adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.464..564L (fulltext)
is the proton/electron mass ratio, Θ = is the electron/proton parallel temperature ratio, and is the parallel electron beta parameter in the Maxwellian limit κ → ∞. The dispersion relation for bi-Maxwellian-distributed electrons is obtained from equation ( 14 ) by changing Z κ with the Maxwellian plasma dispersion function from equation ( 12 ). 3 EFHI SOLUTIONS We have solved the dispersion relation (equation 14 ) numerically, and analysed the unstable firehose solutions for a wide variety of plasma regimes susceptible to this instability. In this section, we present the main features of the EFHI, namely, growth rates, wave-frequencies, and wavenumbers, as well as the anisotropy thresholds, and restrict our discussions only to a number of representative cases. However, the anisotropy thresholds displayed and analysed in the second part of this section cover extended conditions encountered in space plasmas, for instance, in the solar wind and planetary magnetospheres. Since only the electrons are anisotropic, in the next analysis we omit the labelling subscript ‘e’, for the anisotropy, A , and the plasma beta parameter, β ∥ . The effects of Kappa-distributed electrons are triggered by their temperature anisotropy and the abundance of suprathermal populations, which is quantified by the finite (low) values of the power-index κ. 3.1 Unstable solutions First, we examine the growth rates and the wave-frequency of the EFHI for different plasma regimes conditioned in principal by the parallel plasma beta parameter β ∥ , the electron anisotropy A , and the power-index κ. The regimes identified in Figs 1 and 2 are specific to the firehose instability, when a magnetized plasma becomes penetrable by the LH EM fluctuations propagating parallel to the magnetic field with frequencies higher than the proton cyclotron frequency. All the unstable modes, i.e. with γ > 0 in Fig. 1 , exhibit this property that becomes evident in Fig. 2 , where their wavenumber dispersion extends to high frequencies exceeding Ω p . Increasing the presence of suprathermals, i.e. lowering the power-index κ, the range of unstable wavenumbers is restrained, but the wave-frequencies and the instability growth rates are enhanced. These effects are in general stimulated by increasing the plasma beta parameter β ∥ , the temperature anisotropy and the electron–proton temperature contrast Θ. Plots evidencing the influence of Θ are not shown here, being less relevant for the scope of this paper. The unstable solutions displayed in Figs 1 – 4 are obtained for the same value of this parameter, namely, for Θ = 4 in accordance to the observations in the slow solar wind (Newbury et al. 1998 ). Figure 1. Effects of the suprathermal electrons quantified by the power-index κ =2, 2.2, 3, 6, ∞, on the growth rates of EFHI for different plasma beta explicitly given in each panel. Here is the one used in equation ( 14 ). Figure 2. Effects of the suprathermal electrons (κ =2, 2.2, 3, 6, ∞) on the
Paper: 2020MNRAS.493.5301B (fulltext)
f from the MCMC analyses at z = 0.5 and z = 1. We utilize the monopole and quadrupole up to the k max given in Table 1 , and the hexadecapole up to for TNS and for EFTofLSS at z = 0.5, while for TNS and for EFTofLSS at z = 1. In contrast to the exploratory, full P ( k , μ) Fisher matrix analysis performed in Section 4 , at z = 0.5 we find that the EFTofLSS model does significantly better than the TNS model and the gain from the inclusion of the hexadecapole in the EFTofLSS model is also larger with respect to the TNS case. At z = 1, where the models have the same range of validity, the improvement is less dramatic. An important point we wish to reemphasize is that taking the hexadecapole up to too high a k max (the ones found in Table 1 ) produces biased estimates of the growth rate f for both models, with the exception of the EFTofLSS case at z = 0.5 where indeed we can take k max = k max,4 . This is what has been done in the Fisher analysis in Section 4 , which uses the full P ( k , μ) up to the same k max from Table 1 . As we have already stated, the MCMC analysis resembles what is done in a real data analysis procedure, and is therefore more robust and reliable. To reiterate the point above, we have also performed checks to see what happens if we set the same k max = k max, 4 for TNS at z = 0.5. We found that including the hexadecapole at the same k max results in for TNS giving a (barely) unbiased result for f . This results to a larger error of , compared to our default case with error. We have also checked that increasing k max = k max,4 beyond results in a biased recovery of f . A similar test can be found in fig. 4 of Paper I . Further, we note that in earlier work by Taruya, Saito Nishimichi ( 2011 ), Fisher matrix forecasts using the multipole expansion were performed for the TNS model equipped with a linear bias prescription, taking the same k max for monopole, quadrupole, and hexadecapole. The authors showed that the monopole and quadrupole contain most of the constraining power, but the hexadecapole can somewhat help to further decrease the errors. While the model considered in Taruya et al. ( 2011 ) is not the same as the one we consider in this work, the main result is general: it is preferable to include the monopole and quadrupole at a derived common k max and then add the hexadecapole for as long as the constraints are not biased. 5.1 The effect of positivity priors on the EFTofLSS constraints It is important to comment on the effect of the positivity priors imposed on the EFTofLSS parameters ; these can have a non-negligible effect on the EFTofLSS f constraints. More specifically, we have found that if the fiducial values for these parameters are sufficiently away from zero so that no positivity priors are needed, the f constraints can increase substantially. This explains the apparent inconsistency between our MCMC results and some of the MCMC results in Paper III . In this paper a different set of simulations, cosmology,
Paper: 2017A&A...608A..40P (acknowledgments)
The Astronomical Validation activities were made possible thanks to the invaluable support of the local technical staff and the technical commissioning team. The authors thank Cees Bassa and Ramesh Karuppusamy for their important help in setting up the ROACH backend for pulsar observations. D. Perrodin thanks Gemma Janssen for providing WSRT timing residuals shown in Fig. 15 . F. Loi gratefully acknowledges the Sardinia Regional Government for financial support of her PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2007–2013 – Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.). From 2012 to 2014, D. Perrodin was supported by the ERC Advanced Grant “LEAP”, Grant Agreement Number 227947 (PI M. Kramer), which also provided a 10-gigabit-ethernet switch and a storage cluster for SRT LEAP observations. M. Pilia was supported by the Sardinia Regional Government through the project “Development of a Software Tool for the Study of Pulsars from Radio to Gamma-rays using Multi-mission Data” (CRP-25476). A. Ridolfi and C. Tiburzi gratefully acknowledge support from the Max-Planck-Institut für Radioastronomie. C.Tiburzi also acknowledges support from the Universitaet Bielefeld. V. Vacca was supported by the DFG Forschengruppe 1254 “Magnetisation of Interstellar and Intergalactic Media: The Prospects of Low-Frequency Radio Observations”. The development of the SARDARA backend was funded by the Autonomous Region of Sardinia (RAS) using resources from the Regional Law 7/2007 “Promotion of the scientific research and technological innovation in Sardinia” in the context of the research project CRP-49231 (year 2011): “High resolution sampling of the Universe in the radio band: an unprecedented instrument to understand the fundamental laws of the nature”. The Sardinia Radio Telescope is funded by the Department of University and Research (MIUR), the Italian Space Agency (ASI), and the Autonomous Region of Sardinia (RAS). It is operated as national facility by the National Institute for Astrophysics (INAF).
Paper: 2020A&A...639A..88C (fulltext)
along with the RP1 backbone composite series used in our plage area composite (dashed blue) and RP1 backbone composite series keeping only the days common with the RP1 individual series (dashed red). Bottom : RP1 backbone composite series when varying the included individual datasets based on their bandwidth. The different curves show the RP1 backbone composite by keeping Ba, Co, Kh, RP2, SF1, SF2, Te, Up (dashed green), Br, ML, BB (dashed blue) and Ka, PM, VM (dashed red). The RP1 backbone series used for our composite is shown in solid black. Depicted are annual median values (solid lines) along with the 1 σ asymmetric intervals (shaded surfaces). The SC numbers are given below the curves. Finally, we also test the effect of including data taken with different bandwidths in the backbone series. For this purpose, we use the RP1 backbone and produce three different versions of it: (i) keeping only datasets with either bandwidths narrower than 2 Å or broader than 3 Å (Ba, CL, Co, Kh, PS, RP2, SF1, SF2, Te, Up). Since the RP1 bandwidth is 2.5 Å, these limits imply that the chosen bandwidths differ by at