adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...916...33G (fulltext)
− K )/ E ( J − K ); Indebetouw et al. 2005 ; Jiang et al. 2006 ; Zasowski et al. 2009 ; Xue et al. 2016 ) and absolute (right, A ( λ )/ A ( K ); Rieke Lebofsky 1985 ; Rieke et al. 1989 ; Indebetouw et al. 2005 ; Chiar Tielens 2006 ; Fritz et al. 2011 ; Wang Chen 2019 ; Hensley Draine 2020 ) measurements. Real variations are seen in the relative measurements indicating different MIR extinctions between the diffuse and dense ISM. The variations in the absolute measurements are likely due to a combination of real variations and uncertainties in deriving the total column normalizations. Note that the Lutz ( 1999 ) work presented preliminary results that were finalized in Fritz et al. ( 2011 ). In the case of the E ( λ − K )/ E ( J − K ) measurements, these differences are likely due to the relative contribution between diffuse and dense material in the sight lines studied. This can be seen from the Zasowski et al. ( 2009 ) measurements that encompass the full range of values with higher extinctions seen toward the Galactic Center ( l = 10–15) and lower extinctions well away from the Galactic Center ( l = 90). Zasowski et al. ( 2009 ) interpret this as less dense material in the l = 90 case compared to the l = 10–15 case, and this would be consistent with much of the previous work probing denser material given their biases toward high dust column sight lines. In the case of the A ( λ )/ A ( K ) measurements, some of the differences can be due to uncertainties in deriving the total column. The absolute level for the Chiar Tielens ( 2006 ) curves was set by fiat to that of Indebetouw et al. ( 2005 ) and Lutz ( 1999 ) and, hence, their overall high MIR extinction may just be due to this normalization. Similarly, Hensley Draine ( 2020 ) used Fitzpatrick et al. ( 2019 ) to set the absolute level of the Cyg OB2 12 extinction. However, the differences between the Rieke et al. ( 1989 ) and Fritz et al. ( 2011 ) results are enigmatic. The Br α /Br γ ratio for the former reference agrees well with the measurements by Wade et al. ( 1987 ); between these references, the ratio is measured for eight sight lines (one twice) and the (weighted) average Br α /Br γ ratio is 16 ± 1. Some of the flux in the beams used may originate from luminous early-type stars, which typically have a Br γ equivalent width of order 6 × 10 −4 μ m (Hanson et al. 1996 ). The result could be that the ratio is overestimated by about 5%. Fritz et al. ( 2011 ) determined a ratio value of 11.6 ± 2 from the ISO spectrum (for Br α ) and a ground-based spectral image (for Br γ ). The difference in these two measurement approaches suggests that the best value is toward the large end of the ones permitted by Fritz et al. ( 2011 ) and that the Galactic Center extinction in Figure 10 probably lies between that shown for Fritz et al. ( 2011 ) and ours (and the point from Rieke et al. ( 1989 ). Only Rieke Lebofsky ( 1985 ), Rieke et al. ( 1989 ), Chiar Tielens ( 2006 ), and Hensley Draine ( 2020 )
Paper: 2021ApJ...909..200B (fulltext)
photometry for the faintest variable. Multi-epoch data were also used to investigate color–magnitude and Bailey diagrams for M53 RR Lyrae at NIR wavelengths. No significant overlap is seen in the color–magnitude plane between RRab and RRc stars, suggesting that horizontal branch evolution occurs toward the red edge of the instability strip (Arellano Ferro et al. 2012 ). RR Lyrae in the Bailey diagrams follow the expected locus of RRab in a typical OoII cluster. New NIR photometry for RR Lyrae in the metal-poor M53 GC provided an opportunity to constrain the metallicity dependence of their PLRs by means of a quantitative comparison with relatively metal-rich GCs. While single-epoch data for RR Lyrae are available for several GCs, for example from 2MASS, homogeneous JHK s -band time-series photometry is limited to only a few GCs. Time-series data are crucial to determine precise mean magnitudes and the PLRs for RR Lyrae with a dispersion of ∼0.04 mag, thus allowing a rigorous comparison of GCs with different mean metallicities. Combining precise K s -band PLRs in GCs covering a metallicity range of Δ[Fe/H] = 0.9 dex, we determined a metallicity dependence of the PLRs and simultaneously estimated distances to the GCs adopting different calibrations based on theoretical models and Gaia EDR3 data. The quantified metallicity dependence of ∼0.15–0.17 mag dex −1 is in good agreement with theoretical predictions for the relation from Marconi et al. ( 2015 ). When excluding the calibrator relations, the metallicity dependence of the PLRs in the GCs was found to be sensitive to the adopted spectroscopic metallicities, the RR Lyrae sample size, and the number of clusters, and can vary between 0.07 and 0.15 mag dex −1 . Therefore, homogeneous