adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...854..117J (fulltext)
2010 ) can become carbon-rich, although the transition luminosity is lower in metal-poor populations. Sextans A appears to have a higher relative fraction of carbon stars compared to Leo A (Table 9 ), and its evolved stellar population (above the TRGB) is more luminous. In both galaxies, there are a few stars with bolometric magnitudes brighter than the traditional AGB luminosity limit of (Wood et al. 1983 ). These bright sources represent a small population of RSGs or hot-bottom-burning AGB stars (with ), whose ages are consistent with a recent increase in the SFR ∼1 Gyr ago (Dolphin et al. 2005 ; Cole et al. 2007 ). The discrepancy in the size of the AGB populations unlikely due to the difference in stellar mass of the host galaxy. More likely it is due to differences in both the mean metallicity and SFH. 4.2. Dust Production Stars with have a measurable IR excess due to dust. There are 32 stars in Leo A and 101 stars in Sextans A, which meet this criteria, with a low-moderate relative uncertainty in the DPR. As the uncertainty in the chemical classification is high for most sources (Figure 9 ), we compute four values for the total dust-production rate: (1) using the best-fit models irrespective of dust chemistry; (2) assuming all stars are O-rich; (3) assuming all stars are C-rich; and (4) only including stars identified as variable. Table 10 lists the total DPRs for each galaxy. These estimates exclude any source with a relative uncertainty in the DPR > 0 (see Figure 8 ). Table 10 The Global Dust-production Rates for Leo A and Sextans A Note. Best-fitting chemical type: 25 C-rich and 6 O-rich in Leo A, and 68 C-rich and 33 O-rich in Sextans A. The total DPR in Sextans A should be considered a lower limit, as we do not detect AGB stars to the tip of the RGB. In both galaxies, the total dust input into the ISM is dominated by one or two stars. In Leo A, the star with the highest DPR is LeoA-143, with a DPR of . This is two orders of magnitude greater than the second-highest DPR in Leo A. This object has a rising SED and was identified as a candidate PNe from H α observations by Magrini et al. ( 2003 ), hence we exclude this source from the global dust-budget estimate. In Sextans A, the two sources with the highest DPRs are IDs 17 and 448 (SSTDUSTG J101059.43-044249.3 and J101058.05-044304.1, with and , respectively). These also have rising SEDs; however, both of these sources have been identified as variable x-AGB candidates by DUSTiNGS (Boyer et al. 2015c ). Our fitting routine classified SextansA-17 as an oxygen-rich AGB-star; it is responsible for 89% of the global dust production of Sextans A. If this source is instead carbon rich, then its DPR is significantly lower: . If SextansA-17 is confirmed to be an oxygen-rich AGB star, then it conclusively shows that individual metal-poor ( to −1.40) evolved stars can produce significant amounts of dust, and contribute substantially to the enrichment of the ISM in their host system. Figure 11
Paper: 2019ApJS..242....1S (fulltext)
yet implemented in SPECTRUM or AWSoM. The comparison of the coronal hole emission with the EIT images is also misleading for two reasons. First, the SPECTRUM calculations have been carried out with the two fundamental limitations discussed above; second, the coronal hole count rates of the EIT images are affected by instrument-scattered light (Shearer et al. 2012 ), and thus they are an overestimate of the real emission of coronal holes. These limitations prevent us from drawing any conclusions from the comparison with EIT. Finally, we assumed that the kinetic proton and ion temperatures are equal. However, in the region of the data extraction, the proton temperature anisotropy, T ⊥ / T ∥ , is between 1 and 1.3. In regions where there is temperature anisotropy, the kinetic proton and ion temperatures are not equalized by Coulomb collisions. 4.1.3. Sulfur The total intensity of the S x 264.2 Å line is significantly underestimated in both models even if lines formed at similar temperatures are in agreement with observations for the west limb. The reason for this discrepancy is likely due to the assumed abundance of sulfur. The values adopted in this study are photospheric, but the abundance of this element is notoriously difficult to determine because sulfur behaves in an anomalous way with regard to the FIP effect. In fact, sometimes it can behave as a high-FIP element, and sometimes as a low-FIP element, depending on the properties of the region where it is emitted (Laming 2015 , and references therein). Therefore, while the comparison of the predicted intensity of this element is expected to follow the behavior of the other ions formed at similar temperatures (e.g., Fe xi–xii ), the uncertainties in the element abundances make its interpretation more difficult. 