adsabs/WIESP2022-NER huggingface dataset:
Paper: 2015ApJ...811...71Q (acknowledgments)
This work is partly supported by China Ministry of Science and Technology under State Key Development Program for Basic Research (2012CB821800, 2015CB857100) and the National Natural Science Foundation of China No. 11373038, No. 11373045, Strategic Priority Research Program of the Chinese Academy of Sciences, No. XDB09010302. SSRO acknowledges support from NASA through Hubble Fellowship grant #51311.01 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, INC., for NASA, under contract NAS 5-26555. The orion calculations were performed on the Trestles XSEDE cluster and on the Yale University Omega cluster. We are grateful to the referee for their detailed comments and suggestions.
Paper: 2019ApJ...873...69K (fulltext)
with any field characteristics obtained from circular polarization modeling. Considering our ZDI results, the mean vertical field corresponds to the surface-averaged unsigned radial field reported in Table 3 . Our = 98–168 G is a factor of 2.9–5.6 lower than the vertical surface fields suggested by MacDonald Mullan ( 2017b ). If, instead, we choose to compare their predictions with our measurements, the theoretically estimated field should be multiplied by assuming isotropic field vector orientation. This yields a total surface field strength of 850–950 G, which is 3.3–4.0 times lower than determined in our paper. To summarize, theoretical models by Feiden Chaboyer ( 2013 ) appear to predict a more realistic surface field strength for the components of YY Gem than the one found in the approach by MacDonald Mullan ( 2014 , 2017b ). However, this conclusion should be taken with caution given the ambiguity of relating the vertical surface field parameter of the latter theoretical calculations to observations. 7.3. Estimating Magnetic Flux from X-Ray Luminosity In the absence of direct constraints on the surface magnetic properties, many studies resort to an approximate estimate of the mean magnetic field strength. The X-ray emission is often considered to be a particularly useful magnetic proxy. Pevtsov et al. ( 2003 ) demonstrated that the total unsigned magnetic flux measured for solar magnetic regions at different spatial scales shows a power-law dependence on the X-ray spectral radiance. The few measurements reported at that time for active late-type stars fell on the extrapolation of this relation to higher magnetic flux values. Feiden Chaboyer ( 2013 ) recalibrated this relation using Stokes I field strength measurements of low-mass dwarfs. Both Feiden Chaboyer ( 2013 ) and MacDonald Mullan ( 2014 ) attempted to verify their theoretically predicted surface field strengths with indirect field strength estimates using the X-ray emission of YY Gem. This exercise was, however, hampered by the contamination of the ROSAT X-ray measurements of YY Gem (Voges et al. 1999 ) by the nearby early-type companions Castor A and B. More recent Chandra observations of YY Gem (Hussain et al. 2012 ) provided images with a high angular resolution, allowing one to separate the low-mass binary from the A-type stars. The combined quiescent X-ray luminosity of YY Gem is reported to be (3–3.5) × 10 29 erg s −1 . Assuming equal luminosities of the components and using their fundamental radii yields = 770–820 G with the calibration by Feiden Chaboyer ( 2013 ). This is significantly smaller than the observed obtained in our study, indicating that the magnetic flux versus X-ray luminosity relation may be appropriate only for large stellar samples but is unlikely to be precise enough to provide a meaningful field strength estimate for individual stars. Another problematic issue with applying this relation to active stars, apparently entirely overlooked in the
Paper: 2018MNRAS.480.4199F (fulltext)
