adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...829...56R (fulltext)
photons via the inverse Compton process makes it necessary to simultaneously compute the photon distribution . It becomes time dependent and is integrated according toSince all radiation processes are calculated in the isotropic approximation, i.e., from , and no external photon fields are considered, the gradient of the photon distribution is very small. Hence, in this case photon propagation can be neglected. The synchrotron power spectrumis computed in the Melrose approximation (Brown et al. 1983 ): Employing the same approximation, the synchrotron self-absorption coefficientis calculated (Ginzburg Syrovatskii 1965 ). As pointed out earlier, the inverse Compton process takes place in the Klein–Nishina regime. The resulting losses and gains are obtained fromThe catastrophic loss is parameterized by the escape timescale and depends on the radius of the cell. The total flux emerging from all spatial cells is calculated similar to the model of Blandford Konigl ( 1979 ). The integrated flux per cell can be calculated viaThe observed frequency depends on the Doppler factor and the redshift . The flux from one cell is proportional to the photon density and the surface of the cylindrical cell , where is the cell size along z . The speed of light c reflects the escape timescale given above. In the time domain, light-travel times have to be accounted for. The total flux from the source is then the sum over the fluxes F i from all zn cells at the time when they were causally connected to the reference point. This point is chosen to be the end point at , i.e., closest to the observer:The time delay t i can be calculated from the additional distance along the line of sight:where is the angle between the z -axis and the line of sight. For time-dependent simulations any time interval in the observer’s frame is boosted from the shock frame according to . 3. RESULTS In this section we will first present a classical SSC fit to the high-energy data of the observational campaign by Abdo et al. ( 2011a ). Based on the resulting fit, we argue that the artificial spatial boundary condition should and can be replaced by a specific morphology of the radial confinement behind the shock. 3.1. High-energy SSC Fit Similar to our previous work RS13 , the starting point is a classical SSC fit, meaning that the size of the emission region is a free parameter, shown as sim1 in Figure 2 . In addition to the parameters in Table 2 , the parameters r = 2.5, , an injection energy , and an isotropization timescale were used. These values were used for all simulations, except log2c. The fit was done “by eye,” as is common for complex radiation codes. Figure 2. Overall SEDs for various simulations and a detailed view of the radio band. sim1 was obtained with a variable box size z max , altering the resulting SED. Simulations log[1–3] were obtained with a fixed but large z max and show the effect of an increase in expansion speed. The peak in the optical is due to the host
Paper: 2016MNRAS.460.3163M (fulltext)
range 4.7 M ⊙ –7.8 M ⊙ . In Fig. 1 (a)– (c), variations of TCAF model fitted shock location (in r s ), normalization and mass values with time (day in MJD) are shown, when all model parameters were assumed to be free. Fig. 1 (d) shows the variation of model fitted masses when the normalization was frozen at an average value of 139.07. Figure 1. Variations of TCAF model fitted parameters: (a) shock location (in r s ), (b) normalization ( N ) and (c) mass of the black hole ( M BH in M ⊙ ) over the entire period of the 2010 outburst of MAXI J1659−152, when all model parameters are kept as free. In (d), TCAF fitted M BH is shown when the model normalization was kept frozen at 139.07, which we obtained from taking an average of the normalization values of panel (a). Table 1. TCAF model fitted mass and normalization values. Here, X=95358-01, Y=95108-01, and Z=95118-01 are the initial parts of the observation IDs. In columns 4 and 5, the TCAF fitted model normalization and BH mass values are shown when all TCAF-related parameters are free while fitting the spectra and in column 6, model fitted mass values are shown when normalization is frozen at 139.07. 1σ errors for N and masses are shown in superscripts. We can also cross-check the range of the predicted mass by the above method when we vary M BH versus the reduced χ 2 of the best-fitted spectra. The best fit using the current version (v0.3) of the TCAF model was obtained based on the model fitted reduced χ 2 values when found nearly equal to unity. In Paper I and in Table 1 , model parameters are given when best fits are obtained. After achieving satisfactory model fits, we kept all model parameters, such as sub-Keplerian rate , Keplerian rate , location of the shock X s , compression ratio R and normalization N , frozen, except the mass of the black hole, and found how the reduced χ 2 changed with the mass of the black hole. In Fig. 3 (a)–(c), the variation of reduced χ 2 with mass of the BH is shown for three different spectra (obs. IDs: 95118-01-06-01, 95118-01-16-01, 95118-01-21-00) selected from three different spectral states (SIMS, HIMS and HS, respectively). As in the earlier cases, if we consider to be the upper limit for acceptable fits, we obtain a mass in the range 4.4 M ⊙ –7.3 M ⊙ . 