adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...846...68Q (fulltext)
is seen between q 24 and for the entire sample, with a correlation coefficiency of 0.33. The second panel of Figure 5 shows that metal-poor galaxies have on average warmer color than metal-rich ones, but no apparent correlation between q 24 and . Actually, the two galaxies with the highest around 0.4 show almost the largest and smallest q 24 , respectively. The above result confirms a previous finding of no relation between and f (60 μ m)/ f (100 μ m) (Wu et al. 2008 ). The last panel of Figure 5 illustrates some weak dependence of on the far-IR color , with a correlation coefficiency of 0.26. Figure 5. vs. the IR color of , , and . The red points denote metal-poor galaxies and black points are for metal-rich galaxies. The two horizontal dashed lines indicate the average values of metal-poor and metal-rich galaxies, respectively. Figure 6 investigates the dependence of on the three IR colors mentioned above. A weak correlation is seen between and , with a correlation coefficiency of −0.34. Overall, the metal-poor and metal-rich galaxies share the same range in , as already mentioned above. At the same , q 70 is smaller for metal-poor galaxies compared to metal-rich galaxies. The two metal-poor galaxies with the highest show low q 70 , but not as extremely as expected, if there is a relation. The dependence of q 70 on is very weak, with a correlation coefficiency of −0.05. At the same color, metal-poor galaxies on average occupy lower regimes compared to metal-rich ones. Among two metal-poor galaxies with the highest , only one shows a lower q 70 than the remaining galaxies. The last panel shows no strong relation between and the far-IR color , with a correlation coefficiency of about −0.2. Figure 6. Same as Figure 5 but for . Figure 7 shows as a function of the three IR colors mentioned above. The first panel indicates a weak dependence of q 100 on , with a correlation coefficiency of −0.42. At the same color, metal-poor galaxies show somewhat lower than metal-rich galaxies. SBS 0335-052E, which has the highest , does not show the lowest q 100 . In the second panel, a stronger trend is present for the with, with a correlation coefficiency of −0.53. The two galaxies with the highest have the lowest q 100 among the whole sample. In the last panel, the dependence of q 100 on is weak, with a correlation coefficiency of −0.33. Figure 7. Same as Figure 5 but for . as a function of IR color is shown in Figure 8 . An inverse relation of with is present, with a correlation coefficiency of −0.63. The relation between and is also apparent, with a correlation coefficiency of −0.43. The relationship between and is the strongest one, with a correlation coefficiency of −0.71, indicating that the low of metal-poor galaxies is associated with their high . Figure 8. Same as Figure 5 but for . In summary, for the whole sample, including both metal-rich and metal-poor galaxies, only shows good relationships with the three IR colors. Previous studies of
Paper: 2019MNRAS.485..367K (fulltext)
that the following correction terms are subtracted from the metallicity determined from H ii calibration given in Section 3.2 : Combining these with equations ( 5 ) and ( 7 ), they result into the following final calibrations: where x and x are determined from equations ( 5 ) and ( 7 ), respectively. We apply the above corrections to the DIG/LIER-dominated spaxels (i.e. those with emission line ratios beyond the Kauffmann line) to all galaxies in sample. Fig. 3 (bottom row) shows metallicity maps of NGC 1042 obtained from O3N2 (left-hand panel) and O3S2 (middle-panel) after correction, while the corresponding maps for all other galaxies are shown in figures (bottom rows) in the supplementary online material (Appendix A). 4.3 Residual differential metallicity after correction The metallicity maps obtained using these new calibrations for the DIG/LIER regions already show an improvement, especially in terms of much reduced metallicity contrast between H ii regions and nearby DIG/LIER regions. In this subsection, we quantify such improvement. From the corrected O3N2 and O3S2 metallicity maps of each galaxy, we estimate representative values of metallicities for H ii –DIG/LIER pairs using the same methodology described in Section 3.1.1 , and study the distribution of differential metallicities in Fig. 9 . Left-hand panel shows the distribution for [S ii ]-BPT selected H ii –DIG/LIER pairs, while right-hand panel corresponds to [N ii ]-BPT selected H ii –DIG/LIER pairs. By construction, mean differential metallicity is zero for each of the two metallicity diagnostic and for both samples. A comparison of dispersions before and after applying corrections can be found in Table 1 . Figure 9. Distribution of corrected differential metallicity (Δlog Z) of H ii –DIG/LIER pairs selected on the basis of the [S ii ]-BPT (left-hand panel) and [N ii ]-BPT (right-hand panel) diagrams. In each panel, the brown and cyan histograms represent the