adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.457..986Z (fulltext)
smoothing scales. The error bars are given by the standard deviation from 200 realizations. The 5,10 h 1 Mpc smoothing scale gives similar rms and bias for linear redshift distortion parameter but slightly more bias for larger smoothing. Even though is a nuisance parameter in BAO analysis, it is interesting to observe the value fitted, as it indicates the level at which the redshift corrections are using the right value of the velocity field. The 2 /dof values are very similar between the different smoothing scales explored. Table 4. Best fits for and . Mean and rms from 200 reconstructed QPM mocks. Second block refers to the results using the covariance matrix from 1000 mocks with a fixed smoothing scale of 15 Mpc h 1 . The accuracy in the value is very similar between 5 and 15 h 1 Mpc, but visibly degrades when going to higher smoothing scales. The smaller dispersion for is for 1015 h 1 Mpc; however, the 15 h 1 Mpc is significantly biased (5.3), thus, 10 h 1 Mpc is the best option if we considered only . As we find a relative large bias in 15 h 1 Mpc h 1 Mpc compared to 5, 10, 40 h 1 Mpc, we perform a cross-check between the results using the four smoothing scales as a sanity check. We included in Fig. 5 the dispersions plots of the 15 fits from the mocks, and the R = 5,10, and 40 Mpc h 1 . The legend includes the values of the correlation coefficient in the three cases. The results show that effectively we are using the same set of mocks as they show very high correlation coefficient and that changing the smoothing scale of reconstruction affects slightly the correlation between the results. Figure 5. Dispersion plots of 15 versus 5, 10, 40 . The legend includes the values of the correlation coefficient in the three cases. The large values of the correlation coefficient just show that we are effectively using the same set of mocks for the test and that changing the smoothing scale of reconstruction affects the correlation between the results slightly, but remains large. For , the least significant bias ( b / lower) is for the 5 h 1 Mpc smoothing scale. Considering and simultaneously, the best option is 5 h 1 Mpc which gives the less significant bias with smaller dispersion (0.8 in and 0.9 in ). We find maximal variations in the mean value for best-fitting parameters, 0.5percent for and 0.3percent for , producing D A , H 0.5percent. In the case of , the precision seems to be very similar for the 5 and 15 h 1 Mpc smoothing scales and slightly lower for the 40 h 1 Mpc. The accuracy is also similar for 5 and 15 h 1 smoothing scales and clearly degrades when using a higher smoothing scale. The 2 /dof values are very similar between the different smoothing scales explored. 4 Fig. 6 presents the distributions of the uncertainties for and parameters for different smoothing scales and the quantitative fitting results for the uncertainties are summarized in Table 5 . We can see that the smoothing scale strongly affects the error distributions
Paper: 2021MNRAS.501.5964D (fulltext)
(Bovy 2015 , dashed red). We also show recent measurements by Callingham et al. ( 2019 ), Eadie Jurić ( 2019 ), Erkal et al. ( 2019 ), and Vasiliev et al. ( 2021 ) with the filled black star, orange circles, purple triangle, and brown square, respectively. Our circular velocity profile shown in Fig. 6 is in excellent agreement with the Cautun et al. ( 2020 ) profile. This model uses MW rotation curve data derived from Gaia DR2 to fit an adiabatically contracted dark matter halo. Interestingly, Cautun et al. ( 2020 ) find a relatively ‘average’ halo concentration for the MW, of and a total (pre-LMC infall) mass of M ⊙ , in excellent agreement with our total mass estimate. Moreover, our total MW mass also agrees with recent measurement using the more distant satellite galaxies as tracers (e.g. Callingham et al. 2019 ; Li et al. 2020 ). It is reassuring that our results at intermediate radii are in such good agreement with independent measures from both the inner and outer halo. 6 CONCLUSIONS In this work, we have applied a distribution function method to a large sample of distant ( r > 50 kpc) halo stars to estimate the mass of the MW out to 100 kpc. We pay particular attention to the systematic effect of unrelaxed substructure, and the dynamical influence of the LMC. Our main conclusions are summarized as follows: We use a rigid MW–LMC model to constrain the systematic reflex motion effect of the massive LMC on our halo mass estimate. As shown by Erkal et al. ( 2020 ), the (pre-infall LMC) halo masses are overpredicted due to the velocity gradients caused by the recently infalling LMC. However, we find that a simple velocity offset correction in v los and v b can minimize the overestimate caused by the reflex motion induced by the LMC, and, assuming a rigid LMC mass of 1.5 × 10 11 M ⊙ , we can recover the true mass within 1 − σ. By applying