least ±0.5 Å from that of RP1. (ii) including only datasets with the nominal bandwidth between 2 Å and 3 Å, that is, within ±0.5 Å of the bandwidth of RP1. In this case, we further subdivide the datasets according to whether the bandwidths that have been assigned to them; ii (a) appear to be consistent with their actual behaviour (BB, Br, ML) ii (b) or their assigned bandwidth does not appear to be consistent with their actual behaviour (Ka, PM, VM). For this test, we considered the BB and ML datasets only over the periods when the final instrumentation was used (see Appendix B ), in order to avoid inconsistencies due to instrumental changes. Figure 15 b displays the three test backbone series in comparison to the one used in our composite. All three reconstructions of the RP1 backbone series lie within the uncertainties. There are generally small differences, which are greater in 2011, before 2000, and after 2018, reaching up to 0.01 in disc fraction. 5. Summary and conclusions We processed 43 datasets of full-disc Ca II K observations spanning the period 1892–2019 to derive the evolution of plage areas over the last 12 solar cycles. We processed the data consistently with the method developed by Chatzistergos et al. (2018b , 2019b) . An extra step in the processing was added to ensure accurate results from the analysis of images from the Ky and YR datasets, which suffer from specific artefacts along arcs on the solar disc. We adapted our processing such that these artefacts can be precisely accounted for and showed that we can obtain accurate results for those datasets as well. We used our results for the 38 datasets with observations centred at the Ca II K line to create a plage area composite by applying the backbone approach employed by Chatzistergos et al. (2017) to create a sunspot group number composite. We created two backbones, one
Paper: 2020ApJ...899L..30G (fulltext)
show that after ∼5 kyr the combined wind of the stars leaves the cluster itself, forming a collective cluster shell. We calculate the shell radius as the density-weighted average distance from the origin following Equation (23) in Rosen et al. ( 2017 ). We obtain at corresponding to an approximate expansion rate of the shell to be 18.7 . For comparison, we also calculate a density-weighted average velocity expansion of 9.05 , which is a factor of ∼2 lower than the value derived previously. Typically, observations infer the bubble age from the dynamical timescale, where both and are observationally inferred values of the shell radius and shell expansion rate, respectively (e.g., Li et al. 2015 ). Our measured values stated above for the shell velocity imply that inferring the bubble age from these quantities will underestimate the dynamical age of the bubble because the shell velocity is decreasing in time. In the top panel of Figure 5 we compare the time for the bubble material to cool, , to the characteristic bubble expansion time using from above. In each cell we calculate the cooling time given byHere is the thermal energy of the gas parcel, is the energy loss rate via cooling, and is the cooling function taken from Gnat Sternberg ( 2007 ). Here we have used for a fully ionized plasma of solar composition, where and are the electron and ion number density, respectively. To calculate in each cell we use , where assumes all helium is ionized at solar metallicity. Figure 5. Top panel: ratio of the cooling time for the bubble material to the characteristic bubble expansion time at t = 10.5 kyr. For these calculations we remove the cells that are dominated by numerical effects. Bottom panel: Mach number weighted by cell-mass within the cluster wind as a function of temperature and density at t = 10.5 kyr. We show only gas with K so that we do not include contributions from the background ISM. In the top panel of Figure 5 we show using the characteristic bubble expansion time kyr in all cells. As the cooling timescale has a strong dependence, density enhancements due to clumping will lead to faster cooling times. To take this into account we calculate on a cell-by-cell basis. We find a cell-mass−weighted average of kyr and a cell-mass weighted average of for the hot stellar winds inside the bubble shell. Figure 5 also shows that the majority of the gas within the shell, especially at the center where the density is lowest, has . At larger radii closer to the shell boundary we find . This is likely due to the turbulent mixing between the low-density, high-temperature shock-heated gas and high-density, low-temperature shell. Because the energy loss via cooling is negligible within the bubble, and instead the thermalized wind energy is transferred via adiabatic expansion, thereby driving the dense bubble shell. We note that we find that the regions around the most massive stars at the center of the simulation are dominated by numerical effects and