differential metallicity distribution from O3S2, and O3N2, respectively, after applying the correction to the DIG/LIER/Seyfert spaxels presented in Section 4.2 (equations 10 , 11 , 14 , and 15 ). σ(Δlog( Z )) denotes the standard deviation of the differential metallicity distribution. In each case, the average offsets of the distribution are zero by construction. O3N2 shows comparable metallicity dispersions for H ii –DIG/LIER samples obtained from both selection criterion, i.e. 0.04 dex for [S ii ]-BPT selection and 0.03 dex for [N ii ]-BPT selection. The dispersions are smaller than the scatter in differential metallicities before applying correction (i.e. 0.05 dex). Like O3N2, O3S2 also shows comparable metallicity dispersion for H ii –DIG/LIER samples obtained from both selection criterion, i.e. 0.06 dex for [S ii ]-BPT selection and 0.07 dex for [N ii ]-BPT selection. Although the dispersions after correction are still larger than those obtained from O3N2, the improvement is significant with respect to the dispersions
Paper: 2017A&A...603A..45B (fulltext)
more complex calibration model may contribute to the degeneracy and, in the worst case, restore complete degeneracy. 4.5. Dependence on heliotropic coordinates The spherical coordinates , , introduced in Sect. 2.6 define the position of the solar system barycentre in the SRS. This is what matters for calculating the parallax effect, which depends on the observer’s displacement from the barycentre. On the other hand, the physical relevance of the modulation is connected with the changing illumination of the satellite by the Sun, which depends on the heliocentric distance and the heliotropic angles and that represent the proper direction towards the centre of the Sun at the time of observation. The difference between the heliotropic and barytropic coordinates is at most about 0.01 au and 0.01 rad, respectively. This is small but should be taken into account for an accurate modelling of the basic angle variations. In this context it can be noted that the expected thermal impact on the satellite scales as , whereas the parallax factor scales as . On the other hand, the scanning law is chosen to keep as constant as possible, whereas can vary at the level of 1%. If the basic angle varies as , it is no longer strictly of the form of Eq. ( 18 ). The resulting parallax bias can be estimated by means of Eq. ( 19 ). 4.6. Breaking the degeneracy? If the basic angle varies as a consequence of the changing solar illumination of the rotating satellite, we expect to see a parallax bias according to Eq. ( 19 ). However, as discussed above, the degeneracy with the parallax zero point is not perfect, and in principle this opens up a possibility to calibrate the basic angle variations from the observations. At least three different effects that contribute to breaking the degeneracy could be used: the finite size of the field of view (Sect. 4.4 ), the time dependence of due to the eccentricity of the Earth’s orbit (Sect. 4.3 ), and the difference between the barytropic and heliotropic angles (Sect. 4.5 ). Unfortunately all three effects only come in at a level of a few per cent of the variation, or less, which makes the result very sensitive to small errors in the calibration model. Moreover, the finite field of view is of little use if we have to calibrate complex periodic variations of optical distortions independently in each field of view. The best chance may be offered by the time variation of , for which the ratio of the parallax effect to the illumination strength goes as and consequently varies by % over the year. Thus the hope of breaking the degeneracy purely from the observations themselves, based on the self-calibration principle, is somewhat limited. 5. Conclusions We present an analysis of the effect of basic angle variations on the global shift of parallaxes derived from observations by a scanning astrometric satellite with two fields of view. The method of small perturbations was used to derive the changes in the four observables, the across- and
Paper: 2016ApJ...817...64X (fulltext)
equally. The fitting method is the same as the SED analysis. The results are shown in Figure 4 . The arrows indicate the 95% upper limits of those time bins, whose TS values are smaller than 5. No obvious long term variation is found, which is consistent with the emission expected from an SNR. Figure 4. Light curve of source X. The horizontal solid and dashed lines are the average flux and its 1 σ range from the whole data set. 3. DISCUSSION The spatial and spectral association between source X and the HESS observation of SNR CTB 37B suggests that source X is the GeV counterpart of this SNR. The γ -ray emission can be either from the ICS of high energy electrons or the π 0 decay due to the inelastic pp collisions. Due to the lack of non-thermal X-ray emission in the same region of the TeV γ -ray emission, Aharonian et al. ( 2008 ) argued that the TeV γ -rays may have a hadronic origin. The Suzaku observations reveal, however, both thermal