adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...902...22L (fulltext)
3 DM particles (Rasera et al. 2010 ; Alimi et al. 2012 ; Bouillot et al. 2015 ). For the flat ΛCDM model, the six key cosmological parameters were set at the central values determined by the Wilkinson Microwave Anisotropy Probe Seven Year data (WMAP7) (Spergel et al. 2007 ; Komatsu et al. 2011 ). For the other two DE models, the values of the seven cosmological parameters (the six key parameters + w ) were chosen to satisfy the same WMAP7 constraints. Table 1 lists the values of the matter density parameter, Ω m , linear power spectrum amplitude on the scale of , σ 8 , dimensionless Hubble parameter, h , dark energy equation of state, w ≡ w 0 + w a (1 − a ) with scale factor a ( t ) (Linder 2003 ), and mass of individual DM particles, m p , for the three DE models. For a more detailed description of the DEUS, we refer the readers to Alimi et al. ( 2012 ) as well as the DEUS webpage. 4 4 All data from the DEUS are publicly available at http://www.deus-consortium.org/ . Table 1 Initial Conditions and the Particle Mass Resolution A phantom DE ( w CDM) is characterized by its negative kinetic energy that exerts stronger repulsive force than Λ (Caldwell et al. 2003 ). Whereas, a quintessence model is characterized by the potential shape of a scalar field DE. For the DEUS, the Ratra–Peebles potential, whose shape is expressed as powers of linear combinations of the exponentials of the scalar field (Ratra Peebles 1988 ), was specifically chosen. Although the distant-field diagnostics based on the linear observables like the CMB temperature spectra and SN Ia luminosity–distance relations cannot distinguish among the three models (Bouillot et al. 2015 ), an efficient near-field probe based on the nonlinear observables might be able to pull it off since the formation and evolution of the nonlinear structures proceed in a different way among them (e.g., see Alimi et al. 2010 ). Table 2 Halo Spin Transition Zones The halo catalog from the DEUS comprises the gravitationally bound objects identified at various redshifts by the Friends-of-Friends (FoF) halo finder with a linkage parameter of 0.2 (Roy et al. 2014 ) and provides information not only on the center of mass position, , and virial mass, M h , of each object but also on the positions and velocities of its constituent DM particles, which allow us to compute its angular momentum vector, . The well-resolved FoF objects in the mass range of at z = 0 are selected for a sample of the galactic halos to be used for the current analysis. The low-mass FoF objects with are excluded from our analysis on the ground that the angular momentum vectors of the halos composed of less than 300 DM particles are likely to suffer from inaccuracy caused by the shot noise (Bett et al. 2007 ). The routine we go through for the investigation of the DE dependence of the halo spin transitions is similar to that described in Lee et al. ( 2020 ), the summary of which is provided in the following. 1. Divide the simulation volume
Paper: 2018ApJ...867...26B (fulltext)
the Q 21,C ii ,C ii bispectrum should be identical to that of the Q 21, δ , δ bispectrum. That is, both of these cross-bispectra are expected to follow the form. Consequently, we study the statistics of Q 21, δ , δ here rather than Q 21,C ii ,C ii since the latter is more difficult to model. The main shortcoming of this approach is that it neglects the additional nonlinearities in the [C ii ] biasing relation, and these effects may reduce the agreement with the perturbative formula. In estimating the signal-to-noise ratio (S/N) at which may be measured in future surveys (Section 6 ), we assume a linear biasing model for [C ii ] emission. This allows us to predict the [C ii ] auto-spectrum and 21 cm-[C ii ] cross-spectrum (as required for the S/N calculation) from the statistics of the simulated density field. It may be possible to construct a subgrid model to include unresolved small halos with the correct statistical properties (e.g., McQuinn et al. 2007 ). However, the approach adopted in that work would need to be extended to capture the correct quadratic halo bias and is beyond the scope of this paper. 