adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...642A..42S (fulltext)
stars ( Dutra et al. 2014 ; Lourenco et al. 2019 ). From the pool of models studied in Dutra et al. (2014) ; Lourenco et al. (2019) , we chose four: BSR8, BSR9, G2*, and IU-FSU – all of them allow for the DU process to set in at reasonable neutron star masses (between 1.0 and 2.0 solar masses). Furthermore, all of them present microscopic properties different enough to allow us to conclude that the results we find are most likely general. The same behavior is shared among all models studied, with the only difference being the star’s mass at which the DU process becomes available. This paper is divided as follows: in Sect. 2 we discuss the microscopic models used, Sect. 3 is devoted to the review of the thermal evolution of neutron stars, Sect. 4 contains our results for the relaxation time of neutron stars at the onset of the direct Urca process, and in Sect. 5 we present our conclusions. 2. Microscopic models Quantum hadrodynamics (QHD) is a powerful tool used to build models that represent the strongly interacting matter with hadrons being the main degrees of freedom. The first model constructed from such an approach was proposed by Walecka (1974) and Serot Walecka (1997) with the two free parameters fixed to reproduce the nuclear matter energy per particle as a function of the density, and a minimum of B 0 = −15.75 MeV at the saturation density ρ 0 = 0.19 fm −3 . However, the model also presents bad results for the effective mass ratio and incompressibilty, both at ρ = ρ 0 , namely, ( M n is the nucleon rest mass) and K 0 = 540 MeV, respectively. Over the years, many other improved versions of this model were proposed in which and K 0 are fixed to more compatible values with experimental/theoretical predictions. Furthermore, other bulk parameters at the saturation density are also used in order to constrain the free coupling constants of these microscopic models. Here, we investigate the cooling process of neutron stars based on parametrizations of a general model described by the following Lagrangian density ( Dutra et al. 2014 ; Li et al. 2008 ) in which F μ ν = ∂ μ ω ν − ∂ ν ω μ and B μ ν = ∂ μ ρ ν − ∂ ν ρ μ . ψ is the nucleon field and σ , ω μ , and ρ μ represent the fields of the mesons σ , ω , and ρ , respectively. The mean-field approximation is used in order to solve the equations of motion for the fields. This procedure, along with the energy-momentum tensor, T μ ν , allows the construction of all thermodynamics of the system since the energy density and pressure are given by E = ⟨ T 00 ⟩ and P = ⟨ T i i ⟩/3, respectively. These equations of state are evaluated, as a function of the density, by taking into account the auto-consistency of the field equations and the definition of the effective nucleon mass given by M * = M n − g σ σ . More details related to the calculations of these quantities can be found in Dutra et al. (2014) and Li et al. (2008) , and references therein. In order to study neutron stars and their thermal
Paper: 2017MNRAS.466.2352S (fulltext)
(i) the same ratio between PSF size and pixel width (on the detector) at all wavelengths; (ii) the same incident flux at all wavelengths; (iii) the ratio between diagonal and off-diagonal terms. Some values can be null or negative. While it is highly unlikely that the three conditions will be satisfied exactly, there is always a chance of them almost being satisfied. In which case, instead of null eigenvalues we might see significantly smaller values compared to the others. This results in a poorly conditioned matrix. Having a large range of eigenvalues causes precision problems when trying to invert the matrix. A more careful study might be needed to explore the conditions when negative, null or very small eigenvalues can occur. 