adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJS..239....1S (fulltext)
of the 9 year period observed, we find reductions in the GDF, consistent with an average decline in the ram pressure when correcting for solar wind propagation times. The conditions reflected by GDF maps from 2009 through 2016 are consistent with an expanding heliosheath, provided that plasma cooling overcomes any increase in the LOS from expanding heliospheric boundaries. The conditions appear to change abruptly in the 2015–2017 GDF map. In fact, as reported by McComas et al. ( 2018 ), the conditions change so quickly that there appears to be a longitudinal discontinuity near the nose. The brightening observed within this 2015–2017 GDF map reflects a strongly time-dependent change manifesting most prominently in 2017. The heliosheath responds to time-dependent changes in the solar wind ram pressure. Over most of the 9 year period analyzed we have observed drops in the LOS-integrated pressure due to expansion of the heliosheath driven by reductions in the solar wind ram pressure. In the 2015–2017 period, we have observed some recovery in the LOS-integrated pressure due to a significant increase in the ram pressure observed at 1 au beginning in 2015. Despite the large changes near the nose, the LOS-integrated pressure near the tail shown in Figure 6 remain relatively stable. Accounting for the stability of the tail will require understanding how time variations propagate downtail along the LOS. Presumably this stability requires that the LOS in the tail is significantly longer than the LOS near the nose. This reinforces the conclusion that the structure of the heliosphere is not round, and has a long comet-like tail (Schwadron Bzowski 2018 ). 3.3. Time-dependent Changes in the IBEX Ribbon We approach the time-dependent changes in the IBEX ribbon by considering the secondary neutral solar wind as the source. In this model, the solar wind conditions at 1 au are reflected in the ENA flux from the IBEX ribbon after a time delay consisting of a travel time from 1 au to the source region, the charge-exchange lifetime of a newly ionized proton in the ribbon, and the time necessary to return from the source region to IBEX located at 1 au. We use the parallax distance of au based on the study of Swaczyna et al. ( 2016a ). We find that a 1 keV neutral atom moves from 1 au to the source region in years. The charge-exchange lifetime of a newly ionized proton in the ribbon at 1 keV is ∼2.5 years. The transit time of the neutral atom back to 1 au is also years. Adding up these terms, we find a time delay of years between the observation of solar wind at 1 au and the ENA observations of manifestations of this solar wind within the ribbon at 1 keV. At 4 keV, the propagation time from 1 au to the ribbon distance (and vice versa, for propagation from the ribbon to 1 au) is years. The charge-exchange lifetime of this 4 keV ENA is ∼2 years, yielding a time delay of . The estimations of these time delays are rough, largely due to uncertainties in our
Paper: 2016MNRAS.458.1417R (fulltext)
2 sequences, respectively. This results in all periodicities being identified with spacings similar to other Kepler -observed g-mode pulsators (Reed et al. 2011 ). Figure 4. Period spacing sequences indicated by the KS test. The solid blue (green) line indicates the l = 1 (2) period spacing sequence with the dashed line the overtone alias. Figure 5. Period transform of PG 1142 showing evenly spaced periods. Short blue (green) lines indicate the asymptotic l = 1 (2) sequence with full-length lines indicating those which match detected periodicities. The horizontal line indicates 4.35σ for reference. 3.2 Binary analysis PG 1142 has proven to be a very exciting target. The detection of fA and fB prompted follow-up spectroscopic observations (described in Section 2.1 ) to search for velocity variations, which were subsequently detected. Fitting the 24 RVs (in Table 1 ), and assuming a circular orbit, gives an orbital solution period of 0.541089 ± 0.000020 d (21.39 μHz), amplitude of 85 ± 2 km s −1 , and phase of BJD = 2457099.74105 ± 0.00204 (top panel of Fig. 6 ). The eccentricity was subsequently fitted at 0.079 ± 0.028, showing it is negligible for our calculations. Using the values of K = 85 ± 2 km s −1 and P = 0.541 d from spectroscopy, the sdB canonical mass M1=0.47 and assuming i = 90 o , we get the minimum values for M 2 of 0.26 M ⊙ and for the orbital separation a = a 1 + a 2 = 2.52 R ⊙ . Of course we know this is not the inclination as no eclipses are evident in the light curve. Figure 6. Velocity and photometric data folded over the orbital period. The K2 data have been phase-binned into 72 bins with 1σ error bars shown. We attempted several phase-folding and binning combinations of the photometry to improve the period