and non-thermal diffuse X-ray emissions from the western part of the radio shell, which suggests alternatively a multi-zone leptonic scenario for the γ -ray emission (Nakamura et al. 2009 ). We discuss both the leptonic and hadronic models in light of the multi-wavelength data, including the Fermi -LAT ones. In the modeling, both the spectra of electrons and protons are assumed to be power laws with exponential cutoffs, , where i = e or p , and α i and are the spectral index and the cutoff energy, respectively. The distance of CTB 37B is adopted to be 13.2 kpc (Tian Leahy 2012 ), and the radius is taken to be r ≈ 20 pc which corresponds to an angular size of 51 at such a distance. The radiation field includes the cosmic microwave background (CMB), an infrared field with T = 30 K and energy density u = 1 eV cm −3 , and an optical field with T = 6000 K and u = 1 eV cm −3 (Porter et al. 2006 ). The gas density is adopted to be 0.5 hydrogen cm −3 , as inferred from the X-ray observations (Aharonian et al. 2008 ; Nakamura et al. 2009 ). For the region which is coincident with the SNR shell (region 1), no non-thermal emission has been detected (Aharonian et al. 2008 ; Nakamura et al. 2009 ). The 1–5 keV flux of the thermal emission is ∼3 × 10 −13 erg cm −2 s −1 (Aharonian et al. 2008 ). The 2–10 keV unabsorbed flux of the thermal X-ray emission from region 1 is estimated to be ∼6 × 10 −13 erg cm −2 s −1 , based on the non-equilibrium ionization model employed in Aharonian et al. ( 2008 ). This thermal flux is adopted as an upper limit of the non-thermal emission from the SNR. 3.1. Leptonic Model In the leptonic model, the radio to X-ray emission is due to synchrotron radiation of relativistic electrons in the magnetic field, and the γ -ray emission is produced through ICS in the background radiation field and bremsstrahlung in the interstellar medium. To fit the radio to TeV γ -ray data, we find α e ≈ 1.65, , a magnetic field B ≈ 100 μ G, and a total electron energy above 1 GeV W e ≈ 9.0 × 10 48 erg. The fitting
Paper: 2016MNRAS.455.1512C (fulltext)
baryon fraction. Once M g is specified, the gas density is normalized such that, 2.3 Ionization balance In this paper, we consider models that may represent the most metal-poor DLAs currently known. Given the presumably minimal level of recent star formation in these systems, it is reasonable to assume that the local sources of ionizing photons are sub-dominant relative to the extragalactic background. In what follows, we have therefore assumed that the surface of the most metal-poor DLAs is illuminated solely by the Haardt Madau ( 2012 ) ultraviolet/X-ray background radiation from quasars and galaxies with intensity J 0 (ν). We also explore simple power-law models for the ionizing background, which are equivalent to the ‘ table power law slope α ’ command within cloudy , where f (ν) ∝ ν α . We assume that the distributions of gas and dark matter are spherically symmetric. Under this assumption, gas at a distance r from the centre of a dark matter halo is irradiated by background photons from all directions. The radiation field is therefore attenuated by a different amount depending on the density distribution of the gas along a given direction. If J 0 (ν) represents the unattenuated radiation field at a frequency ν, and 2πsin (θ)dθ/4π is the fraction of sky with an optical depth τ(ν, r , θ), then the intensity of the radiation field at a distance r from the centre of the cloud is given by where μ = cos θ, and i corresponds to every element/ion stage that we consider in this work. Unfortunately, our calculations are computationally too demanding to include every ionization stage for all elements with atomic number 30 (unlike cloudy ). We have therefore incorporated only the most abundant metals that are commonly observed in metal-poor DLAs, including H i , D i , He i , He ii , C i –C iv , N i –N iv , O i –O iv , Mg i –Mg iv , and Si i –Si iv . We have adopted the photoionization cross-section, σ i (ν), of each element using the compilation by Verner et al. ( 1996 ). At a distance r from the centre of the halo, the column density of species i in the direction θ can be calculated by integrating the volume density of species i, n i ( R ), from a radial distance r to the virial radius 2 (i.e. integrate along the path defined by the dashed line in Fig. 1 ): where equation ( 12 ) is used for 0 ≤ θ ≤ π/2 and equation ( 13 ) is used for π/2 θ ≤ π. The rate of primary ionizing photons at a given radius from the cloud centre of each species is then where h ν i is the ionization energy 3 of ion i . Each radiation field considered in this work is finely interpolated around the ionization energy of each ion to ensure numerical accuracy in the integrations. Figure 1. The coordinate system used to calculate the angular dependence of the column density of each species at a given radial coordinate (see equations 12 and 13 ). The rate of collisional ionization, Γ ci ( T ), is incorporated in our models using the Dere ( 2007 ) rate calculations.
