adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...904...82Q (fulltext)
are the amplitude index (Belokurov et al. 2017 ) and the Gaia DR2 variability classification (Mowlavi et al. 2018 ), in the left and right panels, respectively. The local sample (without YSOs and MS/RGB stars) is also shown for comparison as a black unfilled histograms. We found that the amplitude index method seems to find variability in objects that were not classified as variables in the Gaia DR2. 5.2. Absolute Magnitudes for the Foreground Mira Population Several studies have been carried out to estimate IR absolute magnitudes of the AGB populations in the LMC, where the distance to the stellar system is known, and therefore, the distance modulus (and presumably also the IR extinction) can be assumed to be the same for each object (see e.g., Whitelock et al. 2008 ). From Figure 6 , we can determine an average absolute K magnitude of mag for the local sample. Although the magnitude values found roughly correspond to those found in the LMC (i.e., between −8.0 and −6.0 mag; see, e.g., Whitelock et al. 2008 ), one should keep in mind that we established a fainter limit of . Nevertheless, with the aim of analyzing the absolute magnitude distribution using the variability classification defined by Gaia (Mowlavi et al. 2018 ) and the amplitude index (Belokurov et al. 2017 ), we made Figure 9 , where the distribution of absolute magnitude for the local sample without considering the filtering of YSOs and MS/RGB (made in Section 4.3 ) is shown. Figure 9 shows that by including these younger sources, the distributions are much broader in terms of absolute magnitude range. This can be partly explained by the current sample being mostly based on a single 2MASS observation and including the effects of large amplitude variability. However, when we filter the sample by a variability qualification (as shown in both panels of Figure 9 ), the low-luminosity tail (in ) is cut out. In this sense, the Gaia classification as Mira or SR variable seems to narrow the distribution more. Following a similar discussion in Mowlavi et al. ( 2018 ), we argue that the low-luminosity tail in Figure 9 and also Figure 7 is due to contamination with YSOs and MS/RGB stars that can also peak in the IR but do not show the same variability (Lewis et al. 2020b ; Y. M. Pihlström et al. 2020, in preparation). 5.3. Bolometric Magnitudes for the Foreground Mira Population The bolometric luminosity is a fundamental property useful for classifying stellar populations and evolutionary stages (Srinivasan et al. 2009 ), as it measures the intrinsic stellar power. Although its definition is straightforwardly formulated as the total integrated power over all frequencies, in practice, complete photometric measurements that allow a direct bolometric luminosity estimate are hardly ever available. Therefore, under various assumptions, a limited set of photometric measurements, preferably near the peak of the SED, can be used to apply a bolometric correction (BC) in order to determine the
Paper: 2018MNRAS.481.5270G (fulltext)
paper, as explained below. 3.1 Peebles–Hauser The Peebles–Hauser (PH) estimator is based on the correlation function proposed by Peebles Hauser ( 1974 ) and can be expressed as where DD (ρ) is defined as the pair of observed quasars counts within a separation radius ρ normalized by the the total number of pairs, N obs ( N obs − 1)/2 and RR (ρ) follows the same definition for the random catalogues. 3.2 Landy–Szalay The Landy–Szalay (LS) estimator is based on the correlation function proposed by Landy Szalay ( 1993 ). In addition to the quantities DD (ρ) and RR (ρ) defined above, DR (ρ) denotes the pair of quasar-random counts normalised by the available number of pairs. Hence, the LS estimator is given by For the PH and LS estimators, the quantity D 2 ( r ) is calculated by replacing equations ( 4 ) and ( 5 ) into equation ( 3 ), respectively. 