Paper: 2017A&A...607A..55V (fulltext)
panels include ellipses representing the 2 fitted disk components to guide the eye. These are drawn at the radial location corresponding to the inner and outer disk radius for disk #1 (thick line) and disk #2 (thin line), as summarized in Table 2 . The right panel ( top ) shows the real part of the visibilities as function of the deprojected baseline for the data (black dots) and the model (red line). The bottom panel shows the residuals. The visibilities are binned in sets of 200. The radial brightness profile for the continuum emission rises monotonically until it peaks at 0.44. After that it decreases and shows a change in slope around 1.15 until it becomes indistinguishable from the background around 1.35, as shown in the bottom left panel of Fig. 5 . The radial brightness distribution of the dust continuum emission between the peak at 0.44 and the outer disk radius at 1.35 can be fitted by two power laws separated at 1.15 with exponents of 6 and 16, respectively. The azimuthal brightness profile for the continuum emission peaks at an azimuth of 135 with a maximum 25% above the median value for the disk and 18 away from the major axis of the disk. To further study the azimuthal structure of the disk we subtract a median radial Gaussian profile from each azimuthal row in the polar projection. We create this average Gaussian by selecting only those radial slices with an integrated value within 1% of the median integrated value over all azimuths (Fig. 6 ). The remaining structure peaks close to the major axis of the disk and is best fit with a 2D Gaussian centered on (radius, azimuth) =(0.43, 18) with a FWHM of 0.15 in radius and 52 in azimuth. This feature is discussed in Sect. 4 . 3.2. CO and HCO<inline-formula specific-use="simple-math"><sup>+</sup></inline-formula> emission We detect spatially and spectrally resolved emission from the 12 CO and HCO + emission lines from the HD 34282 disk. We show the moment maps and line profiles in Figs. 2 and 3 respectively. Fig. 5 ALMA band 7 image reconstructed using superuniform weighting, deprojected using the disk inclination and rotated with the position angle listed in Table 2 to put the major axis of the disk at 0 (north; bottom right panel ), converted to polar coordinates ( top left panel ). This polar map is collapsed along the radial and azimuthal axes to yield the azimuthal intensity distribution ( top right ) and the radial intensity distribution ( bottom left ). In this last panel we also show the size of the beam minor and major axis for reference. Fig. 6 Surface brightness of the disk after subtracting a Gaussian fit to the median radial profile, shown in polar coordinates with the position angle relative to the major axis of the disk on the -axis and the radial distance from the center on the -axis. Contours of the best-fit 2D Gaussian are show at values of [0.25, 0.5 and 0.75] times its peak value. We estimate the systemic velocity from the 12 CO emission line at km s, based on the
Paper: 2016A&A...587A..91B (fulltext)
Fig. A.1 Transitions of CHCN, covered by our ALMA survey. The best-fit LTE synthetic spectrum of CHCN is displayed in red and overlaid on the observed spectrum of Sgr B2(N2) shown in black. The green synthetic spectrum contains the contributions of all molecules identified in our survey so far, including the species shown in red. The central frequency and width are indicated in MHz below each panel. The angular resolution (HPBW) is also indicated. The -axis is labeled in brightness temperature units (K). The dotted line indicates the 3 noise level. Fig. A.1 continued. Fig. A.1 continued. Fig. A.1 continued. Fig. A.1 continued. Fig. A.2 Same as Fig. A.1 for CHCHCN, . Fig. A.2 continued. Fig. A.2 continued. Fig. A.3 Same as Fig. A.1 for CHCHCN, . Fig. A.3 continued. Fig. A.3 continued. Fig. A.4 Same as Fig. A.1 for CHCHCN, . Fig. A.4 continued. Fig. A.4 continued. Fig. A.5 Same as Fig. A.1 for CHCN, . Fig. A.6 Population diagram of CHCN, toward Sgr B2(N2). Only the lines that are clearly detected, do not suffer too much from contamination from other species, and have an opacity below 2.5 are displayed. The observed datapoints are shown in black while the synthetic populations are shown in red. No correction is applied in panel a) . In panel b) , the optical depth correction has been applied to both the observed and synthetic populations