adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020MNRAS.491.4481O (fulltext)
hot Jupiters from Kepler , coupled with the complex targeted nature of the survey (cf. the untargeted surveys used in our sample), means that these results need to be approached with some caution. Our result () confirms the giant planetmetallicity correlation seen in previous studies, but suggests that it is marginally weaker for hot Jupiters than it is for the longer period giant planets such as in the survey outlined above. We summarize our result and the previous results in Table 1 . Table 1. values. Our value is lower than all previously published results in Table 1 . We are within 1 of the result of Johnson etal. ( 2010 ); however, if we accounted for the mass dependency in our calculation as they have, we would expect our value for to decrease further (though not significantly so, as the stellar masses in our sample have a range of only 1M ). We have 2.32 and 2.31 from Valenti Fischer ( 2005 ) and Schlaufman ( 2014 ), the two other results from RV surveys, respectively. While these hint at a difference in the strength of the correlation between cool and hot Jupiters, we have 1.55 from Guo etal. ( 2017 ) and 2.75 from Petigura etal. ( 2018 ), the two hot Jupiter specific studies. Though again it should be noted that both hot Jupiter studies have a very small sample size. 5.2 Potential biases The metallicity offset ( c ) is needed to calibrate between the metallicities in Besanon and SWEET-Cat, but adds an extra degree of uncertainty compared with a survey that has a uniformly determined set of metallicities for both hot Jupiter hosts and field stars. However, the metallicity offset appears fairly well constrained from the sample distribution in Fig. 4 , and is relatively small in comparison to the overall spread of metallicities (cf. Fig. 3 ). The metallicity offset does correlate with (see Fig. 4 ), which results in a relatively large and slightly asymmetric uncertainty on . There is also a correlation between the radius of a star and its metallicity: the increase in opacity with the presence of metals results in the star having a larger radius. As transit depth decreases with the square of a stars radius, planets would be more difficult to detect around higher metallicity stars via the transit method. This would act to decrease our value of , but it has been found by Petigura etal. ( 2018 ) that planet detectability does not significantly depend on stellar metallicity. RV surveys will preferentially find planets around metal-rich stars as it is easier to perform the method when there are stronger and/or more metal lines present in the host star spectra. We do not expect the detection of a planet via the transit method to depend significantly on metallicity of the host star and this is one of the advantages of using a sample of hot Jupiter planets from transit survey discoveries. However, it should be noted that confirmation of planets from transit surveys is based on RV follow-up, which will still be subject to the bias described above.
Paper: 2019ApJ...879...42L (acknowledgments)
J. A. López-Vázquez and S. Lizano acknowledge support from PAPIIT-UNAM IN101418 and CONACyT 23863. J. Cantó acknowledges support from PAPIIT-UNAM-IG 100218. We thank an anonymous referee for useful suggestions that improved the presentation of this paper.
Paper: 2015ApJ...811..105H (acknowledgments)
The authors were supported by a grant from the NASA Living with a Star Program to Ames Research Center. The HMI data used are courtesy of the NASA/ SDO and the HMI science teams. We gratefully acknowledge many useful discussions with Nagi Mansour, Alan Wray, Alexander Kosovichev, and Philip Scherrer.
Paper: 2017ApJ...850....3K (acknowledgments)
We thank the entire Herschel Gould Belt Survey team for their efforts in obtaining and preparing the data used for the dense core catalogs we employed in this paper. We particularly appreciate the efforts of Bilal Ladjelate, Vera Könyves, and Alexander Men’shchikov in their contributions to the getsources extractions used for our analysis. J.K., J.D.F., E.R., M.C.C., and C.D.M. acknowledge the financial support of a Discovery Grant from NSERC of Canada. R.K.F. is a Dunlap Fellow at the Dunlap Institute for Astronomy Astrophysics. The Dunlap Institute is funded through an endowment established by the David Dunlap family and the University of Toronto. P.C., J.P., A.P., and A.C.T. acknowledge the financial support of the European Research Council (ERC; project PALs 320620). The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This research made use of astrodendro, a Python package to compute dendrograms of Astronomical data ( http://www.dendrograms.org/ ). This research also made use of Astropy ( http://www.astropy.org ), a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ). Facilities: GBT , Herschel .
Paper: 2017MNRAS.471.4628R (acknowledgments)
Acknowledgements AM acknowledges funding support from Spanish public funds for research under project ESP2015-65712-C5-5-R from the Ministerio de Industria, Economía y Competitivida (MINECO) and the Fondo Europeo de Desarrollo Regional (FEDER), and from project RYC-2012-09913 under the Ramón y Cajal programme of the Spanish MINECO. The authors want to acknowledge the efforts and very constructive comments and suggestions of the anonymous referee. We also acknowledge Brian Boland and Jose Delgado for the English edition of this manuscript.