4 THE HOMOGENEITY SCALE In order to estimate the homogeneity scale from the SDSS-IV DR14 quasars (henceforth ), we first obtain the values of D 2 ( r ) for each redshift slice using both estimators above. Then, a polynomial fitting is used to obtain the corresponding scale where the transition to homogeneity is identified (we refer the reader to Gonçalves et al. 2018 for more details). Following previous analyses (Scrimgeour et al. 2012 ; Laurent et al. 2016 ; Ntelis 2017 ), we define the homogeneity scale as the characteristic scale where the Universe can be considered homogeneous within 1 per cent of the expected fractal dimension for a homogeneous distribution, D 2 = 3 – thus, the scale where the spatial distribution of QSOs reaches D 2 = 2.97. Although arbitrary, the 1 per cent criterion is widely used in the literature, and is justified given some observational issues, such as the survey geometry and incompleteness, as well as the sample noise. Since we use 20 random catalogues to obtain D 2 , we also perform a bootstrap analysis over the 20 values of , quoting its mean and standard deviation as our measurement of the homogeneity scale and its corresponding uncertainty, respectively. 1 Our first measurements of are presented in the second column of Tables 2 and 3 . For the sake of example, Fig. 1 shows the results for both and D 2 obtained at the redshift using the PH estimator (similar results are also obtained for the LS estimator). The red points stand for the observational data with the respective errors. In the right-hand panel of Fig. 1 , the horizontal lines correspond to the D 2 = 2.97 and D 2 = 3 thresholds. From our analysis, we find that the PH estimator provides smaller error bars than the LS estimator. A possible explanation may be attributed to the fact that our approach does not use mock catalogues. Instead, we relied on an accurate bootstrap analysis of the 20 random catalogues we have produced. A full assessment of the homogeneity scale using mock catalogues that reflects the matter power spectrum, redshift distribution, QSO clustering, and angular selection function of the
Paper: 2019ApJ...879...54L (acknowledgments)
We thank the referee for constructive and insightful comments. This paper makes use of ALMA data ADS/JAO.ALMA#2011.0.00294.S; #2012.0.00307.S; #2013.1.00118.S; #2012.1.00090.S; #2015.1.01528.S; #2016.1.00434.S; and #2017.1.01492.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. This work is based on observations taken by the CANDELS Multi-Cycle Treasury Program with the NASA/ESA HST , which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. P.L., D.L., and E.S., acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 694343). I.R.S. and A.M.S. acknowledge support from STFC (ST/P000541/1). F.B., B.M., E.V., K.H. and EFJA acknowledge support of the Collaborative Research Center 956, subproject A1, funded by the Deutsche Forschungsgemeinschaft (DFG). S.L. acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) Grant BE 1837/13-1 r. E.A.C. acknowledges support from the ERC Advanced Investigator Grant DUSTYGAL (321334) and STFC (ST/P000541/1). M.J.M. acknowledges the support of the National Science Centre, Poland, through the POLONEZ grant 2015/19/P/ST9/04010; this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 665778. S.T. acknowledges support from the ERC Consolidator Grant funding scheme (project ConTExt, grant No. 648179). The Cosmic Dawn Center is funded by the Danish National Research Foundation. E.V. acknowledges funding from the DFG grant BE 1837/13-1. J.L.W. acknowledges support from an STFC Ernest Rutherford Fellowship (ST/P004784/2), and additional support from STFC (ST/P000541/1).