3.4. Estimating the Bispectrum In order to estimate the bispectrum from our 21cmFAST simulation outputs, we follow the method described in Smith et al. ( 2008 ). Throughout we consider two characterizations of the wavevector arguments. In both cases we calculate the average bispectrum in linearly spaced wavevector bins. Our first characterization is for illustration purposes only. Here we fix the magnitude of and while varying the angle θ 12 between the two wavevectors. The third side of the triangle, , is determined by these three parameters and the requirement of translation invariance (.) We consider spherical bins around each wavevector of thickness Δ k = k f , where k f = 2 π / L box = 0.00785 Mpc −1 is the fundamental mode of our simulation box. Our second characterization sets a minimum ( k min ) and a maximum ( k max ) magnitude for each wavevector argument. That is, we enforce for each wavevector , along with the usual constraint that . Owing to homogeneity and isotropy, all bispectra have three arguments: , , and . Our bins are cubes of side length 4 k f in Fourier space. This characterization captures nearly all the available modes. As discussed in Section 2 , we also enforce that k 1 ≥ k 2 ≥ k 3 . In estimating bispectra, we loop through all of the simulated Fourier modes that lie within this wavevector range. We determine B 21, δ , δ and P 21, δ from the same set of Fourier modes to reduce sample variance in our estimates of Q 21, δ , δ . In general, one would like to make k min and k max as small and large as possible, respectively. However, our estimates at small k are noisy, and the perturbative formula becomes less accurate at high k . We chose values of k min and k max throughout that give the best results, but we also show results for different values of k min and k max in Appendix A . 4. Results First, we consider
Paper: 2016MNRAS.459..771H (fulltext)
a slit orientation to better separate the components of the counterpart, but the seeing was not good enough to resolve them. In these observations, spectra of three objects are visible. Source 2 (see Fig. 1 ) corresponds to the elongated counterpart. All three spectra (sources 1 to 3) show a continuum with many absorption lines from neutral metals. The CO bandheads at 1.62 and 1.66 μm are also detected, proving that the objects are all K- or M-type stars (see Fig. 2 for the spectrum of source 2). We find no evidence for velocity broadening of the absorption lines. This is fully consistent with what we expect for RSGs, which have rotational velocities of at most a few km s −1 , undetectable at MOSFIRE's resolution of ∼80 km s −1 in the H band. Figure 2. The normalized spectra of NGC 925 J022721+333500 (source 2, from the Dec. 2013/Jan. 2014 observation, blue line) and a 3500 K model from Lançon et al. ( 2007 , red line, shifted downwards by 0.5 for clarity). The spectrum of J022721+333500 has been shifted by −506 km s −1 and has been interpolated over wavelength ranges that were strongly affected by noise from telluric emission lines (grey shaded areas). The ‘emission lines’ visible near some of these ranges are due to this noise. The dashed lines indicate the positions of absorption lines that are indicative of late-type stars – from short to long wavelength: Mg i triplet (1.574, 1.575, 1.577 μm), Si i (1.59 μm), CO bandhead (1.62 μm), CO bandhead (1.66 μm), Si i (1.67 μm), Al i triplet (1.672, 1.675, 1.676 μm), OH (1.69 μm) and Mg i (1.71 μm). From the cross-correlation with template spectra, we find a radial velocity of 495 ± 11 km s −1 for source 1. For source 2, we find a radial velocity of 506 ± 9 km s −1 for the Dec. 2013/Jan. 2014 spectrum and 500 ± 14 km s −1 for the Nov 2014 spectrum. For source 3, we find a radial velocity of 502 ± 8 km s −1 . This source is not centred on the slit, which causes an offset in the radial velocity. The sources are too faint to be visible in our through-slit alignment images. However, brighter stars selected to check the slit alignment were detected and we use the comparison of their locations on the through-slit image with that of the deep WHT/LIRIS image (Heida et al. 2014 ) to determine the actual location of the slit on the sky. From this, we derive a best estimate of the offset between the location of source 3 and the centre of the slit of 0.5 arcsec ± 0.05 arcsec (see Fig. 1 ). This corresponds to 2.8 ± 0.3 pixels on the MOSFIRE detector, or a velocity offset of 85 ± 10 km s −1 . Because the seeing was comparable to the slit width, this value is an upper limit on the velocity offset. If we correct for this, we find a radial velocity ≥420 ± 15 km s −1 for source 3. The radial velocities of sources 1 and 2 are in agreement with what is expected from the radial velocity field of NGC 925 (de Blok et al. 2008 ). This proves that the objects are located in NGC 925, confirming the absolute magnitude
Paper: 2019A&A...622A..71V (fulltext)