4.1.2 Results for the slicer Fig. 2 shows the result of the super-optimal procedure on a slicer simulated supernova. Using SpecSim, we simulated the observation of a Type Ia supernova at redshift 1.5 with a 2-m telescope of focal length 20m with an exposure time of 8h. The simulated errors include the zodiacal light, detector and shot noise. The high-resolution input spectrum, shown in blue, is the spectrum input to the slicer (theoretical Type Ia supernova spectrum scaled to magnitude, mirror area and exposure time). The red error bars display the 1 errors computed by the super-optimal extraction. The choice of the exposure time yields S/N of 10.4 for the Si ii absorption feature at 1.53m. Figure 2. SPECSim simulation of the Type Ia supernova spectrum and extraction with the super-optimal extractor. The input truth (blue) is simulated and reconstructed (red) with 1 error for a supernova at redshift 1.5 observed with the slicer spectrograph on a 2.0-m space telescope of 20.0-m focal length. The exposure time is 8h. The error bars estimated by the super-optimal extractor result from the zodiacal light, detector noise and shot noise. Fig. 3 shows the percentage error to compare the optimal (grey) and super-optimal (red) spectrum extraction methods highlighting the Si ii line region. The input spectrum and the observational parameters used are identical to those used in Fig. 2 . The super-optimal extraction performs significantly better than the optimal extraction. At the redshifted Si ii absorption line at 1.537m, the super-optimal extraction performs 1.6times better in the error percentage than the optimal extraction, and the residuals (deviation from theoretical) are also smaller, with their distribution compatible with the theory of chi-square fit. The goodness of fit can be tested by the pull distribution, which corresponds to the histogram of the ratio of the residuals and estimated error. Ideally, this should be a normal distribution centred at 0 and with rms of 1. This is the case for the super-optimal extraction and not for the optimal extraction. In our case, this is because the latter underestimates the errors. Figure 3. Comparison of the optimal (grey) and super-optimal (red) spectrum extraction
Paper: 2016MNRAS.461..988K (acknowledgments)
This study was carried out with the support by the FWF NFN project S11601-N16 ‘Pathways to Habitability: From Disks to Active Stars, Planets and Life and the related subprojects S11607-N16 and S11608-N16. HL thanks the support by FWF project ‘Characterizing Stellar and Exoplanetary Environments via Modeling of Lyman-Alpha Transit Observations of Hot Jupiters, P 27256-N27. The authors thank Dr Yamila Miguel and Dr Alessandro Mura for their valuable comments on the mineral atmospheres. RS thanks the support by the FWF project P23810-N16. The authors thank the anonymous referee for valuable comments.
Paper: 2016MNRAS.461.4260S (acknowledgments)
This work was funded with grants from Consejo Nacional de Investigaciones Cientificas y Tecnicas de la Republica Argentina, and Universidad Nacional de La Plata (Argentina). 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 (USA), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência, Tecnologia e Inovação (Brazil) and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). The Gemini program ID are GS-2008B-Q-54 and GS-2009B-Q-65. This research has made use of the NED, which is operated by the Jet Propulsion Laboratory, Caltech, under contract with the National Aeronautics and Space Administration. We thank the referee for her/his significant contributions which greatly improved this work.
Paper: 2018MNRAS.478.4565G (fulltext)
from 0.13 to 0.53 M J , in resemblance to Saturn. The Jupiter mass planets simply occupy the occurrence rate of ∼10 per cent of single stars (Cumming et al. 2008 ). Similarly, for CBPs, Jupiter mass planets appear to be less common (Armstrong et al. 2014 ) as compared to single star systems. Therefore, our study, on the basis of tidal interactions between Jupiter-like planets and their host stars (Faber et al. 2005 ; Liu et al. 2013 ), cannot be applied to estimate the occurrence rate of S-type planet in close binaries. In this sense, the difficulty in more exploration is that the nowadays a dynamical tide model is not suitable for Saturn-like or Neptune-like planets any more. In particular, the inner structure of Neptune-like planets differs a lot from that of gas giants. This may eventually give rise to major differences in the dynamical tides for Saturn-like or Neptune-like planets compared to Jupiter. However, above-mentioned question is beyond the scope of this study, and we will explore this issue in future. The scenario we propose in this work is an exotic formation mechanism for S-type planets in tight binaries ( a B = 0.5 ∼ 3 au). An interesting question is whether there is a limit for the separation of a binary below which in situ formation of S-type planets is impossible. Five S-type sub-Earth-sized planets have been found in Kepler-444A(BC) system (Dupuy et al. 2016 ). Although it has a larger a B ∼ 37 