adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...596A.112T (fulltext)
to solar system icy satellites, it could still possess enough water to be an aquaplanet , the term aquaplanet refering here to a planet where water is abundant enough to flow efficiently on a planetary scale. In practice, in our simulations, this means that the surface acts as an infinite source of water. In this section, we thus make an attempt at quantifying how much is enough. Then, we discuss the various climate regimes available to an aquaplanet and point out the main differences with the dry case. 5.1. Transition from small to large water inventory The exact water inventory for which Proxima b would transition from a land planet (previous section) to an aquaplanet (this section) is difficult to define. In particular, the nature of the transition depends of the amount of greenhouse gas in the atmosphere. For a low atmospheric greenhouse effect (meaning that ice is stable somewhere on the planet), there is a bistability between two possible climate regimes around the transition. To understand this bistability, we introduce a thought experiment in which we consider a planet with two very different initial conditions: Case 1: warm- and/or water-rich-start. We assume that theplanet starts with a global ocean, possibly covered by sea icewherever cold enough. This experiment has been carried outby Yang et al. (2014) with a325 m deep ocean. They showed that in sucha configuration, winds carry sea ice toward hot regions and theocean carries heat toward cold regions, so that an equilibriumcan be found with less than 10 m of sea ice inthe coldest regions. They however acknowledge that the pres-ence of continents could significantly alter this value. Yanget al. (2014) only looked at a syn-chronous planet, but the argument remains qualitatively valid fora non-synchronous planet, even though numerical values willsurely change significantly. Therefore, water content as small as 10 m GEL (and maybe even lower) is sufficient to maintain aquaplanet conditions. If water were to be removed or the temperature decreased, at some point, the oceanic transport would shut down and the planet would transition to a dry, cold regime with glaciers. Case 2: cold- and/or water-poor-start. As discussed in the previous section, if the planet started cold or dry enough, all the water would be trapped in ice caps and glaciers. Now, we demonstrated earlier that in this state, more than m GEL of ice could be stored this way. If the water inventory and/or the temperature were to be increased, glaciers would progressively spill toward hotter regions. This state could resist until the oceanic transport becomes more efficient than the atmospheric transport. After that, an ocean would accumulate and, in turn, warm the cold regions, speeding up the transition to the aquaplanet regime. Therefore, between roughly and m GEL, two distinct climate regimes coexist, depending on the history of the planet. As both the water inventory and the amount of greenhouse gases play a role, it
Paper: 2017ApJ...834...37L (acknowledgments)
B.V.L. was supported by a Stanford University undergraduate research grant. Y.Y.M. was supported by the Weiland Family Stanford Graduate Fellowship. This work received partial support from the U.S. Department of Energy under contract number DE-AC02-76SF00515. We thank Harry Desmond and Andrew Hearin for useful discussions. We thank Rachel Reddick for providing the measurement of observed galaxy statistics used in this work. This research made use of the Dark Sky Simulations, which were produced using an INCITE 2014 allocation on the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory. We thank Mike Warren, Matt Turk, and our other Dark Sky collaborators for their efforts in creating these simulations and for providing access to them. We thank Peter Behroozi for providing the halo catalogs and merger trees for the Bolshoi , BolshoiP , and MDPL simulations, which were run by the MultiDark Collaboration. Additional simulations and computations were performed using computational resources at SLAC and at NERSC. We thank the SLAC computational team for their consistent support.
Paper: 2019MNRAS.485.1876S (acknowledgments)
ACKNOWLEDGEMENTS The authors acknowledge the support from the program of development of M.V. Lomonosov Moscow State University (Leading Scientific School ‘Physics of stars, relativistic objects and galaxies’). We also would like to thank the Professor Maurizio Paolillo (Department of Physics, University of Naples Federico II) and Arina Morgunova (Sternberg Astronomical Institute, M.V. Lomonosov Moscow State University) for the useful discussion.
Paper: 2019MNRAS.490.5270V (acknowledgments)
ACKNOWLEDGEMENTS This research has made use of data obtained through the High Energy Astrophysics Science Archive Research Center Online Service, provided by the NASA/Goddard Space Flight Center. This work is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). We thank the referee for the constructive feedback and suggestions that improved the quality of the paper.
