gas particle lies outside the FE radius but within the radius of the adiabatic blast wave ST phase, SN energy is transferred 30% as kinetic energy and 70% as thermal energy. Finally, if the gas particle is beyond the ST radius and into the realm of the pressure-driven and momentum-conserving SP phase (Chevalier 1974 ), then part of the original SN energy is dissipated and only a fraction is transferred to the gas particle, with the thermal portion dissipating more quickly than the kinetic portion, following a prescription
Paper: 2016ApJ...827...49S (acknowledgments)
We thank all of the members of the Chandra team for their enormous efforts, specifically D.P. Huenemoerder and M. Nowak for easing data processing and fitting procedures.
Paper: 2016A&A...587A..14V (fulltext)
hemisphere (57 deg south). This configuration is ideal for jet monitoring because it provides a view of all local times in a single frame, and the nucleus rotating in front of the camera gives the stereographic views without the need for different spacecraft pointing. From a distance of 29 km, we achieved 3 m/px resolution with the WAC. We found that many more sources were active at that time (at least 10 jets per image), and we could trace them down to the surface (Fig. A.5 ). We consistently found sources on high slopes, such as cliffs and walls of alcoves and pits. There is very little evidence of any activity arising from smooth, flat areas, apart from the larger scale activity in Hapi. The Hapi activity was associated to terrains displaying bluer spectral slopes; when comparing our sources with published color maps by OSIRIS ( Fornasier et al. 2015 ; Oklay et al. 2015 ) or VIRTIS Capaccioni et al. (2015) , it seems to also be true for the smaller sources. It is particularly interesting to see that new jets arise from areas identified as bluer in early images August 2014 but not yet active at that time. Figure 2 shows an example of such a source. Figure A.5 summarizes all inverted sources. Fig. 2 Left panel : map of sources active in December 2015, projected on the 3D shape model of 67P. Right : close-up view of an RGB color composite showing one of the sources with a typical bluer hue, corresponding to the location of the white box in the left panel . The color image reflects the variegation of the terrain in August 2014, when this area was not yet active. Distance from the comet center: 43 km; resolution: 0.75 m/px. The large block in the center is about 100 m wide. Reference image: NAC_2014-09-05T06.35.55.557Z_ID30_1397549300_F22. Active spots are typically observed closer to the equator than in September 2014, actually within 30 of the subsolar latitude. Inverted sources are listed in Fig. A.5 . We also observed that sources switch on and off within 20 min of crossing the terminator, this duration being an upper limit constrained by the cadence of our observations. This is particularly striking for sources in Seth and Hapi, which experience very different diurnal illumination. They are typically illuminated only for three consecutive hours before entering three hours of night because of the strong self-shadowing introduced by the large concavity of the nucleus. As a result, they experience two day/night cycles per nucleus rotation. 3.3. Sources in April 2015, 2 AU, subsolar latitude +9.8<inline-formula specific-use="simple-math">°</inline-formula> The same trend in spatial distribution and behavior of sources is confirmed with later sequences. Our next inversion of a sequence acquired in March 2015 again shows that sources lie close to cliffs, and their average latitude has migrated south, following the Sun. We start to observe new regions becoming active close to the equator in the Ma’at and Imhotep regions (Fig. A.5 ). As we get
Paper: 2016ApJ...819..108B (fulltext)
all models and for both metallicity evolution scenarios. This indicates that BH–BH mergers are expected to be dominant GW sources within the framework of the evolution adopted in models V1, V2, and V4. If no detections are made after a modest increase of LIGO/Virgo sensitivity, these specific evolutionary models will be eliminated in the context of BH–BH formation. Our calculations incorporate the evolution of average metallicity with redshift into the cosmological model of the universe; a broad spectrum of metallicities is employed in our population synthesis rate predictions. Changing the details of the adopted metallicity evolution model, within the uncertainties, produces at best modest effects on the rate predictions. Specifically, the low metallicity models produce somewhat higher merger rate densities for heavy mergers (BH–BH) and somewhat lower merger rate density for low-mass mergers (NS–NS). These trends are understood in terms of the effects of metallicity on the BH–BH formation (low metallicity boosts the formation) and on the NS–NS formation (low metallicity suppresses the formation; see