au, the periastron distance is only ∼5 au ( e B ∼ 0.86). Exotic formation mechanism such as capture seems unlikely to form a multiplanet system. Due to the close pericenter passage of 5 au, the disc of Kepler-444A will be truncated to 1–2 au. Although there are many challenges for forming such a planetary system, in situ formation can be a possible scenario (Dupuy et al. 2016 ). Therefore, future planet-hunting in the binaries with a separation less than 5 au may provide vital clues to in situ or exotic formation to understand their origin. In summary, planet formation in close binaries is a fascinating and challenging subject. As mentioned previously, searching for S-type planets in eclipsing binaries arouses great scientific interest for the forthcoming space missions such as PLATO. Our work provides some suggestions for selecting target binaries in search for S-type planets in the near future. Their presence or absence will deepen our understanding of planet formation. APPENDIX A CODE TESTS We performed additional simulations to examine whether the modified codes can accurately identify pericentre passages and exert the tide effect at each pericentre passage. In order to shorten the integration time and make the dynamical tide operate immediately, we adopt the following initial conditions. We assume that the planet has entered the stable region of the primary star in the beginning. The planet is on a very high eccentric orbit with respect to the primary ( e p = 0.95, a p = 0.2). The parameters of the binary are M 1 = M ⊙ , M 2 =
Paper: 2020A&A...634A..64B (acknowledgments)
We thank the anonymous referee for constructive comments which helped to improve the manuscript. We thank D. Dallacasa and F. Gastaldello for comments on an early version of the manuscript and S. Giacintucci, D. Hoang, R. Kale, T. Shimwell, T. Venturi and R. van Weeren for kindly providing us the displayed radio contours. GB acknowledges partial support from ASI/INAF n. 2017-14-H.O. The work of DR and SR was supported by the NRF of Korea through 2016R1A5A1013277 and 2017R1A2A1A05071429. The scientific results reported in this article are based in part on observations made by the Chandra X-ray Observatory and in part by XMM-Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA.
Paper: 2015ApJ...806...34W (acknowledgments)
The research was supported by the National Science Foundation of China grants 41174123, 41421063, WK2080000031, and WK2080000077, the Chinese Academy of Sciences grants KZCX2-YW-QN512 and KZZD-EW-01, and the Fundamental Research Funds for the Central Universities under grant WK2080000031.
Paper: 2018ApJ...868...58K (acknowledgments)
We thank the anonymous referee for constructive suggestions that helped to improve the manuscript. This work is supported by National Basic Research Program of China (973 program, grant No. 2015CB857005) and National Science Foundation of China (grants No. 11421303 11503024). J.X.W. thanks support from Chinese Top-notch Young Talents Program, and CAS Frontier Science Key Research Program (QYZDJ-SSW-SLH006). Z.Y.C. acknowledges support from the China Postdoctoral Science Foundation (grant No. 2014M560515) and the Fundamental Research Funds for the Central Universities.
Paper: 2018MNRAS.478.5356S (fulltext)
the residuals from the planet fit, we obtain ( r , ρ) BIS = (−0.39, −0.33), and ( r , ρ) S-index = (−0.06, 0.38), which indicates no correlation among these quantities. These results, for the HARPS and CORALIE data, can be seen in Fig. 9 , with the activity indices listed in Tables A1 and A2 . We also performed the bisector analysis on K2-238, and found ( r , ρ) BIS = (0.40, 0.37), which would indicate no correlation between the BIS and the FEROS RVs. For the residuals we found ( r , ρ) BIS = (0.50, 0.36), also indicative of no strong correlation. The results are shown in Fig. 10 , and listed in Table A3 . We did not include the S-index due to the low S/N spectra obtained with FEROS, which prohibited us from measuring them reliably. Figure 10. Correlations between the RVs (top panel) and the residuals from the planetary fit (bottom panel), with the BIS from FEROS for K2-238. The colours represent the orbital phase of the RV and residuals. r and ρ are the Pearson and Spearman correlation coefficients, respectively. 