Paper: 2021MNRAS.502L..45B (fulltext)
considering equation ( 4 ) and measured P values of ∼1–10 h for AMXPs (Salvo Sanna 2020 ). Figure 2. Numerically computed neutron star spin frequency ( ν ) evolution curves due to transient accretion (see Sections 3.1 and 3.2 ). The dotted grey horizontal line shows a constant long-term average accretion rate () of 5 × 10 15 g s −1 . The solid grey curve shows an evolving with a fast decay (see Section 3.1 ) in the RLDP on the right of the dotted vertical line. Various ν evolution curves are for g s −1 , and based on if EM spin-down and evolution are included not, as mentioned on the plot. This figure shows that ν can increase in the RLDP (see Section 3.2 ). 3.2 Spin evolution of transient sources First, we study how ν evolves if the neutron star accretes transiently throughout the LMXB phase (see Fig. 2 ). For an evolution duration of two billion years and for the examples given in this figure, ν remains much lower than ν eq,eff (not shown in the figure) for g s −1 . Note that an –10 16 g s −1 is common for many neutron star LMXBs (Lamb Yu 2005 ), although could be significantly higher in the initial stage of the LMXB phase (e.g. see Tauris 2018 ; Chen, Liu Wang 2020 ). The aim here is to find out how ν evolves in the RLDP. Fig. 2 shows that ν can increase in the RLDP, even if decreases drastically, and regardless of the inclusion of an additional spin-down (e.g. due to the EM torque (see Bhattacharyya Chakrabarty 2017 ) in the quiescent phase; see Section 1 ). This is not unexpected, as ν neither approaches nor tracks ν eq,per during a transient accretion (see Section 2 ). However, we note that ν either approaches or tracks ν eq,eff for such an accretion. Hence, ν either increases or decreases due to transient accretion, both in the RLDP and before the RLDP, depending on whether increases or decreases (see Section 2 and equation 5 ). Therefore, ν can decrease in the RLDP, if decreases. But, since is expected to depend on the binary orbital period P (with an estimated relation of , Lasota 2001 ), and the change of P is moderate (e.g. Bhattacharya van den Heuvel 1991 ), ν should not considerably decrease in the RLDP. 3.3 Spin evolution of persistent sources How does ν evolve for a persistent source? The neutron star first spins up relatively quickly towards the spin equilibrium frequency ( ν eq,per ), and then tracks it (see Introduction and Section 2 , Fig. 3 ). In the RLDP, if decreases rapidly, ν decreases for a persistent source, but cannot track ν eq,per anymore, which was shown by Tauris ( 2012 ), and is confirmed in Fig. 3 . But in reality, a persistent source becomes a transient for , and consequently, ν increases, as it does not track the lower spin equilibrium frequency ν eq,per anymore, and now approaches a higher effective spin equilibrium value ν eq,eff (see Fig. 3 , and also Section 2 ). Therefore, since even an initially persistent source should become a transient at least in the RLDP due to the decrease of , ν should increase in
Paper: 2018ApJS..234...12L (fulltext)
directions, uncorrected for Faraday rotation. The FWHM dimensions and orientation of the elliptical Gaussian restoring beam are indicated by a cross in the lower left corner of the map. Figure 4.4421. Source 1954+513 at epoch 2011-Aug-26. Each panel contains two contour maps of the radio source, the first consisting of I contours in successive integer powers of two times the lowest contour level, with linear fractional polarization overlaid according to the color wedge. A single negative I contour equal to the base contour level is also plotted. The second map includes the lowest positive I contour from the first map, and linearly polarized intensity contours, also in increasing powers of two. The sticks indicate the electric polarization vector directions, uncorrected for Faraday rotation. The FWHM dimensions and orientation of the elliptical Gaussian restoring beam are indicated by a cross in the lower left corner of the map. Figure 4.4422. Source 1954+513 at epoch 2015-May-18. Each panel contains two contour maps of the radio source, the first consisting of I contours in successive integer powers of two times the lowest contour level, with linear fractional polarization overlaid according to the color wedge. A single negative I contour equal to the base contour level is also plotted. The second map includes the lowest positive I contour from the first map, and linearly polarized intensity contours, also in increasing powers of two. The sticks indicate the electric polarization vector directions, uncorrected for Faraday rotation. The FWHM dimensions and orientation of the elliptical Gaussian restoring beam are indicated by a cross in the lower left corner of the map. Figure 