NIR time-series data in additional GCs, particularly in metal-rich ([Fe/H] > −1.0 dex) and more metal-poor ([Fe/H] −2.1 dex) GCs, is required to extend the metallicity baseline and the statistics of both the number of RR Lyrae and the GCs. Our analysis has shown that a robust constraint on the effects of metallicity on the PLRs and precise RR Lyrae distances to GCs can be obtained simultaneously, provided a statistically significant sample of GCs covering a wide range of mean metallicities is available. However, the calibration of the RR Lyrae PLZ relation in the Milky Way is still hampered by uncertainties and a possible zero-point offset in the parallaxes even with Gaia EDR3. Using theoretical PLZ relations to predict parallaxes for Galactic RR Lyrae, we found a small parallax zero-point offset of −7 ± 3 μ as in EDR3, which is significantly smaller than the value (−32 ± 4 μ as) obtained using the recipe of Lindegren et al. ( 2020a ). We note that our estimated parallax offsets are in agreement with the values provided by the corrections of Lindegren et al. ( 2020a ) for faint and distant RR Lyrae stars that have small parallaxes (0.5 mas). When Gaia EDR3 parallaxes were corrected using the formulae of Lindegren et
Paper: 2017ApJ...836L...5B (fulltext)
only investigate H 2 –H 2 O atmospheres, the sole opacity sources in the cloud-free models are the vibration-rotation bands of water vapor in the near- and mid-IR, collision-induced absorption (CIA), and Rayleigh scattering off of H 2 and H 2 O gas (see Freedman et al. 2008 , 2014 and Lupu et al. 2014 for a more detailed description of the opacity data). Exo-Transmit includes H 2 –H 2 CIA opacities but not H 2 –H 2 O or H 2 O–H 2 O. The non-inclusion of H 2 O CIA should not affect our results because these opacities tend to be only weakly wavelength dependent. For our cloudy models, the cloud opacity is treated as an infinite opacity source at the location of the cloud deck. 2.2. Modeling JWST Observations and Instrumental Noise We simulate three different instrument modes: the NIRISS Single Object Slitless Spectrometer (SOSS) ( R = 700, 0.7–2.7 μ m), NIRSpec G395H+f290lp ( R = 2000, 2.8–5 μ m), and the MIRI Low Resolution Spectrometer (LRS) ( R = 100, 5–14 μ m). To calculate the flux and background rates, and , we use the beta version of Space Telescope Science Institute’s online Exposure Time Calculator 6 6 https://devjwstetc.stsci.edu (ETC). The ETC does not contain a systematic noise floor, which has been suggested to be anywhere from 20 to 30 ppm for the near-IR instruments and 50 ppm for MIRI (Greene et al. 2016 ). These noise estimates may shift somewhat but will not impact the conclusions in this paper. The duty cycle is calculated by determining the number of allowable groups in an integration before detector saturation. A group, in JWST terminology, is one or more consecutively read frames—all exoplanet spectroscopy modes have a single frame per group. To determine the number of groups per integration, n grp , we sequentially increase n grp in the ETC, until a single pixel on the detector becomes saturated. The duty cycle is calculated by . We compute noise simulations for a GJ-1214-like host star with a magnitude J = 8, as a realistic system that might be observed with JWST and also discovered by TESS (Sullivan et al. 2015 ). Additionally, a GJ-1214-like star at J = 8 will have a distance of 6.6 pc, which acts as an optimistic comparison with de Wit Seager’s ( 2013 ) assumed system at 15 pc. We calculate , 9, and 29 for NIRISS SOSS, NIRSpec G395M, and MIRI LRS, respectively, corresponding to duty cycles d = 0.33, 0.80, and 0.93. Using the duty cycle, the total shot noise is F out is the flux (e − /s) for the host star computed from the JWST ETC. F in is the in-transit flux, , where is obtained from the transmission spectrum model described in Section 2.1 . The in-transit and out-of-transit time components, t in and t out , are the transit duration and the out-of-transit observing time, respectively, multiplied by the duty cycle. We compute the total noise viawhere n int is the total number of integrations during the entire transit, B λ is the extracted background rate (observatory background plus dark current in e − /s), computed in
Paper: 2017MNRAS.465.1573K (fulltext)
