adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.470.1849E (fulltext)
each ii / jj term and ij / ji term. We start with ii / jj . For a fixed baseline that involves antenna i , there will be at most N ant additional baselines that are redundant with and do not involve gain i . From equations ( 39 ) and ( 40 ), the weighting of the covariance between two different baselines in which only one involves antenna i goes as . Thus, the presence of redundancy adds no more than N ant terms involving antenna i but not antenna j and vice versa, for each ii summand in equation ( 35 ). Multiplying this overall factor of by N ant to account for the N ant different ii sums leads to a contribution to the noise amplitude of the order of , similar to the level of the noise without redundancy. As a result, redundancy changes the modelling error amplitude by a factor of order unity in the diagonal terms. Next, we consider the ij terms in equation ( 35 ). For a given and i j , there will be at most N ant redundant baselines that do not involve the i th or j th gains, causing the weighting of each unique variance term to go as rather than in the non-redundant case. Since there are unique baselines that do not involve i or j , the overall sum of these terms goes as . As a result, the ij terms in equation ( 35 ) will have a similar magnitude as the ii / jj terms but the overall impact on the amplitude of the modelling noise described in equation ( 35 ) still changes the amplitude by a factor of order unity. We confirm these arguments with a numerical comparison between the amplitude of the modelling noise with and without redundancy taken into account for two redundant arrays of 91 and 331 hexagonally packed 14m apertures and S min equal to the naturally weighted confusion limit. We compute the off-diagonal elements of by numerically computing the beam integral in equation ( 5 ) for all with Airy beams and perform the full matrix inversions prescribed in equations ( 16 ) and ( 17 ). We compare our results to the same calculation where all off-diagonal elements of are set to zero (Fig. A1 ) and find that the difference in amplitude is essentially a factor of order unity, leading to a negligible increase in the effective . This calculation confirms our argument for HERA-scale arrays. Figure A1. We compare the amplitude of modelling noise on a short baseline when is assumed to be diagonal (orange line) and the off-diagonal terms of are explicitly included for a 91-element (dashed lines) and 331-element (solid lines) hexagonally packed array of 14m apertures. We find that even in a maximally redundant array, the effect of redundancy is to change the overall amplitude of the modelling noise by a factor of a few. This only has a small impact (10percent) on , the smallest k where the 21cm signal (black line) dominates over the modelling noise, as computed from equation ( 51 ) that ignores the effect of redundancy. APPENDIX B: The Point Source Approximation For Modelled Foregrounds For analytic tractability, we assumed that the modelled
Paper: 2018ApJ...863L..18A (fulltext)
groups as an extra fractional uncertainty, f , on all data points. 10 10 We use this statistically robust approach instead of assuming a fixed fractional uncertainty of 3%–5% at each frequency as done by Dobie et al. ( 2018 ). We use emcee (Foreman-Mackey et al. 2013 ) to perform a MCMC analysis to determine the posteriors of the model parameters. We use a logarithmic flat prior on s (−1.5 log s 1.5) because by definition s must be positive and the data do not provide any constraints once s becomes sufficiently large (corresponding to a sharp break that occurs over a time interval much smaller than our sampling time). We use flat linear priors on all other parameters: 10 t b 1000 days, 1 F ν , b 1000 μ Jy, −20 α 1 , α 2 20, and 0 f 1. Figure 1. Up-to-date X-ray, optical, and radio light curves of GW170817 (solid circles; open circles are the new data presented in Dobie et al. 2018 ). The data are clearly indicative of a decline at ≳200 days. Also shown are our structured jet models from Margutti et al. ( 2018 ); see Xie et al. ( 2018 ) for full details of the simulations. Both jets have an ultra-relativistic core with E K,iso = 6 ×10 52 erg within an opening angle θ jet = 9°. The solid lines are for a model with n = 10 −5 cm −3 , θ obs = 17°, e = 0.1, and B = 0.0005, while the dashed lines are for n = 10 −4 cm −3 , θ obs = 20°, e = 0.02, and B = 0.001. Our new radio, optical, and X-ray observations continue to support these models. We find and , with days, indicative of a clear transition from a rising to a declining light curve; see Figure 2 . We can reject a non-declining light curve (i.e., α 2 ≥ 0) at 7 σ confidence level. These results are consistent 11 11 We repeat our MCMC analysis for the subset of radio data presented