3.2 QPO frequency(ν<sub>QPO</sub>) – time (day) evolution: fitted with POS model Low-frequency QPOs are very commonly seen in PDSs of X-ray intensity variations (light curves) emitted from discs surrounding stellar massive black holes. Sometimes the dominating frequencies of these QPOs (generally type ‘C’) are found to evolve with time, mostly in HS and HIMS from both rising and declining phases of an outburst. The monotonic evolution of these QPO frequencies could be fitted very well by a propagating oscillatory shock (POS) model (see Chakrabarti et al. 2005 , 2008 ; Chakrabarti, Dutta Pal 2009 ; Debnath et al. 2010 , 2013 ; Nandi et al. 2012 ). According to the POS model, the frequency is inversely
Paper: 2016MNRAS.459.1646C (fulltext)
are compatible with there being no correlation of κ 500 with any of the parameters considered. Similarly, for each relationship, Spearman rank correlation tests do not allow us to reject the null hypothesis (of there being no correlation) to a likelihood of 0.01. We note, however, that the size of our sample is small compared to the scale of the uncertainties on κ 500 ; hence we in no way rule out that such correlations could exist, but not be detectable in our data. 7 COMPARISON TO PREVIOUS ESTIMATES As can be seen in Fig. 1 , our determined value of κ d is low compared to existing literature values (although the very commonly used values of Draine 2003 and Draine Li 2007 are within our bootstrapped uncertainty). Converting the James et al. value to a wavelength of 500 μm for comparison with our own, as per equation ( 1 ), translates it to κ 500 = 0.20 ± 0.06 m 2 kg −1 ; a factor of 3.92 larger than our value, despite the fact we employ the same fundamental technique; it is important to address the reasons for this. As discussed in Section 2 , our consideration of systematic effects, using ξ (i.e. the contribution of helium and metals to a galaxy's gas mass) and δ O (i.e. oxygen depletion in H ii regions) will account for a factor of 1.86 reduction in κ d relative to James et al. We also employ a slightly larger value of ε d (0.5 versus 0.456), leading to a further factor of 1.10 reduction in κ d . However, our use of a more modern, smaller value of (0.0134 versus 0.019) works in the opposite direction, diminishing the reduction in κ d by a factor of 0.71. In combination, these global effects correspond to a factor of 1.42 reduction in κ d relative to James et al.; additional effects must be at work to explain the full difference between our values. Fortunately, M 61 (NGC 4303) is present in both the James et al. sample and our own, allowing for a direct comparison of the reasons for the difference in values for a specific case. For M 61, we find that our result of κ 500 = 0.039 m 2 kg −1 (see Table 1 ) is a factor of 5.13 less than the κ 500 = 0.20 m 2 kg −1 value that James et al. found for their sample as a whole. James et al. did not work out values of κ d for individual sources, but do say that there is scatter of a factor of 2 around their sample-wide relation, hence we need to account for a factor of difference between our κ d for M 61, and the James et al. sample-wide value. In Appendix A , we work through each of the sources of difference in our values of κ d in the case of M 61, and find that a factor of 6.31 is to be expected, well within the predicted range – highlighting the dramatic influence of considering systematic effects and utilizing superior observations. During the Herschel science demonstration phase, Eales et al. ( 2010 ) applied a resolved variant of the James et al. method (where they pegged the dust mass surface density to the gas mass surface density, assuming a fixed Schmidt–Kennicutt law and a Milky Way
Paper: 2020A&A...635A.164S (fulltext)
