adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.481..578G (fulltext)
in the analysis for Figs 8 and 9 where we looked specifically for differences in the PL relations derived from known metal-rich and metal-poor stars. Figure 11. Distance moduli for the 20 RRLs in our DZ sample as a function of [Fe/H] (spectroscopic metallicities from C05 ). The solid grey line is the distance modulus ±2σ (dashed lines) estimated using as a reference the PL relation derived by Neeley et al. ( 2017 ) and only the RRab stars in the D4 sample. Metal-poor sources are magenta squares and metal-rich are green triangles. RRab and RRc stars are encircled by blue squares and red circles, respectively. The RRLs marked with a cross are the rejected stars. The least-squares fit (black line) has been calculated in four different cases: considering the whole sample (case A, top left panel), removing the ‘closest’ star (in terms of distance from us; top right panel, case B), removing the ‘most distant’ star (case C, bottom left panel), and excluding both (case D, bottom right panel). In every case, the slope (d μ /d[Fe/H]) is consistent with zero within 1σ (see the text and Table 8 for details). Table 8. d μ /d[Fe/H] derived from the DZ sample. 5 SUMMARY AND CONCLUSION As part of the SMHASH program, using IRAC- Spitzer data we have determined a new distance modulus of the Sculptor dSph using RRLs located in the inner region of the galaxy. We obtained time-series photometry for 49 RRLs, (36 RRab and 13 RRc) at 3.6 μm. Seven stars were discarded from the initial sample mainly due to photometric contamination effects. Adopting periods from Kaluzny et al. ( 1995 ) and Clementini et al. ( 2005 ), we built light curves for the remaining 42 highest quality stars, delineating their mid-IR pulsation properties. In order to investigate the photometric properties of the sample, and to choose the highest quality subsample of stars, we created four data sets (D1, D2, D3, and D4) by removing various problematical stars (see Section 3 for details). PL relations were derived for each RRab-only and RRab+RRc ( fundamentalized ) data set, and were found to be in reasonable agreement (i.e. within 1σ) with the empirical relations published in Madore et al. ( 2013 ) and Neeley et al. ( 2015 ), as well as the revised relations from Neeley et al. ( 2017 ) and the recent PL relations derived by Muraveva et al. ( 2018b ) (Table 4 ). We adopt for Sculptor the distance modulus derived from the D4 sample, as this has the cleanest RRL selection and the best-fitting slopes are the closest to the published empirical relations that we have considered as a reference in this work. We are aware that mid-IR studies of the RRL PL are increasing (Madore et al. 2013 ; Dambis et al. 2014 ; Klein et al. 2014 ; Neeley et al. 2015 , 2017 ; Muraveva et al. 2018b ), but choose the Madore et al. ( 2013 ), Neeley et al. ( 2015 ), Neeley et al. ( 2017 ), and Muraveva et al. ( 2018b ) PLs as fiducial as they are built using the sample of RRL calibrators whose trigonometric parallaxes were
Paper: 2017ApJ...836...21B (fulltext)
analysis to a three-dimensional (3D) turbulent dynamo with periodic boundary conditions and an equator in the middle that cuts the domain into two halves, with opposite helicity of the forcing function. Finally, we employ full-disk vector magnetograms from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory ( SDO ) to obtain all three magnetic field components at the two-dimensional (2D) surface of the Sun, to compute magnetic helicity spectra from a sequence of synoptic maps. 2. Formalism 2.1. The Usual Magnetic Energy and Helicity Spectra In a periodic Cartesian domain, the usual magnetic energy spectrum is given by the integral over shells in wavenumber space of the Fourier-transformed magnetic energy density,where is the surface differential in Fourier space in D dimensions. In D = 3 dimensions, the surface of a D -dimensional unit sphere is . In D = 2 dimensions, is the circumference of a unit circle, while in D = 1 dimensions, corresponds to the two end points of a line. Here and in the following discussion, we measure the magnetic energy density in rather than , so the vacuum permeability factor is dropped. Furthermore, is the radius of a sphere in Fourier space, and hats denote the Fourier transform of the magnetic field, that is,The magnetic helicity spectrum is defined analogously to aswhere is the Fourier transform of the vector potential , with and . These spectra are normalized such that where angle brackets with subscript V denote volume averages. Analogously, one can define the current helicity spectrum such that , where is proportional to the current density. For the following, it is useful to remember that the magnetic energy spectrum is the Fourier transform of the trace of the two-point