f dif – decorrelation bandwidth, t dif – scintillation time, τ sc – scattering time, θ H – angular diameter of the scattering disc, d s – distance to the scattering screen, ρ dif – scale of diffraction pattern, n – spectral index of ISM inhomogeneities. Dynamic spectra of observations conducted in 2014 and 2015 show clearly distinguishable inclined structures that indicate the presence of angular refraction in a given direction. Time and frequency structure functions obtained from analysis of the dynamic spectrum in 2015 showed their power-law character with an equal index of α = 1.13 ± 0.01, which corresponds to n = 3.13 ± 0.01. The similarity of their inclinations indicates strong refraction in the direction to the pulsar. In the 2014 observation, we had a strong modulation in frequency and time, so a quality structure function could not be obtained. Such a modulation is well known as time and frequency ripples caused by the interference of diffracted and direct rays in dual-reflector radio telescopes. It was found that the diffracted rays from the sub-reflector edge can interfere with the main beam to produce a gain ripple of sinusoidal form for the dual reflector system. Analysis of the dynamic spectrum for 2012 gave the same slope of the structure functions, α = 0.83 ± 0.04. For 2014 and 2015 observations, the drift of diffraction spots in the dynamic spectrum is clearly visible. In Bhat et al. ( 1999 ), it was noted that such drift behaviour is typical for this pulsar. Such a drift can last for quite a long time. This effect was observed during the entire series of observations conducted in 1993–1994 (110 days). This suggests that the structure leading to the refraction of radiation passes the line of sight for a time longer than 110 days. Analysis of the complex covariation function module for the space–ground interferometer yielded the scale of the diffraction pattern. In Fig. 6 , this function is shown for the 2015 observation for the RadioAstron–Arecibo (RA–AR) baseline. Applying equation ( 8 ), we found a value of the spatial coherence function B u = 0.25 ± 0.04. Using the values α = 1.1 and , we obtained ρ dif = (6.0 ± 0.9) × 10 9 cm. The distance from the observer to the screen d s was estimated using ( 10 ). Since the tangential velocity of the pulsar is , we neglected the velocity of the Earth and the screen. Accordingly, d s / D = 0.64 ± 0.06, . Using ( 11 ), we calculated , . We estimated the scattering time to be 0.76 μs. Figure 6. PSR B0834+06. Average covariation function from complex cross-spectra (observation 08.04.2015, baseline RadioAstron–Arecibo). The dashed line corresponds to the OFF-pulse window. For 2014, we did not obtain a qualitative covariation function, because of the influence from frequency ripples. In the observation of 2012, the baseline projection of the space–ground interferometer was 2.05 × 10 10 cm. For this day, we found the value B u = 0.38 ± 0.09 and, using α = 0.83, we determined the diffraction
Paper: 2017A&A...608A..96L (fulltext)
to interpret the OPR values of H ( Crabtree et al. 2011c , b ) and subsequently studied in more detail for the specific system ( Gomez-Carrasco et al. 2012 ). The mechanistic distinctions were also recently considered for observations of the OPRs of HO ( Herbst 2015 ) and HCl ( Neufeld et al. 2015 ), providing new insights to explore. A rigorous determination of the mechanisms in a particular system requires a full quantum scattering treatment, but as pointed out by Gomez-Carrasco et al. (2012) , full quantal computations are rarely achievable because of their complexity and the computational cost. The next best approach is the semi-classical trajectory method, in which atoms and molecules undergo classical trajectories on a quantum mechanical PES. It is this approach that is used in this paper. Of course, both destruction and conversion processes must also be considered in determining OPRs, and such processes are also contained in our chemical simulations. In the remainder of the paper, we first consider in Sect. 3 the astronomical observations leading to new OPR measurements for HCl in an extragalactic absorber. Then we discuss in Sect. 4 our modeling approach and present its outcomes for the scrambling versus hydrogen abstraction/proton hop mechanisms. Section 5 describes our semi-classical treatment of reaction ( 4 ), which models show to be the dominant formation process for HCl. Finally, Sect. 6 summarizes our results and draws the main conclusions of the present study. 