Paper: 2017ApJS..228....4Z (fulltext)
case, even if the Coulomb collision scenario outlined above was not valid, the waves generated during the magnetic field reconfiguration could discriminate between fully stripped and normal ions for a short time before being dissipated, resulting in a charge state composition similar to the Outlier one. If such a reconnection event occurred high in the corona where the plasma density is low enough, ionization would not be able to replenish the ions left behind, leaving the decrease in their abundance intact. However, if the wave–plasma interaction was depositing energy to all ions except the fully stripped ones, the Outlier wind should exhibit larger ion temperatures in normal ions than in the fully stripped ones. Also, the presence of an additional energy source to this wind, which is unavailable to the normal slow wind and not directly associated with the reconnection region, would cause the ion temperature in the Outlier wind to be larger than in the normal wind. Figure 10 shows, however, quite the opposite: (1) all heavy ions have the same temperatures in the Outlier wind, and (2) these ion temperature values are lower than in the normal slow wind. However, the signatures of wave–plasma interaction at the reconnection point could easily be lost during the Outlier wind propagation in the heliosphere after the initial release, so that the comparison shown by Figure 10 does not necessarily rule out this scenario. The acceleration of ions during magnetic reconnection in solar flares has also been investigated with simulations and analytic studies by Drake et al. ( 2009 ). They found that the ions with a high mass-to-charge ratio can behave like pickup particles and be strongly heated more easily than protons as they cross the reconnection exhaust boundary; the threshold for ions to be heated also depends on the plasma β . Whether the depletion of the fully stripped ions in the Outlier slow wind is attributed to the lack of this type of acceleration needs further exploration. 5. CONCLUSIONS In this work, we have studied the Outlier slow solar wind, a subset of slow solar winds that shows a peculiar charge state distribution, and compared its characteristics to those of the normal slow solar wind. The Outlier wind, initially identified through a C 6+ /C 5+ ratio that is much lower than expected, was found to be a subset of slow solar winds with the same properties and source regions as the latter, with the exception of a very large decrease in the abundance of fully stripped ions, which causes a decrease of C and He abundance, and much lower ion temperatures. We argue that no matter what the Outlier wind origination mechanism is, the source region must be above 1.6 solar radii and the generating process must be episodic. We discuss three possible scenarios that could explain the peculiar properties of the Outlier wind: wave–plasma interaction, depletion already contained in coronal loops, and Coulomb collisions. In the first scenario, waves
Paper: 2015MNRAS.447.1112B (acknowledgments)
This work used the COSMA Data Centric system at Durham University, operated by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by a BIS National E-infrastructure capital grant ST/K00042X/1, DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure. The authors are indebted to the Virgo Consortium, which was responsible for designing and running the halo simulations of the Aquarius Project. They are also grateful to Gabriella De Lucia, Amina Helmi, and Yang-Shyang Li for their role in developing the semi-analytic model of galaxy formation used in this paper. They also thank Jorge Pearrubia and Carlos Vera-Ciro for valuable discussions and suggestions. They especially thank Nicolas Martin for generously sharing his new position angle measurements. ES gratefully acknowledges the Canadian Institute for Advanced Research (CIfAR) Global Scholar Academy and the Canadian Institute for Theoretical Astrophysics (CITA) National Fellowship for partial support. CB thanks the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement 278594-GasAroundGalaxies for partial support. This work was supported in part by the National Science Foundation under Grant No. PHYS-1066293 and the hospitality of the Aspen Center for Physics.
