adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.486.5558S (fulltext)
Whilst a reasonable harmonic fit to the ESPaDOnS data can be achieved using this period, it does not produce an acceptable phasing of all available 〈 B z 〉 measurements. Figure 1. Photometric ( top ) and magnetic 〈 B z 〉 ( bottom ) measurements, phased with periods determined from (left to right) K2 photometry, all 〈 B z 〉 measurements, and all photometric measurements. 〈 B z 〉 measurements were obtained from ESPaDOnS by Shultz et al. ( 2018 ) and photopolarimetric data by Borra et al. ( 1983 , BLT83) and Bohlender et al. ( 1993 , BLT93). The solid and dashed curves show, respectively, the best second-order harmonic fit to the ESPaDOnS 〈 B z 〉 data and the 1σ fit uncertainty. Photometric data are from K2, SMEI, Hipparcos ( H p ), and Strömgren y photometry published by Sterken et al. ( 1995 , S95), Manfroid et al. ( 1995 , M95), and Pedersen Thomsen ( 1977 , P77). For clarity the K2 and SMEI data have been binned by phase, using bin sizes of 0.01 cycles. The mean magnitude of each data set was subtracted for display purposes. All of the periods obtained since the year 2000 are approximately consistent with one another; the two periods determined using photometric data obtained prior to the year 2000 are also consistent with one another. At the same time, the difference between the Bowman et al. ( 2018 ) and Catalano Leone ( 1996 ) periods, 0.00015 d, is almost eight times larger than the formal uncertainty in the Bowman et al. period (the less precise of the two). This suggests that the period may have changed. 3.2 Period analysis We now turn to a re-examination of the period analysis of the individual data sets; the results are summarized in Table 2 . Period analysis was performed using standard Lomb–Scargle statistics (Lomb 1976 ; Scargle 1982 ), utilizing both the idl program periodogram 4 and the period04 package (Lenz Breger 2005 ). The uncertainties were determined in the same manner as by Shultz et al. ( 2018 ), i.e. according to the analytic method described by Bloomfield ( 1976 ). Examining the ESPaDOnS data in isolation yields P rot = 0.97887(6) d, consistent within uncertainty with the K2 period (Bowman et al. 2018 ). The period determined from all available magnetic data (i.e. including the previously overlooked 〈 B z 〉 measurements of Borra et al. 1983 ) is 0.979855(5) d. The photometric and magnetic data are shown phased with this period in the middle panel of Fig. 1 . Phasing 〈 B z 〉 with this period and fitting the ESPaDOnS data with a second-order sinusoid yields a reduced χ 2 = 5, i.e. a poor fit. This period also does not provide a coherent phasing of the majority of the photometric data set. Notably, the variation in the phase-binned SMEI data almost completely disappears, likely because observations are being binned as though they were at the same phase when, in fact, they are not. We can also combine all available photometric data (shifted to the mean magnitude of each data set, as in Fig. 1 ), which yields P rot =
Paper: 2018MNRAS.478.5533F (fulltext)
comparison once the angular positions of the satellites are randomized around this point. From this tensor we compute its eigenvalues, λ 1 > λ 2 > λ 3 , and corresponding eigenvectors, , , . We define the size of the three ellipsoidal axis as a = λ 1 , b = λ 2 , and c = λ 3 . We also define as the vector perpendicular to the planar satellite distribution. We also define the RMS plane width w as the standard deviation of the satellite distances to the plane defined by the vector . To summarize we characterize the satellite distributionwith the following quantities obtained from the inertia tensor: RMS plane width, w . c / a axis ratio. b / a axis ratio. 