3. Observations 3.1. Description of the observations The ortho and para forms of HCl were observed with the Atacama Large Millimeter/submillimeter Array (ALMA) during Early Science cycle 2, in summer 2014. Two different tunings were observed to cover the – transition of ortho-HCl (rest frequencies 189 GHz, redshifted to 100 GHz, within ALMA Band 3) and the – transitions of para-HCl (rest frequencies 485 GHz, redshifted to 257 GHz, within ALMA Band 6), respectively, as listed Table 1 . The array was maintained in a configuration providing a synthesized beam resolution of better than 0.5, sufficient to resolve the two point-like lensed images of PKS 1830211, separated by 1. The bandpass calibration was done on the bright quasar 1924292, and the gain calibration was solved by regular visits on 18322039 during the observations. The correlator was set up with spectral windows of 1.875 GHz, providing a velocity resolution of 1.3 km s (Band 6) and 2.9 km s (Band 3), after Hanning smoothing. The data calibration was performed within CASA 1 following a standard procedure. A further run of self-calibration using the bright continuum of PKS 1830211 helped us to significantly improve the data quality. The final spectra were extracted toward both lensed images of PKS 1830211, using the CASA-python task UVMULTIFIT ( Martí-Vidal et al. 2014 ), by fitting a model of two point sources to the interferometric visibilities. The ALMA spectra of HCl toward PKS 1830211 are shown in Fig. 1 . The
Paper: 2020MNRAS.499.4854T (fulltext)
each particle by decomposing the gravitational forces into a long-range term, computed from Particle-Mesh methods, and short scale interactions from nearest neighbours using Tree methods. The code is MPI parallel and can be used with periodic boundary conditions in the comoving frame. The scaling of the code with the number of particles is , so it can handle a large number of particles with reasonable computational resources. The simulations were run in the MareNostrum supercomputer at BSC-CNS 4 using a modified version of GADGET-2 in which the free parameters of the code were chosen such that the force computation was accurate enough for PBH binaries to form and survive without being disrupted by pure numerical errors. We took the gravitational softening length ε = 0.001 pc and a time-step of Δ t = 10 −6 in log ( a ). These parameters have a significant impact in the orbit stability, so we ensured that our choices are optimal and that a further reduction of both ε and Δ t does not yield any significant improvement in orbit stability. Previous analytic estimates suggest that the first PBH pairs begin to appear approximately at the radiation-matter energy equipartition epoch ( z ∼ 10 3 ; Nakamura et al. 1997 ; Sasaki et al. 2016 ) or, depending on the PBH fraction, even earlier – during the radiation-dominated epoch. Therefore, we had to modify the cosmological equations in the GADGET-2 code to be valid during the radiation dominated epoch of the Universe since we start our simulations at z init = 10 5 . As was discussed earlier, in this work we focus on the formation of PBH pairs for different fractions of PBH to the total DM component. So, when f PBH 1.0, we have to split the DM into two types: ‘PBH DM’ and ‘normal DM’ (see Section 1 ). As it is shown in Section 3 , in our simulations virialized structures start to form as early as z ≈ 10 3 , which could be caused by the initial Poisson distribution of PBHs. This differs significantly from the case of standard ΛCDM model, where the first virialized structures start to form at z ≃ 40. Therefore, we assume that the ‘normal DM’ behaves as an uniform medium in our simulations. Nevertheless, we will discuss later why the formation of virialized structures is important and how it affects the behaviour of PBH pairs (see Section 3.3 ). In order to quantify the range of redshifts in which this approximation is applicable, we calculate the fraction of virialized normal DM using the analytical Press–Schechter formalism for haloes more massive than 30 M ⊙ (see Press Schechter 1974 ). We find that the fraction of virialized structures made of normal DM is less than 50 per cent at z > 10. (see Fig. 1 ). Figure 1. The fraction of virialized structures is made of normal DM for haloes more massive than 30 M ⊙ , as a function of redshift z . Thus, the presence of normal DM influences the evolution of PBH DM only through its contribution to the computation of the Hubble function in the GADGET-2 code: For our
Paper: 2015MNRAS.452.1553E (acknowledgments)
We are grateful to the referee for his/her constructive comments. This paper is based on data obtained at the European Southern Observatory, Chile, with proposals ESO 68.C-0149(A), ESO 70.C-0008(A) and ESO 382.C-0195(A). This work has been funded by the Spanish Ministerio de Economía y Competitividad (MINECO) under project AYA2011-22614. JGR acknowledges support from Severo Ochoa excellence program (SEV-2011-0187) postdoctoral fellowship.
