adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...637A..52N (fulltext)
Finally, we computed the cumulative number of sources, N (> S ), brighter than a given flux ( S ) in each X-ray band. To this aim, we computed the sky coverage (i.e., the sky area Ω covered as a function of the flux limit) of the J1030 field to correct the incompleteness of our catalog. We computed our sky-coverage by dividing the number of output sources flagged as “good” in our simulations by the number of input sources as a function of input flux and then multiplying for the total geometric area of the J1030 field covered by Chandra . The sky-coverage values were then fitted with a spline to obtain a smooth monotonically increasing function. The sky coverage in the full, soft, and hard bands are plotted in Fig. 11 . The derived flux limits over the central ∼1 arcmin 2 region are ∼3 × 10 −16 , 6 × 10 −17 , and 2 × 10 −16 erg cm −2 s −1 in the full, soft, and hard bands, respectively, making the deep Chandra survey in the SDSS J1030+0524 field the fifth deepest X-ray survey field achieved so far (see Fig. 1 for area-flux curve comparison with other surveys). Once the sky coverage is known, the cumulative source number was computed using the equation: Fig. 11. Sky coverage (i.e., sky area vs. flux limit relation) in the full (black solid line), soft (red dashed line), and hard (blue dash-dotted line) bands derived from our simulations. The horizontal cyan dotted line represents the total geometric area of our J1030 field (335 arcmin 2 ). where N S is the total number of detected sources in the field with fluxes higher than S , and Ω i is the sky coverage associated with the flux of the i th source. The three log( N )–log( S ) relations in the three X-ray bands are reported in Fig. 12 . The red points represent the cumulative number of sources of our J1030 field, while the log( N )–log( S ) of our mock catalog are shown as the blue dot-dashed line. For comparison, we also plotted the log( N )–log( S ) relations found in the 7 Ms (magenta line) Chandra Deep Field-South by Luo et al. (2017) , and the log( N )–log( S ) relation (green solid line) found in the COSMOS field by Civano et al. (2016) . From Fig. 12 we conclude that the log( N )–log( S ) relations derived in the J1030 field are in general agreement with those from the literature. Besides cosmic variance, we caution that some of the differences among the log( N )–log( S ) seen in Fig. 12 could be produced by systematic uncertainties in the different methods used to derive the sky coverage and the individual source fluxes in the various surveys. Fig. 12. Cumulative number counts (number of sources brighter than a given flux) for the main source catalog (red dots) in the full ( top ), soft ( middle ), and hard ( bottom ) bands. The blue dot-dashed line represents the cumulative number of sources from our mock catalog. For comparison, we plot the log( N )–log( S ) relations found in the 7 Ms Chandra Deep Field-South by Luo et al. (2017 , magenta line), and in the COSMOS field by Civano et
Paper: 2016MNRAS.459.3512V (acknowledgments)
GMA and BV acknowledge financial support from the ERASMUS–ASTROMUNDUS programme hosted by the University of Innsbruck, where this article was conceived in 2013. We thank an anonymous referee for useful comments that have greatly improved the quality of the article. BV, MMC and MM acknowledge financial support from the Ministry of Education, Science and Technological Development of the Republic of Serbia through the project #ON176021 ‘Visible and invisible matter in nearby galaxies: theory and observations’.
Paper: 2018A&A...617A..38S (acknowledgments)
We thank the anonymous journal referee for their insightful comments, which improved the manuscript. RS gratefully acknowledges support from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013)/ERC Advanced Grant RADIOLIFE-320745. EKM acknowledges support from the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. The European VLBI Network is a joint facility of independent European, African, Asian, and North American radio astronomy institutes. Scientific results from data presented in this publication are derived from the following EVN project code: GN002. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Long Baseline Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The Arecibo Observatory is a facility of the National Science Foundation (NSF) operated by SRI International in alliance with the Universities Space Research Association (USRA) and UMET under a cooperative agreement. The Arecibo Observatory Planetary Radar Program is funded through the National Aeronautics and Space Administration (NASA) Near-Earth Objects Observations program. Based on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre (CADC/NRC/CSA). This research has made use of NASA’s Astrophysics Data System Bibliographic Services. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research made use of Astropy, a community-developed core Python package for Astronomy ( Astropy Collaboration et al. 2013 ). This research made use of APLpy, an open-source plotting package for Python ( Robitaille Bressert 2012 ).