is consistent with a solar value of log (Al∕H) = −5.55 ± 0.03 ( Asplund et al. 2009 ). However, Al can also be present in other refractory species, in which case our derived estimate is a lower limit on the true Al/H abundance in the atmosphere of WASP-33 b. In particular, the dominant Al-containing species in a solar-composition atmosphere in equilibrium, at temperatures near 3000 K, include Al and AlH ( Woitke et al. 2018 ). Under such conditions, our retrieved AlO abundance is ~ 10 3 × higher than that predicted in chemical equilibrium with solar abundances. Future studies may investigate the feasibility of our retrieved abundance of AlO in WASP-33 b, possibly due to chemical disequilibrium or other mechanisms. Future observations may also provide better constraints on the same. 4.2 The aluminum oxide feature The main spectral feature of the optical transmission spectrum reported in this work is an increase in the planet-star radius ratio blueward of about 560 nm. In general, an increase toward short wavelengths can be caused by third light of an additional stellar companion in the aperture of flux extraction. Moya et al. (2011) detected a stellar object 2 arcsec away from WASP-33, well included within our aperture. The authors estimated that the probable physical companion of WASP-33 should have an effective temperature of about ~3000 K. As carried out in von Essen et al. (2015) , within our wavelength region we estimated that the M dwarf should be at least 10 3 times dimmer than WASP-33. This would create a variability of planet-to-star radius ratio as large as 60 ppm. This upper value is well contained within our error bars given that the median precision of the dataset is 245 ppm. Thus, it is safe to neglect the contribution of WASP-33’s companion. Also, star spots might in principle cause a spectral slope in the transmission spectrum ( Sing et al. 2011 ; Oshagh et al. 2014 ). This possibility plays no role for WASP-33 either, since it has no or a too thin outer convection zone, making star spots unlikely to occur. In consequence, we have interpreted the spectral feature shortward of 560 nm as an increase in opacity in the planetary atmosphere of WASP-33 b. In Sect. 4.1 we retrieved AlO as the opacity source of the spectral feature between 450 and 550nm. Spectral absorption features by gaseous AlO have not been reported in the literature of exoplanetobservations, though atmospheric models predict its importance in hot Jupiters ( Gandhi Madhusudhan 2018b ). AlO was described as a potential condensate forming clouds or hazes in very hot exoplanet atmospheres (e.g., Lodders 2002 ; Wakeford et al. 2017 ; Pinhas Madhusudhan 2017 ). AlO is difficult to observe for M or L type stellar objects, first because the optical spectra of these low-mass stars are dominated by TiO and VO absorption features, and second because such spectral feature at ~ 500 nm is challenging to observe for very late type stars because of their intrinsic faintness at
Paper: 2019MNRAS.490.1325A (fulltext)
measured in the various environments described in the Section 1 (Garrod et al. 2008 ; Barone et al. 2015 ; Codella et al. 2017 ; Quénard et al. 2017 ). Those models rely on different formamide formation routes (the gas-phase reaction NH 2 + H 2 CO in Barone et al. ( 2015 ), Codella et al. ( 2017 ), and a combination of irradiated surface chemistry and gas-phase chemistry in Garrod et al. 2008 and Quénard et al. 2017 ). On the contrary, the destruction routes are common in all models and are dominated by ion–molecule reactions involving formamide and HCO + , H, H 3 O + , He + , and C + . Moreover, some reactions involving neutral radicals have been invoked, such as the one involving the OH radical (Belloche et al. 2017 ), but no experimental or theoretical data are available on the rate coefficients for those reactions, which are, anyway, expected to be very slow based on what is known on the reaction NH 2 CHO + H (Haupa, Tarczay Lee 2019 ). The proton transfer reactions with HCO + and H dominate the formamide destruction in ‘standard’ conditions, while that with H 3 O + is a dominant formamide sink in the water-rich gas. Instead, the reactions involving He + and C + ions, which provide in general a smaller contribution to the formamide destruction, proceed via charge transfer and lead to a significant fragmentation of the ionized formamide, especially in the case of He + collisions because of the large amount of energy released in the process (see for instance, the article by Ascenzi et al. 2019 for the case of similar molecules). In summary, even though proton transfer processes can lead to fragmentation, NH 2 CHOH + is expected to be also an important by-product in the main destruction route of formamide. As already mentioned in the Section 1 , the most probable fate of protonated formamide will be its recombination with interstellar free electrons. The global electron–ion recombination rate coefficient assumed in the KIDA data base, and used in all the models cited above, is given by k ( T ) = 7.5 × 10 −7 ( T /300) −0.5 cm 3 s −1 , and very nicely compares with the value calculated in this work, which is larger by only 10 per cent, contrarily to other similar cases such as the one of protonated methanimine (CH 2 NH) where the guessed values substantially differ (Yuen et al. 2019 ). Our recommendation is to update the data bases for astrochemical modelling with the present value of 8.33 × 10 −7 ( T /300) −0.5 cm 3 s −1 , which is the result of rigorous calculations rather than an assumed value. Concerning the branching ratios, our method is not yet able to determine the yield in the different exit channels and, therefore, an experimental determination, such as those achieved by using the heavy ion storage ring CRYRING (Geppert Larsson 2008 ), is required. We note, however, that it is highly improbable that all the channels mentioned in the KIDA data base have the same yield. It can be instructive to compare the case of protonated formamide
