adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.492.1025C (fulltext)
scale them by the corresponding PFs estimated over the same energy ranges. However, also this procedure might not necessarily yield a realistic estimate of the total flux of the pulsar, as it implicitly assumes that no difference exists between the spectral shapes of the pulsed and pulsed plus unpulsed emission (which is not necessarily the case). 3.2 Estimating the spectrum of the total X-ray emission For all those cases where the spin signal is not detected in the X-ray band, or the photon counting statistics is too low to extract a meaningful pulsed spectrum, the closest approximation to the intrinsic pulsar spectrum and flux can only be derived by a spatial analysis of the total emission (pulsed plus unpulsed). This approach leads to estimate an upper limit to the real flux, owing to the possible residual contamination from the background. It is expected to give a very close approximation to the intrinsic pulsar emission especially in the Chandra data, where this contamination can be minimized. 3.3 Similarities and differences in the pulsed and total X-ray SEDs: a few examples In Fig. 1 , we show a comparison for a few X-ray SEDs extracted considering either the total (pulsed plus unpulsed) emission (in black; this work, with procedural details given below) or the pulsed-only emission (in red; from Kuiper Hermsen 2015 ). In most cases, these SEDs overlap in energy only partially, as the pulsed SEDs were typically extracted at higher energies with respect to the pulsed plus unpulsed SEDs that are extracted here over the soft X-ray energy range (below 10 keV). Figure 1. Total (pulsed plus unpulsed) and pulsed-only X-ray SEDs for a sample of pulsars. The former were derived in this work and are shown in black, the latter are taken from Kuiper Hermsen ( 2015 ) and are shown in red. Clearly, the total (pulsed plus unpulsed) spectrum of J0205+6449 and J1513−5908 cannot be reproduced just by scaling the pulsed spectrum for the (average) value of the PF (≳21 per cent and ∼88 per cent), indicating a strong contamination from diffuse emission in the pulsar spectral data, which can hardly be removed (this is clearly more evident for the case of J0205+6449, and it is consistent with the detection of the PWN also at GeV energies (Li et al. 2018a ). On the other hand, the total and pulsed SEDs of J1813−1246, J1846−0258, J2022+3842, and J2229+6114 are consistent with each other once the pulsed SED is rescaled by the value for the corresponding PF, implying a comparatively small contamination from the diffuse and/or background emission. In particular, the pulsed SEDs of J1813−1246, J2022+3842, and J2229+6114 are slightly below the corresponding total SEDs (note that in all these cases the PFs are >75 per cent; see Table 1 ). Although the pulsed SED of J1846−0258 appears considerably below the SED for the total emission, the two SEDs turn out to be consistent with each other once the rather small PF is taken into account (∼31 per cent; see again Table 1 ).
Paper: 2017ApJ...835...85K (fulltext)
axial fluxes, and encircled by the same twist field present in the two original tubes. In this way, the helicity, in the form of twist, inverse-cascades to ever larger scales. The PIL forms a natural boundary of the flux system: when the twist reaches this boundary, it cannot proceed further, since all of the flux has already reconnected. The end result of the process is a mostly axial and untangled field throughout the interior of the region, and a highly sheared field concentrated near the boundary of the region, i.e., at the PIL. This is precisely what is observed as a filament channel. The smooth internal field corresponds to the laminar coronal loops observed routinely on the quiet Sun and shown in Figure 1 . In this way, the helicity condensation model provides a natural mechanism for the simultaneous formation of highly sheared filament channels and laminar, untangled coronal loops. These two, seemingly unrelated features of the solar atmosphere are actually created by a single process (Antiochos 2013 ). The helicity condensation model was initially simulated by Zhao et al. ( 2015 ), who injected magnetic helicity into a plane-parallel Parker corona (Parker 1972 ). In the plane-parallel representation, the boundaries of the region of imposed twisting flows serve as the boundaries of the flux system, in analogy with the PILs in coronal-field configurations. Those authors found that photospheric motions that inject the same helicity everywhere form filament channels at the flux-system boundaries. Furthermore, randomizing the photospheric