adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.474.3599L (fulltext)
with haloes, and the slight difference is because the two tracer classes do not correspond to exactly the same halo population, since central galaxies are populated in haloes in a random way. Third, for the two cases in which we cross-correlate the dark matter field from the F5 simulation with tracers from the GR simulation (cyan dashed and red dot-dot-dot–dashed), ξ gm decreases towards very small separations ( r ≲ 0.3 h −1 Mpc), and this is because GR tracers usually do not coincide with the highest density peaks in the dark matter field of the F5 simulation so that the highest values of ξ gm appear to be away from the GR tracers themselves. All in all, the test highlights the importance of using carefully constructed mock galaxy catalogues when we compare the predictions of GGL in different models. As an example, the 3D real-space two-point correlation functions and the projected 2D correlation functions in our GR and F5 HOD catalogues agree with each other within 2 per cent–3 per cent in the range of separations considered here, but such agreements are in a statistical, rather than an object-by-object, sense. When cross-correlated with the same (F5) matter field, the ξ gm results by using F5 and GR galaxies are indeed different (cf. blue dotted and cyan dashed curves in Fig. 8 ), in particular at small separations. This indicates that high-resolution simulations enable to accurately resolve the internal structures of haloes and to allow realistic galaxy mocks to be constructed, are necessary in order to make GGL more useful for distinguishing f ( R ) gravity and GR. 3.3 Galaxy bias Finally, we compare the galaxy biases in GR and f ( R ) gravity. We are interested in this comparison this for two reasons. First, in standard ΛCDM, the linear bias factor, defined as is scale-independent on large scales. On the other hand, it is known that f ( R ) gravity, and in general chameleon models, predict a scale-dependent linear growth rate of matter density perturbations, which may result in scale-dependent b lin , because we have tuned the HOD parameters to match ξ gg ( r ) in the two models, while their ξ mm ( r ) can have a scale-dependent difference. We want to check whether, at least for F5, such a scale dependence of b lin is significant enough to make it of interest in observations. Secondly, assuming that the linear bias factor b lin gives a correct quantification of galaxy bias at some given scale r , the galaxy–matter cross-correlation coefficient R gm , defined as is equal to 1. This offers an opportunity to derive the matter correlation function ξ mm from observational determinations of ξ gg and ξ gm . We want to see whether the minimum length-scale R min above which this can be performed without worrying about the non-linear effects in galaxy bias is significantly different in GR and in F5. Fig. 9 shows the results of our tests to answer these questions. The top bunch of curves in each panel (left for GR and right for F5) show the
Paper: 2021MNRAS.502.2565P (fulltext)
of the α-effect and the angular velocity profile, the star with the rotation period of 30 d is slightly above the large-scale dynamo instability threshold. Table 1 lists the integral parameters for the kinematic dynamo models. Our results are in general agreement with the results of the previous paper by Pipin ( 2015 ). The models show a decrease in the dynamo period with an increase in the rotation rate. Fig. 3 shows the time–latitude diagrams for the large-scale toroidal magnetic field evolution at r = 0.9 R in the kinematic models. The models with a period of rotation longer than 10 d show solar-like butterfly diagrams. The star rotating with a period of 10 d shows a double dynamo wave pattern, where the high-frequency high-latitude cycles merge into long cycles at low latitudes. A similar pattern is found for the star rotating with the period of 8 d. For a star with a higher rotation rate, the dynamo wave patterns divide into two: the high-latitude wave drifts to the equator and the low-latitude wave migrates toward the poles. This property becomes very clear for the M5, M2 and M1 models. All these models show a mix of the magnetic parity modes and different systems of the dynamo waves at high and low latitudes. The complicated dynamo wave patterns in these models result in multiple dynamo periods (see Table 1 ). For example, the model M1 shows three different dynamo waves. There are two high-frequency waves migrating toward the poles below 50°. The third wave is a quasi-periodic high-latitude wave migrating toward the equator. We deduce the dynamo periods using the time–latitude diagrams of the toroidal magnetic field in the subsurface layer r = 0.9 R , the variations of the toroidal magnetic field flux F S , and