Dominik et al. 2012 ). For example, the standard model (V2) merger rate density changes from for low metallicity evolution (see Table 3 and Figure 2 ) to for high metallicity evolution (see Table 4 and Figure 3 ) for BH–BH mergers with total mass 25–37 . The decrease of merger rate density from low to high metallicity evolution is rather moderate: only a factor of ∼2. Note that metallicity itself plays a crucial role in the formation process of BH–BH binaries (Belczynski et al. 2007 , 2010a ). For example, a group of stars with solar metallicity will form ∼10–100 times fewer BH–BH mergers than the same group with 10% solar metallicity (de Mink Belczynski 2015 ). However, once we incorporate this very strong dependence into cosmological models of the universe and employ a broad spectrum of metallicities (as in our methods II and III), the merger rate density predictions are not as strongly affected. Both metallicity evolution models generate (alas with a different efficiency) low metallicity stars (typical progenitors of BH–BH mergers). A broad BH–BH merger delay time distribution (Dominik et al. 2013 , 2015 ) allows for both models to produce BH–BH binaries that will merge at low redshifts. 5.2. Comparison of Models with Forecasted Advanced LIGO/Virgo Upper Limits In Figures 5 and 6 and Tables 5 and 6 we present evolutionary model predictions for double compact object merger rate densities. The predictions are constructed with method II (see Section 2.2 ) and contrasted with the forecasted advanced LIGO/Virgo upper limits (see Section 4.2 ). Our models are described in Section 3 . Figure 4. Merger rate density for BH–NS systems for our low metallicity evolution scenario predicted (with method II) for advanced LIGO/Virgo. Note that only the delayed SN explosion model (V4) and the optimistic model (V1) make detections likely, while the other models
Paper: 2017ApJ...834..169T (fulltext)
eigenvalues . The lengths of the semimajor and semiminor axes are and with the axis ratio b ′/ a ′. The details of the iterative method for measuring axis ratios can be found in Tenneti et al. ( 2015b ). In the first iteration, we start with the half-lengths of the principal axes all equal to 1 and determine the eigenvalues and eigenvectors of the ellipsoid. After each iteration, the lengths of the principal axes of ellipsoids are rescaled such that the enclosed volume is constant and particles outside the ellipsoidal volume are discarded. This process is repeated until convergence is reached such that the fractional change in axis ratios is below 1%. In addition to the distribution of the axis ratios, b / a and c / a of the stellar components of subhalos, we are also interested in the orientation of the major axis of the stellar shape with the shape of dark matter in subhalos. So, we compute the probability distribution of the misalignment anglewhere and are the major axes of the shapes defined by the dark matter and stellar matter components respectively. 3.2. Two-point Statistics In this paper we quantify the intrinsic alignments of galaxies with the large-scale density field using the ellipticity–direction (ED) and the projected shape–density () correlation functions. The ED correlation function cross-correlates the orientation of the major axes of the 3D shapes of dark matter or stellar component of galaxies with the large-scale density field. Consider a subhalo centered at position x with the major axis direction . Let the unit vector in the direction of a tracer of the matter density field at a distance r be . Based on the notation in Lee et al. ( 2008 ), the ED correlation function is given bywhich is zero for randomly oriented galaxies in a uniform distribution. In the simulations the matter density field is traced using the positions of dark matter particles. The projected shape correlation function, is directly related to the correlation function measured in observations. Following the notation of Mandelbaum et al. ( 2006 ), we define the the matter–intrinsic shear correlation function and the corresponding projected two-point statistic . In this paper, r p is the comoving transverse separation of a pair of galaxies in the XY plane and Π is their separation along the Z direction. The components of the projected ellipticities of a galaxy are given bywhere b ′/ a ′ is the axis ratio of the projected shape of the stellar component of a galaxy, and φ is the position angle of the major axis with respect to the reference direction (position of the dark matter particle). Here, e + refers to the radial component and e × is the component rotated at 45°. The matter–intrinsic shear correlation function is given by,where S + represents the “shape sample,” selected on the basis of a binning in subhalo mass and the “density sample” labeled by D consists of the dark matter particles used to trace the matter density field. S + D is given by the
Paper: 2018MNRAS.478.1677S (fulltext)