in Dobie et al. ( 2018 ), with α 1 , α 2 , t b , F ν , b , s , and f as free parameters, and find that t b and α 2 are more loosely constrained: t b = 190 ± 30 days and . A value of α 2 ≥ 0 can be ruled out at 3.0 σ confidence level. with the analysis of Dobie et al. ( 2018 ), but provide a much stronger indication of a break due to the longer time baseline of our observations. Both successful structured jet models and pure cocoon models are consistent with the observed α 1 . Unlike the simplest analytic models, which predict α 2 ≈ −2.2 (Sari et al. 1999 ; Nakar Piran 2018 ), the Xie et al. ( 2018 ) successful structured jet simulations predict a shallower initial post-peak decline (Figure 1 ). Therefore, our derived α 2 does not strongly discriminate between successful structured jet and pure cocoon or dynamical ejecta models, although the latter are mildly disfavored (Lamb et al. 2018 ). Of the two Xie et al. ( 2018 ) models shown in Figure 1 , our most recent data at 273 days and 289 days mildly favor the dashed model, which in comparison to the solid model implies a higher circumbinary density of 10 −4 cm −3 , a lower fraction of the shock energy imparted to electrons ( e = 0.02), and an observer viewing angle
Paper: 2017MNRAS.465...19T (fulltext)
excluded in this work due to their CMD locations. Figure 1. Locations of the stars in the 7 deg 2 APOGEE field of NGC 6553. The solid circle indicates cluster tidal radius ( R t ). Non-members outside 1.5 R t are the grey crosses. Stars inside 1.5 R t are colour-coded, according to the colour bar shown in Fig. 2 . Cluster members and non-members are labelled by filled circles and crosses, respectively. Figure 2. [Fe/H] versus radial velocity. Symbol meanings are the same as in Fig. 1 . Figure 3. Differential reddening-corrected CMD of NGC 6553 from PSF photometry of VVV imaging, supplemented with bright stars from 2MASS. The cluster fiducial sequence is indicated as a red solid line. The grey dots are stars within 1.5 R t (tidal radius), and the black dots are stars within the half-light radius. APOGEE targets inside 1.5 R t are colour-coded as in Fig. 1 . Cluster members and non-members determined in this paper are labelled by filled circles and crosses, respectively. See text for more details. From the coordinate information, these 10 cluster members have no overlap with the spectroscopic studies of NGC 6553 published after the year 2006, which include Alves-Brito et al. ( 2006 ), Zoccali et al. ( 2008 ), Gonzalez et al. ( 2011 ), Johnson et al. ( 2014 ), and Dias et al. ( 2015 ). Most of the APOGEE spectra for our sample have signal-to-noise ratio (SNR) higher than 80, except the second star in Table 1 . Each star in our sample has a single one hour visit, so the SNR is correlated with the stellar brightness. The SNR plays an important role in estimating the uncertainties of element abundances (Majewski et al. 2015 ). Three stars are labelled as ‘PERSIST_HIGH’ and six are labelled as ‘PERSIST_MED’. Persistence, where a significant fraction of accumulated charge is released over a long period of time, is particularly strong in one of the detectors (1.51–1.58 μm) used in the SDSS III/APOGEE survey (Nidever et al. 2015 ). However, by comparing the results from spectra containing persistence pixels versus those without, Holtzman et al. ( 2015 ) suggest that persistence does not dramatically impact the parameters in DR12 data. Table 1. Basic parameters for cluster members of NGC 6553. Notes . a Differential reddening corrected magnitude. b Measurement error. c PERSIST: spectrum has a significant number (>20 per cent) of pixels in the high (or medium) persistence region. d N_WARN: parameter value is within half grid spacing of the synthetic spectrum grid edge for nitrogen. SN_WARN: SNR70. From APOGEE measurements, our 10 cluster members show a mean RV of −0.14 ± 5.47 3 km s −1 . Our mean RV agrees with the other determinations: 4 ± 7.1 km s −1 (Cohen et al. 1999 ), 1.6 ± 6 km s −1 (Meléndez et al. 2003 ), −1.86 ± 2.01 km s −1 (Alves-Brito et al. 2006 ), −2.03 ± 4.85 km s −1 (Johnson et al. 2014 ), and 6 ± 8 km s −1 (Dias et al. 2015 ). In addition, the APOGEE data yield a mean [Fe/H] of −0.15 ± 0.05. This GC has diverse metallicity results from
Paper: 2019ApJ...871..246M (fulltext)