density). From the 2D plot, it is seen that the distribution of the dust averaged velocity also has the filamentary structure with several minima of the V d,av, z -component. Positions of these velocity minima correspond to positions of maxima of the number density (it is clear from Fig. 7 B), i.e. dust accumulates in the regions of its deceleration. This is consistent with the continuity equation. Thus, dust number density is not uniform in the astrosphere due to periodical deceleration and acceleration of the dust grains. This non-monotonic behaviour of the dust velocity is caused by gyrorotation around magnetic field lines. For example, for the trajectory started at point P 0 gyrorotation takes place in the ( Z , Y )-plane, and during each rotation at the point with maximal y , velocity vector of gyromotion is opposite to the plasma velocity and, hence, the total individual dust velocity has a minimum; oppositely, at the point with minimal y , total velocity has a maximum (see the schematic illustration of this effect in Fig. 8 ). Figure 7. A. 2D distribution of the averaged dust z -velocity component (absolute value) in the ( ZX )-plane obtained for the perpendicular case. B. Dust number density and averaged velocity (absolute value of the z -component) along the Z -axis ( x = 0, y = 0). All results are presented for the dust grains with a d = 1.8. Distances are normalized to R 0 . Figure 8. Schematic picture of dust grain motion in the uniform perpendicular magnetic field. Green dots correspond to maxima of the local dust velocity, blue dots correspond to the local minima (see the text for details). The distance between the density maxima and their number depend on the period of gyrorotation of dust grains (see Fig. 8 ). Namely, in the case of the uniform electromagnetic field, the distance between two adjacent maxima of y ( z ) along the trajectory is equal to D gyr v p , z T gyr , where the dimensionless period of gyrorotation is T gyr 2/( Ba d ). In our case, when v p , z and B change along the trajectory, the distance between the density maxima depends on the averaged D gyr at each part of the trajectory (values of D gyr averaged over the whole trajectory are given in Table 2 ). Fig. 9 A shows y ( z ) along the trajectories (started at P 0 ) for different a d . It is seen that the grains are involved in the gyrorotation with corresponding gyroradius (given in Table 2 ). Distance between adjacent maxima of y ( z ) along the trajectory correspond to the distance between maxima of the dust number density in Figs 5 and 6 . Fig. 9 B shows D gyr as a function of z for the trajectories started at P 0 and the all considered values of a d . Note that D gyr decreases with approaching to the AP because the magnetic field B increases (see Fig. 3 B). This is why the dust density filaments become more frequent close to the AP (see Fig. 5 ). Figure 9. A. The trajectory ( y ( z )) started at P 0 is shown for dust grains with four different a d .
Paper: 2021ApJ...908..215Y (fulltext)
i.e., the prior distribution of BBH source parameters θ conditional upon some hyperparameters Λ. For this work, we are concerned with the prior distribution of ( D L , m 1 , m 2 ) ∈ θ that is dependent on cosmological hyperparameters ( H 0 , Ω m ) ∈ Λ via Equation ( 6 ). The conditional priors is denoted as π ( θ ∣Λ). The hyper-likelihood is related to the regular likelihood by:where h denotes the GW data. In hierarchical inference, we have access to the posterior distribution of parameters of individual BBHs P ( θ ∣ h ), which is related to the regular likelihood through the Bayes’ theoremwhere is the evidence and π ( θ ) is prior used for the parameter estimation of individual events. Rewriting Equation ( 8 ) by replacing the integral with the summation over discrete posterior samples (e.g., Hogg Foreman-Mackey 2018 ), we obtainwhere n is the number of posterior samples for individual events. Combining N independent events, we obtain the total likelihoodwhere h denotes the collection of data { h i }. The hyper-posteriors are given by , with π (Λ) being the prior distribution of hyperparameters. In order to perform model selection, it is also necessary to calculate the hyper-evidence given a model M The Bayes factor (BF) between model M 1 and model M 2 isWe impose a threshold of the natural logarithm of BF at as the point when one model is significantly favored against another (e.g., Mackay 2003 ). 4. Results In this section, we present results of hyperparameter estimation and cosmological model selection using the simulated BBH population that is expected to be detected by third-generation detectors such as ET. 4.1. Hyperparameter Estimation Figure 3 shows the joint posterior distribution of H 0 and Ω m using 10 3 BBH events detected with ET, while ignoring the delay time between binary formation and binary merger and assuming we know the cosmic star formation rate and black hole mass distribution a priori. In our analysis, uniform priors are used: H 0 ∈ [40, 105] km s −1 Mpc −1 and Ω m ∈ [0, 0.75]. In this example, the Hubble constant is measured with a precision of 5.6%. By performing this analysis for a range of N (the number of BBH events) assuming zero measurement uncertainty of luminosity distance and black hole masses (which we call zero-error injections), we find the measurement precision of H 0 scales linearly with . We expect that one year operation of ET, yielding ∼10 5 BBH detections, will allow H 0 to be measured within ≈ 0.6%. However, this result is too optimistic as it does not account for uncertainties in cosmic star formation rate, delay time distribution and black hole mass distribution. In Figure 6 of the Appendix , we show that the estimates of ( H 0 , Ω m ) are biased if an incorrect model of star formation rate is used. Figure 3. Joint posterior distribution of the Hubble constant ( H 0 ) and matter density (Ω m ) in the ΛCDM model estimated using 10 3 BBH events. The 2D contour regions denote the 1 σ , 2 σ, and 3
