adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017A&A...603A.137M (acknowledgments)
We thank the anonymous referee for his/her detailed and useful report which helped us to improve our manuscript. This work has made use of data from the European Space Agency (ESA) mission Gaia ( http://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. We acknowledge financial contributions from the agreement ASI-INAF I/037/12/0. This work was partially supported by the Regione Autonoma della Sardegna through POR-FSE Sardegna 2007–2013, L.R. 7/2007, Progetti di Ricerca di Base e Orientata, Project N. CRP-60529. A.R. acknowledges the Sardinia Regional Government for its financial support (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2007–2013 – Axis IV Human Resources, Objective l.3, Line of Activity l.3.1.).
Paper: 2018MNRAS.480.2704L (fulltext)
assumption that BBHs trace the location of their parent star particles. The prospects for detecting BHs without stellar companions remain limited. Still, with a refined model of the star formation history in the MW, non-detections with X-ray, radio and microlensing surveys will put constraints on binary evolution models. Conversely, a much higher number of detected systems will be equally constraining, and may inform us of additional BBH formation channels (Rodriguez et al. 2015 ; Marchant et al. 2016 ). Our understanding of massive binary evolution is still very uncertain, especially the modelling of stellar mass-loss through different evolutionary phases, the properties of common-envelope mass transfer, and supernova kicks. Specifically, we have assumed that common envelope interactions during the Hertzprung gap result in stellar mergers, which may underestimate the BBH production rate. Conversely, we assume the BH natal kicks are reduced with respect to NS natal kicks, which may overestimate the number of systems surviving both supernova explosions. As our work highlights the importance of improved galactic models, we do not explore the wide parameter space of current binary models. The different BBH merger rates predicted by Mapelli et al. ( 2017 ) show how different binary evolution models will eventually be ruled in or out by observational data, provided appropriate models are used for the star formation. Based on our assumptions for binary evolution, we predict that LISA will detect about 25 BBHs within the MW halo. The frequency of these systems will not vary over the duration of the LISA mission, which means that the measured chirp mass will be degenerate with the distance of the system (see equation 4 ) and discerning it from a closer NS binary may not be possible. While certain aspects of binary evolution are likely to be revised, we emphasize that the BBH predictions of our model are somewhat on the high end (see a discussion in Lamberts et al. 2016 ) and that fine-tunning it in order to produce more galactic BBHs may overproduce the observed BH merger rate (Abbott et al. 2017c ). Unless a mechanism allows for the formation of BBHs at close to solar metallicity, BBHs are strongly biased towards the galactic halo, which strongly reduces their GW signal on Earth relative to disc populations. Even a single detection with accurate localization will provide information on the binary evolution mechanism and the conditions of its formation. We find that less than a million BBHs have merged in the MW by the present day. Focusing on the mergers within the last 2 Gyr, we find a merger rate of ≃10 −5 yr −1 . Our model, which assumes a binary fraction of unity, is on the higher end of the measured rate. We find the mean mass of the systems to be 29, which is in line with the announced detections (Abbott et al. 2016a ; 2017a , b , c ). We find that sources with represent 8 per cent of the merged systems, meaning there could be roughly 40 000
Paper: 2021MNRAS.504.2062P (fulltext)
sets with each set obtained from weighted average over 100 voxels. This yields an RMS of for clean modes in the range k ≃ 0.02–1 h Mpc −1 . The RMS estimate can be further improved by averaging over all the data for a fixed τ (this leaves the mean unchanged for reasons outlined in the foregoing) and then computing the RMS using bootstrapping (e.g. Kolopanis et al. 2019 ). We plot the power spectra for both the XX and YY polarizations in all the figures to emphasize the long-term system stability in a drift scan. Our calibration does not involve a polarized source so both the polarizations are assigned equal weight in the beginning of the scan. Our results show that no significant deviation emerges at the end of the scan. As the H i signal is unpolarized, the power spectra for the two polarizations can be added in quadrature to yield a further improvement in RMS by nearly a factor of . 