corresponding to the position of the hydrogen-burning shell that is slowly moving outwards in mass as the helium core continues to grow. The surface chemical composition is the same as obtained at 7 R ⊙ since, in the absence of any additional mixing process, there is no change in the surface composition after the RGB-bump (see Table C.2 ). Age differences remain at the same level as they were at 7 R ⊙ (cf., Fig. 2 ) and are sustained in the subsequent evolution towards the RGB tip. 5.2. Evolution of 1.5 M<sub>⊙</sub>, 2.0 M<sub>⊙</sub>, and 2.5 M<sub>⊙</sub> models Using the same initial composition as in the 1 M ⊙ case, we calculated and compared tracks and stellar structures at higher masses: 1.5 M ⊙ , 2.0 M ⊙ , and 2.5 M ⊙ . The first and most evident difference with respect to the lower-mass case is the existence of a convective core during the main-sequence evolution, whose extent as a function of age is depicted in Fig. 6 . Although all codes rely on the Schwarzschild criterion for defining the core convective boundary there are differences in the predicted core size of the order of 10% across codes. Fig. 6. Evolution of the convective-core size during the main-sequence phase as a function of age for the 1.5 M ⊙ ( left ), 2.0 M ⊙ ( centre ), and 2.5 M ⊙ ( right ) solar-radius calibrated models. In many cases a saw-tooth profile is visible at the edge of the core as a consequence of changes in its chemical composition throughout the evolution. Nuclear burning produces changes across the chemically homogeneous central region of the model. In the case of growing convective cores (1.5 M ⊙ ), the sharp increase in density at the edge of the core increases the opacity, which consequently increases the radiative temperature gradient in that position. As evolution proceeds and the discontinuity becomes larger, the radiative gradient outside the core surpasses the value of the adiabatic gradient turning that additional layer convectively unstable. The core suddenly increases in size as is revealed by the sharp spikes in the core profiles (see left panel in Fig. 6 ), and the subsequent fast decrease in the core extent occurs once the composition of the layer is homogenised to the core value. The radiative temperature gradient then decreases as a result of the opacity decrease and the layer becomes convectively stable again (see e.g. Silva Aguirre et al. 2011 ; Gabriel et al. 2014 ). The existence of these “peaks” in the core size as a function of time are the result of the numerical implementation of the Schwarzschild criterion in evolutionary codes, and the behaviour of the core boundary is expected to be smooth as a function of time in real stars. For the higher-mass cases (2 M ⊙ and 2.5 M ⊙ ) the evolution of the convective-core size is indeed smoother as it reaches it maximum extension at the beginning of the main sequence and contracts throughout the core hydrogen-burning phase. One of the expected consequences of the different
Paper: 2016ApJ...818..107G (fulltext)
show that the dependence of the mass loss rates on the EUV SED is mild. Thus, using an arbitrary EUV SED profile provides a reasonable estimation for the mass loss rate. Figure 13. The variations of the total mass loss rate with the variation of spectral index F 50–400 / F 50–900 . The mass loss rates are determined by the amount of effective absorption of stellar EUV radiation. Thus, the heating is related to the product of number densities of species and the absorption cross-sections ( H heat ∼ nσ ν ). High-energy photons can penetrate deeper into the atmosphere due to the smaller cross-sections. In the dense regions of the atmosphere, more species can absorb the stellar irradiation. On the other hand, the regions where low-energy photons are absorbed are at a higher altitude than those where high-energy photons are absorbed. Due to the decrease in the number density of species with increasing altitude, the number of particles absorbing the stellar irradiation also decreases. However, the effective heating by high- and low-energy photons is comparable because the heating of the atmosphere is proportional to the number density of particles and the cross-section of photoionization. For atomic hydrogen, the cross-section of photoionization is inversely proportional to the cube of the frequency (Spitzer 1978 ). This also explains why the total mass loss rates predicted for different EUV spectra are comparable. 4. SENSITIVITY OF ATMOSPHERIC PROPERTIES TO EUV SEDS: DISCUSSION 4.1. The Effect of Hydrodynamic Escape Parameters on the Temperature In Table 2 , we have shown that the hydrodynamic escape parameter (where R p and M p are the radius and mass of the planet, and μ and T 0 are the mean mass per particle and temperature at the lower boundary of the atmosphere) is smaller for Kepler-11b than for other planets. The hydrodynamic escape parameter is defined as the ratio of the potential energy to the kinetic energy of the gas. The gas can be bound to the planet if the temperature of the gas is very low or the radius of the planet is small (the mass of the planet is assumed to be constant). In such a case, the value of λ is large. For higher temperatures and/or larger R p values, however, the kinetic energy of the gas can be greater than the potential energy. Thus, the atmosphere can escape the bounds of the planet. The latter case corresponds to a smaller λ . In an atmosphere heated by XUV radiation of the host star, the temperature of the gas can reach a few thousand kelvin at the bottom of the atmosphere (Yelle 2004 ; Murray-Clay et al. 2009 ; Guo 2011 , 2013 ). Thus, the value of λ can decrease with increasing altitude as a result of high temperatures and a large radius. This means that the gas can escape the planet at some altitudes. The critical value of λ to produce a hydrodynamic wind is about 10–15 (Stone Proga 2009 ). Thus, a thermally driven hydrodynamic wind can be produced easily in the atmosphere with a smaller λ because weak XUV
