adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.468.3883P (fulltext)
FR and SR and high-mass FR and SR are shown. 3 POPULATIONS OF FAST AND SLOW ROTATORS 3.1 Comparing to the ATLAS<sup>3D</sup> survey The first question we seek to answer is whether the Illustris simulation produces a representative sample of fast and slow rotators. The ATLAS 3D survey provides an excellent set of observational data to compare to Emsellem etal. ( 2011 , hereafter E11 ), examining the distribution of ellipticity and R for 260 ETGs in a volume- and magnitude-limited sample. We reproduce their results in the left-hand panel of Fig. 3 . Figure 3. Distribution of ETGs in the r plane, (a) reproduced from the ATLAS 3D survey ( E11 ) compared to (b) those created in the Illustris simulation, sampled based on the same selection criteria (see the text for more details). The magenta line shows the line on which edge-on elliptical galaxies with the same degree of rotational support would lie, as described in the text and re-derived in Appendix C . The green line follows equation ( 5 ), defining the cutoff below which galaxies are classified as SRs. Each galaxy is represented by a circle whose colour and size scale with the stellar mass. E11 selected ETGs based on whether they could clearly discern spiral arm structures in the galaxy. Taking into account the variation due to projection effects in observed galaxy properties, E11 suggested making a divide between FRs and SRs based on their and R properties using the curve above which galaxies are classified as FRs and below as SRs. Of their sample, 224 galaxies, or 862percent, were classified as FRs and the remaining 36, 142percent, as SRs, using equation( 5 ) to divide the population. They found FRs to be roughly oblate, with the ellipticity of a galaxy correlating linearly with its anisotropy, . In other words, the FRs are roughly rotationally supported with stars on mostly circular orbits. Thus, the motions of stars and the projected shape of the galaxy have a simple, linear correlation. SRs are instead galaxies with low degrees of rotational support, with many stars on radial or generally irregular orbits. Thus, we can identify galaxies whose shape is well represented by the assumption of rotational support, those with ellipticity () proportional to their degree of velocity anisotropy (). Cappellari etal. ( 2007 ) and E11 find an approximate expression for the variation of R with for galaxies with . We detail this expression in Appendix C , and follow their lead in showing the relationship for 0.7 as a magenta line in the left-hand panel of Fig. 3 . The E11 sample of galaxies is volume and magnitude limited to those galaxies within 42Mpc, brighter than mag, which are clearly visible from the William Herschel Telescope (declination 2935) and which are far from the Galactic plane to avoid contamination (galactic latitude b 15). Using the same criteria (arbitrarily setting the equatorial direction aligned with the positive z -axis and the galactic plane relative to that), we
Paper: 2020ApJ...904...88A (fulltext)
third harmonic. Figure 8 presents the temporal dependence of the radiation field E x in units, recorded at one point on the boundary of the computational domain for run 1. The emission spectra recorded at the points indicated by the star in Figures 4 – 6 are shown in Figure 9 . The emission line width near the second harmonic of the plasma frequency for all regimes is less than or of the order of 2%. Figure 4. Efficient regime. (a) The map of the electric field E x in the moment (b) radiation spectra of electric field components for the entire simulation time at the outer boundary of the vacuum region as functions of the longitudinal coordinate x ; (c) theoretical growth rate along the line of the local maximum. Figure 5. Weak field. . Figure 6. Unmagnetized. . Figure 7. Strong field. t = 728 ω p −1 . (b) Phase space ( x , v x ) of the beam. Figure 8. The temporal dependence of the E x field in the single point indicated by the white star in Figure 4 . Figure 9. The frequency spectrum of the radiation produced in the single point indicated by the white star on the corresponding Figures 4 – 6 and 10 . Figure 10. Eff. regime + ions. . (a) Ion density in plasma column and radiation field E x in vacuum. (b) Frequency spectrum of radiation field along the boundary of the simulation box. (c) Theoretical growth rate along the line of its local maximum. The beam instability becomes purely longitudinal in the strong magnetic field (Figure 7 ). In this regime, we also observe a noticeable extension of the beam relaxation length. Therefore, we have extended the total system length in order to overlap beam relaxation regions more efficiently. The overlapping degree can be estimated from the image of the phase space (Figure 7 (b)). Figure 7 (a) shows that, in the case of a sufficiently strong external magnetic field, EM emission disappears. Let us consider the efficiency of EM radiation generation in these regimes quantitatively. Figure 11 presents the fraction of the total beam power P b converted into the radiation power P rad for run 1 as a function of time (efficient regime). In this graph, we separate contributions from various radiation polarizations (TM or O mode includes the fields E x , E z , and B y ; TE or X mode— B x , B z , E y ). It is seen that the ordinary TM mode dominates and the efficiency of its generation reaches 4% of the beam power. The total beam-to-radiation energy conversion efficiency is ≈10 −2 . The duration of the main emission can be estimated as an FWHM width of the plot shown in Figure 11 . In all simulations performed for the efficient regime, this duration lies in the range . Figure 11. The efficiency of beam-to-radiation power conversion as a function of time for run 1. The radiation efficiency depends not only on the amplitudes of the plasma oscillations excited by the beams, but also on the degree of their overlap. For this reason, the dependence of the emission level on time has a pronounced maximum corresponding to
Paper: 2020ApJ...892....4D (fulltext)
rate of change in the orbital separation:Here the total rate of change in the orbital angular momentum is determined byThe three terms on the right side of Equation ( 6 ) represent angular momentum losses caused by GR, MB, and mass loss, respectively. The GR-induced rate is calculated with the standard formula (Landau Lifshitz 1959 ; Faulkner 1971 )where G and c are the gravitational constant and the speed of light, respectively. The prescription of Verbuant Zwaan ( 1981 ) is adopted to calculate the angular momentum loss due to MB,For IMXBs and wide LMXBs, the mass-transfer rate may be higher than the Eddington-limit accretion rate of the NS. Thus, the accretion rate of the NS is evaluated usingand the mass loss rate from the binary system isIn the case of super-Eddington accretion, we adopt the isotropic reemission model, assuming that the extra material leaves the binary in the form of isotropic wind from the NS. Therefore, the angular momentum loss rate due to mass loss can be derived to bewhere a 1 is the distance between the NS and the center of mass of the binary. 3. Results of Evolution Calculations We have performed calculations of the binary evolution for thousands of I/LMXB systems. We choose the initial NS mass and the initial donor mass , and set the initial orbital period 1.0 day days. We use exponential diffusive overshooting for the donor stars. Note that there exists a bifurcation period for the initial orbital period day (Pylyser Savonije 1988 , 1989 ). If the initial orbital period is below P bf , the binary systems will evolve with shrinking orbits, possibly forming ultra-compact binaries. This kind of evolution cannot reproduce PSR J1640+2224, and will not be considered here. In addition, we exclude the evolutions with the final orbital periods exceeding 500 days. 3.1. A Typical Example for the IMXB Evolution We present the evolutionary sequences for a typical IMXB system that consists of a 1.4 M ⊙ NS and a 3.5 M ⊙ donor star with an initial orbital period P orb = 6 days in Figures 1 and 2 . The donor star starts to fill its RL and commence mass transfer at the age of 240.6 Myr. The mass-transfer process lasts about 1.9 Myr, at a rate higher than the Eddington accretion rate (the maximum mass-transfer rate >10 −5 M ⊙ yr −1 ). In this case, most of the transferred material from the donor is lost from the binary system. Thus, after the mass transfer, the donor evolves into a CO WD of mass 0.543 M ⊙ , while the NS has accreted only 0.034 M ⊙ mass. The orbital period becomes smaller in the former stage of the mass-transfer process due to the relatively high mass ratio (>1); when the mass ratio is reversed, the orbit begins to expand. The final orbital period is P orb = 17.8 days. Figure 1. Example evolution of a typical IMXB consisting of a NS and a donor star with initial masses M 1 = 1.4 M ⊙ and M 2 = 3.5 M ⊙ respectively, and orbital period P orb = 6 days. The blue and red solid curves denote the evolutionary tracks for
Paper: 2016ApJ...817L..14S (fulltext)