Paper: 2021ApJ...915..106C (fulltext)
range of 60–80 kpc. Similar numbers have been found by Zhang et al. ( 2019 ) from the analysis of about 300 galaxy clusters at redshift 0.2 z 0.3. These authors described the BCG+ICL radial profile by using a triple Sérsic model and found a dominant core in the innermost 10 kpc, a bulge between 30 and 100 kpc and the ICL that dominates the distribution beyond 200 kpc, but already outside 100 kpc the profile transitions from the BCG to the ICL. They suggested that this transition is a potential cut between the two components, in good agreement with Pillepich et al. ( 2018 ) who noticed that simulated halos more massive than 5 × 10 14 M ⊙ have more ICL mass outside 100 kpc than within. All the values of the transition radius, i.e., the radius at which the ICL starts to become the dominant component, reported above are comparable with the range predicted by our model. We stress again that a wide range of possible values of R transition is preferable to a narrow one for the reasons discussed above. It strongly depends on the morphology of the BCG, on the amount of ICL with respect to the other components, and on the dynamical state of clusters, which reflects the merging history of the BCG+ICL system and thus its evolution with time. 5. Conclusions Following the analysis done in our recent paper (Contini Gu 2020 ), we have further improved our model by assuming that the radial mass distribution of a BCG+ICL system can be described by the sum of three profiles: a Jaffe profile for the bulge, an exponential disk, and a modified NFW for the ICL. We have taken advantage of a wide sample of simulated galaxy groups and clusters from our state-of-the-art semianalytic model to study the innermost ∼100 kpc of BCG+ICL systems. Unlike the model used in our previous study, the new one offers the opportunity to go below 100 kpc since bulge and disk are treated separately. The main goals of this study were to confirm that our model can reproduce several observed relations between the stellar mass at a given radius and the total BCG+ICL/halo mass, at different redshift and, give an idea of where the transition between the BCG and the ICL happens, i.e., when the ICL component starts to dominate the total distribution. In light of the analysis done and the overall discussion, we conclude the following: 1. The radial mass distribution of a BCG+ICL system can be described by the sum of three profiles: a Jaffe profile for the bulge, an exponential disk, and an NFW-like profile for the ICL. The model can reproduce several observed scaling relations between the BCG+ICL stellar mass within a given aperture and the BCG+ICL/halo mass, at different redshift with an overall precision below 0.05 dex at z = 0 and 0.3 dex at z ≳ 0.5 along the wide stellar mass/halo mass ranges probed. 2. In good agreement with recent observational studies, our model predicts that R transition , i.e., the radius at which the radial distribution of BCG+ICL systems transitions from the BCG to the
Paper: 2017MNRAS.468.2836L (fulltext)
; Boylan-Kolchin etal. 2012 ), and rotation curves (van der Marel Kallivayalil 2014 ). 2.4 Likelihood from <italic>V</italic><sub>max</sub> distributions Here, we summarize the statistical method for comparing the observational and simulated V max distributions. It is identical to that of Jiang van den Bosch ( 2015 ) except where noted below. The goal is to determine the probability that the Milky Way satellite V max function can be drawn from the distribution of simulated functions. We will establish the statistical scatter between the simulated V max functions, and calculate the mean deviation between the measured Milky Way V max function and the simulated systems. The size of the measured V max function deviation relative to the size of the scatter will tell us about how readily the Milky Way V max function is realized in each of our models. The first step is to define the variation within the set of V max for a given halo mass-dark matter modelgalaxy formation model combination. The n most massive satellites of the i th simulated halo are selected, and their values of V max are sorted into descending order. The V max distribution is then { V i , 1 , V i , 2 , V i , 3 V i , n }; here we have omitted the max subscript for clarity. We can define the difference between this i th halo distribution with respect to the j th halo distribution thus: and if there are N realizations of the model in question, the mean Q for the i th distribution, , is