adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.485.4967F (acknowledgments)
ACKNOWLEDGEMENTS We thank our anonymous referee for their insightful comments and suggestions. This paper comes from work undertaken during a visit funded by the Scottish Universties Physics Alliance (SUPA) Postgraduate, Postdoctoral and Early Career Researcher Short-Term Visits funding. This work benefited from the Exoplanet Summer Program in the Other Worlds Laboratory (OWL) at the University of California, Santa Cruz, a program funded by the Heising-Simons Foundation. KM acknowledges funding by the Science and Technology Foundation of Portugal (FCT), grants No. IF/00194/2015 and PTDC/FIS-AST/28731/2017. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). This study is based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), Ministério da Ciência, Tecnologia e Inovação (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). This work is based on observations collected at the European Southern Observatory under ESO programme 099.C-0728. Some of the observations in the paper made use of the NN-EXPLORE Exoplanet and Stellar Speckle Imager (NESSI). NESSI was funded by the NASA Exoplanet Exploration Program and the NASA Ames Research Center. NESSI was built at the Ames Research Center by Steve B. Howell, Nic Scott, Elliott P. Horch, and Emmett Quigley. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This research has made use of the Exoplanet Orbit Database, and the Exoplanet Data Explorer at exoplanets.org , as well as the Extrasolar Planets Encyclopaedia at exoplanet.eu. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France.
Paper: 2016MNRAS.457.3582P (fulltext)
needed, the sampling has been done over 10 6 randomized maps. To determine the random cases, we explored three different procedures: (1) randomization of the γ-ray sample coordinates by drawing an equal number of positions homogeneously distributed over the sky, making sure that only random sources with b II values in the same range as the original γ-ray catalogue are considered. This leaves untouched the IceCube positions, which are known to be not uniformly distributed, but might lose any large-scale structure present in the γ-ray sample; (2) to at least partially obviate this, we have also randomized only the γ-ray sample right ascensions; (3) randomization of the IceCube right ascensions, making sure that only random sources that can be associated with the γ-ray catalogue within the relevant error circles are considered (for example, a | b II | 10° IceCube random source can still be associated with a 2WHSP object provided it has a large enough error radius). This preserves any large-scale structure present in the γ-ray sample plus the IceCube declinations. However, the requirement above does not conserve the total area sampled by the IceCube error circles, resulting in a biased test statistics. As a result, we used procedures (1) and (2), conservatively taking as our best estimate of the probability of random association the largest of the two probabilities. Since our three catalogues are somewhat overlapping, in particular at large fluxes and FoM (see Section 2.2.2 ), the number of bins per scan is 10, and the points are not independent, we do not correct the probabilities for the ‘look elsewhere effect’ (which would take into account the artificial p-value reduction due to the application of multiple tests). We have also fixed the catalogues studied at the beginning of this work as well as the bins in flux and FoM used for the scans. 4 RESULTS 4.1 <italic>Fermi</italic> 2FHL Fig. 1 shows the chance probability of association of 2FHL high Galactic latitude sources with IceCube events for objects having F (>50 GeV) larger than the value on the x -axis and a randomization of the γ-ray sample on right ascension, which gives the most conservative result. Red squares indicate the whole high Galactic latitude sample, black circles denote HBL only, and blue triangles represent non-HBL sources. The numbers give the observed (above the points) and average random value (below the points) of N ν for HBL. Fig. 1 shows the following: for the whole sample and for HBL the chance probability is strongly dependent on γ-ray flux, with an anti-correlation between probability and flux up to F (>50 GeV) ∼ 1.8 × 10 −11 photon cm −2 s −1 with a p-value 10 −4 according to a Spearman test. We attribute the turn-over in p-value at very large γ-ray fluxes to small number statistics; p-values ∼1.8 per cent are reached for F ( > 50 GeV) ≳ 1.8 × 10 − 11 photon cm −2 s −1 for the whole sample; p-values ∼0.4 per cent are reached for F ( > 50 GeV) ≳ 1.8 × 10 − 11
Paper: 2017ApJ...837...43S (acknowledgments)
This work is based on research supported in part by the National Research Foundation (NRF) of South Africa (grant no. 106049). Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the NRF. N.D. was supported under grant no. 50OC1302 by the Federal Ministry of Economics and Technology on the basis of a decision by the German Bundestag. This work was carried out within the framework of the bilateral BMBF-NRF-project “Astrohel” (01DG15009) funded by the Bundesministerium für Bildung und Forschung and the NRF. The responsibility of the contents of this work is with the authors.
