adsabs/WIESP2022-NER huggingface dataset:
Paper: 2015ApJ...800..125V (acknowledgments)
The research leading to these results received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement n312789. This work has been financially supported by the Programme National Hautes Energies (PNHE). This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG) via the Cologne Bonn Graduate School (BCGS), the Max Planck Society through the International Max Planck Research School (IMPRS) for Astronomy and Astrophysics, as well as special funds through the University of Cologne. M.Z., B.S., S.S., and A.B. are members of the IMPRS. Part of this work was supported by fruitful discussions with members of the European Union funded COST Action MP0905: Black Holes in a Violent Universe and the Czech Science Foundation—DFG collaboration (No. 13-00070J). This work was co-funded under the Marie Curie Actions of the European Commission (FP7-COFUND). M.G.-M. is supported by the German federal department for education and research (BMBF) under project number 50OS1101. We are grateful to all members of the ESO PARANAL team.
Paper: 2019MNRAS.483.5042B (acknowledgments)
ACKNOWLEDGEMENTS We acknowledge the constructive criticisms of the reviewer.
Paper: 2016MNRAS.456..756H (fulltext)
triangles that connect to these points. 2.3.2 Identifying parent triangles Identifying the triangle which contains a given point can be done most easily by checking every triangle individually by brute force. Since the total cost for finding the triangles containing ALL points scales as , this method rapidly becomes too expensive even for a modest number of triangles and will dominate the CPU time for constructing the tessellation. A simple but effective speed-up is the so-called ‘jump and walk’ method. In this method, we select an initial triangle to start the search for a given point. This might be informed from the previous iteration of the triangulation or simply selected at random. The method then tests each face of the triangle to see if the point lies outside or inside the face. If the point is inside every face, then the triangle contains the point. If outside one or more faces, then the triangle is false. We would then ‘walk’ to the neighbouring triangle closest to the point. We then perform the tests again until the triangle has been correctly identified. 2.3.3 Degenerate triangles One common problem that is found in almost all Delaunay triangulation algorithms is that of degenerate triangles. Consider the simple 2D example where four points are exactly the vertices of a square, A, B, C and D. Due to the symmetry, then both of the triangle pairs ABC–CDA and ABD–BCD are possible triangulations. However, both fail the circumcircle test because the exterior fourth point lies exactly on the circle. There is algorithmically no way to distinguish the two cases. A similar related problem occurs when we compute which triangle contains a given point; if the point lies exactly (to floating point precision) on the line dividing two triangles, then there is no way to distinguish which triangle the point lies in. There are three common solutions to this problem: (i) add a small random perturbation to the positions of one of the points to break the degeneracy; (ii) add a virtual perturbation (without physically moving the particles) to each particle position to force the computation of the degenerate state to fall one side or the other (Edelsbrunner Mücke 1990 ), or (iii) use higher precision when computing the determinants, either using exact floating point arithmetic or long-integer arithmetic (Springel 2010 ). For our purposes, we chose the first solution, partly due to simplicity and secondly due to the fact that even though the Delaunay triangulation can be slightly altered, the Voronoi tessellation is (mainly) unaffected. 2.3.4 Creating the Voronoi tessellation To create the Voronoi tessellation, each face of the cell can be constructed by joining the circumcircle centres of Delaunay triangles that are common between two points. In 2D, this is trivial since each Voronoi cell face is a simple line represented by two points. In 3D, it is more complex since each Voronoi face is a complex polygon with an arbitrary number of vertices.