3.3 Characterizing asphericity with normalized scalars We compare each satellite distribution against its own spherically randomized distribution. We keep fixed the radial position of every satellite with respect to the central galaxy and then randomize its angular position. We repeat this procedure 10 4 times for each satellite distribution and proceed to measure the quantities mentioned in the previous section: w , c / a , and b / a . This allows us to build a normalized version of all quantities of interest by subtracting the mean and dividing by the standard deviation of the values from randomized samples. We use the normalized quantities to build the analytic probability distributions for the scalars describing asphericity. 3.4 Building an analytic asphericity probability distribution After building the normalized variables for the simulated data we perform a Kolmogorov–Smirnov test with the null hypothesis of belonging to a normal distribution with mean and standard deviation computed from the mean and standard deviation estimated from the data. Although the physical quantities of interest are bound, we find that the distributions for the normalized w , c / a , and b / a are indeed consistent with Gaussian distributions. Based on this result we build a multivariate normal distribution for the joint distributions of the normalized w , c / a , and b / a where X = [ w , c / a , b / a ] T is a vector variable with the normalized quantities, μ is the vector mean, and the Σ is the covariance matrix. We compute the preferred covariance matrix and the mean distribution values with a jackknife technique. That is, out of the n pairs in each simulation, we perform n different covariance and mean value measurements using only n − 1 pairs. The reported covariance and mean values correspond to the average of all measurements, the corresponding standard deviation also helps us to estimate the uncertainty on every reported coefficient. This compact description allows us to generate samples of size N that are consistent by construction with their parent simulation. Finally, we use the generated samples to estimate how common are the deviations from sphericity that we measure in the observational data. We use a double-tailed test around the mean in this comparison, meaning that we count the fraction of
Paper: 2018MNRAS.480.3062L (acknowledgments)
ACKNOWLEDGEMENTS The authors thank an anonymous reviewer for comments and suggestions that strengthened our manuscript. NWCL gratefully acknowledges support from the American Museum of Natural History and the Richard Guilder Graduate School, specifically the Kalbfleisch Fellowship Program, as well as support from a National Science Foundation Award No. AST 11-09395.
Paper: 2016MNRAS.457.1786M (acknowledgments)
BDM gratefully acknowledges support from NASA Fermi grant NNX14AQ68G, NSF grant AST-1410950, and the Alfred P. Sloan Foundation. We thank the anonymous reviewer for several helpful suggestions and for emphasizing the applicability of our results to Type IIn supernovae.
Paper: 2019MNRAS.482L...9B (acknowledgments)
ACKNOWLEDGEMENTS We thank Dougal Mackey, Adrian Price-Whelan, David Hogg, Gurtina Besla, David Schiminovich, Alis Deason, and Sergey Koposov for illuminating discussions. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 308024. This research was started at the NYC Gaia DR2 Workshop at the Center for Computational Astrophysics of the Flatiron Institute in April 2018 and made use of data from the European Space Agency mission Gaia ( http://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Paper: 2018ApJ...860..147M (acknowledgments)
I thank Duncan Galloway, Zac Johnston, and Hendrik Schatz for useful discussions, and acknowledge the instructive tutorials from Ed Brown, Andrew Cumming, and Rob Farmer located on the MESA Marketplace ( http://cococubed.asu.edu/mesa_market/ ) that helped at the beginning stages of this work. Relevant MESA inputs for this work are available on the MESA Marketplace. This work was supported in part by the U.S. Department of Energy under grant No. DE-FG02-88ER40387. This material is based on work supported by the National Science Foundation under grant No. PHY-1430152 (Joint Institute for Nuclear Astrophysics–Center for the Evolution of the Elements).