our method to a sample of MW-mass haloes from the Auriga simulation, we find that the halo masses are typically underestimated by 10 per cent. However, this bias is reduced to if we only consider haloes with relatively quiescent recent accretion histories. The residual bias is due to the presence of long-lived shell-like structures in the outer halo. The halo-to-halo scatter is for the quiescent haloes, and represents the dominant source of error in the mass estimate of the MW. We apply our distribution function method to N = 483 halo stars when high-probability Sgr stars are excluded. The overall sample has a radial velocity anisotropy, β = 0.5, in good agreement with recent measures in this radial range (Bird et al. 2019 , 2020 ). Our estimated mass within 100 kpc is M (100 kpc) = 6.07 ± 0.29 (stat.) ± 1.21 (sys.) × 10 11 M ⊙ . A systematic bias correction (), and additional uncertainty (), are included based on our results from the Auriga simulations. The mass estimates are slightly higher when we do not include a velocity offset to correct for the reflex motion induced by the LMC, or slightly lower
Paper: 2018MNRAS.477.1508C (fulltext)
emission produced by electrons spiralling on the magnetic field lines in a dipolar magnetic field. From the literature (e.g. Svestka 1981 ; Bastian et al. 1998 ), it is assumed that in the radio range the emission comes from the non-thermal electrons that are accelerated in the corona and then propagate down to the foot-points of the loops with a commonly accepted power-law distribution in energy. Nevertheless, when taking into account the comparison between radio and hard X-ray observations, our 3D magnetic loop radio modelling agrees reasonably well with the literature in relation to the location and sizes of the radio source. The ambient plasma is supposed to be a quiescent background with some temperature and density profiles. It is widely known that the radio emission emanates from the chromosphere to corona where the electrons are trapped due to magnetic mirroring. Thus, the number density of non-thermal electrons refers to different locations in the loop and different distribution of trapped or precipitating particles, from which it follows that a detailed determination of the emitting region parameters in microwaves and hard X-rays is important. We present an alternative approach to the time-consuming process to calculate a non-homogeneous model – each time one is needed for a flare analysis – by providing a data base of pre-calculated models constrained by some statistical properties of a large sample of Nobeyama observations. We know that this data base is still limited in terms of the wide range of parameter combinations needed to calculate the gyrosynchrotron emission to describe all possible scenarios of flares in the microwave. We statistically analysed 10 important parameters, that in general cannot be directly observed, to explain a flare as shown in Fig. 6 . We found that 8 out of 10 parameters in the simulations were recovered with a relative error ≤20 per cent on average. We consider this result an improvement for flare analysis in terms of the time consumed with the calculations. Although the data base was produced using equally distributed parameters from the lower to upper limits, the geometric bias discussed in Section 6.1 results in differences in the distributions of geometry, magnetic field, and energy parameters. In the energy parameters, the observed solar bursts show preferences for lower non-thermal electron density and a slight preference for higher densities for the limits used here. The magnetic field distribution shows some preference for higher values than the centre of the data base range we used. We also noted some shift in the distribution for loop-top events. In general, the geometric parameter distribution shows a tendency for bigger loops. Differences between individual solutions are mainly due to the degeneracy of results with quite similar χ 2 in the dominion of 10 parameters. This is the reason that we preferred the averaged solution weighted by the χ 2 . Table 3 shows the best parameters inferred
Paper: 2017MNRAS.466.4904B (fulltext)
and 0.4307keV, respectively. If that is indeed the origin of these features, it would be consistent with the presence of strong, highly ionized C and N emission lines in the UV (Cenko etal. 2016 ). Interestingly, Miller etal. ( 2015 ) also suggest several highly ionized emission features are present in their high-resolution spectra, particularly near the O K-edge. Figure 6. Merged X-ray spectra of ASASSN-14li seen at early times (first 200d, black crosses) and later times (after 200d, red crosses). Each spectrum is binned with a minimum of 20 counts per energy bin. The early-time spectrum is best fit with two Gaussian emission features superimposed on a blackbody (black solid line), while the late-time spectrum is consistent with a purely blackbody model (red solid line). The