the Poynting flux luminosity L P . To identify the dynamo periods, we employ the standard wavelet package of the scipy distribution (see www.scipy.org ). All these parameters indicate the unique dynamo period for the models in the range of rotational periods from 15 to 30 d. The models for a rotational period of less than 15 d start to show long-term variations. The different dynamo parameters show the different sets of periodic variations. For the long period, we choose the minimal value that is found both in F S and L P and in the time–latitude diagrams. Also, the models for fast rotating stars, M1, M2 and M5, show the short-term periodicity of the L P parameter. Its period is about two times less than the main dynamo period. Figure 3. The time–latitude diagrams for the near-surface ( r = 0.9 R ) toroidal magnetic fields for the kinematic dynamo models. Table 1. The integral parameters of the kinematic dynamo models. The numbers in the model names correspond to the period of rotation in days, which is rounded to the nearest integer. P rot is the period of stellar rotation. Ro* is the estimation of the Rossby number. F T is the magnitude of the total magnetic flux in the convection zone. F S is the magnitude of the
Paper: 2018ApJ...853..119A (fulltext)
and lower values of , respectively. 3.4. Constraining H<sub>0</sub> with BAO plus Deuterium Abundance in ΛCDM Obtaining a constraint on H 0 from the BAO requires adding information to break the degeneracy. One way to do this is to add a constraint on the baryon density (e.g., Addison et al. 2013 ; Aubourg et al. 2015 ; Wang et al. 2017 ). We assume that the photon energy density, or, equivalently, the CMB mean temperature, is also known. The CMB temperature was measured precisely by COBE /FIRAS (Fixsen et al. 1996 ; Fixsen 2009 ) and we view this result as independent of the CMB anisotropy measurements performed by more recent experiments. Note that while the H 0 in the H ( z ) in the denominator of (1) cancels in the product, some residual H 0 dependence still exists because the decoupling redshift and the sound speed depend on the physical matter and radiation densities, and , respectively, while the expansion rate H ( z ) depends on the fractional densities and . In the BAO fit with an external baryon density prior, performs double duty. It not only goes into determining the late-time expansion ( D M and H at the BAO survey redshifts) but also controls the expansion history in the early universe prior to baryons decoupling from photons, since the photon and neutrino properties (with ) are held fixed. As well as providing an indirect H 0 constraint, the BAO+ fit also serves as something of a self-consistency test of early and late-time expansion. The most precise constraints on independent of the CMB power spectrum come from estimates of the primordial deuterium abundance. In standard Big Bang nucleosynthesis (BBN), the abundance of light nuclei is determined by a single parameter, the baryon-to-photon ratio η (see recent review by Cyburt et al. 2016 , and references therein). Taking the photon number density as fixed from the CMB temperature, a measurement of the primordial deuterium abundance in conjunction with knowledge of BBN physics provides a constraint on . Precise estimates of the primordial deuterium abundance have been made in recent years using extremely metal-poor damped Ly α (DLA) systems along sight lines to high-redshift quasars (e.g., Pettini Cooke 2012 ; Cooke et al. 2014 , 2016 ; Riemer-Sørensen et al. 2017 ). Cooke et al. ( 2016 ; hereafter C16 ) reportby combining six such systems. The reaction rate plays a key role in the conversion from D/H to . Using the theoretical calculation for this rate from Marcucci et al. ( 2016 ), C16 findwhich is lower than the Planck value (assuming a standard model throughout). Using instead an empirically derived rate, C16 findwhich has a larger uncertainty but is in better agreement with CMB-derived values. We performed fits to the galaxy-plus-Ly α BAO data with the addition of each of the Gaussian priors on in ( 6 ) and ( 7 ) in turn. We show parameter constraints in Table 3 , including the WMAP 9-year and Planck 2016 CMB anisotropy constraints for comparison. Table 3 Constraints
Paper: 2020MNRAS.492..153B (fulltext)