Paper: 2018ApJ...853..198N (fulltext)
in concert. The quantification of the abundance uncertainties included S/N-dependent cross-validation and an error rescaling for some elements, exploiting the fact that abundance estimates for member stars of open clusters should on average not differ by less than their uncertainties. This provided a set of APOGEE-derived abundances that is unprecedented in its (pertinent) combination of quality, quantity, and homogeneity. We only considered stars with very similar stellar parameters, giants in a restricted T eff and log g range; we have 90 spectra across seven open clusters and thousands of “field stars” with the same data, S/N, and data analysis. For all of these stars, we have individual abundances for 20 elements (Fe, C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Ni, P, Cr, Co, Cu, and Rb), with a median precision of 0.03 dex. We characterize these abundances by their values [X/Fe], except for iron, where we use [Fe/H]. On this basis, we undertook an extensive analysis of the chemical (in-)homogeneity of stars within an open cluster, testing the standard assumption that such stellar “birth siblings” were formed at the same time from chemically homogeneous material. This should result in identical element abundances, including those elements (e.g., C and N), whose photospheric abundance has been altered in giants by mass/age-dependent dredge-up. We confirm that the abundances of cluster member stars typically agree with each other within their small (0.03 dex) uncertainties, as had been found before (see Section 1 ). Moving beyond, we explicitly determined the intrinsic dispersion in these abundances, or derived upper limits for them. We found that (at least in the best sampled clusters), the abundance dispersions are essentially zero, with typical upper limits of ∼0.03 dex. However, there are exceptions: some elements in some clusters show small but significant dispersion, attributable to a subset of member stars. To quantify the abundance similarity among stars of known common birth origin (cluster members) to the (dis-)similarity among field stars, we compared the abundances among pairs of stars. It was tempting to define a distance measure in the high-dimensional space of element abundances, but the “curse of dimensionality” makes distance in a 20 dimensional space very dependent on prior assumptions about distance distributions once the distances become comparable to the measurement uncertainties. Therefore, we resorted to a simple statistic, , quantifying the likelihood of the 20 observed abundances (differences) for any pair of stars, assuming their intrinsic abundances were identical. When we construct for intracluster pairs of stars, we find that its median is similar to , again illustrating the level of chemical homogeneity within clusters. Yet, about 20% of intracluster star pairs have , indicating significant abundance differences in one or more elements. We find open clusters to be mostly, but not exclusively,
Paper: 2020MNRAS.493.3744F (fulltext)
the molecular gas mass determined by Riechers ( 2013 ), and can be a result of the different resolution of the observations. Regardless of this difference, it is clear that the companion has a massive gas reservoir. The quasar was not detected by Sharon et al. ( 2016 ) but a 3σ upper limit is obtained, yielding a CO(1–0) line luminosity upper limit of , which converts to a molecular gas mass of . We also calculated the molecular gas mass from the CO(5–4) emission of the quasar, as this is the lowest J detection available for this source. We assume that the CO(5–4) transition is thermally excited, based on CO(4–3) observations of high- z type I QSOs, which found a unity ratio (e.g. Riechers et al. 2006 ; Weiß et al. 2007 ). With this assumption the molecular gas mass of the quasar is . These results suggest that there is a significant amount of warm gas in the quasar and it cannot be considered gas-poor. 3.5 <monospace>RADEX</monospace> modelling of the CO spectra To put constraints on the excitation properties of SMM J04135+10277, we modelled the SLED of both sources using our ALMA detections combined with CO(1–0) and CO(3–2) line observations from the literature (Riechers 2013 ; Sharon et al. 2016 ). We used the non-LTE molecular radiative transfer code radex (van der Tak et al. 2007 ) to calculate the line intensities of CO from transitions J up = 1 to J up = 8, within a wide range of physical parameters: kinetic temperature ( T kin ), number density of