correlation tensorwhere angle brackets denote ensemble averaging, which could be approximated by averaging over time, and is assumed to be statistically independent of , owing to the assumption of homogeneity. Thus, following standard relationships (e.g., Matthaeus et al. 1982 ; Brandenburg et al. 2011 ), the energy spectrum is then given by , where is the Fourier transform of M ij . Likewise, the scaled magnetic helicity spectrum is given by , where is the unit vector of . (The hat on is not to be confused with the hats on or M ij , where they denote Fourier transforms over .) (Including here the factors 2 and k on the left sides of the two equations has the advantage that and thus have the same prefactors.) Note that under isotropic conditions, . Such spectra show the high-wavenumber range better than just or , and are therefore also considered in some of the following cases. The magnetic helicity spectrum can be defined analogously from a tensorial generalization of the magnetic two-point correlation tensor. This will be done in the following section, where we will also relax the assumption of homogeneity and allow the spectra to be slowly varying functions of . 2.2. Double Fourier Transform To obtain
Paper: 2018MNRAS.475.3764S (fulltext)
where We find that the pairwise kSZ dipole, ξ kSZ, l = 1 , is negative in linear theory. This means that gravitational attraction predicts a slight tendency of any pair of galaxies to be moving towards rather than away from each other at large scales, where we follow the convention that if two galaxies are moving towards each other, their contribution to the pairwise velocity is negative. The linear Kaiser effect from RSDs increases the amplitude of the pairwise kSZ dipole by ∼ 20 per cent for both LOWZ and CMASS at linear regimes. On the other hand, at non-linear small scales RSDs displace galaxies away from each other, and thus, in redshift space the sign of ξ kSZ, l = 1 changes around 10 h −1 Mpc ( k ≃ 0.2 h Mpc −1 ) from negative to positive (Okumura et al. 2014 ; Sugiyama et al. 2016 ; Sugiyama et al. 2017 ). This small-scale effect of RSDs is analogous to the Finger-of-God (FoG) effect but is not the same, because we have shown that this change in sign of ξ kSZ, l = 1 appears even in the Zel'dovich approximation that does not form haloes (Sugiyama et al. 2016 ). Therefore, it could be considered that the change in sign of ξ kSZ, l = 1 is caused by non-linear velocity dispersion effects, part of which is the FoG effect. The latest kSZ measurement using the high-resolution ACT data (De Bernardis et al. 2017 ) clearly shows the expected change in sign of the pairwise velocity at ∼10 h −1 Mpc. Fig. 2 shows the theoretical predictions of the pairwise kSZ dipole and octopole in linear theory (equations 37 and 39 ) for the LOWZ sample ( f σ 8 = 0.392 and b σ 8 = 1.283), where we use the best-fitting value of the optical depth, τ E = 3.95 × 10 −5 , estimated from our kSZ measurement (Section 7.2 ). Since this figure shows the linear theory results that we use in our analysis, the change in sign of ξ kSZ, l = 1 around ∼10 h −1 Mpc does not occur unlike non-linear theories (Okumura et al. 2014 ; Sugiyama et al. 2016 ). As the shape of the pairwise kSZ power spectrum is proportional to P lin ( k )/ k , the amplitude increases at large scales. As the data improves, we will be able to investigate the pairwise kSZ signal at larger scales than ∼200 h −1 Mpc (e.g. De Bernardis et al. 2017 , fig. 3). While the data should show a baryon acoustic peak around ∼100 h −1 Mpc in the linear theory prediction of ξ kSZ, l = 1 (see blue line in the right-hand panel of Fig. 2 ), the peak is significantly broadened by non-linear effects (Sugiyama et al. 2016 ). The octopole, which is generated from the RSD effect and is free of the linear bias, is about 10 times smaller than the dipole. In future kSZ measurements such as Advanced ACTPol and CMB-S4, we will want to measure all of the multipoles up to the octopole. This full measurement will constrain cosmological parameters, especially for the linear growth rate f σ 8 and the Hubble expansion parameter H (Sugiyama et al. 2017 ). However, the data used in this work is too noisy to detect the octopole; therefore, we do
Paper: 2017MNRAS.465..337P (fulltext)