by scaling relations between the H i content and other intrinsic properties of galaxies. These H i scaling relations can be useful in identifying galaxies that have either more H i than expected or less H i than expected, as galaxies like these may have been affected by recent processes including the removal or accretion of gas. One way to characterize the H i gas content of galaxies is with H i deficiency. H i deficiency is the logarithmic difference between the observed H i mass and the expected H i mass: where M H i exp is the expected H i mass calculated from scaling relations, and M H i obs is the measured H i mass taken from observations (Haynes Giovanelli 1984 ). Generally, a galaxy is considered to have a normal H i gas content if its H i deficiency is between −0.3 and 0.3 (Dénes, Kilborn Koribalski 2014 ), with an H i excess if −0.3 and H i deficiency if >0.3. Studies use H i -selected samples combined with SDSS optical properties to derive scaling relations. With the GASS survey, Catinella et al. ( 2012 ) found that a good predictor of the H i content is the linear combination of stellar surface density and NUV− r colour. We examine the H i deficiency of our sample adapting results using the H i to stellar mass fraction function shown in fig. 8 from Catinella et al. ( 2012 ): with a = −0.285, b = −0.366, and c = 2.872. This function is the relation between H i mass fraction and a linear combination of stellar mass surface density and NUV − r colour with the relation obtained using the subset galaxies with NUV − r ≤ 4.5 mag. The stellar mass surface density is calculated from the formula where R 50 z is the radius containing 50 per cent of the Petrosian flux in z- band from SDSS DR7, and NUV − r is the GALEX (Martin et al. 2005 ) NUV magnitude minus the SDSS DR7 r -band magnitude. Figure 8. H i deficiency using scaling relations from Catinella et al. ( 2012 ). A solid grey line is drawn for deficiency = 0 and a dotted grey line for deficiency = +0.3 and −0.3. Fig. 8(a) shows the H i deficiency in relation to the log of stellar mass. Fig. 8(b) shows the H i deficiency in relation to the distance from filamentary structure based on the DisPeSE topological algorithm. Open symbols are small galaxies with uncertain stellar masses. The results for H i deficiency are shown in Fig. 8 . A solid grey line is drawn for deficiency = 0 and a dotted grey line for deficiency = +0.3 and −0.3. Figs 8(a) and (b) show the H i deficiency in relation to stellar mass and distance to filament, with no obvious trends. In Catinella et al. ( 2012 ), the results are for a sample of galaxies with stellar mass greater than 10 10 M ⊙ . Since only three galaxies (1204837, 1437568, 969633) are above stellar mass 10 10 M ⊙ , this method is not well calibrated for our lower mass sample. With that said, none of the galaxies are H i deficient and half of the sample has H i excess. This is of course not surprising for a small H i -selected sample. 4.3 H <sc>i</sc>
Paper: 2020MNRAS.497.2066F (fulltext)
versions of these methods as described in Patiño-Álvarez et al. ( 2013 ). To establish a significance to the correlation coefficients we performed Monte carlo simulations following Emmanoulopoulos, McHardy Papadakis ( 2013 ) and Patiño-Álvarez et al. ( 2018 ). The confidence intervals (at 90 per cent) in the delay were obtained as in Patiño-Álvarez et al. ( 2018 ). To determine the truthfulness of the delays obtained with the cross-correlation analysis, we performed an alias check via Fourier analysis (Press et al. 2007 ). 9 The resulting delays obtained from the cross-correlation analysis are summarized in Table 3 . Table 3. Delays obtained via cross-correlation analysis, for the entire time-range of study. Delays are in units of days. All correlations are at a confidence level , unless otherwise stated. The uncertainty is at a 90% confidence level. a All delays observed were found to be alias. b Inconclusive analysis. It is important to mention that there are non-conclusive results for some of the light curve pairs, where lag periods from 200 to 1000 d, appear as significant correlations (see Fig. 9 ). We explain this effect by taking into account the low-amplitude variability over large periods of the light curves. When the cross-correlation coefficient is calculated for every lag, light curves in quiescent states will be very similar to each other, regardless of the time axis-shift. The same issue also contaminates cross-correlation functions for correlated signals. Figure 9. Example of inconclusive cross-correlation result. The noisy lines represent the significance of correlation at 90, 95, and 99 per cent, below and above correlation coefficient zero. The smooth line is the interpolated cross-correlation function (ICCF). 5.1 <italic>V</italic> band and 15 GHz anticorrelation In Section 5 , we reported finding a weak anticorrelation between the V band and 15 GHz. We decided to investigate this further by analysing the normalized light curves, then we noticed that during the time period JD 245 = 4860–5760, the behaviour of the V band and the 15 GHz light curves is contrary (see Fig. 10 ). However, this anticorrelation does not hold for the entirety of the time-frame studied in this work; which is the likely reason why it only appeared as a weak anticorrelation on the analysis mentioned above. Figure 10. Normalized light curves for the V band and the 15 GHz emission in the period JD 245 = 4860–5760. Moreover, we decided to test it with a cross-correlation analysis, restricted to the period mentioned above. The analysis results in an anticorrelation at a delay of 39.6 ± 3.2 d, with the V band leading the 15 GHz emission (see Fig. 11 ). In blazars, the V band is usually dominated by synchrotron emission from the jet; however, 3C 273 has a history of being dominated by the accretion disc in the optical bands (e.g. Shields 1978 ; Ghisellini et al. 2010 ). This fact that the optical emission is mostly dominated by thermal emission from