Similarly, if we substitute the i th simulated V max distribution to instead be { V MW, 1 , V MW, 2 , V MW, 3 V MW, n }, i.e. the observed V max distribution of the Milky Way satellites, then we obtain Q MW : The probability that a V max function with Q MW could be drawn from the parent distribution is then P ( Q MW ), where P is the cumulative distribution of . We expand on the method described above to describe how we select satellites. The luminosities calculated for the satellites enable us to take account of the fact that the brightest satellites, for which the velocity dispersions have been measured with the highest precision, need not necessarily reside in the most massive haloes. Therefore, we consider two options for selecting our top n satellites to be matched to observations: (i) select the n brightest V -band satellites and (ii) select the n highest V max satellites. We compare the results from these two approaches in Section 3.1.3 . 2.5 Sterile neutrino matter power spectra In addition to CDM, we consider keV-scale, resonantly produced sterile neutrino dark matter. The latter constitutes part of a larger particle physics model called the neutrino minimal standard model (MSM), which explains neutrino oscillations and baryogenesis in addition to yielding a dark matter candidate, see Boyarsky, Ruchayskiy Shaposhnikov ( 2009 ) for a review. The keV sterile neutrino behaves like WDM, in that it free streams out of small perturbations in the early Universe. The resulting matter power spectrum
Paper: 2017MNRAS.472..919K (fulltext)
of galaxies with high SFRs/luminosities to the CSFRD/total IR luminosity decreases much flatter. This discrepancy reflects the tension between the SFRFs of the EAGLE reference model and IR constraints. The later imply larger number densities of objects with SFR ∼ 10–100 M ⊙ yr −1 (Section 4.1 ) at z 2, and the disagreement with the L100N1504-Ref run increases with time. In Section 6 we will see the important role that feedback plays on keeping the number of these extremely high star-forming objects low at these redshifts. 5 THE CONTRIBUTION OF HALOES WITH DIFFERENT MASSES TO THE SFRF AND CSFRD In this section we present the contribution of haloes with various masses to the CSFRD (Fig. 4 ), the number density of haloes of different masses at different redshifts (Fig. 5 ) and their contribution on the SFRF (Fig. 6 ) in the EAGLE L100N1504-Ref run. Figure 4. CSFRD contribution of haloes with different masses in EAGLE. Below redshift 5 objects with masses M halo = 10 11–12 M ⊙ dominate through their high efficiencies besides their low numbers (Fig. 5 ). Figure 5. Left-hand panel: evolution of the number of haloes with different masses in the L100N1504-Ref. Haloes with low masses are numerous even at high redshift while higher mass objects (e.g. M halo ∼ 10 11–13 M ⊙ ) form with high rate up to redshift z ∼ 1.5. Right-hand panel: percentage of star-forming haloes at different masses. Haloes with M halo ∼ 10 11–12 M ⊙ are typically star forming at all redshift. Figure 6. The contribution of dark matter haloes to the SFRF (black stars) for redshifts z ∼ 8.0 (top left-hand panel), z ∼ 4.0 (top right-hand panel), z ∼ 2.0 (bottom left-hand panel) and z ∼ 0.1 (bottom right-hand panel). Galaxies with SFR ≥ 1 M ⊙ yr −1 reside in large haloes M halo ∼ 10 11–12 M ⊙ even at z ∼ 4.0. These haloes represent only ∼1 per cent of the total star-forming population at this era. In the local Universe the number of massive haloes M halo ≥ 10 11 M ⊙ is significantly higher than that of z ∼ 4. They dominate almost entirely the SFRF at SFR ≥ 0.1 M ⊙ yr −1 . Behroozi, Wechsler Conroy ( 2013 ) and Moster, Naab White ( 2013 ) argued that haloes near 10 12 M ⊙ are the most efficient at forming stars at all redshifts with the baryon conversion efficiency ( M ⋆ / M baryon ) dropping rapidly at both higher and lower masses. In addition, the stellar to halo mass ratio also peaks at 10 12 M ⊙ (Behroozi et al. 2013 ). However, in the paradigm of hierarchical structure formation, small haloes form earlier than larger ones, which need some time to emerge. In Fig. 4 we see that at z ≥ 5 the CSFRD is dominated by relatively small haloes of mass M halo = 10 9–10 M ⊙ . 5 The left-hand panel of Fig. 5 reveals that the number of objects of this mass interval is high. In addition, the right-hand panel of the same figure, where we present the percentage of star-forming haloes at different masses (i.e. n haloes, SFR > 0 / n haloes, total ), shows that a large percentage (>50 per