4.4423. Source 1954+513 at epoch 2015-Nov-25. Each panel contains two contour maps of the radio source, the first consisting of I contours in successive integer powers of two times the lowest contour level, with linear fractional polarization overlaid according to the color wedge. A single negative I contour equal to the base contour level is also plotted. The second map includes the lowest positive I contour from the first map, and linearly polarized intensity contours, also in increasing powers of two. The sticks indicate the electric polarization vector directions, uncorrected for Faraday rotation. The FWHM dimensions and orientation of the elliptical Gaussian restoring beam are indicated by a cross in the lower left corner of the map. Figure 4.4424. Source 1954+513 at epoch 2016-Mar-05. Each panel contains two contour maps of the radio source, the first consisting of I contours in successive integer powers of two times the lowest contour level, with linear fractional polarization overlaid according to the color wedge. A single negative I contour equal to the base contour level is also plotted. The second map includes the lowest positive I contour from the first map, and linearly polarized intensity contours, also in increasing powers of two. The sticks indicate the
Paper: 2019MNRAS.486.3741H (acknowledgments)
ACKNOWLEDGEMENTS This study has benefited greatly from discussions with K. Doi. We also thank the reviewer for many useful comments on this paper. The present research used the computational resources of the HPCI system provided by Cyber Sciencecenter, Tohoku University; Cybermedia Center, Osaka University, Earth Simulator, JAMSTEC through the HPCI System Research Project (Project ID:hp160079, hp170047, hp180001, hp190035). This study was supported by JSPS KAKENHI Grant Numbers JP17K05387, JP17H02869, JP17H01101, and JP17H06360. Simulations reported in this paper were also performed by 2017 Koubo Kadai on Earth Simulator (NEC SX-ACE) at JAMSTEC. This work was partly achieved through the use of supercomputer system SX-ACE at the Cybermedia Center, Osaka University. Simulations were also performed by 2018 Koubo Kadai on Earth Simulator (NEC SX-ACE) at JAMSTEC.
Paper: 2015ApJ...807...35M (acknowledgments)
The authors thank the staff at the Subaru Telescope for their excellent support in the observations. The authors thank Takaya Nozawa for his constructive comments on the manuscript. The work by K.M. is supported by the JSPS Grant-in-Aid for Scientific Research (No. 26800100) and by the WPI Initiative, MEXT, Japan. Support for H.K. is provided by the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS. H.K. acknowledges support by CONICYT through FONDECYT grant 3140563. The work by M.T. is supported by JSPS Grant-in-Aid for Scientific Research (15H02075, 15H00788). The work by K.N. is supported by the JSPS Grant-in-aid for Scientific Research (No. 23224004 and 26400222).
Paper: 2018ApJ...860L..26K (fulltext)
Hazra et al. 2014 ; Passos et al. 2014 ). Indeed, LC17 observed that when their model enters into an extended grand minimum of the weaker magnetic field, the generation of poloidal field stops due to a lack of BMRs, and the model never recovers from that quiescent phase. Their model recovers only when the magnetic field does not fall below a certain level. 2. Model In this Letter, we explore the Babcock–Leighton dynamo mechanism during grand minima, focusing in particular on how the dynamo might recover from such episodes through the Babcock–Leighton process alone. To do so, we first produce grand minima. In our study, we build on our recent work, KM17 , which is an updated version of the original model (Miesch Dikpati 2014 ; Miesch Teweldebirhan 2016 ). In this model, BMRs are produced near the surface based on the toroidal flux at the BCZ and most of the statistical properties of BMRs are based on solar observations. We refer readers to Section 2 of KM17 for the details of this model. From KM17 , we consider Runs B10–11, in which the diffusivity in the bulk of the CZ is in the order of 10 12 cm 2 s −1 . The BMR flux distribution is fixed at the observed value and the time delay between two successive BMR emergences is obtained from the observed log-normal distribution, which changes in response to the toroidal field at the base of the CZ to allow more frequent BMRs when the toroidal field is strong and vice versa. The BMR tilt has a Gaussian scatter around Joy’s law with standard deviation ( σ δ ) of 30° (15°) for Run B11 (B10). Another key ingredient of the model is the downward magnetic pumping which has the same form as in KM17 and it is given bywhere γ CZ = 2 m s −1 and γ S = 20 m s −1 . Thus, the pumping has a value of γ S only near the surface, while in the rest of the