and phase, however the thruster firings, at roughly every six hours, and pulsations decrease the accuracy, and so the RV data are better for constraining the period. Still, the photometry show evidence of two-component variations, occurring at similar levels, in agreement with the FT. From the phasing of the variations, we can deduce the sources. A reflection-effect companion in the binary would produce a reflection-effect maximum, which would occur during a Doppler boosting zero-point (halfway between maximum and minimum) or an ellipsoidal-effect minimum. A Doppler boosting maximum would occur during an ellipsoidal-variable maximum. Figs 7 and 8 show the results of two-component fits to the folded light curve (described below). The phasing indicates that the components are Doppler boosting and ellipsoidal variations. These two components indicate that PG 1142 is most likely an sdB+WD binary. Figure 7. Orbital elements removed from the light curve. Top panel shows original folded and binned light curve. Orbital fits are indicated by lines with green being the combined fit, blue the ellipsoidal deformation, and red the Doppler boosting. Middle panel shows the Doppler-corrected light curve (with ellipsoidal deformation
Paper: 2016MNRAS.458L..89C (fulltext)
gives a power spectrum with the desired shape. We then inverse Fourier transform back to get the pink or red time stream. We then take samples with a positive value to contain a pulse and samples with a negative value to contain none. In the stationary Poisson case, the rate of bursts in the immediate follow-up is the same as the multiyear follow-up since all times are statistically equivalent. However, with flicker noise, the variance is strongly time dependent. If we imagine an object that repeated on average once per two hours, then if those pulses were Poisson-distributed, the probability of seeing zero bursts in 11.4 h or longer is ∼0.007. With pink noise, one expects this roughly 20 per cent of the time, since the system prefers either to be in ‘on’ or ‘off’ mode. If the average repeat period were more like 5–20 h, then we would often see nothing in a multiday follow-up observation that took place weeks or years after the initial event. This is consistent with what Petroff et al. ( 2015b ) saw, though the conclusions differ depending on the assumed statistics. In Fig. 1 , we show a sample from this simulation for three repeat distributions. The right-hand panel shows how, if an FRB's burst rate has long-term correlations (1/ f γ ), the likelihood of a repeat is greatly increased if the follow-up observation is immediately after the initial event, rather than months or years after. Figure 1. Realization of our mock follow-up Monte Carlo. Left-hand panel: power spectrum for pulse arrival times of a single FRB. Grey shows a flat spectrum, corresponding to the often assumed Poissonian repetition rate. The red and blue spectra show flicker noise, with pink (1/ f ) noise and Brownian (1/ f 2 ) noise, respectively. Right-hand panel: we found the first ‘event’ in our Monte Carlo (represented by a star) for the three different spectra and plotted their behaviour in the subsequent 48 h of follow-up. Though the average probability over the whole simulation is 0.5 for each distribution, when we zoom in on this short period, the strong time-like correlations in the 1/ f γ cases means there are many repetitions, they are in an ‘on’ state at this time. 2.2 FRBs 110220 and 140514 Using the event rate of roughly 10 4 sky −1 d −1 from Thornton ( 2013 ), it was originally reported that the probability of seeing a new FRB in the field of 110220 during the 85 h of follow-up was 0.32 (Petroff et al. 2015a ). It was then pointed out by Maoz et al. ( 2015 ) that this underestimated the coincidence by an order of magnitude, since they estimated the rate in any one of the 13 beams, while the new event occurred in the identical beam. The probability also dropped due to the updated daily event rate, given the Thornton ( 2013 ) estimate is now thought likely to be too high. In general, we expect the true rate of FRBs to be lower than what is reported due to non-publication bias. If archival data are searched and nothing is found, it is less likely to be published
Paper: 2020MNRAS.497L..13M (fulltext)