disk sources in the Perseus molecular cloud with the data collected in 2013, 2014, and 2015 (D. M. Segura-Cox et al. 2016, in preparation). Here we focus on seven of the candidate disks (Table 1 ; Figure 1 , left) which were observed with A-array prior to 2015 and can be well modeled with the prescription described in Section 4 . The observations were made in three-bit correlator mode, with a bandwidth of 8 GHz divided into 64 sub-bands. Each sub-band has 128 MHz bandwidth, 2 MHz channels, and full polarization products. The two 4 GHz basebands are centered at 36.9 GHz (∼8.1 mm) and 29.0 GHz (∼10.5 mm). Three sources were observed in each 3.5 hr block, and two sources were observed in each 2.75 hr block. Some observations were conducted as 1.5 hr blocks. 3C48 served as the flux calibrator, and 3C84 was the bandpass calibrator. The observations were taken in fast-switching mode to account for rapid atmospheric phase variations, with a 2.5 minute total cycle time to switch between the target source and the complex gain calibrator, J0336+3218. The total integration time on each source was ∼30 minutes for both A-array and B-array. We reduced the data with CASA 4.1.0 and the VLA pipeline (version 1.2.2). We executed additional flagging beyond pipeline flagging by inspecting the phase, gain and bandpass calibration solutions. VLA Ka-band data sets have an estimated amplitude calibration uncertainty of ∼10%, but only statistical uncertainties are considered in our analysis. Figure 1. VLA A+B array data (left), q = 0.25 model from u, v -plane best-fit (center), and residual (right). Images were produced with robust = 0.25 weighting. Contours start at 3 σ ( σ ∼15 μ Jy) with a factor of spacing. The synthesized beam is in the lower left. Table 1 Observations Note. Observation dates mark the start of the observations. Combined beam sizes reflect robust = 0.25 weighting. 3. ESTIMATED MASSES Two key quantities that describe disks are their masses and radii. While accurately determining disk radii requires modeling, masses are estimated from flux measurements (Table 2 ). By assuming optically thin emission, we estimate disk masses from the 8 mm dust continuum flux with the relation (Hildebrand 1983 ):where , d , , and , are respectively the total observed flux, distance, grain opacity, and blackbody intensity at dust temperature T d . We estimate at 8.1 mm by normalizing to Ossenkopf Henning ( 1994 ) at 1.3 mm using a dust to gas ratio of 1/100: = cm 2 g −1 . β = 1 is typically assumed for protostellar disks (Andrews et al. 2009 ), giving cm 2 g −1 . Mass estimates are inherently ambiguous within an order of magnitude due to uncertainties in the dust-to-gas ratio, T d , and β ; rather than compute a single mass estimate, we calculate lower and upper mass estimates for each source by varying T d . Lower masses were estimated by assuming T d = 40 K, and we determined upper estimates using T d = 20 K. These candidate disks range in flux from 867.5 to
Paper: 2020A&A...643L...3L (fulltext)
OLR, corotation (CR), as well as the 4:1 and 6:1 resonances from higher order Fourier modes of the bar potential. We define a quasi-isothermal ( Binney 2010 ) unperturbed distribution function in action space to represent a young and cold ( σ R = 30 km s −1 ), as well as a hotter and older ( σ R = 50 km s −1 ) disc stellar population. Then we use a first canonical transformation to move to the slow and fast action-angle variables, which allows us to determine the regions of phase-space where orbits are trapped at resonances. In the vicinity of these resonant regions, and after averaging over the fast angles, the dynamics of the slow variables is that of a pendulum, for which we make a new canonical transformation to pendulum action-angle variables. We then phase-mix the original unperturbed distribution function over the pendulum angles to obtain the perturbed distribution function in the different resonant zones. We can then compute the perturbed distribution function at different positions and velocities, making use of the epicyclic approximation. We refer the reader to Monari et al. (2017a , 2019) for more details of the method. We note that it gives a very good representation of orbits trapped at resonances, but is not the best possible description of the linear deformation zones. In Fig. 2 , we present the variations in density of the v φ − R plane resulting from the response of both the cold (young) and hot (old) stellar populations to the bar potential of Portail et al. (2017) . As expected, the features are marked much more clearly for the dynamically young population. Interestingly, this is particularly true for the 4:1 resonance, which corresponds to the Sirius ridge, which is also seen very prominently in the data as a young feature in a large range of radii. Moreover, the density contrast of the ridge associated with corotation is most prominent for young stars around R ∼ 7 kpc, which is also where the Hercules ridge appears most clearly both in global density and as a young feature in the data. The ‘hat’ feature, which corresponds to the bar’s OLR in this model, appears prominently for the young and cold population at R ∼ 9 kpc, where it also peaks in differential age in the data. This analytical model thus provides a remarkable qualitative match to the trend uncovered in the data. Fig. 2. Normalised perturbed distribution function in the presence of the large bar perturbation of Portail et al. (2017) . Top panel : for an unperturbed distribution function representing a young and cold disc stellar population with σ R = 30 km s −1 at the Galactocentric solar radius R 0 . Bottom panel : for an older and hotter disc population with σ R = 50 km s −1 at the Galactocentric solar radius R 0 . 4. Arcturus and moving groups of the thick disc We note in Sect. 2 that the ridges from Arcturus down to Ella all have stellar ages that appear slightly but systematically younger than the surrounding population, which, combined with the regularity