Paper: 2019MNRAS.485.5168M (acknowledgments)
ACKNOWLEDGEMENTS This paper is based on observations collected with the MPG 2.2 m telescope, with the Danish 1.54 m telescope and with the Zeiss 1.23 m telescope. The first two telescopes are both located at the ESO Observatory in La Silla, Chile, while the latter is at the Centro Astronómico Hispano Alemán (CAHA) in Calar Alto, Spain. Operation of the MPG 2.2 m telescope is jointly performed by the Max Planck Gesellschaft and the European Southern Observatory. Operation of the Danish 1.54 m telescope in 2015 was financed by a grant to UGJ from the Danish Natural Science Research Council (FNU). Operations at the Calar Alto telescopes are jointly performed by the Max Planck Institute for Astronomy (MPIA) and the Instituto de Astrofísica de Andalucía (CSIC). GROND was built by the high-energy group of MPE in collaboration with the LSW Tautenburg and ESO, and is operated as a PI-instrument at the MPG 2.2 m telescope. The reduced light curves presented in this work will be made available at the CDS ( http://cdsweb.u-strasbg.fr/ ). LM acknowledges support from the Italian Minister of Instruction, University and Research (MIUR) through FFABR 2017 fund. LM acknowledges support from the University of Rome Tor Vergata through ‘Mission: Sustainability 2016’ fund. GC acknowledges the support by the National Natural Science Foundation of China (Grant No. 11503088, 11573073, 11573075) and the project ‘Technology of Space Telescope Detecting Exoplanet and Life’ from National Defense Science and Engineering Bureau civil spaceflight advanced research project (D030201). IJ acknowledges the Austrian Forschungsförderungsgesellschaft FFG project ‘RASEN’ P847963. TCH acknowledges financial support from KASI. DMB acknowledges the support of the NYU Abu Dhabi Research Enhancement Fund under grant RE124. MK acknowledges the support of the International Postdoctoral Fellow of Independent Research Fund Denmark. CITEUC is funded by National Funds through FCT – Foundation for Science and Technology (project: UID/ Multi/00611/2013) and FEDER – European Regional Development Fund through COMPETE 2020 – Operational Programme Competitiveness and Internationalisation (project: POCI-01-0145-FEDER-006922). We thank Fei Yan for useful discussion about the WASP-74 b transmission spectrum. The following internet-based resources were used in research for this paper: the ESO Digitized Sky Survey; the NASA Astrophysics Data System; the SIMBAD data base operated at CDS, Strasbourg, France; and the arXiv scientific paper preprint service operated by Cornell University. Based on data collected by MiNDSTEp with the Danish 1.54 m telescope at the ESO La Silla Observatory.
Paper: 2020A&A...638A..18M (fulltext)
In Fig. 11 we have plotted both IGAPS r and GaiaG magnitudes on the horizontal axis: despite their being very similar for modest r − i , the relation between them has a growing colour dependence for large r − i , as can be seen in Fig. 12 . A minor factor affecting Fig. 11 is that some IGAPS sources might not have a measured r magnitude (either r I or r U ), and so could not be included. Fig. 12. Differences between IGAPS r I and GaiaG magnitudes as a function of IGAPS r I − i colour. The colour scales according to the density of sources in each bin, with square root intervals. Yellow represents the lowest density of at least four sources per 0.02 × 0.02 mag 2 bin. In the same two areas of 1 sq. deg, we have compared the IGAPS catalogue with Pan-STARRS ( Chambers et al. 2016 ). In this case there are more sources in the Pan-STARRS catalogue. In Fig. 13 we can see that Pan-STARRS is only slightly deeper in the r band. In this figure we are directly comparing Pan-STARRS and IGAPS AB magnitudes that are the same by construction. Crowding accounts for less of the difference in this comparison since both catalogues come from ground-based photometric surveys with a similar pixel scale (0.333 vs. 0.258′′ pixel −1 ) and typical seeing. Fig. 13. Results of the cross-match between IGAPS and Pan-STARRS at l = 60°. In blue, rAB magnitude distribution for the IGAPS sources. In red, r magnitude distribution from Pan-STARRS. In green, sources with both IGAPS and Pan-STARRS values. In cyan, sources in IGAPS not crossmatched with Pan-STARRS. In orange, sources in Pan-STARRS but not in IGAPS. When two r magnitudes are available for an IGAPS source ( rAB I and rAB U ) the mean value is plotted. If only one of the two magnitudes has a value, that value is used and the object is included. 