Paper: 2019A&A...627A.127C (fulltext)
scenario is that the gas and ice chemically evolve while accreting onto the disk ( Visser et al. 2009 , 2011 ; Drozdovskaya et al. 2014 , 2016 ). A final scenario is known as reset; this is when all of the molecules are dissociated into their base elements ( Visser et al. 2009 ; Pontoppidan et al. 2014 ; Eistrup et al. 2016 ). The distinction between any of the scenarios is not important to the discussion of C/O, after all, the number of carbon and oxygen atoms in the gas and ice does not change. Nonetheless, which scenario initializes the chemical state of the gas disk can have a drastic impact on the chemical evolution of its volatiles ( Eistrup et al. 2016 ). In this work we follow the initial volatile elemental abundance of Eistrup et al. (2016) , where C/O = 0.34, and assume thatthe volatile molecules are inherited from the pre-stellar core. For now, we similarly assume that the initial chemical state of the refractories follows the ISM, meaning that there was no chemical interaction between the gas and refractory dust during the collapse of the cloud. If the chemical composition of the dust is representative of the ISM then it has an atomic carbon-to-silicon ratio C/Si ISM = 6 ( Bergin et al. 2015 ). Based on chemical equilibrium simulations of condensing refractories in protoplanetary disks, Alessi et al. (2017) found that the most abundant silicates have silicon-to-oxygen ratios no lower than ~ 1∕4, i.e., mostly enstatite (MgSiO 3 ) or forsterite (MgSiO 4 ). While they produce oxygen-bearing iron species (fayalite – Fe 2 SiO 4 , ferrosilite – FeSiO 3 ) within 5 AU, the majority of iron-bearing materials are troilite (FeS) and pure iron. When iron-oxides become more abundant, it is at the expense of enstatite, meaning that C/O remains unchanged, implying that refractories are generally carbon rich relative to oxygen (C/O > 1). If we assume that the dust is composed of only enstatite (Si/O = 1∕3, C/O = 2) and the Sun accretes gas and dustwith the characteristic gas-to-dust ratio (100) then we can estimate the photospheric C/O. The mean molecular weight of the gas is 2.4 g mol −1 , while the molecular weight of the dust is 172.4 g mol −1 (6 carbon atoms per enstatite molecule − m mol,enstatite = 100.4 g mol −1 ). Hence, Table 1 Carbon-to-hydrogen (C/H) and carbon-to-oxygen (C/O) ratios for the volatiles (gas), refractories (gr), and combined (tot) given the simple model (mdl) where dust mass is dominated by enstatite and solid carbon (with C/Si = 6). Notes. We note that C/O tot = (C gas + C ref )/(O gas + O ref ). where n dust is the number of groupings of six carbon atoms and one enstatite molecule. Therefore (see a summary in Table 1 ), and hence The volatile carbon and oxygen abundances of Eistrup et al. (2016) are n C,gas = 1.8 × 10 −4 n H , and n O,gas = 5.2 × 10 −4 n H . Combining these values with our computed elemental abundances from our simple refractory model, we predict a solar photosphere with C/O = 0.52, and C/Si = 15,
Paper: 2015ApJ...809...23G (acknowledgments)
We thank Noemie Globus for help. This research was supported by a grant from the Israel Science Foundation No. 1277/13.
Paper: 2018A&A...620A.183J (acknowledgments)
We thank Hugh Hudson for the comments and discussions on the paper. The authors were supported by the Czech Science Foundation under grants 18-06319S (J.J. and M.Š.) and 16-18495S (J.K.). J.J., J.K., and M.Š. acknowledge the support from RVO:67985815. Hinode is a Japanese mission developed and launched by ISAS/JAXA, with NAOJ as domestic partner and NASA and STFC (UK) as international partners. It is operated by these agencies in cooperation with ESA and NSC (Norway).