of any other considerations. The best-fitting central masses associated with smaller spins were systematically lower, but always with M > 9 × 10 8 M ⊙ . In general, smaller black hole spins produced poorer fits to the observed light curves. A Schwarzschild black hole (best-fitting mass M = 9 × 10 8 M ⊙ ) produced the worst fit,, and would require a 28.3 M ⊙ solar-type star for disruption (fig. 2, Kesden 2012 ). This makes a low spin value very unlikely. For larger spins, the quality of fit is similar to the near-maximal a / r g = 0.99 case. When a / r g = 0.9, the best-fitting light curve has , with a very similar best-fitting black hole mass M = 1.35 × 10 9 M ⊙ . However, even this slight reduction from maximal spin now requires an 8.3 M ⊙ star for a disruption, compared to M ⋆ = 2.6 M ⊙ for a / r g = 0.99. From the light curves alone, we cannot rule out the possibility of an extremely massive star being disrupted by a more moderately rotating black hole. The strong dependence of TDE rate on stellar mass ( as noted above) clearly favours a near-maximal spin. The light curves of Section 3 were produced assuming a face-on inclination. The best-fitting parameters are, in fact, only weakly sensitive to the disc–observer inclination, with only minor changes to the fitted parameters for θ 20 o . For higher inclination angles, larger black hole masses were required. Taking the uppermost black hole mass allowed from the galactic bulge range as a hard upper limit, we may constrain the inclination to θ 60 o . Given that a relatively massive (∼2 M ⊙ ) star has been disrupted, it is somewhat surprising that the disc component of the evolving light curves is powered by only a relatively small accreted mass M acc ≃ 0.07 M ⊙ . It is important to note that this accreted mass value does not include the matter required to power the (extremely bright) early-time emission (observed in all bands), or the late-time UV emission. Rather, the disc mass represents only a lower bound on the debris mass. Given the likely highly relativistic orbit of the disrupted star, it may well be that a significant portion of the stellar mass was directly accreted in the earliest stages. It has also been argued (Guillochon Ramirez Ruiz 2015 ) that relativistic orbital precession of the tidal disruption streams (induced by a large black hole spin) can lead to inefficient disc formation. 3.2 Radio and X-ray observations No radio emission has been detected from ASASSN-15lh, with upper limits of order ν L ν ≲ 10 38 erg s −1 ( L16 ; Kool et al. 2015 ). Given the likely near-maximal spin of its central black hole, it is perhaps surprising that a jet is not observed. [The observed radio emission is at least two to three orders of magnitude lower than the bright, jetted TDE Swift J1644+57 (Levan et al. 2011 ).] Moreover, unlike other jetted TDEs, ASASSN-15lh is X-ray dim. Initially undetected at X-ray energies ( L16 ), X-ray emission has been more recently detected by Margutti et
Paper: 2017A&A...607A..80J (fulltext)
and the microturbulence were related according to Heinzel Anzer (2001) (see Paper I , Appendix A). Therefore, the obvious next step was to use the L and C iii lines to distinguish between kinetic temperature and microturbulence. This is only possible when two species with different masses are formed in the same emitting plasma volume ( Tandberg-Hanssen 1995 ) and are both optically thin. In this section we check whether this condition is met in the spectroscopic UVCS observations of the L, L, and C iii lines. Fig. 2 Doppler velocities of the L, L and C iii lines as a function of latitude along the UVCS slit. Note that the last three points of the L line reach Doppler velocities of several hundred km s. Fig. 3 Profiles of the L, L and C iii lines of the 12th measured point along the erupting prominence. We present a detailed Doppler velocity analysis of all the 68 points we measured along the prominence in the L, L, and C iii lines in Fig. 2 . This shows that for the majority of the points, the Doppler velocities of the C iii line are consistent with the L and L velocities within the uncertainty of the UVCS wavelength determination, which is equal to the spectral UVCS resolution of about 0.2 Å. This gives an uncertainty in the Doppler velocity of about 60 km s. The Doppler velocities of the three lines are almost consistent within the estimated uncertainties. L and L lines must have the same Doppler velocities because they are emitted from the hydrogen atoms. From this we conclude that the C iii ions also move with the same Doppler velocity as hydrogen. Therefore, the L, L and C iii lines are formed in the same or a similar emitting plasma volume with a unique kinetic temperature and microturbulent velocity. In addition, firm proof that all lines are formed in the same plasma volume is provided by the fact that the profiles of all three lines, reported in Fig. 3 in one representative case, exhibit a single peak. We note that their Doppler shifts should be proportional to . If the profiles had more than one peak, it would indicate that the prominence blobs move with different velocities in different directions, and this is not observed. 4. Kinetic temperature and microturbulent velocity The line profiles of optically thin prominences are mainly Doppler broadened as a result of the thermal motions of atoms and because of the turbulent plasma motions. The Doppler line width can then be expressed as Here is the speed of light, is the Boltzmann constant, is the kinetic temperature, is the mass of the atom, is the microturbulent velocity, and is the effective temperature. We introduced the microturbulence because in erupting prominences that are embedded in CME cores, significant nonthermal motions are expected to occur. Equation ( 1 ) shows that the carbon line is more sensitive to the microturbulent velocity and less sensitive to the kinetic temperature, as is clearly shown in Fig. 4 . Fig. 4 Panel a : FWHM of the C iii line plotted as a function