Paper: 2019A&A...628A..35K (fulltext)
of stars in the K2 samples prevent us from drawing any firm conclusion with regards to the slopes. As for the offsets, the effect of such differences seems to be at the level of ∼ ± 7 μ as at most. In addition, the fairly good agreement between SkyMapper and APOGEE does not exclude the possibility that the photometric T eff and [Fe/H] may be influenced differently in different fields (e.g. C3 and C6) by external factors such as, for instance, the extinction. A further point we would like to raise concerns the apparently larger scatter in the K2 fields. In that respect, we identify the order of magnitude of the Gaia and seismic ( PARAM ) parallax uncertainties (Fig. 12 ). The asteroseismic uncertainties are slightly larger in the case of K2. This can mainly be explained by the fact that the original mission, Kepler , continuously monitored stars for four years, whereas each campaign of K2 is limited to a duration of approximately 80 days ( Howell et al. 2014 ). Despite this, photometric systematics in the K2 data leading to, for instance, spurious frequencies are now well understood, and the pipelines used to produce light curves have been developed over time to remove or mitigate these effects. As a result, we do not expect the global seismic parameters to show any significant bias due to the K2 artefacts (see also Hekker et al. 2012 , and the Kepler and K2 Science Center website 5 ). Nonetheless, the astrometric uncertainties are also substantially larger for K2, almost doubled compared to Kepler . A possible reason for this would be that the regions around the ecliptic plane (such as C3 and C6) are observed less frequently, as a result of the Gaia scanning law, and also under less favourable scanning geometry, with the scan angles not distributed evenly ( Gaia Collaboration 2016b ). To test this hypothesis, we use the quantity visibility_periods_used : the number of visibility periods, that is a group of observations separated from other groups by a gap of at least four days, used in the astrometric solution. This way, we can assess if a source is astrometrically well-observed. This variable exhibits significantly higher values for Kepler , ranging from 12 to 17. The number of visibility periods for K2 are lower or equal to ten, indicating that the parallaxes could be more vulnerable to errors. The predicted uncertainty contrast between the Kepler and K2 fields is about a factor 6 of 1.6, which is indeed consistent with the location of the histogram peaks in the top panel of Fig. 12 . Fig. 12. Distribution of parallax uncertainties in Gaia DR2 ( top ) and in PARAM ( bottom ) for the Kepler (red), C3 (orange), and C6 (purple) fields. 5.2. Quasars and colour-magnitude diagram Lindegren et al. (2018) investigated the parallax offset using quasars, and obtained a global zero-point of about −30 μ as. It is no surprise that this value differs from the ones we obtain in the current work. Indeed, this parallax offset depends on magnitude and
Paper: 2019ApJ...882...97P (fulltext)
radiative losses and energy input per unit mass across the atmosphere due to the most important hydrogen transitions in models with the beam flux equal to 3 × 10 9 erg cm −2 s −1 . The figure shows that beams with a low E C (20 keV) disrupt the top chromosphere, shift the transition region to heights of up to 1500 km, and produce emission in the Lyman continuum and Ly α . The figures also show a peak of absorption in the Lyman continuum at the same height where we can see the maximum of radiative losses in the Balmer and the higher-order continua. This means that the Lyman continuum irradiated downward contributes to hydrogen ionization in the deeper layers. The radiative losses for E C greater than 40 keV do not show any such Lyman emission, but they still show the emission in the higher-order continua that are clearly driven by the particle-beam precipitation. Figure 1. Radiative losses during the flare electron-beam precipitation for E C in the range of 20–120 keV. The beam flux is equal to 3 × 10 9 erg cm −2 s −1 . A positive (negative) corresponds to absorption (emission). The profiles are taken 20 s into the simulations. Table 1 shows the gross