5.5.1 Comparison with other H <sc>i</sc> power spectrum measurements Our results can be directly compared to PAPER H i power spectra upper limits as it was also a drift scan experiment. PAPER was located in Karoo desert, South Africa at geo-coordinates 30°43′17′′S, 21°25′42′′E. Its 2-m antenna had a primary beam (FWHM) of nearly 50°. PAPER’s 64 antennas were placed in a 8 × 8 pattern. For the final results presented in Kolopanis et al. ( 2019 ), six of the antennas were flagged and only a subset of baselines were used for the power spectrum estimations. This subset consisted of 30-m East–West baselines between adjacent columns and those staggered by one row (see fig. 2 of Kolopanis et al. 2019 ). The system temperature for the observational runs varied between 400 and 800 K in the frequency range. These data sets had a total bandwidth of 10 MHz with a channel width of 493 kHz, centred around six frequencies in the range 120–167 MHz. The power spectrum estimate was based on 135 d of observations, but only the data from 124 d was used for LST binning. The LST spanned the range 00 h 30 m 00 s to 08 h 36 m 00 s . The data sets were initially binned with a time resolution 43 s. After the LST binning, they were further averaged with a top-hat filter in time which yielded a resolution of 938 s. With these instrumental parameters, the PAPER collaboration recently published the analysis of its final data products. Their upper limits on the H i power spectra at z ≃ 8.13 are: Δ 2 ( k ) ≃ (1000 mK) 2 at k = 0.2 h Mpc −1 and (380 mK) 2 at k ≃ 0.32 h Mpc −1 (Kolopanis et al. 2019 ). Our upper limit at a comparable wavenumber and redshift is ≃(1000 mK) 2 , based on nearly 35 h of MWA drift scan data. While a more direct comparison between the performance of MWA and PAPER is harder, this allows us to note the following salient differences between the two interferometers and analysis pipelines: (i) the collecting area of MWA is nearly five times large, (ii) PAPER baselines have a higher level of redundancy (Section 3 ), (iii) the time over which the visibilities are averaged is
Paper: 2017ApJ...842...74W (acknowledgments)
The present work is supported by the Xinjiang Natural Science Foundation grant No. 2014211A069. This work is also funded by the CAS Key Laboratory of Solar Activity, National Astronomical Observatories, the Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education, China Scholarship Council (CSC), XAO Director Foundation, and NSFC (No.11673056 and 11403087). The authors also thank the support from the Supercomputer Center of University of Arizona. In addition, the authors appreciate Prof. J. R. Jokipii and his group in the University of Arizona, Profs. Hongbo Hu and Hong Lu at IHEP, Prof. Xueshang Feng and his group at NSSC, Profs. W. Gan and Haisheng Ji at PMO, Prof Jun Lin at YNAO, Profs. Quanming Lu and Chenglong Shen at USTC of CAS, and Prof. Liuguan Ding at Nanjing University of Information Science and Technology for their many helpful discussions.
Paper: 2019ApJ...877...51C (acknowledgments)
We are grateful for all of the referee’s suggestions and comments, which greatly improved the scientific quality and information flow of this work. K.C. would like to recognize and thank the Royal Thai Government Scholarship in the section of the Ministry of Sciences Technologies for the National Astronomical Research Institute of Thailand (NARIT) and the University of Florida’s Graduate School Doctoral Dissertation Award for guidance and financial support during the phase of completing this work and his thesis. E.S.S. acknowledges funding and support from the Spanish MINECO under the projects MDM-2014-0369 of ICCUB (Unidad de Excelencia “María de Maeztu”), AYA2015-70498-C02-2-R and AYA2017-88085-R (both cofunded with FEDER funds), and the Catalan DEC grant 2017SGR643. R.C. and J.M.R.E. acknowledge support from the Spanish Ministry of Science under grant AYA2015-70498-C2-1. J.M.R.E. also acknowledges support from the Spanish Ministry of Science through grant AYA2017-84061-P. R.C., J.M.R.E., and A.H. acknowledge the Spanish Ministry of Science’s support through the Severo Ochoa grant SEV2015-0548. A.H. acknowledges support by the Spanish Ministry of Science through grant AYA2015-68012-C2-1 and by the Gobierno de Canarias through grant ProID2017010115. J.G. acknowledges support by the Spanish Ministry of Science through grant AYA2016-75808-R. J.M.M.H. is partially funded by Spanish State Research Agency grants ESP2017-87676-C5-1-R and MDM-2017-0737 (Unidad de Excelencia ‘María de Maeztu’ CAB).