Paper: 2017MNRAS.469..521K (fulltext)
optimize the chances of finding new systems with gas. 4.1 Flux predictions for atoms When making predictions for C i , C ii and O i masses, the conversion to fluxes is more complicated than with CO where the excitation is dominated by the CMB (see Section 3.5.2 ). Since these lines are at shorter wavelengths, the dust radiation field can become dominant (see Fig. B1 ). While the dust radiation field seen by the gas is not easy to assess, especially as most of our targets are unresolved, it is well known for β Pic. Thus, we use the same radiation field as found in Matrà et al. (in preparation) for that system. For other targets, we fit each SED individually and use the ratio of the dust fluxes at 158, 610 and 63 μm to those of β Pic to scale up or down the β Pic radiation field and so get predictions for the fluxes of each individual system for C ii , C i and O i (see Appendix B ). Note that we also take account of the optical thickness of each line in our flux calculations using the method described in Section 3.5.6 . 4.2 C <sc>ii</sc> model predictions and results We start off by comparing our C ii model predictions to Herschel observations. In Fig. 7 , we plot our predictions of C ii mass as a function of distance to the star. To do so, we use our analytical model using equation ( 11 ) to get the total carbon mass and equation ( 15 ) to get the ionization fraction and then compute . The predicted C ii masses are shown as red points in Fig. 7 . We keep the same style as the CO plot (Fig. 4 ), i.e. systems with gas detected are labelled with their names. If in addition, they have C ii detected, the label is blue (not black). NLTE detection thresholds are computed using the same code as described to compute CO population levels in NLTE (see Appendix B for details). We overplot the detection limits at 5σ in 1 h in both LTE (thin lines) and NLTE (thick lines) for Herschel /PACS (in red) where we assumed the typical sensitivity reached for non-detections (Riviere-Marichalar et al. 2014 ), for SPICA/SAFARI (in orange) and for a future far-IR mission (such as FIRS) with a 10 m aperture (in green) where the sensitivities are taken from the SPICA and FIR surveyor documentation. 4 The assumed sensitivities are summarized in Table 3 . We compute the electron density as a function of the C ii mass (using equation 14 ) with our model to compute the NLTE lines. Figure 7. C ii mass (in M ⊕ ) as a function of distance to Earth ( d ). Planetary systems with gas detections are labelled with their names. If C ii is detected, the label is in blue (black otherwise). The C ii mass lower limits worked out from observations are shown as green arrows, and for β Pic we show the mass derived from Herschel observations using Kral et al. ( 2016 ) as a green point. The red points are predictions from our model. The red downward arrows show systems that are in the blue hatched area on Fig. 1 , which cannot keep CO trapped on solid bodies. The purple points show predictions
Paper: 2021ApJ...907...40P (fulltext)
the same linear assumptions previously applied to yield kernel phases, analogous self-calibrating observables also exist for propagation through more complex optical systems such as coronagraphs. However, for the example configuration explored, the rapid onset of a nonlinear response indicates they may be of limited practical applicability to coronagraphic observations except in the extremely high-wave-front-quality regime. Subnanometer wave-front precision may nevertheless be achieved in future large space telescopes with high-order deformable mirrors. 