Paper: 2017MNRAS.468.3718K (fulltext)
the cooling time depends on the gas density, it is expected to vary according to location. This is clearly visible in the top panel of Fig. 16 , where the spherically averaged temperature profiles of galaxies only models are plotted. Here, the temperature in the inner part of the H ii region is lower than the gas temperature immediately behind the I-front, 11 and the spread is large (see the difference in the spread in the temperaturedensity relation inside H ii regions shown in Figs 13 and 17 ). Figure 16. Spherically averaged temperature profiles of galaxies only models (top panel) GAL_0.5R (blue dotted line), GAL_R (black solid), GAL_2R (red dashed), and galaxiesQSO models (bottom panel) GAL_0.5RQSO_UVXsec (blue dotted), GAL_RQSO_UVXsec (black solid), GAL_2RQSO_UVXsec (red dashed). The shaded regions indicate the 1 scatter around the mean. Figure 17. Temperaturedensity diagram for galaxies only models GAL_0.5R, GAL_R, GAL_2R and galaxiesQSO models GAL_0.5RQSO_UVXsec, GAL_RQSO_UVXsec, GAL_2RQSO_UVXsec. The red (blue) colour indicates the H ii (H i ) regions defined as cells with (0.5). The contours are overlaid as a guide. To emphasize the temperaturedensity relation both in H ii and H i regions, two different y -axis scales are used. On the other hand, the thermal structure in the neutral regions is nearly identical in all galaxies only models, as, similarly to Section 4.1 , hydrodynamical processes, and not UV photons, are responsible for controlling the temperature in the neutral gas. 4.3.2 Heating by galaxies and a QSO Fig. 15 shows that as the QSO increases the extent of the fully ionized region, the radius at which the gas is heated to 10 4 K is larger than in the corresponding galaxies only models. However, both the average profiles (Fig. 16 ) and the overall temperaturedensity relations (Fig. 17 ) are similar to those of the corresponding galaxies only models. This indicates that a QSO plays a small role in controlling the thermal state in the inner parts of the H ii region, where the pre-existing galaxies have ionized the IGM before the QSO activity. On the other hand, the top panels in Fig. 17 show that galaxiesQSO models reach temperatures that are typically higher than those in the corresponding galaxies only models. The differences become less evident with increasing ionizing emissivity from the galaxies (from left to right in the three rightmost panels). The reason for this is that when both the H ii and He iii I-fronts from the QSO are larger than the pre-existing H ii region due to galaxies, the gas experiences the highest heating. However, when only the H ii I-front from the QSO is larger than the pre-existing H ii region (but the QSO He iii I-front is still inside it), the portion of the gas newly heated up by H i photoionization of a QSO has not yet undergone QSO-driven He ii photoionization. This lack of He ii photoionization by the QSO results in a slightly lower temperature. This non-linear heating process, which
Paper: 2019MNRAS.484.1347H (fulltext)
being only due to reflection by dust in the coma. The fitted one-dimensional dust spectrum was used to determine the scaling factor for the dust profile by measuring the relative strength of the continuum in the centre of the emission band compared to that in the centre of the dust region. The scaled dust profile was subtracted from the emission profile to leave the contribution due to the gas only. The measured spatial fluxes were converted to column densities using the fluorescence efficiencies for each species. The fluorescence efficiencies for the OH (0-0), NH (0-0) and CN (0-0) features are strongly dependent on the heliocentric velocity of the comet due to the Swings effect (Swings 1941 ), and were determined using D. Schleicher’s online tool 2 . The C 3 (λ4050) and the C 2 (Δ = 0) fluorescence efficiencies were taken from AH95 and C12 , while the NH 2 (0,3,0) fluorescence efficiency was that used by Fink ( 2009 ). The fluorescence efficiencies were scaled as . The Haser model (Haser 1957 ) describes the photodissociation of parent molecules into daughter molecules in a spherically symmetric coma. The column density of the daughter molecules N d as a