8.5. The fully calibrated colour–colour diagrams The creation of the IGAPS catalogue adds to the available colour–colour diagrams. The first of these to mention is the g − r U , r I − i diagram, which uses the three fully calibrated broad bands. An example, constructed as a density plot from the Galactic longitude range 60° l 65°, is shown in Fig. 14 . The tracks overplotted in red have been computed via synthetic photometry using library spectra (see Appendix D ). As the main sequence (MS) and giant tracks sit very nearly on top of each other, we show only the MS track as a red solid line. A reddening line for an A2V star is also included as a dashed line. The comparison of the catalogue data with these reference tracks points out that all stars to K-type fall within a neat linear strip that follows the reddening vector. Only the M stars break away from this trend, creating the roughly horizontal thinly populated spur at g − r U ∼ 1.5 where nearly unreddened M dwarfs are located. This can be echoed at greater g − r U and r I − i by an even sparser distribution of stars to the right of the main stellar locus. Indeed, in the example shown in Fig. 14 , the density of stars
Paper: 2016MNRAS.457.1028S (fulltext)
magnitudes using the parsec isochrones. We then pair the single stars to make binaries assuming a non-random pairing of the binary components. According to the hydrodynamical simulations of star cluster formation by Bate ( 2009 ), the multiplicity fraction is approximately proportional to the logarithm of the primary mass. As a result, we assume that the probability that a single star with mass m prim is the primary component of a binary, scales linearly with the logarithm of its mass, i.e. Prob ∝ log 10 ( m prim ). We also assume a flat distribution function for the mass ratio 0 ≤ q ≤ 1.0. We then calculate the total J and K s magnitudes of each binary from the sum of the luminosities of its components. We also add photometric errors to the magnitudes of single and binary stars. The photometric errors resemble the photometric errors of PPMXL at each magnitude. Using this procedure, we make a synthetic CMD of a cluster with single stars and binary systems. We then apply our photometric membership criterion (Section 4.2 ) on this synthetic CMD through a blind test and count the number of single stars and binaries. Photometrically, a single star is any point that lies within 2.5σ of the isochrone and a binary is any non-single star whose vertical deviation from the isochrone is less than 0.75 mag. Finally, we change the low-mass and the high-mass slopes of the MF (α low , α high ) in ranges (0.70, 1.25) and (2.00, 2.60), respectively, until we find the best match with the observed MF (using K-S test) and the observed binary fraction. For each set of the simulation parameters, we have simulated 40 clusters with different random seed numbers, to reduce the errors caused by statistical fluctuations. Fig. 8 , shows the synthetic CMD of one of our simulated clusters (left-hand panel) and the detected single stars and binaries after applying the blind photometric test. In this figure, the true binary fraction is 23 per cent in the mass range 0.10 ≤ M / M ⊙ ≤ 4.68, but the observed binary fraction is only ∼6 per cent. Figure 8. (Left) Synthetic CMDs of simulated binaries (red filled circles) and single stars (black open squares) for a true binary fraction of 23 per cent over the whole mass range (0.1 m / M ⊙ 4.68). The scatter of single stars is due to the added photometric errors which resemble actual photometric errors in PPMXL. (Right) The detected binaries (red filled circles) as well as single stars (blue filled circles) after applying the blind photometric test on the simulated data. As it can be seen, many of the binaries have been identified as single stars. The observed binary fraction is ∼6 per cent. The parsec isochrone which is used in the blind photometric test, is shown by the black solid line. Table 4 compares the binary fractions of Alpha Persei from different studies. According to the outcome of our Monte Carlo simulations, the binary fraction of the cluster over the mass range 0.70 m / M ⊙ 4.68 is 34 ± 12 per cent, in agreement with