adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...849...84W (fulltext)
redshift of the drag epoch, z d , can be calculated aswhere . Note that z d is well approximated analytically (Eisenstein Hu 1998 ) aswhere We use a fixed value of the baryon density from the Planck result (Planck Collaboration et al. 2016 ). 6 6 We have tested to marginalize over with a Gaussian prior derived from the Planck measurement and find that the result is largely unchanged. The baryon density can also be accurately determined in a CMB-independent way, e.g., using the primordial deuterium abundance in the Big Bang Nucleosynthesis (BBN) theory (Riemer-Sørensen Sem Jenssen 2017 ). Note that the quantities and can be estimated from anisotropic BAO measurements, while the quantity,is determined by isotropic BAO measurements. As shown above, the BAO distance measurements, or , are two-variable functions of and H 0 (once is known) in a flat ΛCDM cosmology, and therefore the Hubble constant can be, in principle, determined from the BAO distances with marginalized over. In what follows, we use isotropic or anisotropic BAO distance measurements to determine the Hubble constant with marginalized over, i.e., our parameter space is simply (assuming a flatness of the Universe)The sound horizon at the drag redshift r d is calculated using Equation ( 3 ). 7 7 Except for 6dF, the value of r d is rescaled by a factor , where value is calculated from CAMB (Lewis et al. 2000 , available at http://camb.info/ ) in the same fiducial cosmology (Bennett et al. 2014 ). We perform a Monte Carlo Markov Chain (MCMC) global fitting for parameter estimation using a modified version of CosmoMC (Lewis Bridle 2002 ). 8 8 http://cosmologist.info/cosmomc/ The BAO data sets used in this work include 1. the isotropic BAO measurements using the 6dFRS (6dF; Beutler et al. 2011 ) and SDSS main galaxy sample (MGS) (Ross et al. 2015 ) at effective redshifts and , respectively; 2. the BOSS DR12 anisotropic BAO measurements at three effective redshifts (BOSS 3 z bin) in Alam et al. ( 2017 ) or at nine effective redshifts (BOSS 9 z bin) in Wang et al. ( 2017 ) and Zhao et al. ( 2017b ); 3. the eBOSS DR14 isotropic BAO measurement at (Ata et al. 2017 ); and 4. a combination of 6dF + MGS + BOSS 3 z bin + eBOSS DR14 (All 3 z bin), or a combination of 6dF + MGS + BOSS 9 z bin + eBOSS DR14 (All 9 z bin). To check the consistency of the H 0 values determined from different data sets within the ΛCDM model, we compute the tension, T , based on the KL divergence (Kunz et al. 2006 ; Paykari Jaffe 2013 ; Amara Refregier 2014 ; Seehars et al. 2014 , 2016 ; Verde et al. 2014 ; Grandis et al. 2016 ; Raveri et al. 2016 ), which quantifies the distance between two probability density functions (PDFs), p 1, and p 2 . If both p 1 and p 2 are assumed to be Gaussian, the relative entropy in bits between the two PDFs can be evaluated aswhere is the best-fit parameter vector, is the corresponding covariance matrix, and d denotes the dimensions of the parameter space (e.g., d = 2 in our case where
Paper: 2018MNRAS.481.3305S (fulltext)
super asymptotic giant branch (AGB) star. In an He star in the binary system, there is no H-rich envelope and, thus, AGB phase does not occur. For such a situation, the CO core growth depends on the efficiencies of the He shell burning and the mass loss of the He envelope. Because the mass-loss process in the binary system is not fully understood and long-time calculation of the CO-core growth through the He-shell burning is required, it is not easy to evaluate whether a CO core less than 1.36 M ⊙ in the binary system grows up to the critical mass and explodes as an ultra-stripped EC SN. The mass range of CO cores for EC SNe was recently discussed for the primary stars in binary system (Poelarends et al. 2017 ; Siess Lebreuilly 2018 ). However, the mass range would be still model dependent and, thus, we are not sure whether the binary evolution affects the mass range of CO cores of EC SN progenitors. The investigation of the growth of the ONe core with thin He layer is important to clarify the possibility of the evolutionary path to an EC SN in close-binary systems. How is low-mass NS formed from EC SN progenitors and ultra-stripped SN progenitors that have a low-mass Fe core? In the case of EC SNe, the electron fraction in the ONe core is ∼0.48 at the central Ne ignition (Takahashi et al. 2013 , 2018 ). The electron fraction in the ONe core scarcely decreases until the central Ne ignition. On the other hand, progenitors of ultra-stripped SNe cause the electron capture during their evolution and, as a result, the electron fraction of the Fe core decreases. The electron fraction at the centre is ∼0.43 when the central density becomes ∼10 10 g cm −3 in the collapsed models in this paper. Thus, the Chandrasekhar mass of EC SN progenitors is heavier than that of the progenitors that have a low-mass Fe core of ultra-stripped SNe. The hydrodynamical simulation in our previous study (Yoshida et al. 2017 ) showed that the baryon mass of the PNS of an EC SN is 1.32 M ⊙ . 