3.5 Planet scenario validation In order to confirm the planetary nature of our photometric and spectroscopic measurements, we performed a blend analysis using the algorithms described in Hartman et al. ( 2011a , b ), which model the observations taking into account the possibility that they could be generated by either a planet, stellar companions physically associated with our target star or by various blend scenarios, including blended eclipsing binary and hierarchical triple systems. K2-237 b is confirmed to be a planet based solely on the photometry; it is practically impossible for the best-fitting blend scenarios to fit the observed photometry in any of the cases consistent with the spectroscopic information. For K2-238 b , the planetary interpretation is also favoured by the data: although there is a detected close-by companion in the Lick 3 m Adaptive Optics (AO) data, the light curve is not consistent with the transit/eclipses arising from the neighbour, as all the simulated light-curve signatures imply J − K colours much less than the observed J − K = 0.631 ± 0.043. Considering that the brighter source could still itself be a blend, we can reject all the blend scenarios at 2.5σ confidence based on the photometry. However, none of them are able to produce the observed 100 m s −1 sinusoidal RV variation. The best-fitting blend scenarios to the photometry also yield large bisector span variations in excess of 1 km s −1 , which are clearly ruled out by our measurements (see Fig. 10 ). We consider thus both planets to be statistically validated given our photometric and spectroscopic measurements. 3.6 Searching for additional signals in the photometry We search for additional signals in our K2 light curves, produced by other companions, orbital phase variations, or secondary eclipses by performing a Box-fitting Least-Squares periodogram (BLS, Kovács, Zucker Mazeh 2002 ) on the light curves, with the transits of the detected
Paper: 2017ApJ...841...28P (acknowledgments)
We thank the anonymous referee for comments which were beneficial to the final version of this paper. M.B.P. and S.E.Z. acknowledge support from NASA through the ADAP grant NNX11AG12G. B.D.L acknowledges support from NASA ADAP NNX16AG06G. The scientific results reported in this article are based on observations made by the Chandra X-ray Observatory . Support for this work was provided by NASA through Chandra Award numbers GO5-16084A (MBP) and GO5-16084B (AK) issued by the Chandra X-ray Observatory Center, which is operated by SAO for and on behalf of the NASA under contract NAS8-03060. Based on observations made with the NASA/ESA Hubble Space Telescope , obtained at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program HST-GO-13942.001-A. Support for program number HST-GO-13942.001-A was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. This research has made use of NASA’s Astrophysics Data System (ADS) and the NASA/IPAC Extragalactic Database (NED). Facilities: CXO, HST , 2MASS. Software: SciPy, NumPy, matplotlib, pythonFSPS, TOPCAT, ds9.
Paper: 2018A&A...618A..26C (fulltext)
qualitatively understood in terms of the geometry of the heliosphere (see Sect. 4.2 ). 3. Comparing the observation regions of HSTOF and Voyager 1 The flux J ENA of the ENA originating in the heliosheath can be approximately expressed as where J ion is the parent ion flux (protons for hydrogen ENA, He + for helium ENA) at the same energy as J ENA , σ cx, H , and σ cx, He are the cross sections for charge exchange between the parent ions and the neutral hydrogen or neutral helium background gas of density n H and n He , respectively, and L is the thickness of the source region along the line of sight (LOS). The values J ion and n H are averages over the distance along the LOS within the source region (the inner heliosheath). For the helium ENA, we omitted the additional contribution from double charge exchange between the He ++ and the neutral He background, which is on the order of 3% ( Swaczyna et al. 2017 ). The equation does not include the ENA losses on the way from the source, but in the energy range where most of the ENA observations are carried out (IBEX Hi, INCA, and HSTOF) these effects are relatively small (about 1% for HSTOF). Fig. 4. Solar wind flux (yearly averages based on OMNI2 data) at 1 AU as a function of time. The time interval starts earlier than the HSTOF observations discussed in this paper because of expected delay between the changes in the solar wind and in the ion distributions in the heliosheath. To compare the observations of Voyager 1 and HSTOF we use the ENA flux averaged over the HSTOF