Paper: 2018ApJ...857...91J (acknowledgments)
The financial support by the Swiss National Science Foundation (SNSF) through grant ID 200021_159206/1 is gratefully acknowledged. The work of Alberto Paganini was partly supported by the EPSRC Grant EP/M011151/1. Special thanks are extended to L. Belluzzi and O. Steiner for reading and commenting on the paper. The authors also are grateful to the anonymous referee for providing valuable comments that helped to improve the article.
Paper: 2016MNRAS.458.1548B (fulltext)
T 0 versus X-ray luminosity from equation ( 21 ) compared to data (Johnstone et al. 2015 ). The solid and dashed curves, respectively, correspond to the equilibrium temperatures for solar minimum and maximum, T x, ⊙1 = 0.97 × 10 6 K and T x, ⊙2 = 2.57 × 10 6 K (Johnstone et al. 2015 ). The plot shows that equation ( 21 ) is consistent with the data. Although the total wind power escaping from the scale height of the corona is given by , the total wind power escaping from the star is at least . Assuming constant , mean molecular mass m p /2, and thus c s = 2 k b T / m p and c V = 3 k b / m p (Hearn 1975 ), the relation between the total wind power and the coronal wind flux is where . Fig. 1(b) shows this total integrated wind power (blue) along with (green), and (orange) with all three quantities normalized to the same constant . We see that in regime II. Note also that when the equilibrium p 0 is plugged back into the expressions for F x and F W , the ratio of scales with . Therefore, although we focus on m * = r * = 1, for the temperature range of interest we would predict for a range of late-type stars, largely independent of mass since for the relevant stellar models (Kippenhahn, Weigert Weiss 2012 ). Figs 1(c) and (d) are similar to Figs 1(a) and (b) but apply when the last exponent in equation ( 16 ) is reduced by a factor of 2, highlighting the sensitivity of the temperature dependence. Here the power-law fit more accurately matches regime II than the exponential of equation ( 22 ). With the physics included in our present model, we do not identify a huge drop in mass-loss rate at high as suggested by two data points in Wood et al. ( 2014 ). More data are needed to confirm this is a statistically significant effect. There does not seem to be evidence of a corresponding change of magnetic configuration (Vidotto et al. 2016 ), so that would not provide an explanation if the effect were real. Suzuki et al. ( 2013 ) suggested that a decrease in mass loss for young active stars might arise because the increase in coronal density could lead to runaway cooling that drains the thermal energy that would otherwise accelerate the wind. This model does not employ coronal equilibrium arguments that we have used herein to determine our coronal properties. We leave further consideration of this for future work. 4.2 X-ray luminosity as function of magnetic field strength Having established that in regime II, we now connect both of these quantities to the source of magnetic energy. Blackman Thomas ( 2015 ) estimated from the dynamo-produced magnetic energy rising up through the convection zone. To accommodate our present result that 1/2 of source magnetic energy goes into , the relevant expression becomes where B ∼ B φ and Θ/4π is the solid angle fraction through which the field rises (proportional to, if not equal to of the previous section), and we have used the expression for u b below equation ( 12 ). Using equation ( 13 ) in equation ( 24 )
Paper: 2021MNRAS.501.3421K (fulltext)