4.2. Narrowband and Spectral Imaging SPECTRUM has the capability to apply instrument effective areas on the calculated intensities and to create 2D narrowband images, such as those observed by SoHO /EIT or the Solar Dynamic Observatory /Atmospheric Imaging Assembly ( SDO /AIA). To obtain such images, we need to save the 3D data domain surrounding the Sun. Then we calculate synthetic spectra for each pixel, weighting it by the effective area of the instrument and calibration factors. Figure 13 was created based on a box with sides of 3 R ⊙ in length, with 200 cells along each side. SPECTRUM calculated the full spectra from 165 to 350 Å for each cell within the box and convolved them with the instrument’s response function to obtain the observed data number count (DN) measured with the imager. The top left panel in Figure 13 shows the simulated plasma conditions of the solar corona as observed from Earth on 2007 November 4 19:12:27 UT, the top right panel corresponds to 2007 November 12 12:32:02 UT, and the bottom one is at 2009 April 23 13:13:36 UT, corresponding to the closest time to the spectral observations when narrowband observations were taken at the 195 Å wavelength. The
Paper: 2017A&A...606A.144A (fulltext)
in the water column density (mainly due to airmass variations). As we can see with panel c, all the telluric lines are corrected to the noise level (including the telluric lines blended with stellar lines). 4. Methods In this section, we describe how the transmission spectrum was derived and how we can search for water vapor in it with the cross-correlation technique. 4.1. Transmission spectrum The transmission spectrum ( Seager Sasselov 2000 ; Brown 2001 ) was obtained during the transit, when the planet passes in front of the star. It is defined as the area occulted by the planet over the stellar disk as a function of wavelength and probes the highest layers of the atmosphere. We computed the transmission spectrum following the same formalism as Wyttenbach et al. (2017) . Once the spectra are corrected from telluric features, weseparated in-transit spectra from out-transit spectra. Anin-transit spectrum, , is a spectrum obtained when the planetoccults a part of the stellar disk, while an out-transit spectrum, ,corresponds to the full-disk stellar spectrum. We corrected each spectrum for the stellar reflex motion induced by the planet, using the orbital parameters in Table 1 . Then, we created the normalized master out-transit spectrum which is the sum of the out-transit spectra: We normalized all the in-transit spectra to the continuum level of the master out-transit spectrum with a fourth-degree polynomial. This normalization was made from 5338 Å to 6900 Å, which corresponds to the red CCD. We applied a sigma-clipping rejection algorithm on the spectra in order to replace all the cosmic ray hits by the mean value of the other spectra at each wavelength. A normalized in-transit spectrum is noted . Then, we computed the transmission spectrum as the sum of each individual transmission spectrum (), after applying a Doppler shift to the planet rest frame to compensate for the planet orbital motion during transit: Finally, we expressed the variation of the occulted area by the planet as a function of wavelength on an absolute scale, by using the known white-light radius ratio (Table 1 ) : 4.2. Cross-correlation function The study of water vapor in the transmission spectrum in the visible is difficult considering the low intensity of the water bands and the noise level, even if we are able to resolve each line. To detect water vapor we need to co-add hundreds of lines to maximize the signal-to-noise ratio (S/N). To do so we used the cross-correlation function (CCF), expressed by: This technique ( Baranne et al. 1996 ; Pepe et al. 2002 ) consists of projecting a binary mask (with an aperture width of one pixel) on the spectrum and to sum the transmitted flux at each wavelength. The mask contains the theoretical wavelengths of water vapor transitions, and is Doppler shifted successively to scan a radial velocity range of –80 to 80 km s with a step of 0.82 km s. This step corresponds to the size of one pixel on the CCD of HARPS. In this study,
