adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...905...63C (fulltext)
by most of the theoretical equations of state (e.g., Özel Freire 2016 ). Therefore, it is unlikely that this system is observed at a very large inclination angle. On the other hand, assuming that binaries are randomly inclined with respect to the observer, i.e., assuming a flat distribution of , we find a median NS mass (corresponding to ) of (see the right panel of Figure 6 ). Such a value would place M13F on the highest-mass side of the known NS mass distribution (e.g., Antoniadis et al. 2016b ; Cromartie et al. 2020 ). All of this suggests that either M13F is observed at quite small inclination angles () or it hosts a massive NS. Indeed, under the assumption of a flat distribution of , there is a probability that the compact object mass is larger than , thus favoring the case of a massive NS. Finally, we further investigate the possibility of having a standard NS mass coupled with a low-inclination angle. To this aim, we used a Markov Chain Monte Carlo sampler (Foreman-Mackey et al. 2019 ) to explore the combination of NS masses and orbital inclination angles able to reproduce the observed mass function. We defined a standard Gaussian likelihood function to minimize the difference between the left and right sides of Equation ( 2 ). We assumed a uniform prior on the distribution of and also a prior on the NS mass distribution following that empirically derived by Antoniadis et al. ( 2016b ), which is roughly a double Gaussian with a main component centered at and with a dispersion of about , and a secondary component centered at and with a dispersion of about . The posterior distribution is shown in Figure 7 , and the results based on the 16th, 50th, and 84th percentiles show that M13F could host an NS with an almost standard mass of if orbiting with an inclination angle °. Figure 6. Left panel: companion mass as a function of the NS mass. The solid horizontal line marks the best-fit value of the companion mass () and the light-gray region delimited by the two horizontal dashed lines marks its estimated uncertainty. The dark-gray shaded area is the region forbidden by the binary mass function (see Equation ( 2 )), while the dotted–dashed colored lines are the curves obtained assuming different inclination angles. The vertical line represents a canonical NS mass of . Right panel: NS mass as a function of the cosine of the orbital inclination angle, for the estimated mass of the companion star: the solid curve marks the combination of values allowed by the best-fit value of the companion mass (), while the light-gray region delimited by the two dashed curves marks the combinations allowed within the uncertainty (see the legend). The rightmost panel shows, as reference, the NS mass distribution empirically derived by Antoniadis et al. ( 2016b ). Figure 7. Corner plots showing the 1D and 2D projections of the posterior probability distribution of the NS mass and inclination angle of M13F. The 1D histograms are the marginalized probability
Paper: 2019ApJ...877...62L (fulltext)
the latter is also unknown for putative extraterrestrial organisms, in principle a comparatively robust estimate is probably set by biophysical considerations: at least one photon must be incident on the area of the photosynthetic apparatus (e.g., photosystem I or II) over a single temporal cycle involving electron transfer. The minimum empirical bound of ∼6 × 10 11 photons cm −2 s −1 has been explained through charge recombination in photosystem II, protein turnover, and H + leakage (Raven et al. 2000 ). Therefore, combining these results, the critical PAR energy flux becomes Ψ 0 ∼ 2.1 erg cm −2 s −1 . Substituting this value into ( 5 ), and solving for the cutoff distance ( d P ) yields the extent of the “photosynthesis zone.” By doing so, d P is given byA couple of important points are worth highlighting regarding the above formula: (i) the net primary productivity of biospheres at these low light levels is potentially several orders of magnitude smaller with respect to modern Earth, and (ii) photoautotrophs require not only sufficient fluxes of photons at appropriate wavelengths, but also reactants (e.g., CO 2 ), nutrients (e.g., ), and electron donors (Lingam Loeb 2018a , 2019b ). The photosynthesis zone has been plotted as a function of M BH in Figure 1 . Substituting the parameters for the Milky Way, we obtain d P ≈ 0.34 kpc. This distance is fairly “large” because it approximately equals the scale height of the thin disk of our Galaxy. If we consider black holes with masses on the order of 10 9–10 M ⊙ , we find d P ≳ 10 kpc. In such instances, large swathes of galaxies would fall within the domain of the photosynthesis zone. As noted in Sections 2 and 4 , UCDs are characterized by very high stellar densities, indicating that the photosynthetic zone calculated above could also encompass most stars in these galaxies. 