residuals from the fits are shown in the bottom panel. Irrespective of the origin of the emission features, the X-ray emission from ASASSN-14li is reasonably well described by a blackbody. The temperature of the X-ray emission shows only a moderate decrease with time, from 810 5 K down to 4.510 5 K. We also find that between the early- and late-time observations, the mean column density decreases by a factor of 2, becoming comparable to at later times. This could indicate that at early times the material surrounding the TDE was dense, not highly ionized, and obscured the lower energy X-rays, while at late times the material surrounding the event has been nearly completely ionized and thus transparent to the lower energy X-ray photons. We note that the amount of material required to produce this absorbing column is small relative to the overall mass budget, even in the contrived scenario where all of the absorbing material is spherically distributed at the H-emitting radius: M, where f is the covering fraction, r 17 is the characteristic radius of the absorbing material in units of 10 17 cm and N 20 is the hydrogen column density in units of 10 20 cm 2 . Furthermore, our modelling suggests there may be a factor of a few variability in the column density between the individual epochs. Unfortunately the spectra for the individual epochs do not reach the S/N required for a precise determination of the column density. We also note similar variability and overall decrease in the absorbing column in the eight temporally binned spectra. The moderately decreasing temperature and variability in the column density are similar to the findings from Miller etal. ( 2015 ), but we have increased the temporal baseline by a factor of 3. Finally, we note that the Swift XRT detector lacks the energy coverage to discriminate between a bremsstrahlung and blackbody spectrum. While we have assumed the X-ray emission to be well described as a blackbody, there is only marginal evidence that the X-ray spectrum turns over at lower energies. It is plausible that the X-ray emission arises from tidal debris shocking the ambient medium in the vicinity of the SMBH. For ionized gas in the strong shock
Paper: 2015MNRAS.447.3797V (acknowledgments)
This work was supported by the Spanish Ministry of Science and Innovation under project number FIS2009-09898 and by the Spanish Ministry of Economy and Competitiveness under project number FIS2013-40653-P.
Paper: 2020MNRAS.495.4334Y (fulltext)
can be noticed as a non-local equation of state as it describes the radial pressure as a function of the energy density at a specific point on the manifold (under the constraint f ( R, T, Q ) = R ). Now, we consider two subcases as follows. 4.1 The Gokhroo and Mehra Ansätz In order to continue the systematic analysis, we take the assumption introduced by Gokhroo and Mehra, as where K ∈ (0, 1), and μ o is a constant. Utilizing equation ( 59 ) in equations ( 10 ) and ( 17 ), we get and Further, from equations ( 11 ) and ( 12 ), we obtain It could be useful to introduce new structural variables as In this context, after substituting equation ( 63 ) in equation ( 62 ), we obtain which appears to be in form proposed by Ricatti. The integration of this expression produces the line element in terms of z and Π in the background of f ( R, T, Q ) theory as where C is integration constant. Thus, one may write the effective forms of physical variables as Such kind of solutions could be helpful to understand some hidden and very interesting features of the spherical systems. Di Prisco et al. ( 2011 ) calculated such results in the background of GR. These results were modified by Sharif and Yousaf for the cases of cylindrically Sharif Yousaf ( 2012b ) and shearfree spherical Sharif Yousaf ( 2012a ) structures. Yousaf ( 2017 ) and Bhatti ( 2016 ) further extended these results for Einstein-Λ gravity. To obtain non-singular characteristics of the obtained solutions, the obtained solutions ( 66 )–( 68 ) should satisfy equations ( 19 )–( 21 ) at the boundary. 4.2 The polytropic fluid with zero complexity factor In this subsection, we study polytropic relativistic fluid in the presence of f ( R, T, Q ) corrections. To deal with the system of equations, we require vanishing complexity factor condition to supplement with the polytropic equation of state. Here, we consider two cases of polytropes separately. The first one is with the polytropic constant K , polytropic exponent γ and polytropic index n . To make the dimensionless form of TOV equation and the mass function, we may define some new variables as where subscript c shows that the quantity is calculated at the centre. At the boundary r = r Σ (ξ = ξ Σ ), we have ψ(ξ Σ ) = 0. Putting these dimensionless variables in TOV equation, we get Differentiating ν(ξ) and substituting the value of m ( r ), we get In two ordinary differential equations ( 72 ) and ( 73 ), there are three unknown functions ν, ψ, and Π. For the unique solution of these equations, we need an extra condition. To obtain this, we assume the vanishing complexity factor condition in form of dimensionless variables. This gives These differential equations have a unique solution for arbitrary values of two parameters n and α. Any solution of these equations provide the mass, density, radius, and pressure of peculiar stellar object for the specified values of n and α. Now, we consider the second case of polytropic equation of state , where μ b
