adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...627A..62G (fulltext)
the far off-limb region but give a hint of slight increase in the velocity up to 1.3 R ⊙ . Fig. 12. Effective velocity (combined thermal and non-thermal velocity) obtained along the coronal loop from various spectral lines as labelled. In Fig. 13 , we plot the non-thermal velocities ( ξ ) obtained from Fe X 184.54, Fe XI 188.23, Fe XII 193.51, and Si X 258.37 Å spectral lines. For this purpose, we incorporated electron temperature T e obtained in Sect. 3.3 to calculate the thermal velocity. Again Fe XII 193.51 Å, the strongest line, shows almost constant non-thermal velocity with height. Other lines show some increasing trend in the non-thermal velocity with height, however, the data points are highly scattered. The average non-thermal velocity, as obtained from Fe XII 193.51 Å is ≈23 km s −1 . Si X 258.37 Å shows an average value of ≈30 km s −1 with some increase with height. However, non-thermal velocities from Fe X 184.54, and Fe XI 188.23 Å show relatively lower values. Interestingly, Del Zanna et al. (2018b) have noted that the instrumental width for Fe XII 193.5 Å line appears to be constantly larger than that of the other lines. They also noticed the overestimation in line widths from Fe XII 193.51 Å line, which is partly due to instrumental issues and partly due to opacity effects in the stronger lines of Fe XII based on line ratio of Fe XII λ 193.51/ λ 195.12. However, in our coronal loop, deviation of line ratio is within 10% (see Fig. 6 ). Thus, non-thermal velocity estimation from Fe XII 193.51 Å is mildly affected. Fig. 13. Non-thermal velocity obtained along the coronal loop from various spectral lines as labelled. 3.6. Alfvén wave energy flux The Alfvén wave energy flux density is given by where ρ is mass density ( ρ = m p N e , m p is proton mass, and N e is electron number density), ξ is Alfvén wave velocity amplitude, 2 for two degrees of freedom, and V A is Alfvén wave propagation velocity given as . Therefore, the total Alfvén wave energy crossing a surface area A per unit time is given by Henceforth, the total Alfvén wave energy propagating along the loop depends on the electron number density, wave amplitude, magnetic field, and area of cross section. However, the product of magnetic field B and cross-section area A is always a constant along the loop owing to magnetic flux conservation. Therefore, the total Alfvén wave energy propagating along the loop is always proportional to . The product of is expected to be constant with height if the total Alfvén wave energy is conserved as wave propagates along the loop. Thus, we obtain the proportional Alfvén wave energy () along the loop using the N e (see Sect. 3.2 ) and ξ (see Sect. 3.5 ) obtained from Fe XII spectral lines. In Fig. 14 , we plot variations of with height. The plot shows that decreases with height. Thus this provides evidence of damping of Alfvén wave energy with height along the coronal loop, although associated error bars are very large on individual data
Paper: 2020ApJ...899...60Y (fulltext)
constrained by the GW signal. We can then obtain that is . Consequently, the absolute charge Q NS is e.s.u. The dimensionless charge of an NS can be estimated as (Zhang 2019a )In order to satisfy the observational constraint, one requires . This implies that the neutron star has to be a millisecond magnetar before the merger. The condition to form such a magnetar in BNS mergers is contrived, so this scenario is disfavored. Similarly, for a charged BH–BH merger system we adopt the following two most straightforward assumptions: (1) the lighter BH has a mass of 2.8 M ⊙ , which is less than 3 M ⊙ and falls into the BH mass regime; (2) only the lighter BH carries a constant dimensionless charge (for the same absolute charge Q , a lighter BH carries a higher which is more relevant). The mass ratio q of this system should be different from the range constrained above assuming an NS–BH merger, but this ratio does not enter the problem in view of assumption (2) above. We constrain the black hole charge as and the corresponding absolute charge e.s.u. The demanded dimensionless charge is comparable to the one required to explain the putative γ -ray event (Connaughton et al. 2016 ) associated with the first BH–BH merger event (Zhang 2016 ). Contrived conditions are again needed for a BH to carry such a large charge. 