data set in the context of ΛCDM is less than . This is an effect approximately at 2σ level. In fact, this probability is even smaller if we consider the exact σ differences that are shown in Table 1 and find the fraction of simulated data sets with simultaneous σ differences larger that the exact corresponding σ differences of the real data. In particular we find that the probability to have simultaneously 1.14σ difference (or larger) in the first bin, 1.48σ difference (or larger) in the second bin, and 0.99σ difference (or larger) in the third bin for , is . Similarly, for we find the same probability to be . Even though this decrease of probability is interesting to note, it is not generic as it is based on the fine tuned σ deviations of the real data bins from the full data best fits (1.14σ, 1.48σ, and 0.99σ). Thus, this is an aposteriori statistic constructed after looking at the data. Therefore, we adopt the more generic and conservative statistical level of significance of corresponding to the simultaneous deviation of at least 1σ for all three lowest z bins. Note that a similar oscillating effect was also observed in Refs. Kazantzidis Perivolaropoulos ( 2020 ), Sapone et al. ( 2020 ) even though its statistical significance was not quantified using simulated data as in the present analysis. Moreover, it is interesting to check if this behaviour is also evident for any three out of four bins. In 1000 Monte Carlo realizations we find that the number of simulated data sets where the derived in any 3 bins is more than 1σ away from the best fit to the whole (random) data sample is while the corresponding number of cases for is as it is demonstrated in Fig. 3 . The probability is smaller if we consider the exact σ difference of Table 1 . In particular, we derive the number of cases where the derived in any 3 bins is more than away from the best fit to the whole (random) data sample is , while the corresponding number of cases for is . A summary of the results can be seen in Table 2 . These results indicate that the aforementioned oscillating effect is much more prominent at low z ≲ 0.5 where the dark energy density is more prominent than in the fourth bin, which involves higher z . This fact favours the possibility that the effect has a physical origin since a systematic effect would probably affect equally all four redshift bins. Figure 3. Left-hand panel: Percent of simulated Pantheon dataset (including systematics) where any three out of four bins all differ simultaneously more than from the best fit of the full data set. Right-hand panel: Same as the left-hand panel for the parameter instead of . Table 2. Summary of the Monte Carlo deviations from the simulated and real data. The obtained results considering the exact σ real differences should be treated with care, since this decrease of the probabilities is not generic and it is based on a fine tuned σ value. 3 CONCLUSION – OUTLOOK We performed a redshift tomography of the Pantheon
Paper: 2016MNRAS.457.4160H (acknowledgments)
We thank the anonymous referee and the MNRAS editorial staff for their comments on this paper. The Australian SKA Pathfinder is part of the Australia Telescope National Facility which is managed by CSIRO. Operation of ASKAP is funded by the Australian Government with support from the National Collaborative Research Infrastructure Strategy. Establishment of the Murchison Radio-astronomy Observatory was funded by the Australian Government and the Government of Western Australia. ASKAP uses advanced supercomputing resources at the Pawsey Supercomputing Centre. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. This work was supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. This research made use of Montage. It is funded by the National Science Foundation under Grant Number ACI-1440620, and was previously funded by the National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project, under Cooperative Agreement Number NCC5-626 between NASA and the California Institute of Technology. This research has made use of NASA's Astrophysics Data System. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. IH acknowledges many useful discussions with Jan Noordam, Rick Perley and Oleg Smirnov.
Paper: 2016ApJ...832..172S (acknowledgments)
This work was supported by the Planetary Sciences and Exploration Programme, Indian Space Research Organization (ISRO), PRL, Ahmedabad under the sanctioned project scheme. Calculations reported in the present work were carried out at the Computer Centre, Banaras Hindu University.