Paper: 2016MNRAS.456.4524Y (acknowledgments)
We thank C. Leonard for the ab initio curves and P. Bernath for the experimental line list. This work was supported by the ERC under the Advanced Investigator Project 267219 and made use of the DiRAC@Darwin, DiRAC@COSMOS HPC cluster and Emerald CfI cluster. DiRAC is the UK HPC facility for particle physics, astrophysics and cosmology and is supported by STFC and BIS. The authors would like to acknowledge the work presented here made use of the EMERALD high performance computing facility provided via the Centre for Innovation (CfI). The CfI is formed from the universities of Bristol, Oxford, Southampton and UCL in partnership with STFC Rutherford Appleton Laboratory. We also acknowledge the networking support by the COST Action CM1405 MOLIM.
Paper: 2017A&A...600A..26S (fulltext)
GO-14161, with R. Peterson as PI. As described further by Peterson (in prep.), this spectrum was acquired with the E230H grating of the STIS echelle spectrograph and the slit for a resolving power . Adopting five of the six E230H “prime” wavelength settings, with central wavelengths 201.3 nm, 226.3 nm, 251.3 nm, 276.2 nm, and 301.3 nm, resulted in an analysis coverage of 188.0–314.0 nm. The total exposure time of 106.4 ks (29.5 h) yielded per two-pixel resolution element at all wavelengths, except for the bluest and reddest. To compare the abundances deduced from the HST spectra and the visible, we also used ground-based spectra from the ESO Very Large Telescope archives (spectrographs UVES and HARPS). For wavelengths shorter than 400 nm we used HARPS spectra. They have a resolving power and a signal-to-noise per pixel (S/N) better than 150 per pixel. At longer wavelengths, in the blue we combined the individual UVES spectra to a mean UVES spectrum with a resolving power and a S/N per pixel better than 500. In the red, in order to minimise the influence of the telluric lines, and taking advantage of the fact that following Carney Latham (1987) and de Bruijne Eilers (2012) , HD 84937 shows no detectable velocity variability, we used three different mean spectra each obtained by combining individual spectra with almost the same geocentric radial velocity. Therefore, the positions of the telluric lines relative to the stellar lines are almost the same. These three mean spectra were obtained consecutively on a single night every year between 2002 and 2004. The resulting mean red spectra reach a resolving power of about 45 000 and a S/N per pixel equal to 310, 510, and 350, respectively, in the region of the red C i lines. 3. Spectral analysis and abundance measurements In our analysis we used OSMARCS model atmospheres ( Gustafsson et al. 2008 ) together with the turbospectrum LTE synthesis code ( Alvarez Plez 1998 ; Plez 2012 ). In this code, the collisional broadening by neutral hydrogen is generally computed following the theory developed by Anstee O’Mara (1991) , Barklem et al. (2000) and Barklem O’Mara (2000) . For the computations we adopted the model parameters and [Fe/H] derived for HD 84937 in the same way as in Peterson Kurucz (2015) and Peterson (in prep.): = 6300 K, = 4.0, and = 1.3 , along with the iron abundance they derived with these parameters, . These values deduced only from the UV spectra are in excellent agreement with the values derived for this star by Lawler et al. (2013) and Sneden et al. (2016) based on photometry and accurate H ipparcos parallaxes. The parameters and deduced abundance for each atomic line considered for this analysis are listed in Table 1 . The mean abundances of the different elements are given in Table 2 . For the computations of [X/H] we adopted the solar abundances (C) = 8.5 4 , (Mg) = 7.54, (Si) = 7.52, (P) = 5.46, (S) = 7.16, (K) = 5.11, and (Ca) = 6.33, following Caffau et al. (2011b) or Lodders et
Paper: 2016A&A...586A..99H (fulltext)