Paper: 2017ApJ...847...30P (fulltext)
λ , to dimensionalize our expressions. It is thus, in Equation ( 26 ), that we use to formally eliminate a . We assume that the horizontal motion of the open flux has deflected it by some small angle, say 10°, relative to the stationary and undeflected arcade flux. This angular difference, ±5°, produces the tangential discontinuity that is the secondary CS. The field strength in the vicinity of the flare ribbon region was estimated by BLQ to be 150 G. The single-sided 10° deflection will therefore produce a reconnecting component of 150 sin (5°) = 13 G. Assuming a typical electron density of gives the value v a = 500 km s −1 , which was used in our nondimensionalization. The sawtooth pattern is observed to propagate with phase velocity perpendicular to the IRIS slit of km s −1 . This was found in the plane of the sky (primed coordinates) but applied to horizontal motion some 40° from disk center. Foreshortening effects will reduce the velocity V in the horizontal x- direction to an apparent velocitywhere is the horizontal distance from disk center. The observed phase velocity of 15 km s −1 thus corresponds to a real horizontal velocity of km s −1 . Scaling this to the reconnection-component Alfvén speed, v a = 500 km s −1 , yields . 3.3. The Saturated State We use these dimensions to construct a synthetic observation from the velocity structure of our model. We do so by first using the velocity structure given by the linear eigenmode, , discussed above. We construct the stream function by superposing this using the amplitude, , in Equations ( 24 ) and ( 25 ). Figure 6 shows the magnetic field lines (black) and stream lines (red) of the resulting construction. This specific eigenfunction results from and , which has a growth rate of in agreement with Equation ( 26 ). The green contour shows the trajectory of a fluid element that begins at . Figure 6. Contours of TM/KH eigenfunctions for , and with . The green contour shows the trajectory of a particle that started at . Because the growing linear eigenmode has been used in the nonlinear regime, the stream lines (red) cross the field lines (black) in this construction. The magnetic field evolution implied by continuing island growth requires this cross-field flow in the regions outside the diffusion region. This cross-field flow will naturally cease when the linear phase ends and the island growth ends. Fully nonlinear treatments of the TM, in which island saturation occurs self-consistently, have found magnetic structures that are very similar to those produced by the linear modes with (Ofman et al. 1993 ; Chen et al. 1997 ; Li Ma 2010 ; Zhang et al. 2011 ; Doss et al. 2015 ). However, these treatments find a velocity field very different from the linear eigenmode. In particular, the velocity must align with the magnetic field outside the diffusion region, which is not the case with the unsaturated linear eigenmode, as seen in Figure 6 . Nonlinear studies of island saturation in the presence
Paper: 2019ApJ...871..217M (acknowledgments)
We thank N. Featherstone for helping to formulate the idea of creating a rotationally unconstrained near-surface fluid layer in simulations by increasing density contrast, as well as for his work as the primary developer of the Rayleigh code. We thank K. Julien for helpful conversations on the subject of rotational constraint. We thank M. Miesch for illuminating discussion with regard to the high-latitude interaction between differential rotation and meridional circulation. NASA grants NNX13AG18G and NNX16AC92G provided the primary support for this research. Additional support was provided through NASA grants NNX14AG05G, NNX14AC05G, and NNX17AM01G. Computational resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center. We thank the Computational Infrastructure for Geodynamics ( http://geodynamics.org ), which is funded by the NSF under awards EAR-0949446 and EAR-1550901, for supporting the development of the Rayleigh code. L.I.M. was supported during this work by a University of Colorado Boulder Chancellor Fellowship and a George Ellery Hale Graduate Fellowship.