4.2.2. cCBC with an Increasing Charge This scenario involves a plunging BH–NS system in which the BH is immersed in the magnetic field of the NS and gains charge via the Wald mechanism (Wald 1974 ) in an initial electro-vacuum approximation. Levin et al. ( 2018 ) suggested that the BH can be charged stably to carry the Wald’s charge quantity Q W until it could transit from the electro-vacuum state to the force-free state thanks to abundant pair production induced by the strong electric field. At this point, the BH may reach the maximal Wald charge. In this scenario, there are four possible pre-merger mechanisms (first and second magnetic dipole radiation, electric dipole radiation, and magnetic reconnection close to BH’s equatorial plane; Dai 2019 ) and two possible post-merger mechanisms (magnetic reconnection at polar regions and the BZ mechanism; Zhong et al. 2019 ) to generate γ -ray emission. Following Dai ( 2019 ) and Zhong et al. ( 2019 ), we calculate (Figure 12 ) that the subthreshold GRB could be produced by the pre-merger magnetic reconnection or the post-merger BZ mechanism if the NS surface magnetic field satisfies or , respectively, given the following conditions: the radiative efficiency η γ = 1, the mass ratio is q = 5, the minimal separation between the BH and the NS is a min = 2 GM BH / c 2 + r NS , the NS mass is M NS = 1.4 M ⊙ and its radius is r NS = 12 km. The following two points are worth mentioning in our calculation: (1) We consider that the pre-merger magnetic reconnection in Equation (19) of Dai ( 2019 ) should be the BH’s magnetic field produced by the Wald charge Q W rather than that of the NS. This is because the
Paper: 2017A&A...604A..23N (fulltext)
lines available in the MUSE spectral range can be seen, and our upper limit on the C iv /Ly flux ratio seems compelling enough to exclude photoionization by the QSO as the main driver of the LAB Ly luminosity. The upper limit on the He ii /Ly ratio is just below the border mentioned, so that based on this criterion alone, our object would be on the verge of being compatible with photoionization by the central source. Interestingly, only one among the B16 radio quiet targets lies in the photoionization region in Fig. 13 , but since its position corresponds to upper limits only, it remains impossible to tell how far its luminosity can really be explained by photoionization. All other objects have a too low line ratio along either one or both axes. Table 2 Upper limits to the SB of lines due to highly ionized species. Fig. 13 Ratios of high-ionization line fluxes for our object (red) and for those of B16 (Table 2) surrounding radio-quiet QSOs (blue). The He ii line is detected in only one object of the latter sample, while the C iv line is detected in three objects. The green hatched area represents the range of line ratios compatible with photoionization models ( Arrigoni Battaia et al. 2015 ). Fig. 14 Relation size–luminosity for our object (red) and for those of B16 that are radio quiet (blue). The magenta full dots represent the 14 giant LABs discovered by Matsuda et al. (2011) . 5.3.1. Two other probable Lyman alpha emitters at the same redshift We have detected two other possible Ly emission features in the neighborhood of the QSO; their coordinates are given in Table 3 . The brighter feature (LAE1) lies away from the QSO, which corresponds to a projected distance of about pkpc, and it emits within the range Å to Å. The vicinity to the QSO and the emission at wavelengths identical to the central LAB suggest that this object is emitting in the Ly line. Figure 15 (left panel) shows the extension of this emitter as found with CubEx, which is about or pkpc in diameter; if the eastern extension is real and physically belongs to the LAE, then the major axis of the LAE would reach or pkpc. This appears very large, but is consistent with the finding of Wisotzki et al. (2016) , who found many examples of LAEs with Ly emission extending to radius at similar redshift. The emission feature of this nebula consists of two peaks, as shown in the right panel of Fig. 15 . This is very typical of some Ly emitters (LAEs). LAE 221745.3+002006, for instance, shows a very similar profile ( Yamada et al. 2012 , Fig. 2). The latter authors concluded from their observation of LAEs that more than % of them show a double peak, and that in most cases the red component is stronger than the blue one, which is naturally explained by transfer effects in expanding media, even inhomogeneous ones ( Verhamme et al. 2006 , 2008 , 2012 ; Gronke et al. 2016 , 2017 ). Fig. 15 Left : CubEx optimally extracted map of the probable Ly emitter LAE1 located away from the QSO. The key
Paper: 2019MNRAS.488.5300C (fulltext)