4 This mass is higher than the baryonic mass range of the NSs formed from CO137–CO144 models. 6 SUMMARY In this paper, we investigated the minimum mass of NSs based on astrophysical scenarios, i.e. stellar collapse and supernova explosion. We calculated the evolution of CO cores with masses 1.35–1.45 M ⊙ . The stars with CO-core masses higher than 1.36 M ⊙ form an Fe core after off-centre Ne, O, and Si-burnings. We found that low-mass CO cores, which eventually form an Fe core and subsequently collapse, could result in an NS with mass ∼1.17 M ⊙ , which is comparable with the lowest NS mass precisely measured. According to our stellar evolution simulations, the minimum mass of CO cores that produce an Fe core is ∼1.37 M ⊙ and below this value an ONe core is formed, which would lead to EC SN instead. Because of its higher value of Y e , an EC SN would produce more massive NSs than a core-collapse SN from an Fe core. Therefore, the minimum mass of NSs is expected to be determined by the
Paper: 2021A&A...646L...3C (fulltext)
Vidal et al. (2017) calculate a CS/HCS + ratio in better agreement with observations, especially at late times. The reason for the low abundance of HCS + in our gas-phase model may be related to the underproduction of H 2 S (see e.g. Agúndez Wakelam 2013 ), which is enhanced when including grain-surface chemistry, and which can increase the abundance of HCS + through reactions like H 2 S + C + and H 2 S + + C. While the calculated abundance of HC 2 S + is in agreement with the observed upper limit, the calculated C 2 S/HC 2 S + ratio is too low because the chemical model severely underestimates the abundance of C 2 S. This is because the chemical network kida.uva.2014 includes an efficient destruction channel for C 2 S through reaction with O atoms, based on estimations by Loison et al. (2012) . If this reaction is neglected, the calculated abundance of C 2 S is shifted up and becomes very close to the observed value, while the abundance of HC 2 S + also increases, remaining consistent with the observed upper limit (see dotted lines in Fig. 3 ). Accurate calculations of the rate constant of the O + C 2 S reaction at low temperatures are needed to shed light on the chemistry of C 2 S in cold dense clouds. 7. Conclusions We report the first identification in space of protonated C 3 S. Four harmonically related lines observed toward TMC-1 using the Yebes 40 m radiotelescope have been unambiguously assigned to this ion thanks to accurate ab initio quantum chemical calculations and laboratory measurements of the rotational spectrum of this species. The derived C 3 S/HC 3 S + ratio of 55 ± 20 is well reproduced by a gas-phase chemical model in which HC 3 S + is mostly formed through protonation of C 3 S and the reactions S + + C 3 H 2 and S + C 3 . Appendix A: Column densities under LTE for linear molecules The determination of column densities from the observed line paramaters of the rotational transitions of a molecule requires knowledge of the collisional rates of the molecular species under study. These values are not always available and rotational diagrams are used to derived rotational temperatures, T r , and column densities ( Goldsmith Langer 1999 ). Often only one line has been observed and therefore an assumption has to be made as to the rotational temperature. The line parameters of the species studied in this paper are given in Table A.1 . Table A.1. Observed line parameters in TMC-1 for CS, HCS + , C 2 S and C 3 S. Notes. (a) Predicted frequencies from the MADEX code ( Cernicharo et al. 2012 ). Numbers in parentheses are 1 σ uncertainties in units of the last digits. (b) Integrated line intensity in K km s −1 . (c) Line velocity with respect to the Local Standard of Rest in km s −1 . (d) Linewidth at half intensity derived by fitting a Gaussian function to the observed line profile (in km s −1 ). (e) Antenna temperature in K. In this section we analyse how the derived column density depends on the adopted T r for a range of
Paper: 2016ApJ...830...95U (fulltext)