Sky Survey (Skrutskie et al. 2006 ) with an overall accuracy of 012–015. To account for the measured absolute offsets of ∼023 and ∼057 in the NUV and FUV image astrometry, respectively, we used a grid of reference stars detected in a ∼12′′ radius around PSR B0540−69 to register the MAMA images onto the astrometry reference frame of the WFPC2 images with an accuracy of better than 001. We used the NUV and FUV images with the recalibrated astrometry as a reference for the pulsar identification. 3. Results 3.1. Imaging and Photometry Figure 1 shows the NUV- and FUV-MAMA images of the PSR B0540−69 field obtained after the processing described in the previous section. We clearly detected PSR B0540−69 in both the NUV and FUV images at a position coincident with its optical coordinates computed by Mignani et al. ( 2010b ): (0009), (005). 21 21 The errors refer to the average of the pulsar coordinates computed on four independent WFPC2 data sets, see Mignani et al. ( 2010b ) for details. Ours is the first detection ever of PSR B0540−69 in the UV, which also makes it the fifth isolated pulsar, among the ∼2300 known, that has been detected in the near-IR, optical, UV, X-rays, and γ -rays, after the Crab and Vela pulsars, PSR B0656+14, and Geminga (the fourth among radio pulsars 22 22 Geminga (PSR J0633+1746) has not yet been unambiguously detected as a radio pulsar despite many searches, see Maan ( 2015 ) and references therein. ). We also detected the PSR B0540−69 PWN in our NUV image (Figure 1 , left), with a structure and extent similar to what is observed in the optical and near-IR (Mignani et al. 2010b , 2012 ). The PWN is at most barely visible, however, in the FUV image (Figure 1 , right). The bright emission knot in the PWN detected at ∼17 southwest of the pulsar in HST /WFPC2 images (De Luca et al. 2007 ) is also visible in the STIS/NUV-MAMA observation, aligned with the major axis of the PWN. Since our paper is focused on the pulsar, a coherent multi-wavelength spectral and spatial analysis of the PWN and its features will be the subject of a subsequent paper. Figure 1. STIS NUV-MAMA (left) and FUV-MAMA (right) images (9650 s each) of the PSR B0540−69 field taken with the F25QTZ filters. The images are aligned in R.A. and decl., with north to the top and east to the left. The image size is 9′′ × 9′′. The pulsar is the source at the center of the nebula, clearly visible in the NUV-MAMA image. The intensity scale of the two images (in counts) is color-coded in the two horizontal bars at the bottom of each image. In both cases, the minimum/maximum of the scale have been adjusted to favor the identification of the pulsar and other stars in the field. For a better visualization, both images have been smoothed with a Gaussian function using a kernel of 2 pixel radius. For both the NUV and FUV images, we computed the pulsar fluxes through aperture photometry employing the tools in the IRAF 23 23 IRAF is distributed by the National Optical Astronomy
Paper: 2020ApJ...897L..23K (fulltext)
six ARs. Each time interval is limited to a duration when the corresponding AR is between −60° and +60° with respect to the central meridian to avoid extreme magnetic field projection effects (Bobra et al. 2014 ). Table 1 includes also the onset time and the associated GOES class of the flares occurred in each AR, based on the GOES solar flare catalog. 9 9 https://hesperia.gsfc.nasa.gov/ Furthermore, Table 1 gives information on the dominant evolutionary phases of the ARs, and on how long their δ -spots were observed. Table 1 Summary Table of the Properties of the Studied Six ARs: NOAA Number and the Study Period of AR, Information about the Investigated Flares, Dominant Evolution Phase of the ARs Note. The last coulomb shows the time interval when δ -spot(s) appeared in an AR. 3. Data Analysis The emergence and shearing components of the magnetic helicity of the six ARs are calculated using Equation ( 1 ). The total magnetic helicity flux for a given AR was generated by summing the emergence and shearing components. The three helicity injection rates for each AR were obtained by integrating the helicity flux over the entire area of the AR. The three helicity flux components are further normalized by their respective largest absolute value in order to facilitate comparison on similar scales. The normalized emergence, shearing, and total helicity fluxes are shown in the top panels of Figures 2 – 3 . In the upper panels of Figures 2 – 3 , the blue line is the emergence term, the red line the magnetic helicity flux associated with shearing motions at the photosphere, and the black line is the total magnetic flux. Figure 2. Time analysis of three flaring ARs, namely (a) AR 11166 (b) AR 12192, and (c) AR 11890. The top panels show time series of the normalized emergence (EM, blue), shearing (SH, red), and total (Total, black) helicity fluxes. The red vertical lines mark the onset time of flares. The second through fourth rows show the wavelet power spectrum (WPS) of the normalized emergence, shearing, and total helicity fluxes, respectively. Rather than plotting power directly, the color bars visualize the logarithm of the ratio of the power to the expected power for Gaussian-distributed white noise and significance compared to noise. The x -axis of each WPS is the observation time, and the y -axis is the period, both in hours. On the WPS plots, the black lines bound the cone-of-influence, i.e., the domain where edge effects become important. The plots to the right of each WPS are the corresponding global wavelet spectra (power averaged over time, similar to a Fourier power spectrum). The orange dashed lines mark the one σ confidence in global wavelet spectrum analyses. Figure 3. Same as Figure 2 , but for the three non-flaring ARs, namely (a) AR 12645, (b) AR 12470, and (c) AR 11785. By inspecting the top panels of Figures 2 – 3 (a)–(c), we can see that the normalized shearing and total helicity flux components show similar evolution trends in