into components which contribute significantly to the total density above z − , labelled Σ b 1 , and those which do not, e.g. are concentrated close to the disc plane below z − , labelled Σ b 0 . For this latter component, above z − the density is close enough to zero for the surface density to be considered constant. Hence, for z > z − , the total surface density is Σ( z ) = Σ b 0 + Σ b 1 ( z ) + 2ρ dm z , and equation ( 9 ) then becomes Note that the baryonic density shape inside z does not enter in the calculation of σ z ( z ); hence, there is no need to model the shapes of the baryonic components inside the z region where we have tracer data. Also note the term 2ρ dm ( h + z ) of equation ( 10 ) can be expressed as Σ dm ( z ) + Σ dm ( h ). Despite having a constant DM density to an infinite height, the contribution of this component to is finite. The term Σ dm ( h ) is a manifestation of the tracer density decaying exponentially with a scaleheight of h . Thus, the impact of the assumption of constant DM density is controlled by the scaleheight of the tracer population, with hotter populations reaching higher above the disc being affected more. 3.3 Tilt term At the midplane ( z = 0), a potential symmetric in z is separable up to second order (Binney Tremaine 2008 ), with the radial and vertical motions decoupling. Thus, at z = 0 the tilt term vanishes, though it may increase rapidly as z increases. Intuitively, the motion of the tracer stars can be viewed as a combination of oscillation around the disc, and orbital motion around the Galactic Centre; the tilt term can then be seen as a consequence of this orbital motion around the Galactic Centre. A star in an elliptic orbit around the Galactic Centre will on the part of its orbit where it is moving towards the Galactic Centre (i.e. has v R 0) typically also move towards the disc plane; hence, this star will for z > 0 have , and for z 0. Similarly, in the part of the orbit where the star moves away from the Galactic Centre (i.e. has v R > 0), it will typically also move away from the disc plane, again resulting in having the same sign as z . Hence, as , we arrive at the result that σ Rz carries the same sign as z . See Silverwood ( 2016 ) for further motivation on this result on the sign of σ Rz . The σ Rz data given in B15 has a sign error, as it is negative for positive z , and so as done in Silverwood et al. ( 2016 ) we apply a sign correction. We again assume symmetry above and below the disc plane, and for z > 0 we model the vertical profile of σ Rz using the functional form σ Rz = Az n , where A and n are fitted to the σ Rz data. For A ≥ 0 and n > 0, this model naturally encompasses the requirements that σ Rz ≥ 0 for z ≥ 0 and σ Rz = 0 for z = 0. The modelling σ Rz = Az n also fits the σ Rz data well, see Fig. 8 and Silverwood et al. ( 2016 ). The tilt term includes a radial derivative of ν and σ Rz , and so we must also model their radial behaviours. From Bovy et al. ( 2016 ), we see
Paper: 2019MNRAS.487L...7M (acknowledgments)
ACKNOWLEDGEMENTS PM would like to thank STFC for studentship funding and Sergey Koposov for use of the wsdb. We would like to thank the referee Daniel Bramich for a review that greatly improved the paper. PM would also like to thank Felicia Gutmans for carefully reading the paper. This work has made use of data from the European Space Agency (ESA) mission Gaia , https://www.cosmos.esa.int/gaia , processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Paper: 2016MNRAS.462.2366O (acknowledgments)
This work is supported by JSPS KAKENHI grant number 24740190 and 24105007. HY is supported by the Advanced Leading graduate school for Photon Science (ALPS). ACF acknowledges support from ERC Advanced Grant Feedback, 340442.
Paper: 2021A&A...650A..22P (fulltext)
to interval E4, as well as the full spectrum irrespective of the angle. The spectral break between the 1∕ f and f −3∕2 ranges for the unconditioned spectrum (black line) is found approximately (by inspection) at f b ≈ 2 × 10 −3 Hz, indicated by the vertical dotted line. Right : spatial energy spectrum of magnetic fluctuations E ( k ⊥ ) reconstructed from the frequency spectra P sc ( ω , θ VB ) for each sampling angle as well as by using the standard TH (black line). 