motions while keeping the same helicity injection rate did not qualitatively affect the accumulation of twist flux. Zhao et al. ( 2015 ) also tested the effect of injecting helicity of opposite signs on adjacent flux tubes, and found that their fields could not reconnect due to the twist components being co-aligned. In subsequent work (Knizhnik et al. 2015 , hereafter KAD15 ), we rigorously tested the helicity condensation model. We found not only that it qualitatively produced results consistent with the properties of filament channels, but that the inverse cascade of magnetic helicity due to reconnection produces a twist flux at the boundary of the flux system that agrees quantitatively with the predictions of the helicity condensation model. Based on this result, we estimated that with the helicity preference observed on the Sun, filament channels will form in about a day or so, in agreement with observations of their formation (Martin 1998 ; Gaizauskas 2000 ). We showed that helicity condensation agreed both qualitatively and quantitatively with observed properties of filament channels, and that the process produced a much less twisted coronal-loop region away from the flux-system boundaries. Those results were obtained for a 100% helicity preference, however, meaning that all of the helicity injected into the corona was of the same sign. An obvious question to be raised is: what happens if a
Paper: 2020ApJ...904...62W (fulltext)
parameters. The AR with the largest area and longest time of occurrence during the time considered, labeled as NOAA AR 12192, is such an example. As shown in the synoptic maps in Figures 2 (a) and (b), AR 12192 is near a large unipolar region (in blue color), which originates from previous ARs in an AC. We will simulate the formation of this unipolar region in Section 4.2 . The standard set of control parameters is able to identify AR 12192 on CR 2156. On CR 2157, the recurring AR 12192, relabeled as ARs 12209, 12213, and 12214, is mixed with new flux emergence near and within it. We find that the standard set of control parameters is unable to identify AR 12192 on CR 2157. Consequently we have to choose a different set of control parameters: kernel threshold 100 G, erosion size 10 Mm, growth threshold 20 G, and dilation size 60 Mm, to identify the more diffusive AR 12192. We refer to this set of control parameters as the special set. As shown in Figures 2 (c) and (d), the two configurations of identified AR 12192 differ vastly. The two configurations of AR 12192 possess different parameters, presented in Table 2 . During CRs 2156–2157, AR 12192 shows an increase in area and a decrease in flux, and a notable change in tilt angle. Our SFT simulation tests show that the evolution of AR 12192 during CRs 2156–2157 cannot be achieved by SFT transport terms only. This implies new flux emergences, as flux emergence is important in changing the properties of AR 12192 (McMaken Petrie 2017 ). Such new emergence superposes with existing flux, and cancels with existing opposite polarities. Thus, it is expected that the observed evolution of the AR during CRs 2156–2157 is different from the evolution solely deduced from the previous occurrence of the AR on CR 2156. Therefore, we choose the special set of control parameters for processing AR 12192 on CR 2157 for the following simulations. Figure 2. Identified ARs on synoptic maps. The left column shows synoptic maps of CR 2156 (a) and CR 2157 (b), with the ARs (enclosed by black curves) identified by the automatic identification method using the standard set of control parameters. AR 12192 and the unipolar region in proximity are indicated with black arrows. The middle and right columns show AR 12192 identified using the standard set of control parameters on CR 2156 (c), and identified using the special set of control parameters on CR 2157 (d), as well as AR 12222 identified using the standard set of control parameters on CR 2157 (e), and identified using the special set of control parameters on CR 2157 (f). Table 2 Parameters of AR 12192 Identified on CR 2156 and CR 2157 The two sets of control parameters produce different results not only for AR 12192, but for other ARs as well. We show the difference for AR 12222 as an example, in the last row of Table 1 . As shown in Figures 2 (e) and (f), when the special set of control parameters is applied, AR 12222 shows a growth in size and flux, as well as a
Paper: 2020ApJ...902L..27F (fulltext)