Paper: 2019MNRAS.488..381W (fulltext)
density. Finally, a summary of our results is given in Section 5 . The transition HD( j 1 ) + X( j 2 ) → HD() + X( j 2 ) is denoted by hereafter. 2 CALCULATION DETAILS The PESs developed for the H 2 –H 2 system can also be applied to the HD + H 2 collisions. Under the Born–Oppenheimer approximation, the H 2 –HD interaction potential is identical to that of H 2 –H 2 . The main difference between H 2 –H 2 and HD–H 2 is that the centre-of-mass of one of the H 2 molecules is displayed from the middle of the interatomic distance to the centre-of-mass of the HD molecule. A coordinate rotation that shifts the centre-of-mass of H 2 to HD similar to that adopted by Sultanov et al. ( 2012 ) is utilized to obtain the H 2 –HD PES from the H 2 –H 2 Patkowski PES. We followed the corrected coordinate rotation approach as described in the appendix of Balakrishnan et al. ( 2018 ). Computations were carried out using the quantum CC method with a mixed-mode OpenMP/MPI version of the non-reactive scattering code molscat (Hutson Green 1994 ) modified by Valiron McBane ( 2008 ) and Walker ( 2013 ), referred to as vrrmm hereafter. Both the HD and H 2 molecules are treated as rigid rotors. The full quantum CC formulation is well documented in Green ( 1975 ). In the scattering calculations, the angular dependence of the interaction PES is expanded as where are radial expansion coefficients and are the bispherical harmonics. (14, 14, 8) quadrature points are used for integration along each of the angular coordinates (θ 1 , θ 2 , φ). As discussed in Balakrishnan et al. ( 2018 ), the leading anisotropic terms on the HD–H 2 potential, A 101 and A 022 , are nearly identical for both the Hinde PES and Patkowski PES. We excluded any terms beyond A 448 , because higher order terms do not make significant contributions and are not included in the PES of Patkowski et al. ( 2008 ). The modified log-derivative Airy propagator of Alexander Manolopoulos ( 1987 ) is applied to integrate the coupled channel equations and the log-derivative matrix propagated to sufficiently large intermolecular separations to yield converged results. The interval from R = 1 a 0 to the asymptotic matching radius R = 60 a 0 with a step size 0.05 a 0 is found to be adequate for the propagation. We also performed several convergence tests to verify the reliability of the computed collision data. Basis sets with [0 ∼ j 1 + 5; j 2 ∼ j 2 + 4] were found to be sufficiently large for collision energies smaller than 20 000 cm −1 . Basis sets [0 ∼ j 1 + 2; j 2 ∼ j 2 + 4] are sufficiently large for collision energies smaller than 1000 cm −1 . Since the full CC calculation is prohibitively expensive at high collision energies, the coupled-state (CS) decoupling approximation (Heil, Green Kouri 1978 ) is used for calculations for collision energies larger than 2000 cm −1 . The total wave function is expanded in the basis in the CC formulation, while in the CS formulation, is chosen to be the basis sets, where
Paper: 2017MNRAS.471.2743T (fulltext)
is produced by the planet. We then Fourier transform the masked data back into phase versus velocity and display that in Fig. 6 . This shows an apparent retrograde trace, which we attribute to the shadow of a planet. This is again similar to what is seen in WASP-33 (Johnson et al. 2015 ). Figure 6. The spectral profiles through transit after removing the stellar pulsations via Fourier filtering. The planet trace is then readily seen moving in a retrograde direction. The left-hand panel shows the simultaneous TRAPPIST photometry of the transit. The planet's Doppler shadow seems to disappear towards the end of the transit (see Fig. 6 ). It is likely that it has been reduced during the filtering process, as a result of imperfect separation of the planetary and stellar-pulsation signals. This might have some effect on fitting the alignment angle λ, which depends on the slope of the Doppler shadow, but should have less effect on the other fitted quantities. In order to parametrize the planet's orbit, we then fitted the CCFs through transit, in a manner similar to the methods in Brown et al. ( 2017 ). Since we had subtracted the ‘minimum CCF’ above, we first add that back in to the filtered CCFs in order to reintroduce the stellar line profile, which is a key feature for constraining the value of v sin i ⋆ . The parameters that define the shape of the CCF line profile are the projected