Paper: 2015MNRAS.451.3249A (acknowledgments)
The authors acknowledge the reviewer for their detailed and thoughtful comments on this work. MA is funded by an appointment to the NASA Postdoctoral Program at Ames Research Centre, administered by Oak Ridge Associated Universities through a contract with NASA, and acknowledges past funding from the University of Western Australia and the University of St Andrews. GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the Sloan Digital Sky Survey and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programmes including GALEX MIS, VST KiDS, VISTA VIKING, WISE , Herschel -ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is http://www.gama-survey.org/ . The VISTA VIKING data used in this paper are based on observations made with ESO Telescopes at the La Silla Paranal Observatory under programme ID 179.A-2004.
Paper: 2018ApJ...860L...2F (fulltext)
40.7 Mpc with a standard deviation of 2.36 Mpc, corresponding to the measured distance and quadrature sum of statistical and systematic errors reported in Cantiello et al. ( 2018 ). Here we have assumed a Gaussian distribution on this distance measurement, which we deem valid for a measurement of this precision and for the purpose of exploring upper and lower bounds. Using the EM observations as the prior on the luminosity distance results in significantly narrower posteriors on inclination angle and source-frame chirp mass shown in Figure 2 . The improved chirp mass measurement is due to the reduced error on d L , as the d L posterior samples are used to convert from the measured detector-frame chirp mass to the source-frame chirp mass (Schutz 1986 ; Finn Chernoff 1993 ). However, the improved precision on distance has no effect on our measurements of the component masses or spins, because at leading order the mass ratio q = m 1 / m 2 and spin parameter χ eff (Cutler Flanagan 1994 ) are not correlated with distance. With the EM observations as the prior on d L and a prior on the inclination angle uniform in , we find that the viewing angle is degrees (90% confidence). Figure 2. Comparison of posterior probability distributions without and with combined EM information. The black contours show the results for using only the GW signal, the blue contours show the results for the fixed sky location of NGC 4993, and the red contours show the results for both the fixed sky location and a Gaussian prior on distance of 40.7 ± 2.36 Mpc from Cantiello et al. ( 2018 ). These analyses used a prior on inclination angle that is uniform in . For each parameter, we quote the median value and the 90% credible interval (shown with vertical solid and dashed lines, respectively, on the posterior plot of each parameter). The EM information on the distance measurement greatly improves the precision with which we measure the inclination angle (and significantly reduces the uncertainty on the source-frame chirp mass). Errors in the calibration of the GW detectors can cause errors in the measured amplitude of the GW signal and hence in the inclination angle of the binary. We treat this as a systematic error, which we measure by shifting the amplitude calibration of the LIGO and Virgo detectors by the 1 σ uncertainty bounds for LIGO/Virgo’s second observing run (Cahillane et al. 2017 ). We find that shifting the calibration to its most sensitive and least sensitive extremes results in a ±17 shift in the peak of the viewing angle when using a prior on inclination angle that is uniform in . We quote this shift as the systematic error on our measurement. Changing the phase error of the calibration within the bounds reported in Cahillane et al. ( 2017 ) produces a negligible effect on the inclination angle. A prior uniform in goes to zero as the viewing angle approaches face on (or face off), so we repeat our analysis using a prior uniform in ι . Figure 3 shows a
Paper: 2019MNRAS.490.4898H (acknowledgments)
ACKNOWLEDGEMENTS AH acknowledges support from the 2018 NRAO summer research program, where the majority of the analysis was done. KPM is a Jansky Fellow of the NRAO. Thanks to Nimisha Kantharia, Preshanth Jagannathan and Gregg Hallinan, who provided help with the GMRT proposal and observing. We thank the staff of the GMRT who have made these observations possible. The GMRT is run by the National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. We thank the anonymous referee for helpful comments on the manuscript.