molecular hydrogen (), and column density of CO ( N CO ). During each run we set the background radiation temperature to the value of the cosmic microwave background at z = 2.84, adopted spherical geometry and assumed that the only collisional partner of CO is molecular hydrogen. As the linewidths of the low- and high- J CO lines are different, we use an average value during the radex modelling. The radex code only considers photodissociation regions and does not include mechanical and X-ray heating. We compared our observations with the models using χ 2 -analysis. In the first run we varied all three physical parameters in case of each source but as most of the models converged to the same CO column density, we fixed N CO and continued with fitting only T kin and . During the radex modelling of the quasar we set the line width of , fixed N CO to and run radex on a grid of and . It is important to note, that as we only have two detections for the quasar and two upper limits, we do not expect to arrive to a straightforward conclusion about the excitation properties of this source. However, by using the available data we can narrow down the possible temperature and density ranges. Fig. 6 shows how the χ 2 value of the models vary as a function of temperature and density. The upper J transitions of the quasar can be fitted by models with a continuous range of kinetic temperatures of and molecular hydrogen density between . Models with the lowest χ 2 values are outside of the typical temperature
Paper: 2020ApJ...902...56C (fulltext)
as different catalogs cover different redshift ranges. We compile a set of small-scale rejection masks (bright stars, bad imaging tracks, and small regions without spectroscopic coverage) provided by these surveys and generate a joint rejection mask by taking the union. 8 8 The masks are available at 10.5281/zenodo.4012781 . Table 1 summarizes the basic parameters of the spectroscopic reference sources that we use after applying the joint SDSS rejection mask, the 60% Galactic mask, and the infrared point-source mask described in Section 3.1.1 . Table 1 Spectroscopic Redshift Reference Notes. a Effective unmasked sky area in deg 2 . b Number of sources in the effective area. c Fraction of the sources in the Northern Galactic Hemisphere. d Median and 68% CL range of the redshift distribution. References. (1) Strauss et al. ( 2002 ), (2) Blanton et al. ( 2005 ), (3) Reid et al. ( 2016 ), (4) Bautista et al. ( 2018 ), (5) Schneider et al. ( 2010 ), (6) Pâris et al. ( 2017 ), (7) Ata et al. ( 2018 ). 3.2. Clustering Redshifts We apply the clustering-based or cross-correlation-based redshift estimation following Ménard et al. ( 2013 ) for both the channel intensity and Planck's Compton y maps. The technique is based on the simple ansatz that all matter tracers appear spatially clustered on the sky if their redshift distributions overlap. One can thus take a “reference” sample of galaxies or quasars with known redshifts and propagate the redshift information into a “test” data set with an unknown redshift distribution. This is achieved by cross-correlating the reference and test samples as a function of redshift of the former. The first applications to large data sets are done in the regime of discrete objects as the test sample to estimate source redshift distributions in photometric surveys (e.g., Rahman et al. 2015 , 2016a , 2016b ; Scottez et al. 2016 ; Hildebrandt et al. 2017 ; Davis et al. 2018 ; Gatti et al. 2018 ; Kitanidis et al. 2020 ; Krolewski et al. 2019 ). It is straightforward to generalize the method and redshift inference to diffuse fields simply by replacing the notion of objects with pixels on a set of predetermined grids (Schmidt et al. 2015 ; Chiang Ménard 2019 ; Chiang et al. 2019 ). Our implementation in this work is largely based on that in Chiang et al. ( 2019 ); here we briefly describe the key steps and minor modifications made for the tSZ tomography. We denote the overdensity field of the 2D test intensity map as (where T = I ν i in the multichannel analysis and T = y for the direct y map tomography) and that for the 3D reference sources as . As both are, in general, biased tracers of the underlying matter density field with the angular correlation function at some effective scale, we can write down a linear expression for their cross-correlation amplitudes evaluated in bins of the reference redshift z i aswhere b T and b R are the effective linear-clustering bias for the test and reference data at the scales considered.