Paper: 2018A&A...617A..26D (fulltext)
Lasota (2007) have updated this work, for a sample of ten CVs. The importance of this test was revived by Schreiber Gänsicke (2002) , who pointed out that the HST FGS parallax distance of SS Cyg, the archetypal dwarf nova, led to a revised mass transfer rate that placed this outbursting system in the stable region of the ( R out , Ṁ ) plane. This led to much hand-wringing ( Schreiber Lasota 2007 ; Smak 2010 ) until a radio VLBI parallax distance ( Miller-Jones et al. 2013 ), confirmed with Gaia ( Ramsay et al. 2017 ), firmly placed the system in the unstable region (as anticipated by Lasota 2008 ). Although there are dozens of CVs with relatively well-known binary parameters, a thorough investigation of this test has yet to be performed. Ramsay et al. (2017) proposed to test the DIM by taking the absolute magnitude at a specific stage of a dwarf nova outbursts (at the end of long outbursts or in stand-stills) to compare the derived mass accretion rate to the critical rate for the system, under the assumption that both should be nearly equal. Good agreement was found for the sample of a dozen CVs with distances in the Gaia first data release. Its main advantage is that the required optical magnitude is straightforward to extract from the light curves. However, a major drawback of this approach is that it relies on assumptions on how matter is transported in the disk because one needs to predict when the mass accretion rate happens to be equal to the critical rate during the outburst cycle. Hence, it is a much less fundamental and robust test of the basic premises of the DIM. Indeed, an important difficulty in using the fundamental test is that it requires extracting the average mass transfer rate from CV optical light curves, which in many cases are unevenly, inhomogeneously, and incompletely sampled. The situation is somewhat easier for X-ray binaries, for which the DIM also applies because X-ray monitors provide continuous coverage and X-rays provide a closer measure of the bolometric luminosity in those systems than optical magnitudes do for CVs, which in outburst emit primarily UV radiation. Coriat et al. (2012) were able to derive average mass accretion rates for ~50 X-ray binaries and showed that the DIM, modified to include X-ray irradiation ( van Paradijs 1996 ), separates consistently transients from steady systems. We carry out, for the first time on CVs, a similar analysis to that performed on X-ray binaries by Coriat et al. (2012) to test the fundamental prediction of the DIM: the separation of CVs into stable and unstable systems is determined by the orbital period (a proxy for disk size) of the binary and mass transfer rate from the companion. We use a subsample of the CV light curves gathered by Otulakowska-Hypka et al. (2016) . We consider all types of dwarf nova outbursts, including so-called super-outbursts that are not supposed to be described by the standard version of the DIM (see, e.g., Lasota 2001 , and references
Paper: 2017ApJ...840...33S (fulltext)
an antenna with cos 2 beam would have a maximum temperature at the LST when the Galactic center is at zenith. This also corresponds to the spectrum having the largest rms residual among the various mock spectra considered here. For various large-area samplings of the sky, we have thus demonstrated that the foreground component in a beam-averaged spectrum is adequately describable by an MS function. Thus for observations with a frequency-independent beam, MS functions can be used to separate foregrounds from the global EoR signal. We next discuss mathematical statistical detection based on MS-form modeling of foregrounds. 5. On the Detection of the Global Reionization Signal A mock observation of the radio sky as observed by a cos 2 beam is shown in Figures 14 . A GMOSS model for the sky has been adopted and CMB with a spectral distortion corresponding to generic global EoR has been added. We fit this mock observation of the sky spectrum with a function given by the sum of an MS function to model the foreground and a Planck spectrum to account for the undistorted CMB, as described by Equation ( 9 ). We use the downhill simplex (Nelder Mead 1965 ) optimization algorithm to iteratively fit this model to the synthetic spectrum, adopting a successive approximation strategy. The MS polynomial is expressed as a polynomial expansion about a frequency given by the parameter . The first iteration fits a function that has four parameters: the CMB temperature , the pivot frequency , and two coefficients that describe a single power law for the foreground. We successively include more terms using the optimized parameters from the previous iteration as initial guess and zero for the new coefficient introduced. The residuals on subtracting a model with a Planckian function and MS functions of degree 2, 5, 7, 8, 10, and 15 are also shown in Figure 14 . This may be compared to the residuals obtained above on fitting unconstrained polynomials to the same mock observation containing the EoR signal, which was shown in Figure 6 . As noted earlier, for the case where the foregrounds were modeled as unconstrained