efficiently accelerate non-thermal electrons (Sironi Spitkovsky 2014 ). Magnetic reconnection is a complex non-linear process and remains the subject of intense research (Yamada, Kulsrud Ji 2010 ; Treumann Baumjohann 2015 ), so we do not pretend to capture the microscopic details of this process in our 1D fluid model. We can, however, put useful constraints on the power dissipated by magnetic reconnection which then goes into accelerating non-thermal electrons, by calculating their emission as they travel along the jet. This power is inevitably linked to the large-scale average reconnection time-scale along the jet. Our approach is intended to be complementary to the detailed numerical simulations which focus on the small-scale reconnection physics but tend to neglect the large-scale fluid flow and energy balance along the jet; in this work, we instead focus on the average large-scale fluid flow and energy balance, at the expense of a detailed treatment of the microscopic reconnection physics. Our results will help to constrain and inform dedicated numerical simulations of the macroscopic reconnection rates required by considering radiative losses and energy constraints along the jet at large scales. In most astrophysical plasmas, the amount of resistive dissipation of magnetic fields is negligible and occurs on a resistive time-scale where η is the electrical resistivity ( 36 ) and δ is the characteristic length-scale of the fluctuation in the magnetic field. This time-scale is usually far longer than the typical observed time-scale associated with the release of energy via magnetic reconnection in solar flares, for example, Priest Forbes ( 1986 ). It is found in MHD simulations and particle-in-cell simulations of reconnection that once the process of resistive dissipation or magnetic reconnection begins to occur in a region with a strong gradient in the magnetic field, the magnetic energy is released in the form of heating and bulk acceleration of an outflow of plasma from the region. This creates a low-density, low-pressure reconnection region which then draws in additional plasma from both sides of the reconnecting current sheet (the magnetic field changes rapidly across the reconnecting region and this necessitates a concentrated thin current sheet since ). The velocities of the outflow and inflow are, typically, close to the Alfvèn speed, v A , with the precise relationship depending on the geometry of the reconnecting region and the proportion of magnetic energy which can be dissipated by the process. In collisionless highly magnetised plasmas, such as jets, inflow velocities of ∼0.1 v A are typical (Sironi Spitkovsky 2014 ). From Fig. 3 , we have already constrained the jet plasma to be highly magnetized, in which case the Alfvèn speed is approximately the speed of light v A ≈ c (e.g. Gedalin 1993 ). We can parametrize the reconnection rate in terms of the power dissipated in a region in which magnetic reconnection occurs, multiplied by
Paper: 2018ApJ...865....5R (fulltext)
The best-fit relation for Type 2 AGNs from Woo et al. ( 2016 ) is also shown (dashed line). The right panel shows the distributions of the ratio of σ [O iii ] to σ * for Type 1 AGNs: double Gaussian [O iii ] (red) and single Gaussian [O iii ] (blue). We perform regression analysis after accounting for the measurement uncertainties in both σ [O iii ] and σ * . The best fit for Type 1 AGNs is shown by the solid line in Figure 4 , while for comparison, we also plotted the best fit for Type 2 AGNs from Woo et al. ( 2016 ; dashed line). Interestingly, when the wing component is present in [O iii ] (left panel), the best fit deviates from unity (dash-dotted line), having a steeper slope of 2.79 ± 0.14 for Type 1 AGNs and 1.66 ± 0.01 for Type 2 AGNs (see Woo et al. 2016 for Type 2 AGNs). However, when the wing component is absent (middle panel), the best-fit line has a slope of 1.32 ± 0.11 for Type 1 and 1.18 ± 0.01 for Type 2 AGNs much closer to the unity relation. We also present the distribution of the velocity dispersion ratio of [O iii ] to σ * (right panel in Figure 4 ). While [O iii ] fitted with a double Gaussian is much broader than stellar lines (red histogram), for many objects, [O iii ] fitted with a single Gaussian (blue histogram) is also broader than stellar lines, suggesting that an outflow signature is significantly present. Considering that [O iii ] dispersion is a combined effect of gravitational potential ( σ gr ) and nongravitational effect, e.g., outflow components ( σ non-gr ), the measured velocity dispersion can be expressed asNote that although σ total may represent a more complex nature of gas kinematics, we simply assume that the line of sight velocity distribution of gas manifests the convolution of gravitational and nongravitational components. We expect σ [O iii ] to be a factor of 1.4 larger than σ gr if σ non-gr = σ gr . Assuming σ gr = σ * , we find that an error-weighted mean ratio of σ total to σ gr is 1.0 and 2.3, respectively, for Type 1 AGNs with single Gaussian [O iii ] and double Gaussian [O iii ]. The nonlinearity of the σ [O iii ] – σ * relation suggests that the effect of the nongravitational component is significant and outflows are common phenomena in both Type 1 and Type 2 AGNs. 3.2. Outflow Fractions To study the outflow fractions, we estimated the fraction of AGNs with double Gaussian [O iii ] in the sample. In the case of Type 1 AGNs, 89% have double Gaussian [O iii ], while only 11% have single Gaussian [O iii ]. The double Gaussian [O iii ] fraction in Type 1 AGNs is a factor of 2 larger than that of Type 2 AGNs (43%). Since the mean luminosity of Type 1 AGNs is much higher than that of Type 2 AGNs, the difference of the double Gaussian [O iii ] fraction is due to the luminosity effect. In Figure 5 , we present the fraction of double Gaussian [O iii ] as a function of [O iii ] luminosity (left panel). The double Gaussian fraction steeply increases with increasing [O iii ] luminosity, from ∼20% to