Paper: 2020ApJ...890...98H (fulltext)
observers (black dot, Vaquero et al. 2016 ) and additional sunspot observations by Fisher (gray triangle, Denig McVaugh 2017 ) and Hallaschka (green triangle, Carrasco et al. 2018 ). In Figure 5 , we have incorporated the sunspot group number in Derfflinger’s summary manuscript and logbooks as they are and contextualized them upon the existing sunspot group number by contemporary observers. The summary manuscript and logbooks occasionally provide observations with different group number and observations on different days. As observed in Figure 6 , based on our revised sunspot group number series, we have revised Derfflinger’s yearly sunspot group number during 1802–1824 in comparison with the number before our revision in the existing data sets (Hoyt Schatten 1998a , 1998b ; Vaquero et al. 2016 ). Our revision shows a significantly different trend of yearly Derfflinger’s sunspot group number (Figure 6 ) in comparison with that before our revision (Figure 7 ). We observed that Derfflinger’s trend is much more consistent with the sunspot number (v. 2; Clette et al. 2014 ; Clette Lefevre 2016 ) than the group sunspot number series (Svalgaard Schatten 2016 ; Usoskin et al. 2016 ) in Cycle 5. However, Derfflinger’s data are more consistent with the group sunspot number series than the sunspot number (v. 2; Clette et al. 2014 ; Clette Lefevre 2016 ) in Cycle 6. The cycle amplitude seems slightly larger in Cycle 5 than in Cycle 6. Figure 6. Derfflinger’s revised yearly sunspot group number in the summary manuscript (red diamond) and logbook (blue square) with standard errors from averaging, in comparison with the yearly number before our revision (black circle, Hoyt Schatten 1998a , 1998b ; Vaquero et al. 2016 ). Figure 7. Comparison of the yearly international sunspot number (Clette Lefevre 2016 ) divided by 20 (Muñoz-Jaramillo Vaquero 2019 ) and existing yearly sunspot group number series with backbone method (green curve, Svalgaard Schatten 2016 ; purple curve, Chatzistergos et al. 2017 ) and active day fraction method (blue curve, Usoskin et al. 2016 ). 6. Measurements of the Sunspot Positions It is difficult to determine the heliographic coordinates of the sunspots from Derfflinger’s drawings as he had not recorded an explicit time for each drawing. There are horizontal and vertical lines in the drawings, which are supposed to be parallel to the horizon and pointing to the zenith, respectively, as the manuscripts mention that the observations are made with an azimuthal quadrant. The manuscript also states that the images are upside down, i.e., the lower end of the vertical line points to the zenith. An indication of the observing time comes from the logbooks in which the sunspot drawings are inserted above, below, or within the solar elevation timings. Hence, we are using two ways of fixing the position angle of the solar disk to obtain the heliographic coordinates. The first method uses the nearest time of the solar elevation measurements to
Paper: 2018ApJ...865...44A (fulltext)
(bottom) lines represent the synthetic spectra for assumed 12 C/ 13 C ratios. The red line is the best matching synthesis to the observed spectrum. Assumed 12 C/ 13 C ratios are also given in the panels. 4. Abundances from IGRINS Near-infrared Spectra High-resolution, high-S/N H - and K -band spectra of RHB stars reveal a rich assortment of atomic and molecular features (Figure 3 ). Having this opportunity, we were able to compute the abundances of 21 species of 20 elements along with 12 C/ 13 C ratios. For our three RHB stars, abundances of P, S, and K were obtained for the first time. All abundances and 12 C/ 13 C ratios were derived by applying the SYN method to the observed spectra. The atomic and molecular data used for abundance calculations and the analyses of all species are discussed in the following subsections. In all cases the individual line abundances are given in Table 3 and the abundance summaries for the stars are given in Tables 4 – 6 . 