Paper: 2021MNRAS.500..911C (fulltext)
, d v1 ) and ( V v2 , d v2 ) (not shown here). Figure 8. Left-hand panel: 2D distribution ( V v3 , d v3 ). There is a linear trend that is correctly described by the linear relation given by equation ( 16 , dashed line). Right-hand panel: the correlation disappears after correcting the LOS displacements with this equation. This is a statistical demonstration of the v-RSD off-centring effect. From this analysis, we arrive at the third important conclusion of this work: void centres shift preferentially along the LOS when they are mapped from r - into z -space, and this displacement can be statistically quantified by means of equation ( 16 ). These results give support to the v-RSD off-centring effect postulated in Section 4.5 . 5.3 Cross-correlations Since the ratio (or equivalently δ R v ) characterizes the change of volume in voids, the statistical analysis of Section 5.1 gave support to the t-RSD expansion effect postulated in Section 4.2 . On the other hand, as the displacement and velocity characterize the movement of voids, the statistical analysis of Section 5.2 gave support to the v-RSD off-centring effect postulated in Section 4.5 . It only remains to test if both effects are statistically independent by looking for cross-correlations between these quantities. Fig. 9 shows the 2D LOS distributions (left-hand panel) and (right-hand panel). The horizontal lines are the theoretical predictions (dashed) and (solid). No correlations can be seen, giving support to the postulated independence. It is worth mentioning that the analogue POS distributions (not shown here): , , , and show a similar behaviour. These results allow us to interpret the large-scale dynamics of the whole region containing the void (v-RSD) as decoupled from the dynamics of the galaxies at scales of the void radius (t-RSD). This also suggests that potential distortion patterns in observations due to these two effects can be treated separately. This is the fourth important conclusion of this work. Figure 9. 2D distributions (left-hand panel) and (right-hand panel). The horizontal lines are the theoretical predictions (equation 7 , dashed) and (equation 10 , solid). No correlations can be seen in both panels, suggesting that the t-RSD expansion effect and the v-RSD off-centring effect are statistically independent. 6 IMPACT ON THE VOID SIZE FUNCTION This section has a double intention. On the one hand, we will finish the analysis of the last section incorporating the additional AP change of volume not treated yet. On the other hand, we will study the impact of all the z -space effects on the void size function. For this reason, we turn now to the FC void catalogues, fully affected by z -space systematics (see Table 1 ). 6.1 Generalities of the <xref ref-type="bibr" rid="bib69">VSF</xref> modelling We start with some generalities concerning the VSF modelling. The real-space VSF can be modelled using the excursion set formalism combined with the spherical expansion of matter
Paper: 2017MNRAS.470.1943C (fulltext)
A MannWhitney U test of the distributions about this relation does not rule out at 95percent confidence the null hypothesis that the two redshift sub-samples have the same parent population. We do, however, acknowledge that weak evolution of the relation of equation ( 1 ) cannot be ruled out by our data, and this should be investigated with deep X-ray survey data and/or further deep X-ray observations of carefully selected samples. 2.3 Using pressure to predict X-ray luminosity We have demonstrated that we can reliably predict the ICM pressure at a single distance within the group/cluster surrounding an FRII radio galaxy. To be able to predict the overall group/cluster richness, we need to be able to translate this pressure measurement into an estimate of cluster mass and/or X-ray luminosity. We therefore examined whether the universal cluster pressure profile of Arnaud etal. ( 2010 , hereafter A10 ) could be applied to FRII environments. We first examined the sub-sample of I17 for which reliable spectral temperature estimates were available. Our pressure profiles have large uncertainties, and were obtained from either beta or double-beta model fits