4.2 Reconstructing field-perpendicular spectrum E(<italic>k</italic><sub>⊥</sub>) In the following, we proceed to reconstruct the reduced energy spectrum E ( k ⊥ ) using data from within each angle bin above 30° and for which we find a large statistical sample. For illustration purposes, we concentrate on a 24 h interval just before the fourth perihelion (E4), which was also investigated using standard TH analysis by Chen et al. (2021) . Table 3 shows the values of ε , V ⊥ , and the number of statistical samples (counts) associated with this interval for each ten-degree angle bin around 30°, 40°, 50°, and 60°. In this statistical sample, ε ≤ 0.2, in which case the BP19 methodology in the TH limit ( ε → 0) is applicable, that is, combining Eqs. ( 2 ) and ( 3 ) leads to The most common form of TH follows from this expression by performing the integral in a Cartesian coordinate system with one axis along the streamwise direction so that Here E 1 D ( k s ) is the one-dimensional energy spectrum with respect to the streamwise (sampling) direction. This expression resembles Eq. ( 6 ) with Λ α , ε = 1 and leads to the familiar ω = − k s V expression to relate spacecraft frequencies with streamwise wavenumber k s . Table 3 Relevant parameters for each angle bin considered in our analysis with a sufficiently large number of samples. Notes. The parameter ε remains below 0.15, which allows for the application of the TH in the framework of BP19. For reference, at 1 s resolution, the total number of counts in any given 24 h interval is 86 400 samples. It is also important to note that values for Λ α , ε were calculated for α = 3∕2 and using Eq. ( 18 ) Λ TH ≃ 0.7628. However, because we are interested in reconstructing the angle-integrated field-perpendicular spectrum E ( k ⊥ ), Eq. ( 16 ) must be integrated in cylindrical coordinates. Using the change of variables x = ω ∕ k ⊥ V ⊥ , we obtained 3 where Again, Eq. ( 18 ) also resembles Eq. ( 6 ) with Λ α , ε replaced by Λ TH . In fact, it can be shown that , which is consistent with the BP19 model in the limit when ε → 0. The scaling factor Λ TH is due to the integration with respect to the angle φ in the dot product k ⊥ ⋅ V = k ⊥ V ⊥ cos φ ( Bourouaine et al. 2012 ; Bourouaine Chandran 2013 ; Martinović et al. 2019 ). Using Eqs. ( 11 ) and ( 12 ) within each statistical sample described in Table 3 , P sc ( ω , θ V B ) was calculated for magnetic field measurements b ( t ) according to Eq. ( 12 ). The left panel of Fig. 3 shows frequency spectra P
Paper: 2017MNRAS.468.3361G (fulltext)
scale rad is equal to the radial distance separating adjacent arms near the Sun and the scale azim is the angular distance between them. To reiterate, this method does not afford a reliable estimate of the angle p and the number m but only of rad for a given m . The best-fitting values of , , , , u 0 , v 0 , p and 0 for a given m are found by fitting that minimizes 2 defined as In equation ( 2 ), i counts the N stars in the sample, q is the number of parameters to be derived, v obs, i and v mod, i are the observed and modelled velocities and v obs, i is the uncertainty of v obs, i . The model depends linearly on the set of six parameters (), which can be determined by the standard least-squares fitting for specified 0 , m and p . The quantities and are periodic with respect to 0 with period , while the quantities and are periodic with period 2. For a trailing pattern and the potential , out of the two Suns phase values 0 and 0 that minimize 2 , the values that satisfy the condition are chosen (Mishurov etal. 1997 ; Griv, Ngeow Jiang 2013 , 2014 ; cf. Mishurov etal. 1979 , p. 149). The values of () will completely determine the Galactic differential rotation and the values of ( m , p ) (or rad ) and 0 will determine the nearby spiral pattern. 3 DATA The data of stars were collected from the APOGEE-RC DR13 catalogue of 19291 objects, which is constructed using the method described in Bovy etal. ( 2014 ). This catalogue of intermediate-old giant stars in the near-infrared on scales between 25pc and 10kpc has the estimated uncertainties in the distances and the line-of-sight velocities of 5percent and 0.1kms 1 . Ages of 705 local APOGEE giants with the mean age t age 310 9 yr were estimated by Feuillet etal. ( 2016 ). Details on the APOGEE project in the Northern hemisphere and the dual-hemisphere APOGEE-2 project can be found in Bovy etal. ( 2015 ) and Majewski etal. ( 2016 ), respectively. 1 Fig. 1 shows space locations of all 19291 stars with known d projected on to the Galactic X Y , X Z and Y Z planes. As is seen, the stars are limited mainly within 5kpc from the Sun. In the object distribution, there are no obvious arm-like structures in the X Y plane (Fig. 1 , left-hand panel). A scaleheight of the distribution is h 200300pc (Fig. 1 , middle and right-hand panels). Such a scaleheight h 250pc is typical to intermediate-old population of the local Galactic disc (Kharadze etal. 1989 ; Rix Bovy 2013 ; Camargo, Bica Bonatto 2015 ). Figure 1. The space distribution of all 19296 stars with known photometric distances from the Sun projected on to the Galactic X Y , X Z and Y Z planes. Cartesian X , Y and Z coordinates are shown, the X -axis points to the direction of the Galactic rotation, the Y -axis points to the Galactic anticentre, the Z -axis points to the North Galactic pole, and the axis of the Galactic rotation is taken oriented along the Z -axis, with the Sun at (0, 8.3, 0). The solar circle ( r 0 8.3kpc) and the Z 0 plane are indicated by