4.1. Atomic and Molecular Data We adopted the molecular data described in Afşar16 (and references therein) for CN, CO, and OH features and generated the atomic line list (Table 3 ) following a similar procedure to that of Afşar16 . We used the Arcturus infrared atlas (Hinkle et al. 1995 ) 10 10 Available at ftp://ftp.noao.edu/catalogs/arcturusatlas/ . along with atomic line lists from the National Institute of Standards and Technology (NIST) Atomic Spectra Database 11 11 https://physics.nist.gov/PhysRefData/ASD/lines_form.html and from the Kurucz ( 2011 ) database 12 12 http://kurucz.harvard.edu/linelists.html to identify promising atomic lines in our RHB stars. Accurate transition probability information for the atomic and molecular data is essential for a robust abundance analysis. For lines that have NIST transition probabilities rated A and B accuracy (uncertainty ≤10%), we adopted the NIST log gf values. However, laboratory transition probabilities for most of the species transitions in the IR are lacking. Therefore, we had to apply reverse solar analyses to determine the “astrophysical” log gf values of mostly neutral species of elements listed in Table 3 . Only a few ionized species could be identified (see Section 3.2 ), and among them we only applied reverse solar analysis to Yb ii . The transition probabilities ranked as C (accuracies 20%) in the NIST database were needed to be justified in most of the cases with their own reverse solar analyses. The sources of all transition probabilities are provided in Table 3 . For the reverse solar analyses we used the high-resolution infrared solar flux spectrum of Wallace Livingston ( 2003 ) and adopted the solar model atmosphere of ASF12 . The derived transition probabilities then were tested on the Arcturus atlas spectra (Hinkle et al. 1995 ), adopting the Arcturus abundances and model atmospheric parameters from Ramírez Allende Prieto ( 2011 ). Satisfactory agreement with the abundances derived by Ramírez Allende Prieto was achieved for
Paper: 2017ApJ...834...59G (fulltext)
of these frequencies as the result of nonlinear mode coupling. Note that we focus on the significant frequencies (S/N > 4) that appear both in the spectra of single-quarter data and all-quarter data. Other tidally induced pulsations with low amplitudes could also exist. For example, the peaks at 0.7095 day −1 and 4.3990 day −1 are 5 and 31 times the orbital frequency, respectively. Figure 5. Fourier spectrum of light curve residuals with eclipses masked. The upper panel was calculated from the quarter 1 long cadence data. The lower panel presents a similar plot but using all quarters (0–17) of long cadence data. The 10 dominant frequencies listed in Table 2 are labeled. Filled and open circles mark the harmonic and nonharmonic orbital frequencies, respectively. The spectral window is shown in the inset. Table 3 Main Oscillation Frequencies of KIC 3230227 Many frequency triplets can be seen in the spectrum, with equal spacing of orbital frequency. Close examination reveals that all these triplets have frequencies that are equal to times orbital frequency. Thus they can be explained as a combination of one real oscillation peak and two side-lobes due to the spectral window (Figure 5 ). The nonharmonic peaks generate side-lobes at and . We find that after pre-whitening , and f 4 , these triplets essentially disappear, and only peaks remain, supporting the above argument. Low amplitude m = 0 modes can also exist. At high inclination, these modes are expected to have low amplitudes. As discussed below, the Ledoux constant C nl (Ledoux 1951 ) is about 0.16 for the g -modes in the observed frequency range. This means the splitting is about for modes with frequencies much higher than f rot . If we adopt pseudo-synchronous rotation day −1 , are then 0.49 and 0.98 day −1 for m = 1 and m = 2, respectively. Thus the splittings will be located several orbital harmonics away from their central m = 0 peak. This also suggests that the splittings are not due to rotation. The amplitude variations of these oscillations are shown in Figure 6 and listed in Table 4 . Most of the frequencies have relatively stable amplitudes over 16 quarters, with variations less than 0.05 milli-mag. The exception seems to be f 3 , which decreased from 0.174 milli-mag in quarter 1 to 0.078 milli-mag in quarter 16. Figure 6. Amplitude variations of 10 dominant oscillation frequencies (Tables 3 and 4 ). Filled and open circles indicate the harmonic and nonharmonic orbital frequencies, respectively. Table 4 Amplitude Variations of the Main Frequencies ( mag) 3.2. Mode Identification from Phases For tidally induced oscillations in KIC 3230227, it is sufficient to consider only the dominant l = 2 term since higher order terms are at most times (an order of magnitude) smaller than the l = 2 term (Equation (8) in Burkart et al. 2012 ). Additionally, the observed amplitudes of higher order modes () are reduced by geometrical cancelation upon integration over the stellar surface, so