Paper: 2021MNRAS.500.3414P (fulltext)
al. 2007 ), photodesorption (Bertin et al. 2013 ), and cosmic ray induced photodesorption (Hasegawa Herbst 1993 ). These three switches are set to ‘on’, with the photodesorption yield set at 1 × 10 −4 . 2.2 Network The nautilus reaction network used in this study has been expanded to include more complex chemical species and intermediates that lead up to C 2 H 4 O 2 isomers, as well as dimethyl ether. The base network of gaseous reactions is taken from the KIDA network (Wakelam et al. 2012 , http://kida.astrophy.u-bordeaux.fr/ ). The granular reaction network is from the nautilus package, with additional thermal grain-surface reaction pathways leading to C 2 H 4 O 2 isomer precursors from Garrod Herbst ( 2006 ). Used initially in hot core models, these reactions have been included here to provide likely thermal pathways to COMs in competition with radiolysis. Other notable additions include new gas-phase reactions for acetic acid and glycolaldehyde from Skouteris et al. ( 2018 ). The full list of additional reactions is contained in the supplementary material , Tables S1–S3 (available online), with important reactions discussed in Sections 3.1 , 3.3 , and 3.2 . Species with the first letter ‘J’ are those on the surface of the ice mantle, while species with a ‘K’ lie within the bulk of the ice, which, after the simulation has completed running (10 7 yr), contains close to 100 monolayers of ice. Additional binding energies E D for the four principal molecules studied were taken from Garrod ( 2013 ) and are listed in Table 3 . The diffusion barriers ( E b ) for all granular species are 0.4 × E D for ice surface species, and 0.8 × E D for bulk species. 2.3 Radiolysis The method used for radiolysis was first developed by Shingledecker Herbst ( 2018 ). It describes four different results that follow an incoming cosmic ray or secondary electron interacting initially with a target species A, for which we use a ‘’. The subsequent results can be described by the following equations, which represent different paths to ions and suprathermal neutrals, the latter denoted with an asterisk: The first process leads to the ionization of A, while the second process describes the formation of suprathermal fragments B and C with an intermediate suprathermal A formed via dissociative recombination of the A ion. The third process describes the direct formation of an intermediate suprathermal species followed by its fragmentation into non-excited products, while the last process describes the formation of suprathermal A, which does not subsequently fragment. In order to implement both generation of suprathermal species, and fast suprathermal reactions into the rate-equation based model used, we can derive a pseudo-first-order rate coefficient by multiplying a cross-section (σ e ), in cm 2 , with a cosmic ray flux (φ) ,in cm −2 s −1 . In order to obtain a cross-section, we start with a stopping power cross-section S ( E ), which is defined through the loss of kinetic
Paper: 2016A&A...586A..31Z (fulltext)
flux from near the heliotail. Figure 3 can be directly compared to Fig. 9 of Funsten et al. (2015) , who analyzed the symmetry of the IBEX ribbon; however, note that Funsten et al. plot the IBEX ribbon flux and location as a function of angle from the ribbon symmetry axis, which lies within 40 of the plane (0 azimuth in our Fig. 3 ). The IBEX ribbon flux appears to maximize close to the symmetry axis for 0.71 and 1.11 keV, relatively close to the plane (within 30), similar to our results at low ENA energies for the case of slow SW, and high ENA energies during fast SW – both in the absence of an asymmetric SW. At higher energies, the IBEX ribbon flux appears bimodal, likely due to the asymmetric SW, which is excluded in our study. Nevertheless, in all cases, our results appear to be symmetric about the plane. This suggests that, besides the possible influence of the latitudinal asymmetry of the SW, the hydrodynamic interaction of the SW with the LISM, centered on the trajectory of the heliosphere through the interstellar gas, is an important ordering process for the ribbon flux, as also found by Funsten et al. (2015) . 