RWI is an analysis of a configuration with only one local pressure extremum). This analysis of the barotropic shear layer model gets simplified if we implement the strategy used in Umurhan ( 2010 ), wherein is assumed. In this limit, the continuous shear layer flow profile becomes an analytically tractable problem comprised of three piecewise linear shear profiles. The solution procedure follows the steps detailed in the Appendix relating to the same type of analysis done on the asymmetric jet (examined in the next section). 12 12 It ought to be noted that there are subtleties introduced because of the use of piecewise linear profiles. For instance, it is sometimes the case that piecewise linear profiles predict modes that do necessarily have counterparts in analysis of flow profiles that are infinitely differentiable. The normal modes generated herein, through the use of these piecewise linear representations, all have counterpart modes in their corresponding infinitely differentiable flow profiles. For the shear layer considered here it is a slight generalization of Rayleigh’s shear layer analysis (Rayleigh 1880 ) with the additional superposition of the background Keplerian shear profile. The resulting frequency response is given byInspection shows that this system becomes unstable when ω goes through zero. The physical condition for instability is the phase locking of two counter-propagating Rossby waves (Baines Mitsudera 1994 ; Heifetz et al. 1999 ). By setting we find the condition for marginal growth to be given by where is the shear layer amplitude corresponding to marginality. We do not consider the positive roots, , because these generally correspond to composite flows that are strongly cyclonic, i.e., flows in which while the negative root, on the other hand, often corresponds to anti-cyclonic shear profiles. 13 13 This is generally true on examination of the marginal condition in Equation ( 53 ). If we consider the setting examined in M13 , we suppose that the smallest azimuthal wavenumber is the one fitting the box considered therein. The corresponding azimuthal length scale in their box units is approximately . This corresponds to a fundamental azimuthal wavenumber . Similarly, the width of the shear line charge is approximately 0.28 (also in their code units). Translating this into our setup, the total width is , and thus we adopt a value of . In this case, the corresponding critical value of for the R-mode is given from Equation ( 53 ) by , with a corresponding critical Rossby number of . Values of should then be stable to the RWI. We find that the value of predicted in Equation ( 53 ) generally predicts more negative values of that are borne out in our models. The reason for this is that we adopt non-zero values of () for all the models reported in this section. The discrepency is generally 2%–5% of the predicted values. 14 14 We have separately checked (but have not included in this manuscript) and verified that as the model
Paper: 2019A&A...629A...7L (fulltext)
the redshift of the clusters. 4.1. Ridge fitting Since we are trying to create a general method to use for many different sets of galaxies at different redshifts, we have decided to use a single set of parameters across all the bins. By doing so we retain a generally low dispersion in the fits as it makes sure that there are no systematic errors due to using different parameters. As can be seen in Fig. 4 , we mostly obtain good fits for both the ridges with a few outliers and even fewer ridges that we do not find at all. The main exceptions to this are at around redshift z 0.05 where the bins do not contain many galaxies and we end up with sparse ridges. For the redshift bins very close to us we cannot fit anything below redshift z = 0.021 as the ridges are too irregular and the method can no longer fit them to satisfying precision. For the star-forming ridge we see a large increase in dispersion at higher redshift. This is mainly due to how sparse the ridge becomes at this point, making it harder to detect. Fig. 4. Standard deviation of the fit of the ridges in each of the redshift bins. At redshifts of z 0.021, the method does not detect any ridges due to the number of galaxies in the bins. We also see increasing irregularity in the ridges below about redshift z 0.05. Besides this we the star-forming ridge having very high dispersion at redshifts above z 0.15 due to being very sparse at these redshifts. 4.2. Evolution of ridges in redshift By plotting each of our measured ridge SFRs, we can then see a relationship between the SFR and redshift (Fig. 5 ). It is obvious that the relationship is linear for the whole range of redshifts for the star-forming ridge and linear in about half the redshift range for the quenched ridge. It could be argued that a logarithmic fit would make a better fit for the quenched ridge but it seems more likely that there is another answer for the high redshift part of the quenched ridge, since the SFR values for the ridge stay almost constant in that part. This is likely due to a few different factors such as difficulty in observing the quenched ridge at this redshift or due to us using a shallower incline in the SFR– M ⋆ plane than appropriate or that the ridges are not linear in the SFR– M ⋆ plane but actually curve downwards at higher mass ranges as suggested by Schreiber et al. (2014) and Ciesla et al. (2017) . This would lead to higher estimates as we go to higher z since we only detect the high mass end of the ridge in that region. This fit does not seem to be widely accepted as people are still using linear ridges in some papers ( Renzini Peng 2015 and Davies et al. 2016 ). Fig. 5. Evolution of star-forming ridge and the quenched ridge SFR with redshift. For the star-forming ridge the best fit seems to be a linear one whereas for the quenched ridge it seems that above about redshift log( z + 1)=0.055 the measurements diverges from the linear fit. This could be due to a systematic error in the measurement of