there is a normally distributed scatter around the mean gas metallicity given by the MZR, with the dispersion σ 0 = 0.1 dex (the intrinsic scatter in the MZR). We allow σ 0 to increase linearly with decreasing mass at M * 10 9.5 M ⊙ . 2.3.3 Distribution of metallicity within galaxies The average metallicity of a galaxy roughly corresponds to the metal content that would be measured in this galaxy at the distance of one effective radius from its centre (Kewley Ellison 2008 ). Due to metallicity gradients present within galaxies there is a certain range of metallicities with which stars can form inside their host. The azimuthal metallicity variations are typically negligible (e.g. Sánchez-Menguiano et al. 2017 ). However, typical radial abundance gradients in the local disc galaxies are around −0.1 dex/ r e (∼−0.03 dex kpc− 1 ) (e.g. Sánchez et al. 2014 ; Sánchez-Menguiano et al. 2016 ). The detailed studies of the distribution of metallicities of H ii regions within z ∼ 0 disc galaxies carried out by Sánchez et al. ( 2014 ) and Sánchez-Menguiano et al. ( 2016 ) with the CALIFA survey show that the distribution of metallicities at which the star formation proceeds is roughly symmetric with respect to the average value found at . However, the exact range of metallicities of the H ii regions and the metallicity gradient depend on the assumed metallicity calibration (see e.g. table 1 of Sánchez-Menguiano et al. 2016 ). 2 Our choice : We assume that the distribution of metallicities at which the star formation proceeds within galaxies can be represented by a normal distribution with dispersion = 0.14 dex. This particular choice of corresponds to half of the average range of metallicities of H ii regions 2 found by Sánchez et al. ( 2014 ) and Sánchez-Menguiano et al. ( 2016 ), averaged between the two studies. See Appendix E for additional discussion. 2.4 Star formation–mass relation The stellar mass and star formation rate of star-forming galaxies are strongly correlated, giving rise to the star formation–mass relation (SFMR; e.g. Brinchmann et al. 2004 ; Salim et al. 2007 ; Speagle et al. 2014 ; Tomczak et al. 2016 ; Boogaard et al. 2018 ) also called the star-forming main sequence. SFRs are measured based on the luminosities of galaxies observed in certain bands that correlate with the recent (≲100 Myr) star formation activity. Those luminosities need to be corrected for dust attenuation, which is one of the main sources of uncertainty in SFR estimation. Furthermore, conversion from luminosity to SFR is sensitive to the assumed IMF and metallicity. The most common tracers of SFR are UV luminosity (usually at ∼1500–2800 Å), certain recombination lines e.g. Hα and IR continuum emission between ∼3 and 1100 m. Different indicators are sensitive to the star formation on different time-scales: H α line typically probes short ∼10 Myr time-scales while UV and IR provide information on SFR averaged over longer ∼100 Myr time-scales (see e.g. Kennicutt Evans
Paper: 2021ApJ...908..203F (fulltext)
ψ at 89°, as a function of the resonance order N for both inside (a) and outside (b) the plasmapause. Here the results are presented for the broadband MS wave model at the wave lower ( f lc ), peak ( f m ), and upper ( f uc ) frequencies, respectively. Clearly, to evaluate the resonant scattering effect for the ring current protons with energies ∼1 keV–1 MeV, the high order cyclotron resonances must be considered. As shown in Figures 1 (a) and (b), due to the variation of the k ∥ which is modified by the background plasma density, the MS waves with the given frequency distributions can cyclotron resonate with ring current protons at orders N = 3–11 inside the plasmapause, while at orders N = 4–9 outside the plasmapause. The determined resonance orders can be applied to QLT method for calculating the diffusion coefficients. Figure 1. Resonance energy of ring current protons for interactions with MS waves with different resonance orders at L = 4.5: (a) inside the plasmapause; (b) outside the plasmapause. Here the blue, green, and red lines denote the upper cutoff frequency, the peak frequency, and the lower cutoff frequency, respectively. 3.2. Scattering of Ring Current Protons In this part, we examine the MS wave-induced scattering effect of ring current protons in the form of the bounce-averaged diffusion coefficients, and compare the results obtained from both TP simulations and QLT calculations (e.g., Ni et al. 2008 ) with the given MS wave model. Figure 2 shows the 2D plots of bounce-averaged diffusion coefficients , , and as a function of equatorial pitch angle α eq and proton kinetic energy E k at L = 4.5, both inside ((a)–(c)) and outside ((d)–(f)) the plasmapause. The diffusion coefficients obtained from TP simulations, QLT calculations, and the corresponding normalized differences are presented in each column (from left to right, respectively). Here the cyclotron resonance orders discussed above are adopted for the QLT calculations. First, MS waves ∼218 pT can remarkably affect the ring current protons with the equatorial pitch angles ∼50°–90°. Specifically, the energy diffusion () dominates the wave-driven interactions with the rates at the order of ∼10 −4 s −1 for both inside and outside the plasmapause (Figures 2 (b1) and (b2), (e1), and (e2)). The pitch angle scattering () and the mixed diffusion ( ) effects are weaker than the energy diffusion effect, with the rates ∼10 −6 s −1 and 10 −5 s −1 , respectively. The results imply that the MS waves can effectively accelerate and heat the ring current protons, rather than pitch angle scattering. Clearly, the accelerating becomes more important for ring current protons at high pitch angles. Second, the maximum diffusion rates are located at several keV for ring current protons inside the plasmapause, while at several tens of keV outside the plasmapause. These differences in the diffusion rates are mainly caused by the change in the MS wave phase velocities, which are modified by the