Paper: 2018A&A...619A.137B (fulltext)
their stellar mass by less than 10% across cosmic time. This result illustrates the uniqueness of these galaxies, suggesting a minimal contribution from merging events. Upcoming surveys such as WAVES ( Driver et al. 2016 ), covering wider regions of the sky, and deeper in flux than GAMA, will allow us to produce a high enough number of MUGs, to explore their formation channels in detail. It is remarkable that our sample of massive compact galaxies appear – at least in projection – in a relatively crowded environment (see Figs 1 and 2 ), complicating the photometric and spectroscopic analysis. Therefore, the inferred sizes and densities only provide upper bounds, as the light from the minor companions may contaminate the surface brightness profiles in a way that is impossible to mitigate at the spatial resolutions available to us. Follow-up observations, including imaging at high spatial resolution with HST or JWST, and deeper spectroscopy with IFU cameras at 8–10m-class telescopes, such as MUSE or MEGARA, will be required to better comprehend the intriguing properties of massive ultracompact galaxies. Appendix A: Robustness of the measured structural parameters We simulated 3840 galaxies, both in the KiDS r -band and the VIKING Z -band, with the tools described in Buitrago et al. (2013) . We explored the range of structural parameters shown by our sample, namely: where mag, R e , n , a r and p a stand out for magnitude (either in the r - or Z -band), effective radius, Sérsic index, axis ratio and position angle. The structural parameters of the mock galaxies were randomly distributed in a linear way, considering the full parameter space defined by the previously mentioned values. The mock galaxies were placed randomly on the KiDS and VIKING images, only imposing the condition that they should not overlap with any detection defined by the SExtractor segmentation map of each survey. Each mock galaxy was convolved with a representative PSF from those images, and analyzed with the same code we utilize to investigate the real galaxies. According to Figs. A.1 and A.2 , our ability to recuperate the structural parameters degrades at fainter fluxes and higher Sérsic indices. This result is expected, because dimmer and more concentrated objects are more difficult to analyze (a similar behaviour is found in the simulations of Buitrago et al. 2013 ). In the Z -band, the results are somewhat worse because of the coarser pixel scale (0.21 arcsec pix −1 versus 0.339 arcsec pix −1 ). However, for the data at hand, caution needs to be taken only for faint objects with a high Sérsic index. Fig. A.1. Relative errors – (output-input)/input – of the structural parameters (magnitude, effective radius and Sérsic index) of our simulated KiDS galaxies in the r -band. The right-hand column shows the average values in 1 mag bins (derived after a 5 σ clipping). The error bars represent the standard deviation within each bin. Fig. A.2. Relative errors –
Paper: 2015MNRAS.446.1820N (acknowledgments)
CN acknowledges financial support from PRIN MIUR 2010-2011, project ‘The Chemical and Dynamical Evolution of the Milky Way and Local Group Galaxies’, prot. 2010LY5N2T. JB was supported by STFC by grants R22138/GA001 and ST/K00106X/1. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 321067.
Paper: 2018MNRAS.475.5133R (fulltext)
for the 77 pairs with r proj > 5 kpc (blue). Primaries with more than one companion were counted more than once. The pairs with r proj r vir are shown in red. In the case of pairs having both their primaries and secondaries in the parent sample, only the most massive companion was considered. Table 1. Uncertainties associated with the use of z grism on the fraction of pairs. Very close pairs with r proj 5 kpc are superimposed in the CANDELS images so it was not possible to identify them as two distinct objects using the 3D-HST photometric catalogue (see example in the last panel of Fig. 5 ). The images of the galaxies in the parent sample were visually inspected to identify such cases. 10 pair candidates were found. Six of them had z spec , so we visually inspected the associated slit data. For three of them, both nuclei were included into the slit width (see Appendix A ) so we considered them as real pairs, which brings the total number of pairs in the parent sample to 80. The seven remaining ones correspond to a possible systematic underestimation of the number of pairs. Correcting factors to the number of pairs related to, e.g. projection effects are discussed in Section 2.2.4 . 2.2.2 Comparing observational and theoretical merger rates The goal of this paper is to compare observational estimates of the (major) merger rate to the predictions of the ΛCDM model. To avoid introducing systematics in the comparison, one therefore needs to adopt consistent definitions and assumptions. Merger rates in simulations can be defined in several ways. One of them consists in defining the merger rate as the rate at which a secondary halo is accreted on to a primary halo and becomes a sub-halo. This halo–halo merger rate then needs to be corrected by an estimate of the extra time needed for the secondary galaxy entering the primary halo to actually merge with the primary galaxy. Estimates based on semi-analytical or semi-empirical models need to explicitly account for such a correction, which can rely on several methods (e.g. based on the dynamical friction time). This can introduce a systematic uncertainty of a factor of ∼2 on the expected merger rate (see extensive discussion on these aspects in, e.g. section 2.4 of Hopkins et al. 2010a ). Using cosmological simulations, one can directly estimate the resulting galaxy–galaxy merger rate by intrinsically accounting for this delay. However, they are not immune to other uncertainties such as how and when the mass ratio and infall time are defined (Rodriguez-Gomez et al. 2016 ). In this paper, we choose to rely on the semi-empirical approach in which dark matter haloes are populated with realistic galaxies using the halo occupation distribution and empirical scaling relations that are relatively well constrained in the redshift and mass ranges considered in this study. 2.2.3 Identifying merging pairs Since the halo–halo merger rate is the most direct predictions of semi-empirical models, we designed a method
Paper: 2015A&A...575A..33C (acknowledgments)
We thank Nicole Nesvabda, Pierre Guillard, and Nicolas Bouché for useful discussions of the SNe and SMBH feedback. We also thank Jérémy Blaizot and Andrea Cattaneo for very insightful discussions.