Paper: 2017ApJ...841..112L (acknowledgments)
We are grateful to the referee for the constructive comments and substantial improvements to this work. We thank Dr. Chang Liu for providing ideas to explain the observation, and Dr. Xin Chen for providing the tracing method. The observations presented in this paper were mainly obtained with NST at the Big Bear Solar Observatory, which is operated by New Jersey Institute of Technology. The data are courtesy of the BBSO and NASA/ SDO teams. This work was partially supported by NASA grants NNX13AF76G, NNX16AF72G, NNX13AG13G, and NNX14AC12G, and by NSF grants AGS 1348513, 1408703 and 1620875. BBSO operation is supported by NJIT, US NSF AGS-1250818 and NASA NNX13AG14G, and NST operation is partly supported by the Korea Astronomy and Space Science Institute and Seoul National University, and by strategic priority research program of CAS with grant No. XDB09000000.
Paper: 2016A&A...593A..94F (fulltext)
consequence of the release of the S-species from the icy grain mantles because of thermal and nonthermal desorption and sputtering. Contrary to dark clouds and prestellar cores, in B1b the S depletion is comparable to that of C and O, which explains the high abundances of S-bearing species in this core. Table 4 Elemental abundances relative to H. Notes. (a) From the compilation by Agúndez Wakelam (2013) ; (b) this work; (c) elemental abundance in the gas phase; (d) depletion factor = (X/H)/(X/H). The abundance of sulfur in grains is very uncertain. Thus far, the only S-bearing molecule unambiguously detected in ice mantles is OCS, because of its large band strength in the infrared ( Geballe et al. 1985 ; Palumbo et al. 1995 ), and maybe SO ( Boogert et al. 1997 ), but there are only upper limits of the solid HS abundance ( Jiménez-Escobar Muñoz Caro 2011 ), which is thought to be the main sulfur reservoir in the ice. Measurements of the sulfur depletion are derived by comparing the predictions of gas-phase chemical models with the observed abundances of the main S-bearing species. As reported above, chemical models are limited by the uncertainty in the reaction rates and the poorly known [O]/[O] ratio and the exact value of the sulfur depletion is uncertain. Our results suggest, however, that the sulfur depletion in Barnard 1b is not as extreme as in dark clouds where the abundance of S-bearing species is lower. This moderate sulfur depletion could be the consequence of the star formation activity in the region. B1b is irradiated on its eastern side by UV photons from the nearby Herbig Ae stars LkH 327 and LkH 328 ( Walawender et al. 2009 ; Young et al. 2015 ). On its western side, B1b is impacted by the outflows associated with B1a and B1d ( Hatchell et al. 2007 ). Not unrelated, the level of turbulence in B1 (0.4 km s) is higher than in cold cores such as L1544, TMC-1 or L134N (0.1 km s). Sputtering induced by collisions may be efficient in this turbulent environment to erode the icy mantles and release the sulfuretted species to the gas phase. Sputtering could also explain the high abundances of complex molecules observed in this core ( Marcelino et al. 2009 ; Cernicharo et al. 2012 ). The main drawback of this interpretation is that it would also imply a low depletion of CO that is not observed. Another possibility is that the moderate sulfur depletion is a consequence of the rapid collapse of B1b. Aikawa et al. (2005) investigated the impact of different collapse timescales on the gas chemistry. The initial conditions of their first model lie close to the critical Bonnor-Ebert sphere with a central density of 10 cm. When the central density of the core reaches 10 cm, the S-bearing species were totally depleted at AU. In the second case, internal gravity overwhelms pressure and the collapse is much faster (about 0.1 Myr). When the central density of the core reaches 10 cm, molecules such as CS, SO, and CS have abundances of 10 in the
Paper: 2019ApJ...885L...8W (fulltext)