Paper: 2016MNRAS.458.1504S (fulltext)
et al. ( 1989 ) code using a model of 14 configurations, our grasp 1 levels in the excited configurations lie closer to the NIST ones. In the case of four energy levels ( 2 D 5/2,3/2 , 4 P 5/2,3/2 ), our calculated energy levels are more closer to NIST than those of Aggarrwal et al. ( 2003 ). The average percentage difference of these grasp energies with NIST values is 2.2 per cent, and the maximum error of 6.3 per cent appears for the first excited state ( 2 D 3/2 ) of ground configuration. For more accurate target states we adopt a model of 13 electronic configurations which yields target-states energies as closer to NIST values as possible. For instance, our calculated energy levels 2 D 3/2,5/2 associated with the configuration 3s3p 4 lie more closer to the observed values as compared to existing calculations. The remaining set of energy levels are almost of the same accuracy as those of previous data when compared to experimental energies. From this comparison it is evident that present grasp 1 calculated N -electron target wavefunctions demonstrate good agreement with experimental values, which is necessary for more accurate PI cross-sections of Ni xiii . In order to further ascertain the accuracy of target wavefunctions, we have compared weighted oscillator strength ( gf -values) of electric dipole (E1) transitions of Ni xiv where available (Fritzsche et al. 1998 ; Landi Bhatia 2010 ) in the literature. In Table 2 , we present our calculated gf -values of E1 transitions in length form only from the ground state and first exited states to the 41 excited levels of Ni xiv ion. The online version of Table 2 contain the gf -values of all dipole transitions among the 41 fine-structure levels. The indices of lower and upper levels of a transition used in Table 2 are defined in Table 1 . The agreement between our calculated oscillator strengths and grasp 92 calculations (Fritzsche et al. 1998 ) is within 15 per cent for majority of the strong dipole transitions with gf -values ≥ 0.01, while fac oscillator strengths (Landi Bhatia 2010 ) lie within 25 per cent with the exception of few transitions for instance ( 2 D 3/2 – 2 P 3/2 ). We note some significant differences with those of Fritzsche et al. ( 1998 ) and Landi Bhatia 2010 within 70 per cent for weaker transitions like in the case of 4 S 3/2 – 4 F 5/2 and 2 D 3/2 – 2 S 1/2 transitions. Apart from these differences, the overall agreement between present MCDF and previous MCDF (Fritzsche et al. 1998 ) oscillator strengths is quite good. This gives confidence in the accuracy of target states to calculate PI cross-sections. Table 2. Comparison of present weighted oscillator strengths ( gf -values, dimensionless) for E1 transitions in Ni xiv with other theoretical calculations. The complete table is available online only. Notes. a fac : Oscillator strengths from fac code (Landi Bhatia 2010 ). b grasp 92: Oscillator strengths from grasp 92 code (Fritzsche et al. 1998 ). Figs 1 – 3 display our
Paper: 2019MNRAS.489.5109G (acknowledgments)
ACKNOWLEDGEMENTS The author would like to thank the principle investigator of the CAMS project, Dr. Peter Jenniskens of the SETI Institute, for providing the CAMS California data set from the peak nights of the 2012 and 2015 Geminids that were used for this project. In addition, the FDL team members of Susana Zoghbi, Antonio Ordonez, Marcelo De Cicco, Andres Stapper, and Jack Collison, all deserve credit for their groundbreaking work in initially developing and applying deep learning techniques to the various components of meteor analysis in the CAMS pipeline. Finally, the author would like to thank the anonymous reviewers for their comments and suggestions, as well as Dr. Huzefa Rangwala of George Mason University for his instructional insights during his deep learning course presentations and careful review of this paper.
Paper: 2017ApJ...835...34T (acknowledgments)
We thank the anonymous referee for the fruitful comments. We also would like to thank R. Yamazaki, Y. Ohira, and J. Shimoda for their fruitful discussion. This work is supported in part by Grant-in-Aid for Scientific Research of the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, No. 22684012 and 15K05107 (AB), 15K17657 (MS), and 16K17673 (SK). Facility: Suzaku .
Paper: 2019ApJ...872...63L (acknowledgments)
This project is supported by the National Science Foundation of China under grant No. U1831205 and grant No. 11603058. S.W. acknowledges support from the Chinese Academy of Sciences President’s International Fellowship Initiative (grant No. 2017VMB0011). The authors wish to thank Caroline Bertemes for fruitful discussions. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is http://www.sdss.org . SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration, including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatory of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University.