Paper: 2018A&A...618A..93C (fulltext)
of these values, with discrepancies too large to be explained by instrumental errors (e.g. NGC 2509, Fig. 6 ). We also remark that on average, our distances estimated from parallaxes tend to agree more often with the more distant literature value. We suggest that the isochrone fitting procedure from which most photometric distances are estimated might be biased in many of those instances, as they might be affected by field contamination, as well as the degeneracies between distance, reddening, and metallicity. The strength of the Gaia data does not only reside in parallaxes from which distances can be inferred, and in precise and deep photometry, but also in our ability to obtain clean colour-magnitude diagrams from which astrophysical parameters can be infered. Fig. 6. Sky distribution ( top left panel ), colour-magnitude diagram ( top right panel ), and parallax as a function of magnitude ( bottom panel ) for the members of NGC 2509. The median parallax is 0.36 mas. Available literature distances for this cluster are 912 pc (DAML, blue), 1700 pc (MWSC, green) and 2900 pc ( Carraro Costa 2007 , red), which correspond to parallaxes of 1.09 mas, 0.59 mas, and 0.35 mas (respectively). 5. Specific remarks on some clusters It would be lengthy and impractical to devote a Section to every object under analysis in this study, but we provide additional comments on the specific cases of two clusters classified as OCs that are likely globular clusters, and on the previously unreported OCs discovered in this study. 5.1. BH 140 and FSR 1758 are globular clusters The colour-magnitude diagrams of BH 140 and FSR 1758 (shown in Fig. 7 ) present the typical aspect of globular clusters, with a prominent giant branch and an interrupted horizontal branch displaying a gap in the locus occupied by RR Lyrae. They are also clearly seen as rich and regular distributions on the sky. These two objects, located near the Galactic plane ( b = −4.3° and −3.3° for BH 140 and FSR 1758, respectively) also exhibit small parallaxes ( π = 0.16 mas and 0.09 mas), so their distances cannot be estimated accurately from parallaxes alone. FSR 1758, with a Galactic longitude l = 349.2°, seems to be located deep in the inner disk, as the distance estimated from its parallax yields a Galactocentric radius R GC = 1560 pc, and altitude Z = −470 pc. Fig. 7. Top left panel : colour-magnitude diagram for BH 140. Middle left panel : distribution on the sky for the same stars. Bottom left panel : proper motion diagram showing the field in light grey, and the cluster stars as black dots. Right column panel : same for FSR 1758. The broad appearance of the CMD of BH 140 can be explained by blended photometry in the inner regions. As discussed in Evans et al. (2018) and illustrated in Arenou et al. (2018) , the G BP and G RP fluxes of Gaia DR2 sources might be overestimated as a result of background contamination, and the effect is especially relevant in crowded fields such as the core of globular
Paper: 2016MNRAS.455..499L (acknowledgments)
We thank Andrew Hearin for help in using his mock catalogue, Yu Lu for using his semi-analytical data, and Zhankui Lu for helpful discussion and the anonymous referee for helpful comments that greatly improved the presentation of this paper. HJM acknowledges the support from NSF AST-1109354. This work is also supported by the 973 Programme (nos 2015CB857002, 2015CB857005).
Paper: 2017ApJ...850..198Y (fulltext)
Batalha et al. 2013 ; Petigura et al. 2013 ). Radial velocity measurements (Weiss Marcy 2014 ) and transit timing variations (Wu Lithwick 2013 ) manifest that the masses of these super-Earths are in the range of 2–20 M ⊕ . The abundance of super-Earths presents a puzzle for the core instability theory. This theory indicates that when a protoplanet reaches super-Earth size, two physical processes make the survival of super-Earths difficult, leading to a planetary “desert” in this size range (Ida Lin 2004 ). Super-Earths excite density waves in protoplanetary disks (PPDs) and give rise to rapid type I migration. This type of migration causes the planet to be engulfed by its host star if the disk inner edge touches the stellar surface. Recent studies have sought various remedies for type I migration (Yu et al. 2011 ; Fung Chiang 2017 ). PPDs are expected to have an inner edge at the stellar magnetosphere (e.g., Long et al. 2005 ). For planets undergoing disk-driven migration, they are expected to pile up near this edge. They stay either at the edge because the gas runs out, inside the edge down to 2:1 resonance because that is where the tidal torque tapers off, or outside the edge because the standing waves generated by wave reflection off the inner edge stall planet migration (Tsang 2011 ). In this paper, we focus on another threat for super-Earths. Super-Earths have low mean density, which suggests that they must be surrounded by gas envelopes (Rogers Seager 2010 ). Since these observed super-Earths are in the range of critical mass, they trigger efficient gas runaway and accumulate massive gas envelopes. They become gas giants. As a result, super-Earths are supposed to be rare. However, Kepler ’s discovery wrecks these predictions. Lee et al. ( 2014 ) proposed a metallicity gradient inside the PPDs or the late assembly of cores to resolve the puzzle of super-Earth formation. Lee Chiang ( 2016 ) stressed that the late core assembly in transitional PPDs is more consistent with observations. In gas-poor environments, gas dynamical friction has weakened to allow proto-cores to stir one another and merge. In addition, this formation scenario ensures that super-Earth cores accrete mass with a few percent envelope mass fraction (EMF). Guillot Showman ( 2002 ) argued that the dissipation of the kinetic energy of the atmospheric wind, driven by intense irradiation, could bury heat inside the planet. Many studies extend this idea to explain the radius anomaly of hot Jupiters (Youdin Mitchell 2010 ; Ginzburg Sari 2015 ; Komacek Youdin 2017 ). These investigations focus on the late evolution after the disk dispersal. Unfortunately, this is invalid for the early evolution of super-Earths because they are still embedded within disks. The irradiation may not penetrate the disk and is not able to bury heat in the exoplanets. However, we note that tidal interactions between the host star and planet can periodically perturb the planet and generate mechanical
Paper: 2015MNRAS.450.1388C (acknowledgments)
TIM is supported by an appointment to the NASA Postdoctoral Programme at the Goddard Space Flight Center, administered by Oak Ridge Associated Universities through a contract with NASA.