Apart from the much higher signal-to-noise ratio, the real part of the visibilities show a behavior similar to that shown by Andrews et al. (2012) , with a drop off from 1.72 Jy at the shortest baselines to a minimum of 0.057 Jy near 190 k, followed by a second maximum of 0.123 Jy around 275 k and a subsequent decrease to 0.028 Jy at our longest baseline of 410 k. The data of Andrews et al. (2012) continue to longer baselines of 600 k with fluxes approaching 0 Jy. Fig. 2 Real (black points) and imaginary (red points) parts of the visibilities of project 340.S (previously presented in Qi et al. 2013 ) in Jy vs deprojected baseline length in k. The inset shows the region of 120–420 k in greater detail. The dashed black curve shows the recalculated single power-law model of Andrews et al. (2012, their model pC) , which fails to reproduce the data on baselines 290 k. A much better fit is found for a broken power-law model described in Sect. 3 (solid blue curve). Because both models are symmetric, the imaginary parts of their visibilities are all 0 (not shown). We overplot the data with the model presented by Andrews et al. (2012, their model pC) which they find to best fit the continuum data. We recalculate the model at the exact observing wavelengths of the ALMA data. Our model is geometrically thin and uses only the midplane temperatures as calculated by Andrews et al. (2012) (see Sect. 3 ). Our model curve therefore differs in detail from the one shown by Andrews et al. (2012) , but qualitatively shows the same behavior. In particular, it fits the data well out to baselines of 290 k but severely underpredicts the emission on baselines of 300 and 400 k, corresponding to angular scales of – (27–37 au). In the next section we attempt to find a model description that provides a better fit. 3. Model fitting To further investigate the disk structure that best describes the observed emission, we consider the deprojected and radially binned real visibilities of data set 340.S. We fit to the 41 data points plotted in Fig. 2 rather than the full set of complex visibilities to speed up calculations. Since the observed emission is symmetric (imaginary visibilities are all 0) and the 41 data points in Fig. 2 fully sample the curve and do not average out any detected asymmetric structure, this simplification is allowed. As a consequence of using only the binned visibilities, our fitting places relatively little weight on the shortest baselines, which represent the total flux. Because we are primarily interested in the spatial distribution of the emission, rather than the total amount, our results are not affected. However, even a small discrepancy in total flux would be accentuated if we would calculate a residual image. For this reason, we refrain from imaging the residuals and exclusively work with the visibilities. The real part of the visibilities, , are related to the intensity distribution of the source by where is the baseline length, is the
Paper: 2019A&A...624A.114L (fulltext)
proportional to the disk pebble surface density, but weakly dependent on time. In the default model we adopt a constant pebble flux of Ṁ peb = 100 M ⊕ Myr −1 (consistent with Eq. ( 14 ) of Lambrechts Johansen 2014 ) and neglect the time dependence for simplicity. A lower Ṁ peb means an intrinsic metal-deficient/less massive disk and the planetesimals and planets take longer to grow by pebble accretion. On the other hand, the pebble flux cannot be too high as the density ratio between pebbles and gas would otherwise exceed unity ( ρ peb ∕ ρ gas > 1) and the streaming instability will be triggered over the entire disk. The above inequality can be written as Σ peb ∕ H peb > Σ gas ∕ H gas where the scale height of the pebble is given by ( Youdin Lithwick 2007 ) The pebble flux therefore needs to be smaller than 2 πrv r Σ gas H peb ∕ H gas . Our default value is well below this limit. We also explore two additional pebble fluxes, Ṁ peb = 200 M ⊕ Myr −1 and 50 M ⊕ Myr −1 , in Sect. 4 . The pebble mass can be measured from (sub)millimeter dust continuum emission from the young protoplanetary disks. The inferred values are from a few Earth mass to a few hundreds of Earth mass ( Ricci et al. 2010 ; Andrews et al. 2013 ; Ansdell et al. 2017 ), which is correlated with the gas disk accretion rates ( Manara et al. 2016 ). For a typical T Tauri star with a disk accretion rate of 10 −8 M ⊙ yr −1 , the dust mass is ~ 100 M ⊕ (Fig 1 of Manara et al. 2016 ), consistent with our adopted fiducial value. In Eq. ( 8 ), α t is the coefficient of turbulent gas diffusivity, which is different from the concept of turbulent viscosity α ν . We nevertheless note that the above two quantities are approximated to be the