3.2. Timescales for the Evolution of Photosynthesis Finally, we must also examine the issue of timescales. Analyses of isotopic ratios on Earth favor the existence of anoxygenic photosynthesis by ∼3.5 Ga (Tice Lowe 2004 ; Olson 2006 ; Butterfield 2015 ), or possibly even earlier; recall that we posited life had originated by ≳3.7 Ga. The origin of oxygenic photosynthesis remains less tightly constrained, with some proxies (e.g., U–Th–Pb isotopic ratios) placing it as far back as ≳3.7 Ga (Rosing Frei 2004 ), although unambiguous evidence from microfossils appears to date back only to 1.9 Ga (Lyons et al. 2014 ; Fischer et al. 2016 ). Despite this uncertainty, there are multiple grounds for contending that oxygenic photosynthesis may have evolved by ≳2.7 Ga, based on isotopic ratios, phylogenetics, and microfossils (Buick 2008 ; Schirrmeister et al. 2016 ), thus amounting to its emergence by at least several 100 Myr prior to the Great Oxidation Event at ∼2.4 Ga; see also Knoll et al. ( 2016 ), Lingam Loeb ( 2019c ), and Cardona et al. ( 2019 ). In toto , it is not altogether unreasonable to surmise that both anoxygenic and
Paper: 2019MNRAS.483L..47C (fulltext)
absent in the previous models, – the neutron transfer reactions, consisting in ‘hopping’ (quantum tunnelling) of a neutron from weakly bounded state in one (donor, ‘d’) nucleus to another (acceptor, ‘a’) nucleus. Neutron transfer reactions are well known in nuclear physics for near-barrier and sub-barrier energies (e.g. von Oertzen et al. 1987 ; Zagrebaev 2003 ; Zagrebaev, Samarin Greiner 2007 ; Canto et al. 2015 ; Karpov, Rachkov Samarin 2015 ) and the multinucleon transfer reaction can be used to synthesize previously unexplored superheavy elements (Zagrebaev, Karpov Greiner 2013 ; Wuenschel et al., 2018 ). The specific feature of neutron transfer reactions in NS envelope would be that the main contribution to the neutron transfer rate comes from nuclei located at distance l pk , which can be as large as 100 fm, strongly exceeding nuclei radii. As for thermonuclear reactions, this distance is determined by two competing factors: decrease of the tunnelling probability with increase of internuclear distance and decrease of number of nuclei, which can approach closer, because of Coulomb barrier. In some sense, the neutron transfer reactions are similar to a hopping transition between localized states of electrons, which is the basis of hopping conductivity (see e.g. Gantmakher 2005 ). Note, as shown by Zagrebaev, Samarin Greiner ( 2007 ) neutron transfers can also increase fusion rates due to modification of internucleus potential, but I do not discuss this effect here, limiting myself to neutron transfer reactions only, and, specifically, to order-of-magnitude estimates, which demonstrate that neutron transfer is applicable for astrophysics matters. Namely, I start from calculation of the transfer probability (per unit time) between static nuclei at given distance, following the approximative approach by Bonaccorso, Piccolo Brink ( 1985 ) and Monaco Brink ( 1985 ). Then I estimate the reaction rate λ (number of reactions per one donor nuclei per unit time) for certain plasma conditions by averaging transfer probability over realistic internuclei separation in plasma. A simple approximating expression is suggested. Surely, the model (especially its nuclear part) should be improved, and at the end of the letter I briefly discuss its further refinement, leaving detailed calculations for the future. The neutron transfer rate strongly depends on S n of the donor nuclei, charges Z d and Z a of both donor and acceptor nuclei, temperature T , and electron chemical potential μ e (which determines density ρ for given composition). Typically, neutron transfer is relevant for donor nuclei with S n ≲ 3 MeV, which may exist in envelopes of accreting NS. In particular, it can burnout strongest among URCA pairs – specific pairs of nuclei which can coexist at respective electron chemical potential μ e (density) and cools down the envelope by neutrino emission associated with cycles of beta-captures/decay (see Schatz et al., 2014 ; Meisel et al., 2015 for
Paper: 2019MNRAS.482.5401S (fulltext)