Paper: 2016ApJ...833...84X (fulltext)
and thus star formation (see Gnedin 2000 ; Wise Abel 2008a ). Lastly, because ionizing radiation originates from massive stars, the fraction of halos generating escaping photons, shown in the right panel of Figure 22 , follow a similar trend as the stellar occupation fraction. Figure 22. Fractions of halos with active star formation (left; stars younger than 20 Myr) and photons escaped from their virial radius (right) as a function of the halo mass for the Rarepeak region (dashed blue) at z = 15, Normal region (solid black) at z = 15, 12.5, and Void region (red solid with boxes) at z = 15, 10, 8. 4.5. Comparison to Other Work Early efforts to constrain the escape fraction with simulations found wildly varying f esc values over a range of halo masses 10 7 –10 12 , ranging from less than 1% to unity (e.g., Fujita et al. 2003 ; Razoumov Sommer-Larsen 2006 ; Gnedin et al. 2008 ; Wise Cen 2009 ; Paardekooper et al. 2011 ; Yajima et al. 2011 ). However, in the past few years with higher resolution simulations that resolve star-forming clouds, there has been some convergence, showing that low-mass () halos have higher mean f esc values ≳25% (Wise et al. 2014 ; Paardekooper et al. 2015 ) that then decrease with halo mass to approach ∼5%–15% in more massive (log M vir / = 8–11) galaxies at z ≥ 6 (Kimm Cen 2014 ; Ma et al. 2015 ). Using the star formation surface density as a proxy to f esc , Sharma et al. ( 2016 ) suggested that the brightest galaxies dominated the photon budget at the end of reionization with 50% of the photons originating from galaxies with UV absolute magnitudes M 1500 ≤ (−18, −16.5) and at z = (6, 8), respectively. Our results are in agreement with these latest works with f esc decreasing with halo mass, given that M vir ≲ 10 9 . Here we only compare the mean values, but in reality, it should be noted that escape fractions greatly vary between galaxies and temporally. In the smallest galaxies, mean f esc values are ∼50% in the range of 10 7 –10 7.5 with a large scatter (see Wise et al. 2014 ; Paardekooper et al. 2015 ), spanning the full range from zero to unity, depending on previously discussed conditions. This then decreases to ∼0.05 at higher halo masses, agreeing with Kimm Cen ( 2014 ) and Ma et al. ( 2015 ). The Void simulation at z = 8 contains two galaxies with M ⋆ ≃ 3 × 10 7 contained in halos with M vir = 2 × 10 9 that have escape fractions of 25% that are undergoing strong starbursts. These galaxies may be analogs of the ones explored in the “brightest galaxies reionized the universe” scenario posed by Sharma et al. ( 2016 ). In the same data, we are also in agreement with Sharma et al., where we both find that 50% of the ionizing photons (see Figure 18 ) come from galaxies with an SFR ≥ 0.1 yr −1 in halos M vir ≥ 10 9 at z = 8. 5. CONCLUSIONS In this paper, we have quantified the properties and the UV escape fraction of galaxies during the epoch of reionization, expanding upon the work of Wise et al. ( 2014 ) with
Paper: 2018MNRAS.481..981H (fulltext)
it is not objectively easy to determine the optimal deblending performance because it is not fundamentally trivial to determine if an object is actually composed of two galaxies or a single morphologically disturbed galaxy with two cores. This fundamental problem is unfortunately not unique to our morphological measures, but can occur in any qualitative or quantitative investigation of morphology. Fortunately, we only have two deblending statuses, i.e. ‘deblended’ or ‘blended’, therefore the effect of ‘deblending ability’ remains roughly constant down to a certain ‘deblending limit’, then it drops. Note also that this deblending limit is independent from a particular choice of desired morphological measure. Furthermore, this deblending ability can be roughly characterized only by the apparent size of an object (e.g. the number of pixels of a galaxy), because the average surface density of galaxies (or the inverse of average separation between two galaxies) is related to the apparent magnitude of galaxies, and this magnitude is approximately correlated to the number of pixels of a galaxy. Because of this simplicity, when we plot the mean value of