provide the best overall fits in terms of visual inspection of the residuals and through comparison of likelihood ratios between models. Thus, we adopt the BaSTI models for this Letter. We adopt a metallicity grid that ranges from −2.3 ≤ [M/H] ≤ −0.5 with a resolution of 0.1 dex and an age grid that ranges from with a resolution of dex. We find that including ages younger than did not change the SFHs (as these galaxies have no young populations) but increased the computational time. Thus, we simply exclude ages with from our CMD modeling. Finally, given that our CMDs do not reach the oldest MSTO, we follow Weisz et al. ( 2011 , 2014 ) and adopt a prior on the age–metallicity relationship that requires the metallicity to increase monotonically with time, with a modest dispersion allowed at each age. This choice helps to mitigate some of the age–metallicity degeneracy on the RGB and RC. We compute random (which accounts for the finite number of stars on the CMD) and systematic uncertainties (which are a proxy for uncertainties in the physics of the underlying stellar models) as described in Weisz et al. ( 2014 ). Finally, as detailed in Section 3.6 and Figure 6 of Weisz et al. ( 2014 ), SFHs measured from CMDs that include the HB but do not include the oldest MSTO have larger systematic uncertainties, but are consistent with SFHs measured from the oldest MSTO-depth CMDs. Figure 1 illustrates the CMD modeling process. Panel (a) shows the observed Hess diagram of And XXI, and panel (b) shows the best-fit model for the BaSTI stellar library. A visual comparison of the model and data indicates good overall agreement. Panel (c) is a residual significance diagram, i.e., (model–data)/model, which quantifies the level of (dis)agreement. The majority of populated pixels are consistent within ∼1 σ , while only a handful of pixels are highly discrepant (i.e., 3 σ ). There are no signs of poorly fit regions of the CMD (e.g., large swaths of only black or white pixels), which indicates that the model is a good fit to the data. Panel (d) shows the cumulative SFH of And XXI, i.e., the fraction of total stellar mass formed prior to a given epoch. The solid black line is the best-fit BaSTI solution, and the gray-shaded band reflects the 68% confidence interval of the total (i.e., random plus systematic) uncertainties. The random uncertainties are negligibly small compared to the systematic uncertainties. The thin colored lines in panel (d) are the best-fit SFHs from the other four stellar libraries. These SFHs are similar in shape to the BaSTI solution, though particular features can be shifted by up to ∼2 Gyr, which is due to differences in the underlying stellar physics (e.g., Gallart et al. 2005 ). As intended, the scatter in the SFHs from different models is well captured by the gray-shaded error envelope. The small amount of apparent star formation ∼1–3 Gyr ago is likely due to a handful of blue stragglers that occupy similar portions of the CMD as
Paper: 2015ApJ...801...31R (acknowledgments)
The authors thank Catherine Espaillat and Lynne Hillenbrand for providing the optical spectra and physical parameters of a few objects, and John Kwan for providing the mid-IR hydrogen line ratios. E.R. is supported by the NASA's Astrophysics Data Analysis Program research grant to I.P. (ID: NNX11AG60G).
Paper: 2021A&A...651A...8F (fulltext)
to be around 2.5 throughout the whole envelope in good agreement with previous measures. All these conclusions lead us to think that Y CVn is a quite active star that could have developed structure in its envelope. Due to the physical mechanisms that are producing the suggested asymmetries close to the star, the high temperatures, gas densities below ~ 10 8 –10 10 cm −3 , the influence of the Galactic UV radiation field in the chemistry at different distances from the star, and its proximity, Y CVn is a good candidate to better understand the physical and chemical evolution of the envelopes of low mass-loss rate C-rich stars. Appendix A Brief description of HCN Hydrogen cyanide is a linear triatomic molecule that belongs to the C ∞ group and displays a permanent dipole moment of 2.985 D ( Ebenstein Muenter 1984 ). The vibration of its atoms can be described with the aid of four normal modes, two of which are degenerate and are combined into the bending mode ν 2 ( π ). These two degenerate normal modes involve atomic oscillations out of the molecular axis that allow the molecule to spin around it. This results in the creation of a vibrational angular momentum that splits the rotational levels in sublevels with different e − f parity ( Brown et al. 1975 ). The other two normal modes, ν 1 ( σ + ) and ν 3 ( σ + ), are stretching modes that can be essentially described as the HC–N and H–C vibrations along the molecular axis, respectively (e.g., Herzberg 1956 ). Fig. A.1 Vibrational energy diagram of HCN below 5000 K. The lines that connect the vibrational states represent the strongest allowed vibrational transitions. The transitions in blue lie around 13.5 μm, in red around 7.5 μm, and in orange at other wavelengths (3.0, 3.6, 4.8, and 8.2 μm, depending on the transition). Those plotted with ticker lines are detected in the spectra presented in the current work ( Figs. 1 and 2 ). In the current work, we adopted two different notations commonly used