Figure A1. A convergence study of the evolution of the mass of dense gas with n ≥ n cloud /3. For all cloud sizes the three highest resolution levels show good agreement, especially at early times t ≲ 5 t cc , implying that these simulations are well converged. At the same time, the low-resolution simulations with R cloud /Δ x = 76 (dashed lines) systematically differ from the other simulations. Thus, only the dense gas survival mass fraction in the 2D simulations with R cloud /Δ x ≥ 152 are converged. The evolution of the number of FoF groups for the 2D simulations is shown in Fig. A2 . We confirm the conclusion that clouds with R cloud ≫ l cool undergo excessive fragmentation for a resolution of R cloud /Δ x ≥ 152. Only the low-resolution simulations with R cloud /Δ x = 76 are not capturing this result in 2D. Figure A2. The number of FoF groups in 2D simulations with a resolution of 607, 304, 152, and 76 cells per cloud radius (from left to right). For R cloud /Δ x ≥ 152 the 10 and 100 pc cloud simulations have a higher peak number of FoF groups in comparison to the non-radiative and 1 pc cloud simulations. A resolution of R cloud /Δ x = 76 is evidently not enough to cause fragmentation of a 100 pc cloud since the peak N FoF value is comparable to the 1 pc simulation with radiative cooling. We conclude that a resolution of R cloud /Δ x ≥ 152 for our 2D simulations is sufficient to capture fragmentation in 1, 10, and 100 pc simulations with cooling. A2 3D simulations Because 2D and 3D clouds may be affected differently by instabilities, we carry out the same resolution studies for the 3D simulations. First, we remind the reader that the dense gas mass survival fraction studied in Fig. 1 behaves very similar for our low- and high-resolution 3D simulations, with the only notable difference being that the dense gas in the 100 pc simulation at low resolution survives longer in comparison to the high-resolution simulation. To further study convergence properties we show the evolution of N FoF in Fig. A3 . The figure confirms that the 10 and 100 pc simulations are more fragmented than the non-radiative simulation and the 1 pc simulation with radiative cooling. Overall, this confirms our result that clouds larger than the cooling length shatter to smaller cloudlets. However, the figure also shows that fragmentation of the 100 pc cloud in 3D is not fully captured, because the 10 and 100 pc clouds here have a similar number of FoF groups throughout the simulations. This conclusion is further supported by the evolution of the power spectral slope of the low-resolution simulations shown in Fig. A4 because the peak value of the slope is not larger in the 100 pc simulation in comparison to the 10 pc simulation. Excessive fragmentation of the 100 pc cloud in comparison to the 10 pc cloud is thus only seen in our high-resolution 3D simulations. Figure A3. N FoF as a function of time for the low-resolution 3D simulations (with R cloud /Δ x = 80). The
Paper: 2021A&A...647A..37K (fulltext)
zones leads to the depletion of stars in the regions around these resonances. These results do not strongly depend on the choice for the gravitational potential assumed for the Milky Way, provided this is generic enough (and not integrable, e.g., of Stäckel form). We also find that in two other commonly used Galactic potentials ( Piffl et al. 2014 ; Bovy 2015 ), very similar substructures can be observed in R max vs. z max , as is shown in Appendix A , and that they are associated with resonant families, and hence with the presence of non-integrability in the system. Our analysis (as also concluded by Amarante et al. 2020 ) therefore directly shows that not all substructures are related to accretion, as is often considered in the literature, nor to the settling of the gravitational potential after major merger activity as had been suggested by Haywood et al. (2018) . Nonetheless, the characteristics of substructures from merger events may sometimes be related to the presence of resonances. For example, we see that some of the stars in the Helmi streams appear to be close to a resonance, Ω z : Ω R ∼ 1:2. This perhaps explains why its stars are distributed asymmetrically in velocity space (the stream with v z 0 has more stars than that with v z > 0). This asymmetry was used to constrain its time of accretion to approximately 5-8 Gyr ago, which is a puzzlingly low value given that these stars are on relatively bound orbits. Since stars near a resonance take longer to spread out in space ( Vogelsberger White 2008 ), this can lead to an underestimation of their time of accretion. This means that chaotic dynamics may have to be considered when modelling the evolution of tidal debris, as already hinted by Price-Whelan et al. (2015) in their modelling of cold thin streams farther away in the halo. Probably the best way to tell whether substructures are related to accretion events is via a chemical tagging analysis. Stars that originate from a satellite will follow characteristic tracks in chemical abundance space. On the other hand, stars that group together because of dynamical resonances have no reason to be chemically distinct from other stars. How stars populate different resonant families depends on their distribution function and the gravitational potential in which they move. In this work, we only considered that of the Milky Way, but a recent analysis of nearby thin-disc stars has shown that the Sagittarius dwarf also plays a role in their dynamics (see Antoja et al. 2018 ). Interestingly, if we integrate the orbit of Sagittarius in the McMillan (2017) potential for 40 Gyr with the initial conditions derived by the Gaia Collaboration (2018c) and apply the SuperFreq code to obtain its frequencies (i.e. we repeat the analysis done for the stars in our sample), we find that Sagittarius falls on the Ω z : Ω R = 1:2 and Ω z : Ω φ = 1:1 resonance. The stars in our sample on the Ω z : Ω R = 1:2 resonance (green points in Figs. 7 – 9 ) define a