energy range: E 1 E E 2 where E 1 = 58 keV and E 2 = 88 keV. Eq. ( 1 ) then becomes where ⟨ A ⟩ denotes the average over the HSTOF energy range When used for the case of HSTOF, Eq. ( 2 ) must also be averaged over directions of the lines of sight, and over the observation time periods. As explained in Sect. 2 , we divide the HSTOF field of view into four ecliptic longitude sectors (Fig. 1 ). Because of proximity to the Voyager 1 and 2 trajectories, the forward (LISM apex) sector is most suitable for comparison with Voyager observations. A study of the energetic ion distribution in the heliosheath ( Czechowski et al. 2012 ) and the Bonn model of the heliosphere, on which this study was based ( Fahr et al. 2000 ), implies that the thickness L , the energetic ion fluxes, and the neutral density do not vary much over this region. We also consider the two flank sectors. Table 1 lists the hydrogen ENA flux values, which is also illustrated in the Fig. 2 . Table 1 includes also the estimated hydrogen ENA flux from the Voyager 1 direction. It is calculated by substituting for J ion in Eq. ( 2 ) the energetic proton flux J p, V1 at Voyager 1 and for L the distance L V1 = 28 AU between the termination shock and the cliff along the Voyager 1 trajectory. In the HSTOF energy range, Voyager 1 LECP measures only the Z ≥ 1 ion flux , which includes the protons along with the heavier ions. We assume that J p, V1 is approximately equal to . For J Z ≥
Paper: 2020MNRAS.494.1089D (fulltext)
Dynamical Time, J2000.0 ecliptic and equinox). Source: JPL’s SBDB (solution date, 2020-Mar-03 08:13:59). The orbit determination in Table 1 is still uncertain and its associated evolution rather chaotic (see Section 3 ). Wiegert, Innanen Mikkola ( 1998 ) have shown that the statistical analysis of an extensive set of numerical simulations accounting for the uncertainties associated with an orbit determination can produce reliable results. In order to obtain sufficiently robust conclusions, we apply the Monte Carlo using the Covariance Matrix (MCCM) method detailed in section 3 of de la Fuente Marcos de la Fuente Marcos ( 2015b ) to generate initial conditions for our N -body simulations that have been carried out using Aarseth’s implementation of the Hermite integrator (Aarseth 2003 ); the direct N -body code is publicly available. 1 The covariance matrix required to generate initial positions and velocities for 10 4 control or clone orbits has been obtained from Jet Propulsion Laboratory’s Solar System Dynamics Group Small-Body Database (JPL’s SSDG SBDB, Giorgini 2015 ). 2 This is also the source of the data in Table 1 ; JPL’s horizons 3 ephemeris system (Giorgini Yeomans 1999 ) has been used to gather most input data used in our calculations (e.g. data in Table 2 ). Some data have been retrieved from JPL’s SBDB using the tools provided by the Python package Astroquery (Ginsburg et al. 2019 ; Mommert et al. 2019 ). Extensive details on our calculations and physical model can be found in de la Fuente Marcos de la Fuente Marcos ( 2012 ), de la Fuente Marcos de la Fuente Marcos ( 2018a ), and de la Fuente Marcos de la Fuente Marcos ( 2018b ). Table 2. Cartesian state vector of 2020 CD 3 : components and associated 1σ uncertainties. Epoch as in Table 1 . Source: JPL’s SBDB. In order to analyse the results, we produced histograms using the Matplotlib library (Hunter 2007 ) with sets of bins of constant size computed using Astropy (Astropy Collaboration 2013 , 2018 ) by applying the Freedman and Diaconis rule (Freedman Diaconis 1981 ). Instead of using frequency-based histograms, we considered counts to form a probability density so that the area under the histogram will sum to one. 3 ORBITAL EVOLUTION With our calculations, we aimed at answering four main questions: (i) When did this capture take place? (ii) When is 2020 CD 3 leaving its current geocentric path? (iii) How was the pre-capture orbit of 2020 CD 3 ? (iv) How diverse is the evolution in the neighbourhood of the initial conditions in Table 2 ? Answering the first two questions, we can find out if 2020 CD 3 is experiencing a temporarily captured fly-by or a temporarily captured orbiter episode. The third question is directly related to the origin of 2020 CD 3 and the fourth question can inform us on the reliability of our results. In order to provide robust answers to all these questions, we first focus on the initial conditions in the form of Cartesian state vectors and then we use