Paper: 2020ApJ...899..118C (fulltext)
of the DM from the detected giant micropulses. The red dashed curve stands for the constrained optimal Gaussian distribution with the best-fitting parameters shown in the text. As reported by Johnston et al. ( 2001 ), a threshold of R larger than 25 was taken to pick out the giant micropulses at 1413 MHz. The R parameter is defined as R i = (MAX i − m i )/ σ i , where MAX i is the maximum intensity, m i is the mean intensity and σ i is the rms in the i th bin. The phase-resolved R at 6800 MHz is shown in Figure 4 . Two peaks are clearly presented in the leading component and leading edge of the pulse, which implies the existence of giant micropulses with extremely high amplitude with respect to the mean intensity in these phase ranges. And the value of R decreases exponentially in the center and trailing of the pulse, which is inconsistent with the Gaussian statistics at 1413 MHz. As shown in Figure 1 , the giant micropulse emission in the center component has a higher occurrence rate and higher amplitude than that in the trailing component. Furthermore, no detection of giant micropulses with significant R -value occurs in the bump region. Figure 4. R -parameter as a function of pulse phase at 6800 MHz. The main panel of Figure 5 shows a scatter plot of peak flux density and the FWHM for detected giant micropulses. The pulse broadening caused by inner-channel dispersion (1.4 μ s) and scattering (1.0 ns) is negelected due to high observing frequency. To obtain the FWHM, a Gaussian function is adopted to fit each giant micropulse. The position of the center of the peak is kept fixed with the pulse phase of the maximum value. Then FWHM is given by , where σ is the determined parameter using the least-squares method. The giant micropulses have timescales (1.55 ms) much smaller than that of the average pulse profile (2.62 ms). As can be seen, the majority of giant micropulses tend to cluster in width of 50–500 μ s. In this interval, the width of a giant micropulse seems to be independent of its peak flux density, which is consistent with the result at 2.3 GHz (Kramer et al. 2002 ). The pulse-to-pulse energy distribution is served as one of the important differentiator between various emission processes (Burke-Spolaor et al. 2012 ). Probability density function (PDF) for the peak flux densities is shown in the upper panel, which is well described by a power law. The slope, obtained via least-square fitting, is give by α = −3.54 ± 0.04, which is the steepest distribution to our best knowledge. While a logarithmic normal distribution is presented for the PDF formed from the peak flux densities of normal pulses, as shown in Figure 6 . The distinctive distributions between giant micropulses and normal pulses may indicate their different emission mechanism. The pulse energies of the bursts from the Vela pulsar do not exceed 10 times the corresponding mean quantity. Nevertheless, the peak flux densities of the bursts are very large in absolute terms. For
Paper: 2018MNRAS.480.3780K (acknowledgments)
ACKNOWLEDGEMENTS We thank Alexei Pevtsov for helpful discussions and suggestions. We also thank anonymous referee for his/her helpful comments. SDO is a mission for NASA’s Living With a Star (LWS) programme. The SDO/HMI data were provided by the Joint Science Operation Center (JSOC). Hinode is a Japanese mission developed and launched by ISAS/JAXA, with NAOJ as domestic partner and NASA and STFC (UK) as international partners. It is operated by these agencies in co-operation with ESA and NSC (Norway). This study was supported in part by the Russian Fund for Basic Research (RFBR) projects 17-52-53203 and 17-02-00049, by the National Natural Science Foundation of China projects 11673033, 11703042, 11427901, U1731241, by the cooperative NSFC-RFBR grant 11611530679 and by the Strategic Priority Research Program of the Chinese Academy of Sciences XDA15052200. AK and KK would like to thank the staff at HSOS of National Astronomical Observatories of China for warm hospitality and excellent working environment during their visit.
Paper: 2015A&A...575L...2Z (acknowledgments)
The research leading to these results has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under the grant agreement eHeroes (project No. 284461, www.eheroes.eu ). We also acknowledge the financial support by the Academy of Finland to the ReSoLVE Centre of Excellence (project No. 272157).