Paper: 2015ApJ...798..111P (acknowledgments)
A.-M.M. is supported by the National Aeronautics and Space Administration through Einstein Postdoctoral Fellowship Award Number PF2–130095 issued by the Chandra X-ray Observatory Center, which is operated by the Smithsonian Astrophysical Observatory for and on behalf of the National Aeronautics Space Administration under contract NAS8–03060.
Paper: 2018MNRAS.474.3576X (fulltext)
the deeper optical potential of antikaons , the earlier onset of K − and condensations in NS matter. Moreover, condensation in comparison with K − condensation always occurs at a higher density. It can be seen from equation ( 10 ) that the threshold condition for condensations is more difficult to fulfill compared with the threshold condition for K − condensation in NSs. In our model, condensations only appear in stable NSs when the optical potential of antikaons = −100, −110, −120 and −130 MeV, and the presence of them suppresses the increase of the fraction of K − mesons as indicated in Fig. 2 (a) and Table 1 . Besides, it can be also seen from Figs 2(b) and (c) that the increase of K − mesons makes the dramatical decrease of e and μ fractions in NSs. It is because that K − meson is boson in the lowest energy state, replacing negatively charged particles, makes the system energy lower. In order to keep the conditions of the electrical neutrality, the proton fraction grows faster and then the growth is suppressed when mesons appears in NSs. Eventually, the proton fraction turns out to be almost identical neutron fraction in NS matter. Therefore, according to equation ( 15 ), the Fermi momenta of neutrons decrease and the corresponding values of protons increase including K − and condensations in NS matter, which are displayed in Fig. 3 (a). Figure 1. EOSs and NSs masses as a function of nucleon density n N . In the paper, the black, red, blue, dark cyan, magenta, dark yellow and green lines represent the seven cases of = −80, −90, −100, −110, −120, −130 and 0 MeV, respectively. The dots stand for the maximum masses of NSs for the seven cases. Figure 2. Composition of NSs as a function of nucleon density n N with and without antikaon condensations. We take the optical potential of antikaons = −80, −90, −100, −110, −120, −130 and 0 MeV. Figure 3. The nucleon Fermi momentum, the effective mass and the single particle energy E N ( p F ) at the Fermi surface versus nucleon density n N in npeμ and npe matter. We take the optical potential of antikaons = −80, −90, −100, −110, −120, −130 and 0 MeV. Table 1. Maximum masses m max = M max /M ⊙ , and the corresponding radii R and the centre densities n C as well as the threshold densities and of K − and condensations for the optical potential of antikaons = −80, −90, −100, −110, −120, −130 and 0 MeV, respectively. Fig. 3 gives the nucleon Fermi momentum p FN , the effective mass and the single particle energy E N ( p F ) at the Fermi surface as a function of nucleon density n N including and not including K − and condensations in npeμ matter. We could see that the nucleon effective mass with K − and condensations is smaller than the corresponding values without K − and condensations, and it gradually declines along with the optical potential of antikaons growth in npe matter in Fig. 3 (b). The individual single particle energies of neutrons and protons in npe matter are all much less than their values in