Paper: 2018MNRAS.477..699M (fulltext)
be coincidental. The small number of galaxies in our sample and the substantial uncertainties on the measurements of the dust mass and gas metallicity preclude us from drawing any solid conclusion regarding a possible physical link between metallicity and circumgalactic dust mass fraction. Our data do not allow us to determine unambiguously the origin of the circumgalactic dust. The best case in our sample for dust entrained in a galactic wind is NGC 1569, but we cannot rule out the possibility that cold dust features that appear to lie in the zone of influence of the galactic wind are instead dust features of external (e.g. tidal) origin in front or behind the wind. Future studies that not only consider the distribution of the circumgalactic dust with respect to the wind and tidal features but also the kinematics of dust tracers such H 2 , CO, Na I D, and H i will help shed light on the origin of this far-flung dust in NGC 1569 and other dwarf galaxies. APPENDIX A: NOTES ON INDIVIDUAL GALAXIES For the discussion in these notes, please refer to Figs 1 (a)–(f) and Figs 2 (a)–(f). He 2-10 : broad cold dust emission regions surround He 2–10 out to ∼1.5 kpc in all SPIRE bands as well as the 160 m PACS map. The 160 m emission map appears similar in extent and morphology to the H i distribution observed by Kobulnicky et al. ( 1995 ). The 70 m PACS map exhibits a bit more anisotropy in its distribution of circumgalactic emission with tapering features extending to the north–northeast, southwest, and east-southeast. The north–northeast feature extends furthest to ∼2.6 kpc. The orientation of these tapering features appears similar to the smaller 0.5 kpc-scale Hα bubbles (Méndez et al. 1999 ; Zastrow et al. 2013 ), but appears less correlated with the shape of the X-ray emission (Kobulnicky Martin 2010 ). Several knots of emission separated from He 2–10 by ∼3–4 kpc to the north-west and southwest in the PACS maps cannot be definitively ruled out as background sources given the signal-to-noise levels or foreground Milky Way dust clouds, but coincident emission in the SPIRE maps seems connected to the disc via colder streams bridging their separation. The most prominent emission in the SPIRE maps towards the knots north-west of the disc seems similarly oriented to an X-ray feature observed by Kobulnicky Martin ( 2010 ). NGC 1569 : a dense complex of filamentary and broad circumgalactic cold dust features stretch out from all along the disc of NGC 1569. The prominent Hα, 8.0, and 24 m filament (Waller 1991 ; Hunter et al. 1993 ; Westmoquette et al. 2008 ; McCormick et al. 2013 ) extending southwest from the western edge of the disc also shows up in the PACS maps, but becomes fainter and disappears in the SPIRE bands. A clump of cold dust directly south of the galactic nucleus resides ∼1.8 kpc from the disc mid-plane and appears to have made a clean break with the other circumgalactic cold dust emission in the PACS maps. Some faint 8.0 m emission
Paper: 2016A&A...592A..87A (fulltext)
weak correlation of the slope with observed for the full sample is due to a second-order age effect, since the correlation of with age was negligible. We should also note that the sample plotted in the right panel of Fig. 10 consists of only 18 stars, which is less than the half of the whole sample, thus it suffers (more) from low number statistics. 7. Summary and conclusion To explore the effect of the star formation place (birthplace) on the chemical abundance trend with the condensation temperature, we selected a sample of main-sequence stars that have similar stellar parameters and expected similar ages as our Sun. The sample consisted of 39 stars, for 25 of which we carried out new observations with HARPS and UVES spectrographs. The spectra of the remaining 14 stars were taken from HARPS and UVES archives (ESO archive). We performed a detailed differential chemical abundance analysis and derived stellar ages for this sample of stars. When analysing the effect of the GCE on the [X/Fe] elemental ratios, we found that many elements display a strong dependence on stellar metallicity and most of the elements show a statistically significant trend