function of distance from the nucleus ρ for a molecular production rate Q is given by the equation where K 0 ( y ) is a modified Bessel function of the second kind. The quantities l p and l d are the scalelengths of the parent and daughter molecules, respectively, and is the radial outflow velocity which is calculated using the equation from Cochran Schleicher ( 1993 ): A model Haser profile was fit to the measured column density profile of each observed species to determine its production rate. The nucleus was placed at the centre of the slit for each exposure, and therefore the extracted profiles have components at ρ values either side of the nucleus. As the Haser model assumes spherical symmetry in the coma, the profiles in the two directions were averaged and the production rates were determined using these average profiles. A series of model profiles for different values of the production rate were generated, and the model profile where the χ 2 statistic was minimized gave the best-fitting production rate. The additional uncertainty in Q was determined by finding the difference between the best-fitting production rate and the model production rate where (Hughes Hase 2010 ). We measured the molecular production rates using scalelengths from two sources, which are given in Table 4 . The production rates in Table 5 were determined using the scalelengths from AH95 , while those in Table 6 used the scalelengths from C12 . We chose to use the same set of fixed scalelengths as previous authors to allow direct comparison of our abundance ratio measurements with the ratios determined in those studies. The parent and daughter scalelengths are usually scaled as , but Langland-Shula Smith ( 2011 ) note that the theoretical scalelength variation should instead be . Power-law fitting can be carried
Paper: 2019ApJ...878...96H (fulltext)
dependent on the grain shape or composition. Indices fall between −2.4 and −2.74, which gives slightly less steep fits than in the analysis in LH07 , where a spectral index −3 provides the best fit for a severely more limited set of shapes. Therefore, while RAT scaling predictions can be done with a simple power-law estimate, our study suggests that a power-law index −2.6 should be used for a general fit. Figure 20. Q Γ (Θ = 0) as a function of λ / a eff for the silicate base shapes. The shaded area around the mean indicates the range of all samples. The power-law fit is done for data between λ / a eff = 2 and 20. Figure 21. Same as Figure 20 , but for coated silicates. Figure 22. Same as Figure 20 , but for carbonaceous shapes. Due to the limited wavelength range available, Figures 20 – 22 cannot show whether or not the RAT efficiency is best fitted by a constant below λ / a eff = 1.8, an estimate of the threshold between constant and power-law fits that originates from LH07 . The region λ ∼ a eff is not possible to account for properly using the limited wavelength range. Due to this, the full RAT fit provided in LH07 cannot be confirmed for the ensembles. Finally, we consider the precession-inducing RAT component Q e 3 . From LH07 , we know how to estimate Q e 3 for the AMO spheroid, here chosen to have an aspect ratio a : b = 2:3, aswhere e is the spheroid eccentricity, a is its minor axis, and K is a fitting factor. For rough estimates of the maximum value , which are found at cos Θ = ±2 −1/2 , LH07 showed that K is near unity. We study the wavelength-dependent normalized values of for the silicate oblate and prolate ensembles. Using Equations ( 3 ) and ( 4 ) with K ≈ 1, we also study the deviation from the estimated functional form of AMO. The results, presented in Figure 23 , reveal that there exists a critical value of λ / a eff ≈ 5, where the maximal value and shape deviation against AMO of Q e 3 are clearly anticorrelated, such that the maximum value of Q e 3 is noticeably less and the shape is considerably different than what AMO predicts. Such a clear discrepancy against AMO was identified to be a property of only the Q e 3 component. Above λ / a eff ≈ 5, quickly peaks, after which it gradually decreases. Also, below the critical value, another strong decrease is visible, although it is limited by the wavelength range used. That behavior, which is almost identical for the two different shape ensembles, can possibly be identified as being due to changing scattering regimes. To fully understand the universality of the critical grain size, which implies that in some situations certain grain size groups may exhibit a different types of alignment, further studies are needed. Figure 23. Top panels: mean values of the component, normalized with the corresponding AMO component, as a function of λ / a eff , for the silicate oblate and prolate ensembles. The bars represent 1 standard deviation around the mean. Below: mean deviation from AMO