CZ it is γ CZ . Pumping is a process in which the magnetic flux can be transported in a stratified convective medium due to the topological asymmetry in the convective flow. A variety of theoretical and numerical models suggest that it is operating in the solar CZ, particularly near the surface where both the velocity amplitude and the density stratification are largest (e.g., Drobyshevski Yuferev 1974 ; Krause Rädler 1980 ; Petrovay Szakaly 1993 ; Tobias et al. 1998 ). Although at present, we do not have a stringent constraint on the strength of the pumping, previous studies (Käpylä et al. 2006 ; Karak et al. 2014b ) suggest its value to be at least a few tenths of the convective velocity. Keeping in mind that the upper layer of the Sun is highly convective with the observed surface convection speed of a few km s −1 , a value of 20 m s −1 pumping speed is realistic (Spruit 1997 ; Nordlund et al. 2009 ). This value is also large enough to make the magnetic field near the surface approximately vertical, which improves the agreement between Babcock–Leighton dynamo models and Surface Flux Transport (SFT) models. 4 4 Cameron et al. ( 2012 ) showed that the inclusion of
Paper: 2019ApJ...882...71L (fulltext)
layer is still unknown. It may be either an ice-rock layer between a rocky core and a water-rich outer mantle, or an outer rocky core composed of hydrous silicates (Lunine et al. 2010 ; Fortes 2012 ). If a mixed ice-rock layer indeed exists in the interior of Titan, then a newly discovered high-pressure solid solution in the H 2 O–CH 4 binary system called MH-III (also CH 4 filled-ice Ih) may hinder the outer transport of internal CH 4 into the atmosphere. The formation of MH-III was first reported by Loveday et al. ( 2001a ). Contrary to what was previously assumed for the H 2 O–CH 4 system, upon increasing the pressure above about 2 GPa, at room temperature, the classical structure H cage clathrate transforms into MH-III, rather than experiencing phase separation into water ice VII and solid CH 4 . In this way the solubility of H 2 O and CH 4 is kept throughout the entire pressure range inside Titan. This is also likely true for the much higher pressures spanning water ice mantles of water-rich exoplanets, given that MH-III was found to be stable up to about 100 GPa (Hirai et al. 2006 ), and temperatures higher than 1000 K (ichi Machida et al. 2006 ). We refer the reader to Loveday et al. ( 2001b ) for a detailed description of the crystallographic nature of MH-III. If CH 4 is indeed locked in grains of MH-III, scattered within a deep ice-rock layer above the rocky core of Titan, then how much of it may be transported outward depends on the buoyancy of these grains with respect to melt pockets and the solubility of CH 4 under such conditions. Such an analysis requires thermophysical data for MH-III at the appropriate conditions. In the context of Titan-like exoplanets, previous work on the transport of CH 4 across the interior pinpointed the necessary thermophysical data and the lack thereof (Levi et al. 2013 , 2014 ). Quantifying the needed thermophysical data is the prime object of this work. In Section 2 we probe the pressure regime in a possible undifferentiated ice-rock layer inside Titan, to better pinpoint our molecular simulations and the possible role of MH-III. In Section 3 we explain our computational methods. In Section 4 we derive the thermal equation of state (EOS) and thermal expansivity. Section 5 is dedicated to the heat capacity. In Section 6 we estimate the thickness of an MH-III enriched layer in Titan’s interior, and its carbon content capacity. In Section 7 we use a 1D thermal evolution model to asses the stability of such a layer. Section 8 is a discussion, and Section 9 is a summary. 2. The Pressure Regime in the Undifferentiated Ice-rock Layer In this section we estimate the pressure at the boundary between a possible internal ice-rock layer and Titan’s inner rocky core. We consider three basic constraints for Titan, its radius, mass and normalized MOI. We adopt a layered model for the internal structure (see Figure 1 ). The outermost condensed layer is a shell composed of ice Ih with a mass density of
Paper: 2015MNRAS.451.2814V (acknowledgments)
We thank the referee for a thorough, probing report that has improved the manuscript. We also thank Holly Capelo, Alan W. Harris, and Anders Johansen for useful discussions. BTG and DV benefited by support by the European Union through ERC grant number 320964. SE would like to acknowledge the support of the NEOShield project funded by the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no. 282703 (NEOShield) as well as Paris Observatory's ESTERS (Environnement Spatial de la Terre : Recherche Surveillance) travel grants.