polynomials, the residual amplitude gets progressively lower with increasing polynomial order and there are additional turning points introduced. On the other hand, while using MS functions, the residual remains unchanged once the MS function encounters a shape that cannot be described as smooth and thenceforth increasing the order of the MS function no longer changes the residual. This would continue to be the case on fitting the spectrum with MS functions of arbitrarily higher order. The residual recovered on fitting the mock spectrum with such components that may accurately model the undistorted CMB and foreground appears qualitatively similar to the adopted global EoR signal, which was included in the simulation. We now proceed to quantify the confidence in signal detection and the likelihood of false positives. Figure 14. (a) Mock spectrum generated
Paper: 2018MNRAS.479.4760F (fulltext)
origin. Our findings from this Section for the radial metallicity distribution of red and blue GCs in massive ETGs are summarized in a schematic way in Fig. 2 . This representative diagram shows the same radial metallicity gradient for the red (metal-rich) GCs and the blue (metal-poor) GCs, only offset by 1 dex in metallicity. We have argued that the stellar metallicity gradient is similar to that of the red GCs. At some large effective radius (8 R e in the diagram), the blue GC metallicity gradient gives way to an outer halo gradient that is flat, i.e. constant metallicity. We refer the reader to fig. 1 of Forbes et al. ( 2011 ) and fig. 10 of Faifer et al. ( 2011 ) for real-world examples of our schematic plot, which show not only the mean gradients but also the individual GC data points. Figure 2. Schematic representation of globular cluster metallicity gradients as described in Section 3 . The diagram shows the metallicity gradient of the metal-rich GCs in red. This gradient is similar to that of the stellar metallicity gradient. Approximately 1 dex lower in metallicity, the metallicity gradients for the metal-poor GCs are shown in blue. At several effective radii ( R e ), this radial gradient may give way to a constant metallicity for metal-poor GCs to large radius. 4 COMPARISON WITH MAGNETICUM SIMULATIONS Although a number of simulations have modelled the formation of GCs in a hierarchical cosmology (e.g. Bekki et al 2008 ; Muratov Gnedin 2010 ; Li Gnedin 2014 ; Kruijssen 2015 ; Choksi et al. 2018 ) with reasonable success in reproducing bimodal metallicity distributions and some global scaling relations, none have predicted the radial colour or metallicity profiles for GC subpopulations (although see Pfeffer et al. 2018 , for a first step in modelling the spatial distribution of GC systems in Milky Way-like galaxies). However, simulations of the stars in massive ETGs are available and, as noted above, GC metallicity gradients tend to be similar to those of their host galaxy stellar metallicity profiles. Thus, it is useful to investigate simulated stellar gradients as a proxy for GC gradients. The Magneticum hydrodynamical cosmological simulation suite (Dolag et al., in prep.) produces in its highest resolution ( and as every gas particle can spawn up to four stellar particles) massive ETGs with properties that successfully reproduce observations (e.g. Teklu et al. 2015 , for stellar-mass–angular-momentum properties; Remus et al. 2017 , for mass–size relations at different redshifts and dark matter properties; Teklu et al. 2017 , for satellite fractions and the morphology–density relation; Schulze et al. 2018 , for kinematic properties). The simulation adopts a WMAP7 (Komatsu et al. 2011 ) ΛCDM cosmology with σ 8 = 0.809, h = 0.704, , Ω M = 0.272, and Ω B = 0.0451, and an initial slope for the power spectrum of n s = 0.963 (see Hirschmann et al. 2014 ; Teklu et al. 2015 , for more simulation details; Dolag, Mevius Remus 2017 , for
Paper: 2016ApJ...821..119C (fulltext)
of the CSM density profile and the forward shock evolution together determine the temporal behavior of the optically thin radio flux density. For s = 0 and , the flux density rises with time as once , the flux density evolution steepens to (visible as a kink in the model light curve of Figure 1 with cm −3 around 100 days after explosion; such a kink was also noted by Pérez-Torres et al. 2014 ). Meanwhile for s = 2, the optically thin scaling is when later, when bottoms out at unity, then . Therefore, the temporal evolution of the optically thin flux density can be used to estimate the density profile of the CSM. 4.5. Caveats and Complexities As discussed by Horesh et al. ( 2012 , 2013 ), the most significant uncertainty in converting radio luminosity to a density of the CSM is the efficiency of magnetic field acceleration, . In the case of uniform density CSM and optically thin synchrotron emission, the density derived from a given flux density will depend on as when , and when . For s = 2 and , we find , and when converges to unity then . In addition, as seen by comparing Figures 3 with 4 , and Figures 5 with 6 , the synchrotron emission becomes optically thin faster for lower values of . Figure 3. Each panel shows a set of radio