varies less with MC sampling rate than the ejected mass (see below). Figure 4 also shows why it is so difficult to accurately estimate the amount of unbound material in a simulation: the steepness of the Lorentz factor distributions implies that a very small error in the estimated location of the boundary between bound and unbound material has large effects on the predicted mass of unbound material. The second, more minor difference is that the cutoff of the velocity distribution is slightly lower in both MC simulations ( v max ∼ 0.5) than with M1 ( v max ∼ 0.55). We can compare these results to Sekiguchi et al. ( 2016 ), who performed a longer simulation with M1 transport, the same equation of state, and slightly more symmetric masses. They find 0.005 M ⊙ of ejecta with and , and a Y e distribution fairly similar to those obtained with our M1 code—results that are broadly consistent if the higher mass asymmetry led to the ejection of an additional 0.003 M ⊙ of neutron-rich material. The properties of neutrinos escaping the system are summarized in Figure 5 . After the first 2 ms, the average energy of neutrinos and the ν e and luminosities in the M1 and MC simulations agree within ∼20%. The ν x luminosity, on the other hand, is nearly twice as large in the MC simulation. Differences between the two MC simulations are negligible compared to differences between MC and M1. The luminosity of heavy-lepton neutrinos is one of the most uncertain observable in our M1 scheme, because a large region close to the neutron star surface has negligible κ a but large κ s . In that region, neutrinos are trapped, but out of thermal equilibrium with the fluid. As the energy closure is the most ad-hoc part of our M1 algorithm, and the diffusion rate of neutrinos strongly depends on the choice of energy spectrum, this is a particularly difficult situation. The MC scheme has no particular reason to perform poorly in that regime: while it corrects large absorption opacities, its treatment of high-scattering regions is better motivated than that of the gray M1 scheme. Nevertheless, it may be premature to assume that the MC scheme provides the better answer, as the two schemes may be impacted in different ways by the grid resolution. In Sekiguchi et al. ( 2016 ), neutrino luminosities reach values similar to those measured here within ∼5 ms, before decreasing on the cooling timescale of the remnant. Figure 5. Left panel: angular distribution of the electron antineutrinos leaving the grid (3–5) ms after merger; θ is the angle with respect to the angular momentum vector, i.e., polar neutrinos are on the left, equatorial neutrinos on the right, and each bin covers the same surface area. Middle panel: neutrino luminosity in the high-resolution MC simulation and the M1 simulation. Right panel: average (number-weighted) energy of neutrinos leaving the computational grid in the same simulations. The angular distribution of neutrinos shows clearer differences between
Paper: 2019ApJ...870...49S (acknowledgments)
We would like to thank the anonymous referee for useful comments that improved the paper. This work was supported by the European Research Council (ERC-2015-AdG grant No. 695008), FCT (Portugal) grant SFRH/IF/01780/2013, DOE grants DE-SC0008409 and DE-SC0008655, and NASA grant NNX16AB28G. D.A.U. gratefully acknowledges the hospitality of the Institute for Advanced Study and the support from the Ambrose Monell Foundation. Simulations were carried at MareNostrum (Spain) under a PRACE award. Software: OSIRIS (Fonseca et al. 2002 ).
Paper: 2016MNRAS.463.2363K (acknowledgments)
We thank the anonymous referee for their detailed comments and suggestions that improved this study. We also thank Anahita Alavi, Philip Best, Rychard Bouwens, Naveen Reddy, and Irene Shivaei for their insightful discussions and comments. The catalogues used in this analysis are publicly available from Sobral et al. ( 2013a ). DS acknowledges financial support from the Netherlands Organization for Scientific research (NWO) through a Veni fellowship, from FCT through an FCT Investigator Starting Grant and Start-up Grant (IF/01154/2012/CP0189/CT0010) and from FCT grant PEst-OE/FIS/UI2751/2014. IRS acknowledges support from ST7C (ST/L00075X/1), the ERC Advanced Grant DUSTYGAL (321334) and a Royal Society/Wolfson Merit award. BD acknowledges financial support from NASA through the Astrophysics Data Analysis Program (ADAP), grant number NNX12AE20G. JPS gratefully acknowledges support from a Hintze Research Fellowship.
Paper: 2019MNRAS.489L..58Z (acknowledgments)
ACKNOWLEDGEMENTS I thank Marek Sikora for valuable discussions and suggestions, Łukasz Stawarz for providing the quiescent spectra of Mrk 421 and Mrk 501, and the referee for valuable comments. This research has been supported in part by the Polish National Science Centre grant 2015/18/A/ST9/00746.