Paper: 2017MNRAS.464.2730V (fulltext)
v t ) = 0.03 km s −1 and a total error on [Fe/H] of 0.03 dex. This total error takes into account all the errors on the atmospheric parameters. The two methods agree well, and we adopted σ( T eff ) = 40 K, σ(log( g )) = 0.12, σ( v t ) = 0.04, and σ([Fe/H]) = 0.03 dex as our final errors on the atmospheric parameters. Then, we choose star no. 1367 as representative of the sample, varied its T eff , log( g ), [Fe/H], and v t according to the atmospheric errors we just obtained, and redetermining the abundances. Results are shown in Table 4 , including the error due to the noise of the spectra. This error was obtained for elements whose abundance was obtained by EQWs, as the errors on the mean given by MOOG, and for elements whose abundance was obtained by spectrum-synthesis, as the error given by the fitting procedure. Δ tot is the squared sum of the single errors, while Δ tot (obs) is the error as obtained from the four repeated stars. Δ tot (obs) is equal to or smaller than Δ tot . In Table 4 , for each element, we report the observed spread of the sample (RMS obs ) with its error and in the final column the significance (in units of σ) calculated as the absolute value of the difference between RMS obs and Δ tot divided by the error on RMS obs (we choose Δ tot instead of Δ tot (obs) to be conservative). This tells us if the observed dispersion RMS obs is intrinsic or due to observational errors. Values larger than 3σ imply an intrinsic dispersion in the species chemical abundance among the cluster stars. Table 4. Estimated errors on abundances due to errors on atmospheric parameters and to spectral noise for star no. 1367 (columns 2–6). Column 7 gives the total error calculated as the root squared of the sum of the squared of columns 2–6. This total error must be compared with the total error as obtained from the 4 repeated stars (column 8) and with the observed dispersion (RMS) of the data with its error (column 9). The last column gives the significance of the difference between the total error for star no. 1367 and the observed dispersion, in units of σ. 4 RESULTS 4.1 Iron-peak, α elements and heavy elements The mean iron content we obtained is with a dispersion of Reported errors are errors on the mean. Harris ( 1996 ) gives [Fe/H] = −1.32 and we can consider the agreement satisfactory. The measured iron dispersion in Table 4 well agrees with the dispersion due to measurement errors so we have no evidence for an intrinsic Fe abundance spread. As far as iron-peak elements are concerned, Cr and Ni are solar scaled, Mn and Cu are sub-solar, and V, Co and Zn are supersolar. The α elements Si, Ca, and Ti are overabundant compared to the Sun. This is a feature common to almost all Galactic GC and halo field stars as well as to very metal-poor stars ([Fe/H] −1.5) in outer galaxies. Based on these elements, we derive for the cluster a mean α element abundance of [α/Fe] = +0.34 ± 0.01 As far as heavy elements are concerned Y, Zr, Ba, and Eu are
Paper: 2019MNRAS.489.2395T (fulltext)
error margin at three different observatory sites. When dealing with PRV data that supplies the photon-weighted mid-point time and yet no flux curve, the simplest assumption is that the flux was uniformly distributed in time. 2 This allows us to add a second-order correction to the calculated barycentric correction, v (〈 t 〉). To obtain 〈 v 〉, we can either subtract the result of equation ( 10 ) or get the difference from a simple numerical simulation as in Section 4 . In fact, if we do not apply a second-order correction to v (〈 t 〉), we implicitly assume that all photons arrived in the same instant at the photon-weighted mid-point time. We argue that the assumption of a uniform flux distribution is much more realistic. Fig. 5 shows how the second-order errors of three different flux scenarios are all closer to the uniform scenario than zero. In Fig. 7 , we calculate the second-order error using real exposure meter data from 1315 observations with EXPRES (see Blackman, Ong Fischer 2019 ), combining all colour channels to one. The data was obtained during commissioning between March 2018 and February 2019. No exposures were longer than 20 minutes, and