Paper: 2021ApJ...915...66T (fulltext)
the width of the frequency band, andIn our case, and , so ν 0 = 80 MHz and δ ν = 40 MHz. To seed the beam variations in our training set, we draw a 0 , a 1 , and a 2 from independent normal distributions,with the means and standard deviations μ k and σ k given in Table 3 . An extra constraint is applied to exclude FWHM( ν ) curves which dip below 15° in the 40–120 MHz band. The resulting training set of FWHM curves is shown in Figure 3 . We simulate the beam-weighted foreground temperature for ten different bins in LST by smearing the map through LST before computing the spectra. 20 20 By smearing, we refer to averaging many rotations of the foreground map corresponding to LSTs between the edges of each LST bin. The spectra from the 10 LST bins are then concatenated into the final data vector. It is important to note here that the Legendre-polynomial based toy model used to generate the beam training set in this paper is a placeholder for performing detailed electromagnetic simulations specific to the antenna used by the experiment being analyzed. Such simulations can output both antenna power patterns and S -parameters while accounting for effects such as ground plane interactions, scattering/reflections from nearby objects, and local soil properties. Since the gain, G ν , represents the instrument’s transducer gain, which includes the antenna reflection coefficient (Nhan et al. 2019 ), these simulations will also inform the model of the gain. In addition, if these simulations find that a given antenna design is sufficiently inefficient, the noise level from Equation ( 10 ) could be modified to account for the fact that the system temperature of an instrument with that antenna would not be dominated by beam-weighted foreground sources. 3. Signal training set : is formed from a training set where each curve is created by applying the turning point nonlinear signal model to a sample from the prior distribution of its parameters (see Section 4.2 , Figure 2 , and Table 2 ). Figure 3. Ten percent of the training set of 500 beam FWHM. The curves are quadratic Legendre polynomials with coefficient distributions given in Table 3 . Table 3 Parameters of Legendre Coefficient Distributions. Note: See Equations ( 14 )–( 16 ). 5. Results This section lays out the results of applying the general AMLP method described in Section 2 to the procedure put forth in Section 3 for fitting sky-averaged radio data measured with a nonideal receiver using the models laid out in Section 4 . The initial fit using the linear signal model (following Section 3.2.2 ) is shown in Section 5.1 and the final fit using the nonlinear signal model (following Section 3.2.4 ) is shown in Section 5.2 . 5.1. Fit with Linear Signal Model The signal uncertainties implied by the first MCMC fit with the linear signal model are shown in Figure 4 . The main mode of uncertainty is line-like, with a width of approximately 50 mK. This width comes from the similarity, or overlap, between the
Paper: 2016ApJ...823L..39G (fulltext)
that the angle between and x is . Second, we restrict to layers with the minimum variance component . This configuration restricts the analysis of the current to the cases reported in the cartoon of Figure 2 (b). These constraints ensure that the above surrogate computed from the Cluster 4 data is very close to the actual current density. The current layers detected by the PVI method under the above constraints seem to have a self-consistent shape at time lags that range from inertial to sub-proton scales, resembling equilibrium solutions of both fluid-like and kinetic plasmas. The simplest analytical model that could represent thin current layers on kinetic scales is the well-known Harris sheet (Harris 1961 ), which is widely used to describe a plasma sheath confined between two regions of oppositely directed magnetic field. It is a kinetic equilibrium and the earliest exact analytical solution of the Vlasov equation. Under some assumptions, the magnetic field follows a 1D hyperbolic-tangent profile. The number of structures selected by PVI ranges from thousands () to hundreds . Applying the geometrical restrictions of the minimum variance analysis