each measure versus the number of pixels of an object inside the entire image, one can find that the mean value of any measure is approximately flat down to a certain number of pixels, then an object consists of smaller number of pixels starts to show much different (usually bigger) mean value. By monitoring the sudden change in the morphological measures, such as the concentration and the asymmetry of a galaxy, for various input npix values, we estimated that the optimal minimum number of pixels related to this deblending ability should be set around npix = 50 for the nominal setting related to ‘deblending’ of our extracting software, sextractor . Note that the minimum number of pixels related to a single object, explained earlier in this section, varies among various morphological measures. Therefore, the ‘overall’ minimum number of pixels should be determined by comparing the two types of minimum number of pixels (i.e. one type related to a single galaxy, and another related to ‘deblending’) on measure-by-measure bases, and that overall threshold, usually ranging between 20 and 100 pixels, should be applied to a different set of measures for various analyses. 3.3 Variation in detection fraction To investigate variations in the detection and measuring ability of sextractor , we performed a simulation through adding of galaxies into random positions of the each real cluster image. These added galaxies were randomly selected from a list of galaxies satisfying the specific selection conditions (e.g. magnitude and colour ranges) identical to each of the investigations in the next section. The list was constructed from the outer part (2 Mpc or more from the cluster centre) of the same cluster image. The exclusion of galaxies inside 2 Mpc radius was to ensure that added galaxies were free from any
Paper: 2019MNRAS.484.1050D (fulltext)
in the bottom panel we show distributions as a function of R and φ . Figure 2. The – plane coloured by mean φ for the simulation described in Section 2.2.1 . The bins have size Δ = 0.05 kpc, and Δ = 3 km s −1 . The bins with fewer than 10 particles are ignored. Fig. 2 shows the – plane coloured by mean φ . Stars at large radii in the – plane, i.e. stars with high E preferrentially come from stars in population 1, which have lower φ , as seen in the bottom panel of Fig. 1 . The initial DF shown in Fig. 2 is perturbed by displacing it by in . The particles are then evolved using a standard leap-frog algorithm for and the results are shown in Fig. 3 . The number density shows the spiral pattern typical of phase wrapping. Stars orbit the – plane in a clockwise sense and since those at large E have a lower vertical epicycle frequency, the DF quickly develops a trailing spiral. Also shown is the – plane coloured by mean R and φ . Here, the pattern is essentially featureless, apart from the imprint of the number density distribution: with our separable potential the in-plane and vertical epicyclic motions completely decouple. Figure 3. Number density and mean radial and azimuthal velocities in the – plane for the toy model considered in Section 2.2.1 (toy model with additively separable potential). Top panel shows the number density. The bins in the middle and lower panels are coloured by R and φ , respectively. The bin sizes and particle number threshold are the same as in Fig. 2 . 2.2.2 Kinematic spirals in a coupled potential Next we consider the addition of a R – term to the toy model potential R s is a constant radius characteristic of the distribution [if a Taylor expansion of the effective potential is performed for each L population, R s is replaced by R c ( L )]. For illustrative purposes, we choose and . These values are consistent with the cubic terms that arise in realistic Milky Way potentials. The main effect of the new term is to introduce a linear dependence in R to the vertical epicyclic frequency. In particular the difference between the vertical frequency for a population 1 star with and a population 2 star with is . As before we sample an initial distribution (equation 3 ) with 500k particles, perturb it and integrate the perturbed distribution for . The results are shown in Fig. 4 . Not surprisingly, the spirals that appear in the – number density distribution are virtually identical to the ones that appear in our separable model. However, the R and φ spirals are qualitatively different. In particular, there are gradients across the spiral arms and along their inner and outer edges. The addition of the ( R − R s ) 2 term implies a coupling between the in-plane and vertical epicyclic motions. Figure 4. Same as Fig. 3 but for the model considered in Section 2.2.2 (toy model with R – coupling included in the potential). 2.3 Realistic Milky Way potential We conclude this section on test-particle simulations by considering a realistic