in Molecular Spectroscopy and Astrophysics to label a vibrational state: v 1 ν 1 + v 2 ν 2 + v 3 ν 3 ( l ± ) and , where i = 1, 2, 3. The v i is the vibrational quantum number related to the i -th normal mode, l the quantum number for the vibrational angular momentum, which can be expressed with the Greek letters σ , π , δ , φ , γ , ... for l = 0, 1, 2, 3, 4, ..., ± is the total parity of the molecule in this vibrational state (usually given only for the σ states), and p is the e − f parity. The v = 0 means the vibrational ground state (G.S.). The change of the rotational number in a transition is given by B p ( J l ) for the ro-vibrational lines, where J u and J l are the upper and lower rotational levels, respectively, B is the branch (P: Δ J = J u − J l = −1, Q: Δ J = 0, and R: Δ J = +1), and J = J u − J l for the pure rotational ones. More information about the selection rules and the line intensities of linear molecules can be found, for example, in Fonfría et al. (2008) . Figure A.1
Paper: 2016A&A...591A.124K (acknowledgments)
WASP-South is hosted by the South African Astronomical Observatory and we are grateful for their ongoing support and assistance. Funding for WASP comes from consortium universities and from the UK’s Science and Technology Facilities Council. J.A.K.-K. and D.F.E are also funded by the UK’s Science and Technology Facilities Council. A.M.S. acknowledges support from grants SGR1458 (Generalitat de Catalunya), ESP2014-56003-R and ESP2015-66134-R (MINECO). This research has made use of “Aladin sky atlas” developed at CDS, Strasbourg Observatory, France and NASA’s Astrophysics Data System.
Paper: 2018ApJ...857....9D (fulltext)
from the SDSS DR11 quasars at z = 2.34, we reconstruct the evolution of the dark energy EoS using the polynomial expansion, PCA, and the correlated prior method, respectively. In this paper, we pay attention to the high-redshift BAO measurement of BOSS DR11 quasars, the Ly α FB data, which have been widely used to constrain cosmological parameters (Lee et al. 2015 ; Delabrouille et al. 2015 ), especially the EoS of dark energy. However, we note that this measurement slightly prefers a negative dark energy density, so the second main purpose of this paper is to study the possible influence of this Ly α FB data on the constraints of the EoS of dark energy. In the next section, we show the numerical method and measurements used in this paper. Then, we use different reconstruction methods to explore the evolution of the dark energy EoS. In particular, we carefully check the impact of Ly α FB data on the results. The final section presents our conclusions and a discussion. 2. Method and Data We perform a global fitting analysis of cosmological parameters, using the CosmoMC package, 3 3 http://cosmologist.info/cosmomc/ a Markov Chain Monte Carlo (MCMC) code. We assume purely adiabatic initial conditions and a flat universe. Our parameterization is thus:the and are the baryon and cold dark matter physical densities, Θ s is the angular size of the sound horizon at decoupling, τ is the optical depth, n s and A s are the spectral index and the primordial power spectrum, and P w will be the parameters that describe dark energy. In order to understand the nature of dark energy well, we consider the following data sets. 2.1. SN As standard candles, the SN measurements provide a luminosity distance as a function of redshift z , making them a powerful probe for cosmology, particularly for the EoS of dark energy. In this paper, we use 740 samples from JLA (Betoule et al. 2014 ), in which the redshift spans 0 z 1.299, and there are only eight high-redshift samples ( z > 1). The absolute magnitude comes from the logarithm of the luminosity of an astronomical object seen from a 10 parsec distance, which can also be used to give its brightness. We use the distance modulus μ th to relate the expansion history of the universe to the apparent magnitude of an SN at a given redshift. It is defined as the difference between the apparent magnitude m and the absolute magnitude M of the SN and relates to the distance throughwith μ 0 = 42.38 and dimensionless D L = H 0 d L . The luminosity distance is given by d L = (1 + z ) r ( z ), with r ( z ) standing for the comoving distance: The observation of distance modulus μ obs is given by an empirical linear relation:where α and β are set to nuisance parameters in our numerical calculations, , x 1 , and are parameters describing the SN, and M B is related to the host stellar mass M stellar by a simple step function (Betoule et al. 2014 ):so we can calculate the χ 2 of SN following:where σ i is the error of i th SN sample.