spin–orbit misalignment angle λ, the stellar line-profile full width at half-maximum (FWHM), the FWHM of the line perturbation due to the planet v FWHM , the stellar γ-velocity and v sin i ⋆ . These parameters were fitted using an MCMC fitting algorithm which assumes a Gaussian shape for the line perturbation caused by the planet. Both v sin i ⋆ and v FWHM have two data constraints, one from the shape of the line-broadening profile, and one from the slope of the trajectory of the bump across the line profile (given knowledge of λ). The value of v sin i ⋆ obtained in the spectral analysis was used as a prior in the fit. Initial values for the stellar line FWHM and the γ-velocity were obtained by fitting a Gaussian profile to the CCFs. The λ angle and v FWHM were given no prior. Details of the fitting algorithm are given in Collier Cameron et al. ( 2010a ), and the resulting system parameters are listed in Table 3 . Lastly, in Fig. 7 we show the pulsations without the planet trace, obtained by filtering to leave only the prograde quadrants, and then transforming back into velocity space. Figure 7. The spectral profiles through transit after removing the planet shadow via Fourier filtering. The stellar pulsations are seen moving in a prograde direction. 6 EVOLUTIONARY STATUS We then used MINES weeper , a newly developed Bayesian approach to determining stellar parameters using the MIST stellar evolution models (Choi et al. 2016 ). Examples of the use of MINES weeper in determining stellar parameters are shown in Rodriguez et al. ( 2017a , b ). We model the available B
Paper: 2019ApJ...883...69I (acknowledgments)
The authors are grateful to the anonymous referee for improving the original manuscript.
Paper: 2017ApJ...835..210H (acknowledgments)
The Canadian Galactic Plane Survey is a Canadian project with international partners, supported by the Natural Sciences and Engineering Research Council. We thank Blakesley Burkhart and Mordecai-Mark Mac Low for useful conversations, Sean Dougherty for his encouragement through the process of completing this paper, and the referee for insightful comments that have improved this paper. C.A.H. acknowledges financial support received via an Australian Postgraduate Award, and a Vice Chancellor's Research Scholarship awarded by the University of Sydney. B.M.G. acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through grant RGPIN-2015-05948. N.M.M.G. acknowledges the support of the Australian Research Council through grant FT150100024. E.P. acknowledges the support from the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement n. 617199. The Dunlap Institute for Astronomy and Astrophysics is funded through an endowment established by the David Dunlap family and the University of Toronto. This research made use of APLpy, an open-source plotting package for Python hosted at http://aplpy.github.com .
Paper: 2016A&A...593A.107L (fulltext)
variability, although this is also consistent with the calibration error. The future simultaneous, multi-frequency ALMA observations at the 300 GHz bands, are still required to provide better constraints on the instantaneous spectral energy distributions. The detailed analysis the Stokes flux variability, and the analytical modeling of the spectral energy distribution, are beyond the scope of this paper. They will be elaborated in forthcoming papers. 3.2. Polarization fitting, intrinsic polarization angle, and rotation measure During the observations, the target source is rotated with respected to the receiver frame, owing to the alt-azimuth mount of the ALMA antennae. If the polarization position angle and the polarization percentage do not vary significantly in a shorter timescale than the durations of our observing tracks (1–2 h, typically), then the polarization percentage and the polarization position angle can be fitted according to the following formula by definition: where denotes the observed Stokes flux, ( offset, hereafter) is an assumed constant normalized offset of observed Stokes as a result of the amplitude calibration errors or polarization leakage; is the polarization percentage; , , and are the polarization position angle in the sky (e.g. right ascension/declination) frame, the parallactic angle, and the Evector (Sect. 2 , Table 2 ), respectively (see also Hildebrand et al. 2000 ; Li et al. 2005 ). The application to the interferometric observations of point sources is straightforward since the visibility amplitude does not vary with uv distance. A generalized