Paper: 2020ApJ...901..123F (fulltext)
strong enough, it will be difficult to detect them in low resolution spectra especially when the data is noisy. Unfortunately, the Ly β spectral region in most of our spectra has low SNR. This limits our ability to accurately measure the H i column density in individual systems. We, therefore, try to stack these spectra to increase the SNR. The higher SNR of the stacked spectrum would in principle allow us to determine the H i column density by fitting the damping wings of the Ly β absorption. If the majority of the systems have high H i column density, the damping wings of the Ly β absorption should be clearly and prominently visible in the stacked spectrum. In order to increase the chances of clearly detecting the damping wings of the Ly β absorption in the stacked spectrum, we carefully choose the quasar spectra that are used in the construction of the stacked spectrum using the following procedure. We first create a synthetic Ly β absorption line with H i column density of N (H i ) = 10 21 cm −2 and b -value of 100 km s −1 . We then exclude quasar spectra whose Ly β absorption are narrower than this synthetic absorption line. For example, in the lower panel (upper panel) of Figure 8 , we show two quasar spectra with their Ly β absorption being narrower (broader) than the synthetic one. This conservative approach ensures that only spectra of the absorbers with relatively higher H i column density and stronger damping wings contribute in the construction of the stacked spectrum. Applying this constraint on the S β sample leaves us with 21 spectra. We then median-stack these spectra following the method described in Paper I . Figure 8. Ly β spectral regions of four ghostly absorbers. In each panel, the red curve shows a synthetic Ly β absorption line with log N (H i ) = 21.0 and b -value of 100 km s −1 . The upper panels (lower panels) show Ly β absorption that are broader (resp. narrower) than the synthetic spectrum. Figure 9 shows the resulting stacked spectrum around the Ly α , Ly β , and Ly γ spectral regions. The Voigt profile fit for log N (H i ) = 21.5 and b -value of 50 km s −1 is overplotted on the observed spectrum as the red curve. This column density is 0.5 dex higher than the H i column density derived from the stacked spectrum of Paper I . As shown in Figure 9 , the damping wings (especially the red wing) of the Ly β absorption is clearly visible in the spectrum. This implies that the majority of the ghostly absorbers whose spectra are used in the stacked spectrum, are DLAs. Follow-up observation with higher resolution and SNR are needed to precisely measure the H i column density in the individual systems, and confirm which of them are indeed DLAs. Figure 9. Voigt profile fit (red curves), with log N (H i ) = 21.5 and b -value of 50 km s −1 , on the Ly β and Ly γ absorption lines in the stacked spectrum of the ghostly absorbers with strong Ly β absorption lines (see the text). The H i column density is constrained by the strong
Paper: 2019MNRAS.482.4146D (fulltext)
three regimes: the stable regime, the intermediate regime, and the unstable regime. In the stable regime, the Laplace–Lagrange rapid angular momentum exchange between the two inner planets dominates over the gravitational perturbations of the outer orbit. The left column of Fig. 3 depicts this evolution; the system in this column resides in the bottom left of the parameter space in Fig. 2 . The system remains stable for 10 Gyr of evolution and the orbits never come close to one another. The two inner planets’ inclinations oscillate together around the initial z -axis, due to the precession of the nodes (see below). In the intermediate region, systems might appear to be stable for a long time but the outer orbit perturbations become too dominant, causing instability of the inner system. We show the dynamics of this type of evolution in the middle panel of Fig. 3 . This system resides very close to the stability criterion plotted in Fig. 2 , inside the transition zone between e 3,c ( f LL,min ) and e 3,c ( f LL,max ). In the unstable regime, gravitational perturbations from the third planet cause high amplitude eccentricity excitation in the inner planets, causing orbits to cross. We show this behaviour in the right column 3 . This system lies in the top right of the parameter space depicted in Fig. 2 . Figure 3. Here are the three prominent types of dynamics from each region. From left to right, stable, intermediate, and unstable, for our nominal system. Shown in the top panels are the apocentres and the pericentres of the innermost planets ( m 1 in blue and m 2 in red). The middle panels show the inclinations of the inner planets ( i 1 in blue and i 2 in red). Note that this inclination is not with respect to the total angular momentum, but rather with respect to the initial angular momentum of the two inner planets . The bottom panels show, coloured in black, the difference between the longitude of ascending nodes of the two inner planets as cos (Ω 1 − Ω 2 ), and, in yellow, the difference between the longitude of the periapsis as cos (π 1 − π 2 ). These two parameters are present in equation ( 11 ). Recall that the nominal system has the following parameters: M = 1 M ⊙ , m 1 = m 2 = 1 M ⊕ , m 3 = 1 M J , a 1 = 1 au, a 3 = 100 au, and we set initially ω 1 = ω 2 = ω 3 = Ω 1 = Ω 2 = Ω 3 = 0, e 1 = e 2 = 0.001, i 1 = i 2 = 0.001 and i 3 = 45°. The only difference between each integrated system is the second planet’s separation and the third planet’s eccentricity. For the stable system we chose a 2 = 3 au and e 3 = 0.8. For the intermediate system we had a 2 = 3 au and e 3 = 0.9 (which placed it on the stability curve), and finally for the unstable system we had a 2 = 5.9 au and e 3 = 0.551. In the system’s reference frame (see Fig. 1 ), the inner planet’s inclinations are the angles between their respective angular momenta and the normal to the initial orbits. Thus, the inclination modulation shown in the stable system (the left column in Fig. 3