light curve models at a distinct frequency (marked top left), assuming , , , , and s = 2. The model grids span a range of values () with spacing of 1 dex, with purple-blue models corresponding to low values and red models to high . Observed 3 σ upper limits at each frequency are overplotted as black triangles. Figure 4. Each panel shows a set of radio light curve models at a distinct frequency (marked top left), assuming a more conservative (compared to in Figure 3 ) and retaining , , , and s = 2. The model grid and color scheme represent the same range of values as in Figure 3 . Observed 3 σ upper limits at each frequency are overplotted as black triangles. Figure 5. Each panel shows a set of radio light curve models at a distinct frequency (marked top left), assuming , , , , and s = 0. The model grids span a range of n 0 values ( cm −3 ) with resolution of 1 dex, with purple-blue models corresponding to low n 0 values and red models to high n 0 . Observed 3 σ upper limits at each frequency are overplotted as black triangles. Figure 6. Each panel shows a set of radio light curve models at a distinct frequency (marked top left), assuming a more conservative (compared to in Figure 5 ) and retaining , , , and s = 0. The model grid and color scheme represent the same range of n 0 values as in Figure 5 . Observed 3 σ upper limits at each frequency are overplotted as black triangles. Radio light curve models are also affected by the assumed values of SN ejecta mass and explosion energy. If we take conservatively low values of and , we find forward shock velocities of and at t = 10 days since explosion for and , respectively. These blast wave velocities are high enough that synchrotron self absorption still dominates over
Paper: 2016ApJ...828...88H (acknowledgments)
We first thank M.S. Ruderman for helpful comments. A.H. thanks the School of Mathematics and Statistics, University of Sheffield (UK) for the support received. R.E. is thankful to the NSF, Hungary (OTKA, Ref. No. K83133) and is grateful to STFC (UK) for the awarded Consolidated Grant, and, The Royal Society for the support received in a number of mobility grants.
Paper: 2017A&A...600A..34T (fulltext)
where and are two cylindrical coordinates; is the axial ratio, is a structure parameter, and is a function of , such that becomes the density at radius , which defines a volume containing half of the total mass of the component (for details, see Tamm et al. 2012 ). The gravitational potential derivative of a component, necessary for calculating epicyclic frequencies, can in this case be expressed in a convenient form (see e.g. Tenjes et al. 2001 ) where is eccentricity and . The formula is general, but the epicyclic frequencies were calculated within the galactic plane only. While the mass distribution parameters of the spheroidal components and the main stellar disc can be used as presented in Tamm et al. (2012) , the flat young disc needs to be split further into main subsystems. At this stage we do not yet know the relative contributions of different flat components to the overall galaxy stability, thus we have to analyse each one of them. First we separated gas and stars by taking the mass of young stars to be and the gas mass to be ( Tamm et al. 2012 ). Thus we now had two stellar discs, one containing old and intermediate-age stars, and the other containing young stars. The parameters of these flat stellar components are given in Table 1 . Several subcomponents can also be distinguished in the gas disc. Although four to five different phases can be distinguished in the interstellar medium ( McKee Ostriker 1977 ; Chemin et al. 2009 ), here we have considered the neutral gas (including both the atomic and the molecular component) to consist of two main phases, the cold neutral medium (CNM) and the warm neutral medium (WNM; see Brinks Burton 1984 ; Wolfire et al. 2003 ; Kalberla Kerp 2009 ). For M 31 Dickey Brinks (1993) estimated that the mass of WNM is about 60 per cent and the mass of CNM is 40 per cent of the total neutral gas mass. These proportions have been found to also be valid in the Milky Way: according to Heiles Troland (2003) , the mass of WNM is 60 per cent of the total neutral gas mass. Thus we took the masses and . Unfortunately, direct measurements of the thickness of these two gas components in M 31 do not exist. Brinks Burton (1984) estimated from their gas velocity dispersion measurements that CNM has . In addition, taking into account the broad similarity between M 31 and the Milky Way, we have applied here the estimate by Kalberla (2003) that at the solar distance, the WNM disc of the Milky Way is about three times thicker than the CNM disc. Thus we take and . However, to consider the impact of uncertainties, we also performed calculations for some other flatnesses. All the other structure parameters and effective radii were kept unchanged. The parameters of the two gas discs are given in Table 1 . To complete the set of input parameters for stability calculations, we also needed to know velocity dispersions and the speed of sound in the disc components. Following Dorman et al. (2015) , we took velocity dispersions of