with stellar ages. The latter result ([X/Fe] – age correlation) agrees well with that of Nissen (2015) . To minimize the GCE effect on trend that is due to time or age, we selected a sub-sample of stars that lie in a narrow range of ages from 2 to 4.5 Gyr. The stars were also selected to have errors on ages smaller than one Gyr. For this sub-sample of stars we found that the [X/Fe] abundance ratio of some elements correlates with the mean of the apo- and pericentric distances, . These Galactic radial abundance gradients qualitatively agree with those obtained from Cepheids (e.g. Lemasle et al. 2013 ) and from open clusters (e.g. Yong et al. 2012 ; Magrini et al. 2015 ). When considering only stars that are younger than 7 Gyr, we found no significant dependence of the trend on stellar age, as was observed in previous works (e.g. Adibekyan et al. 2014 ; Nissen 2015 ). As in Adibekyan et al. (2014) , when using the full sample of stars, we also observed some hint that the slope depends on , although not in a simple way. However, this dependence completely vanished when a sub-sample of stars with similar ages (the sub-sample mentioned in the previous paragraph) was considered. Here, we should note that this sub-sample consists of only 18 stars. With this small sample we still cannot firmly conclude whether the trend depends on the formation place of stars. A larger sample size and improvement in the data quality (higher S/N) as well as the use of very similar stars in terms of stellar properties (e.g. metallicity, temperature, age) that are observed at a wide range of Galactocentric distances will help to finally answer this question. In this context, the role of large surveys and missions, such as Gaia ( Perryman et al. 2001 ), Gaia -ESO ( Gilmore et al. 2012 ), and APOGEE ( Ahn et al. 2014 ), is
Paper: 2018ApJ...860...67H (fulltext)
the distance of the star–planet system in parsecs (PC), and Hill radius is expressed in (au). Finally, we calculate the RV semi-amplitude, K , of each planet given its estimated mass:We further assume an orbital inclination of ∼90° and e = 0. Table 2 includes each of the parameters used in our calculations which have been extracted from the HZ catalog (Kane et al. 2016 ), as well as the NASA exoplanet archive. Table 3 presents our calculations of planet mass, Hill radii (HR), estimated RV semi-amplitudes and angular separations of the planet–star systems and potential planet–moon systems at both the full HR and Hill radii ( HR). Table 2 Habitable Zone Candidates with R p > 3 R ⊕ Note. a Semimajor axis. Table 3 Radial Velocity, Hill Radius, and Angular Separation Calculations for HZ Candidates with R p > 3 R ⊕ Tables 4 and 5 then present our calculations of HR, angular separations of a potential planet–Moon systems at the full Hill radius and RV semi-amplitudes for each exoplanet with a radius of 10 R ⊕ or greater with our chosen quantified masses: one Saturn mass ( M sat ), one Jupiter mass ( M J ), and 13 Jupiter masses (13 M J ). Table 4 Radial Velocity Semi-amplitude Calculations for Category 4 HZ Candidates with R p > 10 R ⊕ Table 5 Hill Radii Calculations for Category 4 HZ Candidates with R p > 10 R ⊕ Notes. a Angular separation of exomoon at full Hill radius for . b Angular separation of exomoon at full Hill radius for M p = M J . c Angular separation of exomoon at full Hill radius for . We plot a histogram of the effective temperatures of Kepler host stars to determine if there is a similar distribution of temperatures among both the HZ candidates and the full catalog. Figure 11 shows the stellar temperature distributions for both the HZ Kepler candidates (green), as well as the full Kepler catalog (gray). The histograms show that there is a similar distribution of temperatures among both the HZ candidates and the full catalog, with the HZ host star temperatures dropping off (around) 7000 K. As the HZ of stars with greater effective temperatures will lie further away from the star, planets in this zone are harder to detect. Thus, this drop is likely a false upper limit. Figure 11. Stellar temperature distributions. Habitable zone Kepler candidates in green overlays the distribution of the full Kepler catalog in gray. The histograms show that there is a similar distribution of temperatures among both the HZ candidates and the full Kepler catalog. While the distribution of the habitable zone candidates drops off at 7000 K, this could be a false upper limit as the habitable zone of stars with greater effective temperature lies further away from the star and current transit detection methods are less sensitive to planets at these longer orbits. Using the calculations from our Tables above, we plot the Kepler magnitude of the host star of both the unconfirmed and confirmed HZ planets and their expected RV signatures to determine the