2.5. Zernike Basis Taking the Jacobian of one large pixel grid by another is expensive in terms of memory; it is therefore desirable to bring down the dimensionality of either the input or target space. One way to do this is to use a Zernike basis to represent the wave front (Zernike 1934 ). One advantage of this is that we can examine only up to a certain order in Zernike polynomials if we believe that wave-front errors are negligible below a certain spatial scale. We can then differentiate just with respect to Zernike coefficients, which is much more computationally efficient and allows us to run simulations on grids that are many times larger than otherwise. Similar bases (e.g., the “hexike” basis) can be constructed for arbitrary noncircular telescope apertures. We have rerun the simple diffraction simulation from Section 2.1 with 200 Zernike modes on a 256 × 256 grid, and the coronagraph from Section 2.4 on a 128 × 128 grid using 300 Zernike modes. The calculations now take around 4 s, rather than many minutes, with peak memory use around 1.5 and 3 GB rather than 7 and 9 GB respectively. The Jacobian derived from this basis for simple diffraction is an excellent approximation to the full calculation on a grid. Given that in problems such as HST data analysis we believe that PSF variations are dominated by low-order modes (Lallo et al. 2006 ), this allows us to calculate a larger kernel space with a more accurate pupil model than has previously been possible. While it is a huge advantage in simulation speed and fidelity, this basis does not accurately represent the high-spatial-frequency sine waves corresponding to speckles far from the core of the PSF. Nevertheless, it does an adequate job representing the effects of low-order wave-front error, and this may be sufficient for many purposes, for example in optimization of optical design. 3. Discussion A phase transfer matrix constructed analytically with a discrete pupil model contains no a priori information about the pixel sampling, binning, or windowing. This means that the implicit convolutions in the plane, and therefore the associated correlation between adjacent baselines, are not taken into account. Therefore, the normally orthonormal kernel phases (calculated analytically for an infinite field of view and fine sampling) are no longer fully linearly independent. For example, Martinache et al. ( 2020 ) note that in the analysis of
Paper: 2018MNRAS.478..386P (fulltext)
are thought to be representative for larger areas of the comet, locally there may be large deviations from the global value as detected at CG’s final landing site. Figure 18. Diurnal temperature variation for example facet in TM's FOV at 3 au. Thermal inertia: Γ = 67 Jm −2 K −1 s −1/2 . Due to an instrument failure to penetrate the subsurface layers, the depth- and temperature-dependency of the local layering remain unknown. Therefore, in this first refinement study, facet thermal inertia is assumed to be constant. The results for simulation durations of 10 d (without optimized starting conditions) are compared to those of 50 d (with and without optimized starting conditions), one orbit and three orbits. It can clearly be seen that a few comet rotations of simulation time are not sufficient to obtain a converged solution. Increasing the simulation time improves the results, especially when optimized starting conditions are used. Most facets in the TM's FOV require simulation times above 50 up to 200 d, when using appropriate initial conditions. Above a couple of 100 d, the starting conditions begin to lose importance unless the initial guess is rather far off. The limiting factor for a final in-depth TM data analysis lies in the still prevailing uncertainties for the detailed local landing environment and the final lander orientation. The preliminary shape model used in this example does not reflect the correct illumination duration on the lander itself. The model facets in the TM’s FOV are cumulatively illuminated for about the measured amount of time if not the correct time spans, pointing to the wrong lander or individual facet orientation for the preliminary DTM used. 5 CONCLUSIONS Highly resolved celestial body shapes are computationally very demanding when performing a thorough thermophysical investigation. This is especially true for highly non-convex shapes by itself or in combination with disc-integrated data. Also highly resolved local landing sites on nearly convex shapes such as expected to be obtained for Ryugu will benefit from a model that can take into account a local DTM with large detail. A method is presented for efficiently calculating converged surface temperatures and temperature-dependent quantities. While the equations applied solve for subsurface temperatures as well, the method is only applicable to surface temperatures. The software itself can be used to calculate depth profiles as well, losing the method’s advantage, however, of a reduction in computation time. The only speed-enhancing measure in that case is the implemented parallelization. The method presented adds several pre-processing steps to the actual thermophysical modelling. Some calculation steps, however, are fast – the iterative evaluation of the upper boundary condition to obtain starting conditions takes seconds to minutes depending on the size of the shape model. The calculation of illumination conditions is not fast, but is already required as input