radiative losses in the atmosphere due to the free–bound Lyman and Balmer transitions (LyC and BaC, respectively) and the bound–bound Ly α and H α transitions, respectively. The radiative losses were integrated only over the chromosphere and the temperature minimum region—between the height of 300 km above the photospheric floor and the height where the temperature begins to exceed 10 5 K. These numbers do not include any possible reabsorption outside these atmospheric heights; therefore, they do not quantify the radiative output of the atmosphere as seen by an observer. The table shows that there is an overall stronger emission for particle beams with higher flux with the LyC showing the greatest variation. In models with E C = 20 keV the LyC losses reach values of 10 19 –10 20 erg cm s −1 g −1 ; however, models with high E C show that the LyC remains at the quiet level (∼10 16 erg cm s −1 g −1 ). The situation is the same for the Ly α , where the quiet level reaches ∼10 17 erg cm s −1 g −1 . The radiative losses listed in Table 1 show that when the beam flux is increased, the maximum radiative losses in the studied lines and continua occur at higher values of E C . With increasing E C it is the LyC that reaches the peak of the radiative losses first and then the Balmer continuum. This is due to the greater penetration depth of the beams with higher E C and the fact that the LyC originates higher in the atmosphere than the BaC. Table 1 Atmospheric Heating and Radiative Losses Due to the Hydrogen Transitions in Electron-beam-driven Models Note. The radiative losses and the beam heating, as shown in Figure 1 , were integrated over the temperature minimum region and chromosphere. The first row shows the radiative losses for the quiet atmosphere. Figure 2 shows the RH synthetic spectra for the
Paper: 2016ApJ...827....5J (fulltext)
an underlying population with (red lines) and (cyan lines). All results are for the ShGeLMC simulation, with . The discussion above is simplified in a number of ways. First, we assume that the individual members of the galaxy population all have the same escape fraction, i.e., the distribution of is a delta function. As we noted previously, this is not likely to be true. Second, we assumed that all galaxies in our mock samples could be observed at the same signal-to-noise. A realistic sample would contain galaxies with a range of luminosities, and would therefore have a different noise level for each object. Nevertheless, this simple test shows that measuring the mean escape fraction of a large sample is possible even if the errors are large for individual objects. 5. SUMMARY AND DISCUSSION In this paper, we have shown how machine learning algorithms such as lasso regression can be applied to simulated galaxies to obtain a function that maps a spectrum to a Lyman continuum escape fraction. Our method uses the entire observable spectrum, which leads to an increase in performance over, for instance, using only the UV slope and the H β line (Zackrisson et al. 2013 ). We find that using the lowest NIRSpec resolution, R = 100, gives the best results for a given exposure time. Interstellar dust presents a challenge for our method, but it can be dealt with unless the dust attenuation law is very poorly known. Training a model on data with low noise results in a more complicated model that uses a larger number of spectral bins compared to a model trained on data with high noise. The performance of the model is always better when the training and testing data have the same noise level. Therefore, in a real-world situation, a suitable approach might be to train several models using different noise levels, and apply them depending on the noise level for each specific observed galaxy. In a sense, this method represents the reverse of the more traditional approach used when fitting SEDs, which has also been used to constrain (Ono et al. 2010 ). In SED-fitting, one usually generates a large grid of model galaxies, and looks for a model galaxy that looks similar to a given observation. It is then assumed that the properties of the observed galaxy will be similar to that of its closest match in the model grid. The problem with this approach lies in defining “similar.” Typically, the similarity of two SEDs is measured simply as mean of the squared difference between the fluxes in all filters. However, when constraining a single property, such as , not all wavelength regions are equally important. In fact, our results show that most of the information about comes from only a few wavelength bins. The method presented here automatically finds the optimal way of comparing observations to simulations, when the goal is specifically to infer the escape fraction. We emphasize, however, that this is not a model-independent method. Like all other indirect approaches to