Paper: 2017MNRAS.469.4718B (fulltext)
et al. 2017 ). To decouple the effect of Z and Y , we compute five tracks of mass 1.50 M ⊙ and Z = 0.031 ([Fe/H] = 0.25), but with initial helium Y = 0.25, 0.28, 0.30, 0.35 and 0.40 (with Y = 0.28 as our default value). Fig. 4 , right-hand panel, shows the evolution of ΔΠ 1 with central helium for the five tracks. We note that the effect on ΔΠ 1,max and ΔΠ 1,min grows linearly with Y . However, for a variation of Δ Y = ∼0.02 from the default value ( Y = 0.25 and 0.30), the deviation on ΔΠ 1,max (ΔΠ 1,min ) lies within the observed uncertainty on ΔΠ 1 (∼3–4 s). The deviation we tested is compatible with typical spread on disc populations (Casagrande et al. 2007 ). Nevertheless, the effect becomes substantial for extreme enrichments, for example, in bulge or globular clusters where populations with very different He abundances and the same metallicity may coexist (Renzini et al. 2015 ). Figure 4. Left-hand panel: RC-ΔΠ 1 evolution as a function of central helium mass of an M = 1.50 M ⊙ solar metallicity star with three different mixing schemes and four different 12 C(α, γ) 16 O reaction rates. Right-hand panel: RC-ΔΠ 1 evolution as a function of central helium mass fraction of an M = 1.50 M ⊙ star computed assuming three different combinations of Z and Y . 5 ADDITIONAL UNCERTAINTIES ON ΔΠ<sub><bold>1</bold></sub>: <sup><bold>12</bold></sup>C(α, γ)<sup><bold>16</bold></sup>O NUCLEAR REACTION RATE As shown above, ΔΠ 1 is strongly dependent on assumptions related to core convection and metallicity; however, additional parameters and uncertainties may also have impact on ΔΠ 1 , in particular the 12 C(α, γ) 16 O reaction rate, which, along with triple-α, plays a fundamental role, especially at the end of the HeCB (e.g. see Cassisi et al. 2003 ; Straniero et al. 2003 ; Constantino et al. 2015 ). We compute a series of HeCB evolutionary tracks ( M = 1.5 M ⊙ , solar abundance) in which we adopt four 12 C(α, γ) 16 O reaction rates in conjunction with three mixing schemes: Bare–Schwarzschild model (BS), 1.0 H p step function overshooting (HOV) and 1 H p penetrative convection (HPC). The 12 C(α, γ) 16 O reaction rates considered are the tabulated values given by JINA (Cyburt et al. 2010 ), K02 (Kunz et al. 2002 ), CF88 (Caughlan Fowler 1988 ) and NACRE (Angulo et al. 1999 ), and already made available in mesa . While no difference can be noted at the beginning of the phase, the impact of the different mixing schemes is evident at the maximum period spacing (end of the HeCB), where HOV tracks show a scatter of around 6–7 s between them, compared to only ∼2 s for BS (Fig. 4 ). We therefore expect an uncertainty between 6 and 2 s on the MPC and MOV models. This value is comparable with the observed average ΔΠ 1 uncertainty for clump stars (∼4 s). 6 DISCUSSION AND CONCLUSION The precise measurements of gravity-mode period spacing (ΔΠ 1 ) in thousands of HeCB stars have opened the door to detailed studies of the near-core structure of such stars, which
Paper: 2020ApJ...896L..39J (fulltext)
to the distance of 2I. The small apertures sample near-nucleus variations, whereas the 15,000 km aperture is a better measure of the total light scattered by the comet. The use of fixed linear (as opposed to angular) apertures provides a measure of the scattering cross section within a fixed volume surrounding the nucleus and obviates an otherwise necessary geocentric distance correction that is dependent on the surface brightness distribution. We obtained background subtraction from the median signal in a concentric annulus with inner and outer radii of 20,000 km and 30,000 km, respectively. The apparent magnitudes, V x , with x = 500, 1000, 2000, and 15,000 are listed in Table 2 , along with absolute magnitudes, H x , computed fromwhich corrects for the inverse square law and for the phase function, f ( α ) at phase angle α . In the absence of a measured value, we assumed f ( α ) = 0.04 mag degree −1 . The table also lists the scattering cross section, in square kilometers, computed fromwhere p V is the geometric albedo. We use p V = 0.1, as appropriate for dust in solar system comets (Zubko et al. 2017 ), while noting that the albedo of this unique interstellar object is likewise unmeasured and could be significantly different. The dust albedo is ∼2.5 times higher than the average comet nucleus albedo. Table 2 HST Fixed-aperture F350LP Photometry a Note. a For each date and aperture radius, l , the table lists the apparent magnitude, V , the absolute