described above, we found that the Harris profiles well describe 5% and 10% of the discontinuities. Figure 3 shows two examples of such as 1D current sheets in their local minimum variance reference frame. The time duration is (a) and (b), which correspond to 60 km and 420 km along x , respectively, assuming the validity of the Taylor frozen-in hypothesis. The maximum variance component B l performs a smooth, large-amplitude rotation, while . Note that B m , albeit very small, displays a multi-polar signature, typical of solar wind reconnection exhausts (Eriksson et al. 2015 ). B l , B m , and B n refer to the fluctuating part of the magnetic field after that the mean value, computed on the time duration of the discontinuity, is subtracted. For the above structures, the direction of the removed average field is almost aligned to and perpendicular to . Moreover, the full magnetic field component along is constant and equal to the removed average field over the interval so that the geometry corresponds to that of a common Harris sheet + guide field, as reported pictorially in Figure 2 (b). Figure 3. Fluctuations of the magnetic field (after subtracting the mean value) in the local minimum variance frame, in a discontinuity at electron (a) and sub-proton (b) scales. B l is reported with a solid (blue), B n with a dotted–dashed (green), and B m with a dashed (red) line. The thick (black) line represents the Harris profile. Panels (c) and (d) report the surrogate current density (red dashed) for the discontinuities in panels (a) and (b), respectively, together with the Harris current (black solid). The best fits from the Harris model , where and λ is the half-thickness of the layer, are also compared in the same figures (thick lines), indicating very good agreement with the plasma equilibrium theory.
Paper: 2018MNRAS.478.5240M (fulltext)
optical reflected spectra from exoplanets. The major results of this work can be summarized as follows: the CCF technique permits the recovery of the wavelength dependence of reflected spectra of exoplanets, and consequently their albedo function; the CCF technique is able to distinguish between possible albedo configurations and hint at the planetary atmosphere composition; it will be possible to probe at planetary atmospheres for planets in a vastly accessible orbital parameter range; even in cases where a low number of wavelength bins are possible, the ratio between the albedo at redder and bluer wavelengths should help constrain possible chemical and physical atmospheric configurations. as close as 2018, ESPRESSO should already be able to recover the albedo function from hot Jupiter class exoplanets with a few hours of exposure time (e.g. 51 Pegasi b with 1h); the advent of HIRES will allow to probe smaller planets (e.g. hot Neptunes and Super Earths) with just with a few hours of exposure time (e.g. HD 76700 b with around 5h and 55 Cnc e with about 4h) However, the albedo from an exoplanet is not the only factor that modulates the reflected spectrum from an exoplanet. An exciting prospect to further characterize the planet comes from the observation of the phase function of the planet and how it varies with wavelength. This function describes the light curve of a planet as a function of orbital phase. It depends not only on the fraction of the planet illuminated by the host star but also on the general scattering properties of the planet’s atmosphere. The detection of the reflected optical from exoplanets is a daunting task that requires a broad spectral coverage and a extremely high-resolution to achieve the level of S/N we require, as well as an extremely high PSF stability for long periods of time. This can be achieved with ESPRESSO@VLT, HIRES@ELT, and similar observing facilities, which will lead the way towards a more detailed characterization of exoplanets and their atmospheres. The sheer collecting power of giant telescopes, combined with the precision and stability of next-generation high-resolution spectrographs, will put researchers in a position to not only look at the reflected light from exoplanets, but use it to characterize their atmospheres in great detail. A very bright future awaits exoplanetology indeed. APPENDIX A: OPTIMAL OBSERVATION WINDOWS AND DETECTABILITY The reflected signal of the planet is small, and to detect it is essential to choose orbital phases where that signal is close to its maximum. As per equation ( 2 ), this occurs at superior conjunction (or opposition in case of a transiting planet). Unfortunately at that point of the orbit, both the planet and star will have matching radial velocities and thus be difficult – if not impossible – to separate. Even with the precise stellar template built with the CCF technique, fluctuations on the stellar flux will leave residuals in the radial velocity domain at