Paper: 2017MNRAS.466.4917D (fulltext)
contradicting the original hypothesis. A way to avoid this problem could be to look at the incidence of AGN activity as a function of environment, for example as was done by Arnold etal. ( 2009 ). With a sample of 10 groups and six clusters, they find that the fraction of AGN (with L 0.3-8keV 10 41 ergs 1 ) in groups is a factor of 2 higher than in clusters, although the result has marginal significance. Due to concerns about the differing morphological mix of galaxies in these different environments, they also looked at the AGN fraction in early-type galaxies, finding a similar result. Many other studies of the environment of X-ray selected AGN have used correlation analysis to assess the clustering bias on different scales as well as the typical halo mass. Recent examples include Gilli etal. ( 2009 ) who found a strong clustering signal for AGN with median z 0.98 and L 0.5-10keV 10 43.8 ergs 1 . The correlation length matched that of galaxies with stellar mass 10 10.5 M , and implied a typical halo mass of 10 12.4 M . Fanidakis etal. ( 2013 ) found that AGN (10 42 L 2-10keV [ergs 1 ] 10 44 ) typically reside in haloes of mass 10 13 M . DiPompeo etal. ( 2014 ) found similar halo masses for the infrared selected AGN they studied at z 1, with 10 13.3 M and 10 12.8 M depending on whether the AGN are obscured or not. These papers highlight that the luminosity, selection technique and redshift of an AGN population are also important factors in assessing environment. Indeed, Martini etal. ( 2013 ) show that at z 11.5 there may be a reversal in the incidence of AGN in clusters versus the field. More locally, using a large number of galaxies from the Sloan Digital Sky Survey (SDSS), with AGN identified via their [O iii ] line emission, Li etal. ( 2006 ) found little difference between the clustering of AGN and control galaxies on scales greater than 1Mpc, suggesting that the haloes of active and inactive galaxies have similar masses. Between 100kpc and 1Mpc AGN were more weakly clustered, a result these authors argued could be explained if AGN are preferentially located at the centres of their haloes. And at scales less than 70kpc, AGN appeared to be marginally more clustered. The importance of the local environment was also highlighted by Serber etal. ( 2006 ), who looked at quasars (identified by M i 22) in the SDSS, finding they are overdense on scales 100kpc, but have no difference compared to the general population at larger 1Mpc scales. This suggests that galaxygalaxy interactions might be important. However, from a snapshot survey of AGN (selected in the 0.33.5keV soft X-ray band, and identified as AGN using optical emission line widths and ratios), Schade, Boyle Letwasky ( 2000 ) emphasized the lack of evidence for strong interactions or merging activity. Indeed, Sabater, Best Heckman ( 2015 ) have argued that the availability of cold gas in the nuclear regions, rather than local galaxy density or galaxygalaxy interactions, is the key
Paper: 2020A&A...638A..56N (fulltext)
note that the good agreement of the TSI with the red filters is expected to be valid only for the rotational variability, which is dominated by spots. In contrast, the solar irradiance variability on the activity cycle timescale is given by the delicate balance between facular and spot components, and consequently has a very sophisticated spectral profile ( Shapiro et al. 2016 ; Witzke et al. 2018 ). Thus, values of slopes from Table 1 cannot be extrapolated from rotational to activity cycle timescales (see Shapiro et al. 2016 , for the detailed discussion). Fig. 3. R 30 in different filter systems compared to the TSI (black solid line) for a time span of 1000 days over solar cycle 22. Top panels : Kepler , CoRoT, and TESS. Middle panels : three Gaia passbands. Bottom panels : three VIRGO/SPM channels. See the main text for the definition of R 30 . Table 1. Slopes of the linear regressions in Fig. 4 . Table 2 lists the cycle-averaged values of R 30 for all passbands in mmag. Together, Fig. 4 , and Tables 1 and 2 show that the TSI is a passable representative for the variability on the solar rotation timescale as it would be observed in the TESS, Kepler , CoRoT, Gaia G , Gaia G RP , and VIRGO-red filters, but it noticeably underestimates the variability in G BP , VIRGO-green, and VIRGO-blue. Fig. 4. Linear regression between the R 30 values calculated with the TSI and with the solar light curves as they would be recorded in different filter systems. The values of the slope, s , are given in the legend. The black lines have a slope equal to 1. Several studies (see e.g. Basri et al. 2010 ; Harrison et al. 2012 ) have assumed that the amplitude of the rotational solar variability as it would be measured by Kepler is very close to the amplitude calculated for the combined green and red VIRGO/SPM light curves (in the following VIRGO/g+r). Here we test this hypothesis. The variability R 30 for Kepler compared to VIRGO/g+r is shown in Fig. 5 , which is limited to the same time interval as Fig. 3 . The two curves are remarkably similar to one another. To test the similarity quantitatively, we show the linear regression of R 30 between Kepler and VIRGO/g+r for four solar cycles (21–24) in Fig. 6 . The Pearson correlation coefficient is very high (0.999) and the slope deviates by only +0.8% of unity averaged over four solar cycles. While these calculations are related to the amplitude of the rotational variability, R 30 , we additionally calculate regressions between Kepler and Virgo light curves in Sect. 4 . We also directly connect the TSI and VIRGO/g+r rotational variability. The linear regression between R 30 in TSI and VIRGO/g+r results in a slope of 0.88 (±0.002) and a Pearson correlation coefficient of 0.995. Fig. 5. R 30 over cycle 22 for VIRGO/g+r and Kepler . Fig. 6. Linear regression between the R 30 calculated for VIRGO/g+r and for Kepler light curves. The black solid line represents a linear regression with slope = 1. 3. Correction for the
Paper: 2019ApJ...878..105G (fulltext)
model is able to replicate the spread in the CRIR. There are two main controlling factors for the CRIR in the clouds: the number of embedded sources and the cloud environment. Embedded sources create a natural dispersion in ζ for different molecular cloud masses and star formation efficiencies. Without internal sources, there is no spread in our modeled CRIR as a function of column density. In order to represent the observations, the external CRIR must be increased instead for different regions. Local sources of CRs, such as nearby OB associations or supernova, contribute significantly to the CR flux at the cloud boundary. As the external CR flux is increased, the impact of attenuation also increases due to the rapid reduction in low-energy CRs. Figure 11 shows that the impact of attenuation is different between the HNI and LNI models. For the LNI model, ζ changes by less than an order of magnitude across four orders of magnitude in A V . Conversely, the HNI model CRIR decreases by two orders of magnitude due to an overall reduction in MeV-scale CRs. The HNI model overpredicts the CRIRs measured in diffuse gas to CRIRs measured near high-mass protostars, excluding the galactic center sightlines. However, the spectrum is the maximal CR spectrum from Voyager -1 observations (Stone et al. 2013 ; Ivlev et al. 2015 ). The LNI and LNA models underpredict the observed CRIR for all but a few sightlines. Thus, Figure 11 demonstrates that it is essential to consider the cloud environment and properly treat the CR physics and cloud density distributions. Models without attenuation only represent the CRIR within narrow ranges of A V and not in the cloud interiors. Figure 11 also underscores that the low = energy CR spectrum, which, if often adopted in astrochemical modeling, is a poor fit to the majority of the observations. 4.3. Challenges for Deriving the CRIR from Chemical Diagnostics There have been numerous attempts to find chemical diagnostics that are strong tracers of the CRIR (Caselli et al. 1998 ; Indriolo et al. 2007 , 2015 ; Neufeld et al. 2010 ; Indriolo McCall 2012 ; Neufeld Wolfire 2017 ; Albertsson et al. 2018 ). Some of these, such as [DCO + ]/[HCO + ], cannot be modeled with the current 3 d-pdr version due to the lack of deuterium and isotopic chemistry. Most probes of the CRIR are based on the local abundance, which is difficult to directly ascertain from observations. The use of column density ratios typically assumes the line emission observed between species is cospatial. Figures 8 and 9 examine effects of CR physics on the , and column density ratio diagnostics. However, we find that none of these ratios are monotonically sensitive to the average CRIR, shown for the LDI model in Figure 5 . The [CO/C + ] column density ratio is anti-correlated with the density-averaged CRIR because the amount of CO declines while the amount of C + increases in large, massive clusters. Local abundance ratios are used for fitting observations with