to the surface brightness profiles (see I17 ). The environments are also mainly galaxy groups ( L X 10 44 ergs 1 ), and so lower in mass than the sample of A10 ; however, Sun etal. ( 2011 ) have shown that the universal profile also applies on galaxy group scales. Fig. 2 (right-hand panel) shows that our scaled pressure profiles are in broad agreement with the A10 profile shape and normalization. We therefore tested whether the universal cluster pressure profile could be used to translate our single-radius pressure estimates, from our radio predictions, into cluster mass and X-ray luminosity. We used the profile of A10 (equations 1113 of that work) and our input values of distance and P ext to estimate M 500 for each radio-galaxy environment. We then applied the L X M 500 relation of Pratt etal. ( 2009 ) (using the relation for L X R 500 ) to obtain a predicted L X for each radio-galaxy environment. Finally, we compared our radio-predicted X-ray luminosities with the measured X-ray luminosities for our sample from Ineson etal. ( 2015 ). Fig. 3 (left-hand panel) shows the remarkable agreement between our radio-predicted X-ray luminosities and the measured values across more than three orders of magnitude in X-ray luminosity (from poor groups to moderate clusters) our method appears to predict ICM X-ray luminosity to within roughly a factor of 2 based only on the measured properties of the embedded radio galaxy. We have included four additional FRII sources from the sample of I17 for which external pressure distributions could not be measured, but which meet the morphological selection criteria, leading to a sample of 31 objects, as listed in Table 1 . Figure 3. Left-hand panel: the predicted group/cluster X-ray luminosities determined from the radio-measured pressures for the morphologically
Paper: 2017ApJ...850...69C (fulltext)
data presented here from the ion trap measurements are not of the bare species itself because of the slight perturbation induced by the weakly bound helium atom; however, the shift is expected to be small. Laboratory data on the band at and the weak features near for are shown in Figure 2 and indicate that the helium causes a shift on the electronic transition of of around . Considering the width of the absorption band profiles, for comparison with astronomical data, the spectrum of (Figure 1 ) can be considered to be that of the bare cation. In Table 1 , the band maxima of the absorptions shown in Figure 1 are listed. Figure 2. Electronic spectra of below 10 K. Data are shown for two of the bands presented in Figure 1 . The similarities between the spectra indicate that the shift on the electronic transition of due to the presence of the helium is small. Therefore, the spectrum of may be regarded as that of for comparison with DIB data. A linear offset has been applied to the vertical axis of the data shown. Table 1 Absorption Maxima of the Bands Shown in Figure 1 and Compared with the Gas-phase Prediction Reported by Steglich et al. ( 2011 ) Note. a Weak absorptions with intensities lower than 20% of the band at are not listed. Steglich et al. ( 2011 ) predicted the gas-phase transition energies of based on their matrix data using a model (Gredel et al. 2011 ) to account for the polarizability of the different rare gases used. The present work enables a direct evaluation of this model and their results are also given in Table 1 . The predicted values are found to be in accord with the gas phase; however, the large error bars () indicate that the extrapolation is not quite good enough for an unambiguous comparison with DIB data, especially if the latter are weak (). For this, gas-phase measurements as done here on are required. In the context of astronomical detectability it is important to determine the oscillator strength f of the individual transitions, in particular for the strongest band in the spectrum. This is because the theoretically calculated values are not expected to be very accurate because of the nature of the method that had to be used (TD-DFT). Furthermore, these f values correspond to the the whole electronic absorption system(s). To obtain the oscillator strength for the origin band at , its Franck–Condon factor relative to all the other transitions has to be estimated, as carried out by Steglich et al. ( 2011 ). Thus, it is more reliable to measure absolute absorption cross sections directly in the ion trap, as previously demonstrated by measurements on the fullerene cations (Campbell et al. 2016a , 2017 ). Absorption cross-section measurements were carried out at the wavelength of the band maxima of the two strongest absorptions in the spectrum. Plots showing typical exponential fits to the data are shown in Figure 3 . The characteristic fluence, , was found to be 0.8 and at 8281 and , respectively. These values correspond