and the contamination from all other species included in the full model has been removed from the observed datapoints. The purple line is a linear fit to the observed populations (in linear-logarithmic space). Fig. A.7 Same as Fig. A.6 for CHCHCN, . Fig. A.8 Same as Fig. A.6 for CHCHCN, . Fig. A.9 Same as Fig. A.6 for CHCHCN, . Fig. A.10 Same as Fig. A.1 for CHCHDCN. Fig. A.11 Same as Fig. A.1 for CHDCHCN in its out-of-plane conformation. Fig. A.12 Same as Fig. A.1 for CHDCHCN in its in-plane conformation. The full synthetic model (in green) does not contain any contribution of CHDCHCN in its in-plane conformation (in red). Fig. A.13 Same as Fig. A.1 for CHCN, . Fig. A.14 Same as Fig. A.1 for CHCN, . Fig. A.15 Same as Fig. A.1 for CHCN, . Fig. A.16 Same as Fig. A.1 for CHCN, . Fig. A.17 Same as Fig. A.1 for CHCN, . Fig. A.18 Same as Fig. A.1 for CHCN, . Fig. A.19 Same as Fig. A.1 for CHCN, . Fig. A.20 Same as Fig. A.1 for CHCN, . Fig. A.21 Same as Fig. A.6 for CHCN, and . Fig. A.22 Same as Fig. A.6 for CHCN, and . Fig. A.23 Same as Fig. A.6 for CHCN, , , and . Fig. A.24 Same as Fig. A.1 for CHCN, . Fig. A.25 Same as Fig. A.1 for CHCN, . Fig. A.26 Same as Fig. A.1 for CHDCN, . Fig. A.27 Same as Fig. A.6 for CHDCN. Fig. A.28 Same as Fig. A.1 for HCN, . Fig. A.29 Same as Fig. A.1 for HCN, . Fig. A.30 Same as Fig. A.1 for HCN, . Fig. A.31 Same as Fig. A.1 for HCN, . Fig. A.32 Same as Fig. A.1 for HCN, and . Fig. A.33 Same as Fig. A.1 for HCN, . Fig. A.34 Same as Fig. A.1 for HCN, . Fig. A.35 Same as Fig. A.1 for HCN, and . Fig. A.36 Same as Fig. A.1 for HCN, . Fig. A.37 Same as Fig. A.1 for HCCCN, . Fig. A.38
Paper: 2020ApJ...899L..23H (fulltext)
, will produce a bow shock if their radius is larger than the mean free path of gas molecules (Landau Lifshitz 1959 ). The mean free path for molecular collisions is with being the H 2 cross-section. Thus, for objects larger than km, bow shocks are formed if the gas density . The post-shock gas has a high temperature and can be efficient in thermal sputtering. However, bow shocks are not expected to form for objects of and . 5. Numerical Results 5.1. Destruction in the ISM Assuming that H 2 objects of various sizes are produced in a nearby GMC, we estimate the minimum size of objects that could reach the Earth. The closest GMC, W51, is located at a distance of . Thus, at a speed of , it takes yr for objects to reach the solar system. Figure 1 compares the various destruction times with for different object radii at a typical speed. The sublimation time is obtained using the equilibrium temperature (see Section 2 ). We find that only very large objects of radius could survive thermal sublimation and reach the solar system. The minimum size of the objects that can survive is obtained by setting , yielding Figure 1. Comparison of various destruction timescales (slanted colored lines) as a function of the object radius (in meters) to the travel time from a GMC at a distance of 5.2 kpc, assuming a characteristic speed of 30 km s −1 (horizontal black line). 5.2. Destruction on the Way from the Center of GMCs to the ISM The total gas column density toward the densest GMC amounts to extinction of (see e.g., Mathis et al. 1983 ), which corresponds to a hydrogen column density of based on the scaling with (Draine 2011 ). Assuming a mean GMC density and a GMC radius , the travel time is . Equation ( 22 ) implies a destruction time by gas collisions of , which is longer than the travel time. As shown in Section 2 , collisional heating is important in GMCs because of their high density, . Assuming that a fraction η of the impinging proton’s energy is converted into surface heating to a temperature below , collisional heating raises the temperature of the surface towhich implies effective cooling by evaporation (see Equation ( 9 )). To find the actual equilibrium temperature, we solve Equation ( 12 ) and obtain for , assuming . Substituting this typical temperature into Equation ( 3 ) yields The minimum size of the objects that can survive is obtained by setting , yielding We conclude that H 2 objects cannot survive their journey from their GMC birthplace to the ISM if their radius is below . The actual value would be larger because the surface temperature is higher when objects are moving through the core of GMCs with higher gas density. 5.3. Destruction in the Solar System When ‘Oumuamua entered the solar system, solar radiation heated the frontal surface to an equilibrium. We numerically solve Equation ( 12 ) and obtain the equilibrium temperatures and at heliodistances D = 0.25 and . Substituting these temperatures into Equation ( 4 ), one obtains and