magnitude, H , and the scattering crossection, C e (in units of square kilometers), in the form V / H / C e . H and C e are computed using Equations ( 1 ) and ( 2 ). Temporal variations in the absolute magnitudes are plotted in Figure 4 , in which the UT 2020 March 4.3–9.3 dates of photometric outburst (Drahus et al. 2020 ) are marked by a vertical gray band. Our data show that the outburst was preceded by a progressive brightening of 2I by about 20% in all apertures. This brightening is not correlated with the phase angle, which changes modestly and nonmonotonically in this period (Table 1 ), and therefore cannot be ascribed to uncertainties in the phase function. Neither does it vary with the angle from the orbital plane. Instead, the data show a steady brightening from January 3 to February 24, indicating a period of increasing activity. We conjecture that this is due to a seasonal effect on the nucleus: according to the pole solution proposed by Kim et al. ( 2020 ), the subsolar latitude on the nucleus moved from the southern hemisphere to the northern during this period, bringing the Sun’s heat to previously unexposed ice. Figure 4. Absolute magnitudes vs. date for the 500, 1000, 2000, and 15,000 km radius apertures. Smoothed dashed lines are added to guide the eye. The shaded vertical band marks March 4–9, the dates of the reported photometric outburst. Our first postoutburst HST observation occurred about 2 weeks later (March 23), by which time the near-nucleus brightness had jumped
Paper: 2020MNRAS.494.2471J (fulltext)
does not need to precompute and store anything for these surfaces. To solve equation ( A4 ), we need only the current position and direction, and the θ boundaries of the current cell. A1.3 Constant phi surfaces The constant φ surfaces are planes. Determine the Cartesian coordinates for the four edgepoints of the surface. Then, define, for the k plane, the vectors Take the cross-product and normalize to get the normal vector to the plane . Note that an intersection point on the plane will obey . For the k + 1 plane use instead and then corresponding operations for and d . The intersection point ( x i , y i , z i ) and the distance to this point, d s , are then found from solving the linear equation system No solution means the packet is moving parallel to the plane and will not impact it. If d s 0, the packet is moving in the wrong direction and will not impact the plane. The four numbers are computed once and stored for all surfaces. A1.4 Entry surface flagging If the surface through which the packet entered the cell is allowed also an as exit surface, small numeric errors could lead to the packet getting stuck at the boundary. To avoid this the entry surface is initially flagged to be disallowed as solution for which next boundary to encounter. This flagging is removed if an interaction occurs inside the cell. For two surfaces, the ‘convex out’ boundaries at the constant r outer radius and constant θ conical boundary closer to the equatorial plane, it is possible for the packet to enter and exit the same boundary on a straight path. However, this occurrence is very rare, in particular for a high-resolution grid as those used here. We therefore kept the flaggings also for these cases, and was removed only after a small distance into the cell had been travelled. This treatment implies a small distortion to the flight path for photons gracing these surfaces but the effect of this should be very small, and this treatment avoids an more expensive algorithm for doing the transfer exactly. A1.5 Positional corrections Once the shortest distance has been calculated, the new Cartesian coordinates of the packet are calculated from . The spherical coordinates are then calculated from these. It may sometimes happen that in these transformations small numeric errors lead to the new spherical coordinates being (slightly) outside the limits of the new cell. If that happens severe errors can occur in calculating the next movement. Therefore, at each new cell entry, this is checked and if a discrepancy has arisen, the coordinates are adjusted to be just inside (by a multiplicative factor 1 ± 10 −5 ) the surface in question. A1.6 Next interaction point For continuum opacity, standard Monte Carlo procedures are followed by drawing a random optical from τ = −ln z where z is a random number between 0 and 1. Then As the distances are computed in the stellar rest-frame one should ideally use the direction-dependent opacity in this frame. We did not make the