same when the turbulence is driven by magnetorotational instability ( Johansen Klahr 2005 ; Zhu et al. 2015 ; Yang et al. 2018 ). The value of α t can be constrained from the moleculeline broadening measurements ( Flaherty et al. 2015 , 2017 ) and the level of dust settling ( Pinte et al. 2016 ). We adopt a fiducial value of α t = 10 −3 and test the influence of a lower turbulent disk ( α t = 10 −4 ) in Sect. 4 . For pebbles we adopt a fiducial aerodynamical size of τ s = 0.1. This chosen value is consistent with the sophisticated dust coagulation model ( Birnstiel et al. 2012 ) and disk observations ( Tazzari et al. 2016 ). A lower τ s = 0.03 is also explored in Sect. 4 . A fraction of pebbles will be accreted onto a planetesimal when pebbles drift through its orbit. The pebble accretion efficiency ( ε PA = Ṁ PA ∕ Ṁ peb ) is taken from Liu Ormel (2018) and Ormel Liu (2018) . This quantity depends on the disk properties ( τ s , η , α t ) and the planet properties ( m p , a , e , i ). In the limit of 2D and 3D pebble accretion these are given by and respectively, where Δ v is the relative velocity between the planet and the pebble, h peb = H peb ∕ r , is the aspect ratio of the pebble disk. In Eq. ( 10 ) we have already assume that i h peb . The above
Paper: 2019A&A...622A.186S (fulltext)
WDs should have lower Ṁ than novae with lighter WDs. The fact that heavier WD novae require a smaller total accreted mass to ignite the thermonuclear explosion ( Shara et al. 2010 ; Glasner Truran 2009 ; Yaron et al. 2005 ) does not seem to provide any insight into how this would control Ṁ between OBs. Furthermore, while binaries with massive WD and high Ṁ may “disappear” from the diagram because they become common envelope systems ( Nomoto 1982 ), there appears to be no obvious way to hide the other missing M WD − Ṁ combinations (unless they become other kinds of CVs?). A tempting speculation, if this distribution is confirmed, is that the strip populated by the old novae discussed here may define the range of Ṁ and t 3 values where novae can occur. Observational Ṁ determinations for more (old) novae are clearly needed before exploring this idea any further. 12.5. Correlation between <italic>Ṁ</italic> and OB amplitude An interesting correlation exists between the OB amplitude and Ṁ (see Fig. 6 ) that associates large amplitude to low Ṁ . In hindsight, this is not completely surprising given the correlation between and Ṁ reported in Sect. 12.1 . Unlike that correlation, which requires knowledge of several physical parameters (distance, inclination, etc.), this one has as independent variable: the observed OB amplitude (i.e., the difference between two apparent magnitudes). Fig. 6. Correlation between accretion rate and the OB amplitude. Top panel : Ṁ computed using individual WD masses. Bottom panel : Ṁ computed using M WD = 1 M ⊙ for each nova. That the correlation exists without the correction for inclination is somewhat surprising, and may be due to the fact that for most of our novae the correction is relatively small. This would also explain why DQ Her, the nova in the sample with the largest i correction, appears as an outlier in Fig. 6 . Nevertheless, this correlation provides a direct way to estimate Ṁ for any nova for which only the OB amplitude is known. Thus, the OB amplitude acts as a proxy for Ṁ , much in the way that t 3 does (see Sect. 12.4 ). It should be noted that Schmidtobreick Tappert (2015) suggested that novae with large OB amplitudes are candidates for low-mass-transfer-rate systems, but they assumed that the absolute magnitude of a nova explosion differs only slightly for different systems, unlike what is shown by the L / L Edd and the MMRD relations; see Figs. 1 and 2 . The fits in terms of the explicit observables, computed as described in Sect. 2.2 , are 13. Concluding summary The new and precise distances from Gaia , together with the data of Paper I and from the literature, allowed us do determine new system parameters for our sample of 18 old novae with UV data (e.g., M v max , , L OB / L Edd , , Ṁ ). The final values include a detailed treatment of the errors and their propagation and are summarized in Table 2 . Functional relations are provided in the text for all correlations (either revisited or new) presented