formulation for extended sources is given in Martí-Vidal et al. ( 2016 ). In practice, we determine our fitting results and errors using an iterative process. First, for each epoch of observations at each frequency, we perform a prior fitting of , and , based on XX and YY observed at various . From all data points, we then derive the standard deviation () of the difference between the observed and the priori fit. Finally, we disturb each data point of with a Gaussian random number of which the standard deviation is , and then re-fit , , and . In the end, , , and and their errors are determined by mean and standard deviations of fittings to 1000 independent realizations of the perturbed . We checked that those means converged to the values close to a priori fittings. We reject records which present poor signal-to-noise ratios with the measurements of Stokes fluxes, which could be observations at low elevation, or with poor weather conditions, or are observations of mosaic fields of which the pointing centers are far away from Sgr A* (e.g. outside of the central seven fields, see Figs. 1 , 2 ). Table 4 Polarization properties of Sgr A*. Notes. (1) Polarization position angle. (2) Measurements made from combining all ACA observations of B9-a and B9-b setups. We note that the extended emission surrounding Sgr A* is dominated by the unpolarized, free-free emission of the
Paper: 2018A&A...609A.114G (acknowledgments)
We thank the anonymous referee for a very thorough report, which led to numerous improvements in this manuscript. GCS was supported by NASA through Contract Number 1257184 issued by the Jet Propulsion Laboratory, California Institute of Technology under NASA contract 1407 and the NSF through Award 1108645. We thank Jean-Baptiste Marquette (IAP) for providing some EROS2 photometric data, Alistair Glasse (UKATC) for providing the JWST/MIRI filter response curves, and Sara Bladh (Padova University) for providing the results for her M-star models, including the gas-to-dust ratios. This publication is partly based on the OGLE observations obtained with the Warsaw Telescope at the Las Campanas Observatory, Chile, operated by the Carnegie Institution of Washington. This paper utilizes public domain data originally obtained by the MACHO Project, whose work was performed under the joint auspices of the U.S. Department of Energy, National Nuclear Security Administration by the University of California, Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48, the National Science Foundation through the Center for Particle Astrophysics of the University of California under cooperative agreement AST-8809616, and the Mount Stromlo and Siding Spring Observatory, part of the Australian National University. This research made use of the SIMBAD database, operated at CDS, Strasbourg, France. This publication makes use of data products from WISE and NEOWISE, which a joint projects of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Based on data from the OMC Archive at CAB (INTA-CSIC), pre-processed by ISDC. The CSS survey is funded by the National Aeronautics and Space Administration under Grant No. NNG05GF22G issued through the Science Mission Directorate Near-Earth Objects Observations Program. The CRTS survey is supported by the U.S. National Science Foundation under grants AST-0909182. Based on observations made with VISTA at ESO under programme ID 179.B-2003.
Paper: 2016A&A...586A.127F (acknowledgments)
We thank Magnus V. Persson, Thanja Lamberts, Vianney Taquet, Catherine Walsh, and Maria N. Drozdovskaya for interesting discussions. We also thank the referee for his/her comments. Astrochemistry in Leiden is supported by the Netherlands Research School for Astronomy (NOVA), by a Royal Netherlands Academy of Arts and Sciences (KNAW) professor prize, and by the European Union A-ERC grant 291141 CHEMPLAN. K.F. is supported by the Research Fellowship from the Japan Society for the Promotion of Science (JSPS). Numerical computations were, in part, carried out on PC cluster at Center for Computational Astrophysics, National Astronomical Observatory of Japan.
Paper: 2016MNRAS.459.3363Q (acknowledgments)
The authors acknowledge the helpful comments and ideas of B. Ruiz Cobo and H. Socas Navarro provided during the development of the manuscript. We also want to thank Professor M. Carlsson for his helpful comments and corrections after reviewing the manuscript. JdlCR is supported by grants from the Swedish Research Council (VR) and the Swedish National Space Board (SNSB).