Paper: 2018MNRAS.479.2007A (fulltext)
the low-temperature value for the rate constant of reaction FAR2 compared to FAR1 . For FAR2 a C–O bond is formed and as heavy-atom tunneling is less efficient than hydrogen atom tunneling, the rate constant is much lower than what would be expected from the barrier height only (compare for instance MF4 , GA6 , and FAR2 ). Similarly for the reaction of FA with , where a C–C bond is formed ( FAR3 ), again the low-temperature rate constant is very low. Note also the lower values for the T c of FAR2 and FAR3 compared to the hydrogen transfer reactions, indicating that tunneling also sets in at lower temperatures. Comparing the rate constant to the typical value assumed for radical–radical barrierless reactions, ∼10 12 s −1 , it is clear that these reactions are much less likely to contribute to COM formation. On that note we do wish to stress, however, to keep in mind that although radical–radical reactions may be able to proceed without a barrier, this does not mean that all reaction pathways are open, see for instance Lamberts ( 2018 ). 5 ASTROCHEMICALLY RELEVANT CONCLUSIONS Unimolecular reaction rate constants have been calculated and are provided for hydrogen addition and abstraction reactions from methylformate, glyoxal, glycoaldehyde, an ethylene glycol and are thus available to be implemented in both rate-equation and kinetic Monte Carlo models aimed at studying the formation of COMs at low temperatures. Our results are generally in agreement with experimental work, although some discrepancies exist on the efficiency of specific reaction paths, such as the formation of or after hydrogen abstraction from methanol, which impacts on the ease of methylformate formation (for which is needed) or ethylene glycol formation (for which is required). A microscopic model aiming to reproduce experiments may be able to provide a clear picture of how the reactions are intertwined with each other. The reaction could not be studied and thus deserves further attention. We found that one cannot predict average rate constants solely based on the type of the reaction. The spread in the low-temperature rate constant can be roughly seven orders of magnitude for a single reaction type (e.g. hydrogen addition to an aldehyde carbon) showing a strong dependence on the other functional groups that are attached to the carbon backbone. Within a single molecule, on the other hand, one can loosely say that hydrogen abstraction from an aldehyde group is faster than hydrogen addition to the same carbon. Both of these have a rate constant that is larger than hydrogen abstraction from a methyl group. Care should be taken with extrapolating rate constants based on the height of the barrier alone, as calculations show that reactions with narrow barriers can have rate constants at low temperature that are higher than those with a lower activation energy. Reactions that include the breakage or formation of a bond between two heavy atoms generally have low-temperature rate
Paper: 2016ApJ...830...22H (fulltext)
Results of our simulations indicated the following: 1. Considering the current range of impact velocities in the asteroid belt, the depth of an impact crater is only slightly larger than 10 m even when the target is assumed to be of the softest material. This suggests that if the activation of MBCs is due to the sublimation of water–ice, ice must be buried in the top few meters of the object. The latter is consistent with the model by Schörghofer ( 2008 ), who showed that a layer of dust with a thickness of only a few tens of centimeters can prevent water–ice in asteroids from sublimating for the age of the solar system; 2. A substantial amount of ice may be exposed on the inner surface of an impact crater, contributing to the activation of an MBC. The size and therefore the inner area of a crater is larger for targets with a higher water-mass fraction; 3. Some of the ejected material may be re-accreted when the impact velocities are low (e.g., ≲100 m s −1 ), and may contribute to the activation of MBCs. Calculations by Hsieh et al. ( 2004 ) and Hsieh Jewitt ( 2006 ) suggested a very large ice exposure area to account for the activity of MBC 133P/(7968) Elst-Pizzaro. Our simulations indicated that while for an object similar to this MBC, the exposed ices on the bottom and interior surface of an impact crater, combined with the ice re-accreted on the surface of the object may be large enough to account for its activation, in general, due to the small gravity of MBCs, the amount of re-accreted material on the surfaces of these objects is too small to contribute to their activity. The latter implies that the activity of MBCs is most likely due to ice sublimation from multiple impact craters and/or the water content of these bodies are larger than the anticipated 5%, providing more ice in exposed areas for sublimation. Although the simulations presented here portray the impact scenario as a viable mechanism for exposing sub-surface ice to ignite the activity of MBCs, they contain some limiting assumptions. First, we did not consider porosity for our targets. Including porosity is a complicated task that requires developing complex mathematical models. This is currently under development and we expect the results to be ready for publication in the not-so-distant future. However, given the simulations presented here, we do not expect the result to change drastically, even when porosity is included. We considered the target material to be basalt or tuff. However, MBCs are most likely made of carbonaceous chondrites. That means, in order to simulate impact events, the knowledge of the equation of state of this material, as well as the flaw distribution for the fracture model, are required, which are unknown. Our approach, that is, simulating impacts using a very hard (basalt) and a very soft (tuff) target, allows us to portray a general picture of the impact scenario as a mechanism for ice exposure that can be applied to a variety of targets with