3.2.2. Comparing simulated and IBEX ribbon geometry Fig. 3 Simulated ribbon flux from locations along the elliptical fits, as a function of azimuthal angle about the circularity plot center (negative to positive angle is counterclockwise in Fig. 2 ). In the left panels we show the ribbon flux at each energy (different rows) for the slow, intermediate, and fast SW (solid curves), as well as the heliolatitude location of the ribbon (dashed curves). The shaded regions correspond to locations of the ribbon lying within 35 heliolatitude. Notice that the left -axis is in scaling. In the right panels we show the components of the ribbon flux that originated from outward-propagating, primary ENAs born in the IHS, the supersonic SW (SSW), and the total. Fig. 4 Simulated angles between the line-of-sight view from IBEX and the local interstellar magnetic field vector, at distances of 20 au ( left ) and 200 au ( right ) from the heliopause (HP). We show the locus of points defined by (black dots), fit a circle to that locus of points (black curve), and plot the center of the fitted circle (black dot near the center of the plot). The frame of reference of these plots is the same as those shown in Fig. 2 . Table 2 Weighted averages of geometric properties. Notes. The IBEX data are from Funsten et al. (2013) . All results in this table are weighted over energy and circle/ellipse fits. The aim of this analysis is to determine if there is a significant dependence of the ribbon’s geometric properties on the SW speed and ENA energy. Since the draping of the LISM magnetic field is a function of distance from the heliopause (e.g., Grygorczuk et al. 2011 ; Pogorelov et al. 2011 ; Zirnstein et al. 2015a ), the energy of the parent SW and ENA distribution may affect the distances at which the majority of ribbon ENAs (observed at 1 au) originate. We
Paper: 2018ApJ...863..133N (fulltext)
configuration of the non-Gaussianity vs. the sound speed in a nonminimal derivative model in which both the nonminimal coupling and potential are E-model. The left panel corresponds to N = 60, and the right panel is for N = 70. The stars show the maximum values of the sound speed in each case. Table 3 Constraints on the Sound Speed and Tensor-to-scalar Ratio Obtained from 95% CL of Planck2015 TT, TE, EE+lowP Data Note. These constraints are corresponding to a nonminimal derivative model in which the nonminimal coupling is a constant and the potential is E-model. Table 4 Constraints on the Sound Speed and Tensor-to-scalar Ratio Obtained from 95% CL of Planck2015 TT, TE, EE+lowP Data Note. These constraints are corresponding to a nonminimal derivative model in which both the nonminimal coupling and potential are E-model. 5. Summary The aim of this paper was to study the nonminimal derivative model in the context of α -attractor. In this regard, we have considered a two-fields action in which both the fields and their derivatives are nonminimally coupled to the gravity. We have used the conformal gauge to convert the two-fields model into a one field, which its derivative (and not the field itself) is nonminimally coupled to the gravity. After obtaining the the background equations of this NMDC model, we have studied the linear perturbations theory. By adopting the ADM formalism, we have studied both the scalar and tensor perturbations in this setup and found some important perturbation parameters such as the power spectrum, scalar spectral index, and tensor-to-scalar ratio. After that, by introducing the types of the general functions and U ( ψ , φ ), and using the conformal gauge, the NMDC function and potential have been obtained. In this paper, two types of NMDC function have been considered: and . We have shown that in both cases, there is an attractor line in large N and small α limits that the r − n s trajectories tend to, while in the the single-field α -attractor model, there is an attractor point in these limits. In fact, the presence of attractor line instead of attractor point is due to the nonminimal derivatives coupling that causes the scalar spectral index of this model to be a functions of two parameters, α and M . In the limit , one recovers the attractor point as is usual in α -attractor models. We have numerically studied the linear perturbation in two cases with constant NMDC coupling and E-model NMDC coupling (in both cases the potential is considered to be E-model). With these choices, the power spectrum of the perturbations in NMDC model can get the observationally viable value (almost 2.4 × 10 −9 ) in some ranges of χ and α . Also, in this model, the r − n s plane lies in the background of Planck2015 TT, TE, EE+lowP data for all values of M (or χ ) and in some ranges of α , for both N = 60 and N = 70. We have obtained some constraints on the free parameter α which lead to the values of the scalar spectral index and