as a result the second-order errors are relatively small. We plot the real second-order errors against the errors estimated with equation ( 10 ) for a uniform flux curve. The plot shows that within 1 cm s −1 , the uniform flux approximation is indeed a realistic assumption, and much more realistic than setting the error to zero. There is no indication that longer exposure times would change this picture. Figure 7. For nearly one year of EXPRES observations, we compare the actual second-order error of each measurement with the uniform flux approximation. The multichannel exposure meter data have been binned to one channel. EXPRES mainly observes bright targets, so the exposure times are short (≤1200 s), resulting in errors of only a few cm s −1 . Nevertheless, the data show that the uniform flux curve is a good approximation if we for some reason do not have access to the exposure meter flux curve when we want to calculate the second-order error. In the rare case of dealing with data that completely lacks information about the temporal flux distribution, there is no way of knowing if the individual observations have large first-order barycentric errors. One can in principle use equation ( 8 ) to estimate the RV error bars, but it requires a qualified guess at the offset (〈 t 〉 − t 0 ). In our experience, a typical offset is of order 5 per cent of the exposure time, with some larger, negative offsets from when the star was lost on the fibre before the end of the exposure. The actual distribution depends on the instrument and the local sky conditions. 6 CONCLUSIONS This paper describes how to correctly apply a barycentric correction to an exposure using the exposure meter flux curve or an equivalent measure. Rather than following the common practice of computing a single value at the photon-weighted
Paper: 2018MNRAS.475.4434K (fulltext)
− 2.4 per cent and a dispersion of ∼ 10 per cent (8.7 per cent when adjusted for random uncertainty), in the outer r / r max > 0.2 region of the haloes. Similar level of dispersion ( ∼ 7 per cent or ∼ 6 per cent when adjusted for random uncertainty) is also observed for the case of the DM haloes of BJ05 but with positive bias of ∼ 3 per cent. In perfect conditions, a spherical system in equilibrium, we can correctly reconstruct the mass profile and there is no bias and dispersion that can be accounted by random uncertainty on the estimated mass. When applied to data from simulations, we do see some bias and dispersion, meaning the bias and dispersion can be either due to system being aspherical and out-of-equilibrium or due to an unknown effect in simulations. We find little correlation of mass offsets with asphericity, so out-of-equilibrium effects seem to be the main cause behind the observed mass offsets. The out-of-equilibrium effects will shift the mass estimates at any given radius and this will lead to a non-zero dispersion. So the dispersion, in principle, sets a limit on the accuracy with which we can expect to measure mass at a given radius for any halo using the spherical Jeans equation. This limit is set due to the inherent nature of the ΛCDM haloes and is independent of the quality and the quantity of the observational data. Using galaxia (Sharma et al. 2011b ), a stellar population synthesis software utilizing Padova isochrones (Marigo et al. 2008 ), we estimate that, till the magnitude limit of 17 in V band, Gaia will have more than 5 times the number of tracers that we have investigated here. So, the error on the mass estimates of the Galaxy using stellar tracers provided by the Gaia using the Jeans analysis will be limited by the non-equilibrium effects as well as uncertainties in observed distances and tangential velocities rather than the sample size. The fact that the two different simulations (with DM particles as tracers) give similar level of dispersion (10 per cent for surfs and 7 per cent for BJ05 haloes) in estimated masses is reassuring for our estimate of the dispersion. When applied to BJ05 stellar haloes (stellar particles as tracers), we get a higher dispersion (14 per cent). This is the case that is of practical use, as we observe stars rather than dark matter particles. The higher dispersion here is also as expected, since the stellar haloes have more sub-structures than dark matter haloes. Dispersion could also be due to random uncertainty associated with our mass estimator (effect of Poisson noise). We have shown that the random uncertainty is a factor of 2 smaller than the measured dispersion for all analysed cases. The total variance being the sum of squares of random and intrinsic scatter, the random uncertainty should make very little contribution to the dispersion. Taking the random uncertainty into account, we estimate the intrinsic scatter due to non-equilibrium effects to be 12 per cent for the case