Paper: 2021MNRAS.501.1823B (fulltext)
this figure; both parameters are prior dominated. 5.1 CMB lensing with <italic>A</italic><sub>s</sub> prior and supernova Ω<sub>m</sub> constraint With a conservative external A s prior, the L eq – A s degeneracy can be broken, leading to a tight constraint on L eq . Since L eq depends only on Ω m and H 0 , given an additional Ω m constraint, H 0 can then be determined. As described in Section 4 , our conservative A s prior (10 9 A s = 2.19 ± 0.09) is motivated by current constraints from the primary CMB, while our Ω m constraint (Ω m = 0.3 ± 0.012) is forecast for future supernovae measurements. Both the A s and Ω m constraints are independent of assumptions about r s . In Fig. 3 , we show (blue curves) the results of imposing the CMB-motivated A s prior on the CMB lensing constraints from Fig. 2 . Imposing this prior results in tighter constraints on L eq than in Fig. 2 , as expected. The results of additionally imposing the supernova prior on Ω m are shown with the red curves in Fig. 3 . We find that an uncertainty on H 0 of 3.5 km s −1 Mpc −1 is achieved. We will refer to this as our pessimistic forecast, since it assumes a conservative A s prior and a modestly improved Ω m from future surveys. Figure 3. Forecast constraints for a CMB-S4 measurement of the CMB lensing power spectrum upon including external A s and Ω m information. By imposing a conservative, CMB-motivated (but r s independent) prior on A s we break the A s – L eq degeneracy, leading to a constraint on L eq ≡ k eq χ * (blue curves; compare to Fig. 2 ). Since , the resultant constraints on Ω m and H 0 are degenerate (the black dashed curve illustrates the expected degeneracy). By including an additional Ω m constraint from supernovae we break the Ω m – H 0 degeneracy, and obtain a constraint on H 0 (red curve, with marginalized parameter uncertainties reported along the diagonal). As an alternative to a supernova constraint on Ω m , we also consider adding information from an LSST-like measurement of cosmic shear (orange curve); in combination with CMB lensing, galaxy lensing also provides Ω m information that serves to break the H 0 –Ω m degeneracy. Because of large-scale information in the CMB lensing power spectrum, our constraints are not very sensitive to the A s prior. If this prior is completely removed, we still obtain a constraint on H 0 (grey dashed curve). We note that the constraints on H 0 are not very sensitive to the A s prior. As can be seen in Fig. 2 , even in the absence of any A s prior, we obtain some constraint on L eq . This is likely because low- L information in the lensing power spectrum breaks the degeneracy between A s and L eq . In the absence of the A s prior, the combination of CMB lensing and the Ω m constraint yields an H 0 uncertainty of roughly 6 km s −1 Mpc −1 (grey dashed curves in Fig. 3 ). It is also interesting to consider how the constraints on H 0 could improve with more optimistic constraints on A s , Ω m , and Ω b . For
Paper: 2017ApJ...849..124O (acknowledgments)
We thank Hao-Yi Wu and Olivier Doré for the theoretical prediction for the cross-correlation between CIB and CMB lensing. The South Pole Telescope program is supported by the National Science Foundation through grant PLR-1248097. Partial support is also provided by the NSF Physics Frontier Center grant PHY-0114422 to the Kavli Institute of Cosmological Physics at the University of Chicago, the Kavli Foundation, and the Gordon and Betty Moore Foundation through Grant GBMF #947 to the University of Chicago. The McGill authors acknowledge funding from the Natural Sciences and Engineering Research Council of Canada, Canadian Institute for Advanced Research, and Canada Research Chairs program. C.R. acknowledges support from a Australian Research Council Future Fellowship (FT150100074). B.B. is supported by the Fermi Research Alliance, LLC under Contract No. De-AC02-07CH11359 with the United States Department of Energy. This publication makes use of data products from the Wide-field Infrared Survey Explorer , which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. Argonne National Laboratorys work was supported under U.S. Department of Energy contract DE-AC02-06CH11357. Computations were made on the supercomputer Guillimin from McGill University, managed by Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), the ministère de l’Économie, de la science et de l’innovation du Québec (MESI) and the Fonds de recherche du Québec—Nature et technologies (FRQ-NT). This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. This work used resources made available on the Jupiter cluster, a joint data-intensive computing project between the High Energy Physics Division and the Computing, Environment, and Life Sciences (CELS) Directorate at Argonne National Laboratory. The results in this paper have been derived using the following packages: Astropy , a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013 ), CAMB (Lewis et al. 2000 ; Howlett et al. 2012 ), cmocean , 49 49 http://matplotlib.org/cmocean/ HEALPix (Górski et al. 2005 ), IPython (Pérez Granger 2007 ), LensPix (Lewis 2005 ), Matplotlib (Hunter 2007 ), NumPy and SciPy (van der Walt et al. 2011 ), PolSpice (Chon et al. 2004 ), and quicklens (see footnote 39).