Paper: 2016A&A...590A..80R (acknowledgments)
We thank an anonymous referee whose comments have contributed to improving the presentation of this paper. P.R. acknowledges a grant from the Greek General Secretariat of Research and Technology in the framework of the programme Support of Postdoctoral Researchers. F.J.C. acknowledges support by the Spanish ministry of Economy and Competitiveness through the grant AYA2012-31447.
Paper: 2021ApJ...911...39S (fulltext)
any fixed z , and , where . Note that f is zero at the boundaries of these intervals and similarly g is zero at r = 0 and r = R 0 , so the functions are continuous. We solved for V Z 2 in Equation ( 26 ), in order to study the nature of the vertical motion induced by the magnetic shear and potentially explore the relationship between this type of perturbation and the mass flux observed in the chromosphere in the form of jets. The solution for V Z 2 in the zero-beta case is simply proportional to the square of B θ 1 ,and zero otherwise—see Figures 2 and 3 . Hence V Z 2 will be of a similar form to B θ 1 , i.e., a pulse propagating in the z -direction. We suggest that a pulse in the form of this example could possibly influence plasma and contribute to the formation of a jet. Using our perturbation model we can approximate the vertical mass flux of the plasma within the locality of the pulse, φ z = ρ v z ≈ ε 2 ρ 0 V Z 2 . Figure 2. A 2.5D representation of the given example of a V Z 2 pulse from Section 3.5 , at particular times: t = 0.2 τ , t = 0.5 τ , and t = τ . The pulse appears as two peaks due to the axisymmetry around the z- axis of the tube, and has been rescaled so that the V Z 2 (vertical) axis is in units of , while the r- and z- axes are in units of R 0 and v A τ , respectively. An animated version of this figure, showing the evolution of the pulse from t = 0 to t = 2 τ , is available online. (An animation of this figure is available.) Figure 3. Rotated views of the 2.5D plot in Figure 2 at t = τ , showing the z dependence (left) and the r dependence (right). Interestingly, the nature of the perturbation also suggests that there could be plasma with lower velocity near the center of the flux tube, since we would have φ z = 0 at r = 0. Once the pulse is fully formed at t = τ , it will maintain its shape while it propagates in the positive z- direction. For t ≥ τ , φ z will be constant on concentric toroidal surfaces, which can be partially seen in Figure 4 . The toroids degenerate into a cylinder, bounded by the limits for V Z 2 stated in Equation ( 31 ), as the magnitude of φ z approaches zero; outside of this cylinder there is no perturbation. The maximum magnitude of φ z is reached where both f and g are maximized, on the circle with radius r = R 0 /(2 1/ λ ) at z = v A ( t − τ /2). Other forms of the B θ 1 pulse are of course possible as solutions to the first-order equations, but solutions that are suitable for the model are often similar to our example in form and behavior. Figure 4. Left: a 3D plot of the vertical mass flux φ z at time t = τ /2, for the example in Section 3.5 (with scaled Cartesian axes for simplicity). At this point the pulse is “half formed” and there is positive mass flux for 0 ≤ z ≤ v A τ /2 within the tube. The blue arrows at the lower boundary represent the driver for the torsional magnetic field perturbation. An animated version of this figure, showing the evolution of the pulse from t = 0 to t = τ /2,
Paper: 2019ApJ...877..139G (acknowledgments)
The authors acknowledge support from NewCompstar, COST Action MP 1304, and HIC for FAIR. R.O. Gomes would like to thank V. Dexheimer and S. O. Kepler for fruitful discussions and suggestions. P. Char acknowledges support from the Navajbai Ratan Tata Trust.