adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...899L...6L (fulltext)
instead. In addition, some R 50 of SLSN hosts are provided in Lunnan et al. ( 2015 ). Otherwise, we use the the half-light radius within the SDSS catalog 13 13 http://www.sdss3.org/ by matching the SN host galaxies with SDSS galaxies within 3′′. In the SDSS catalog, the half-light radius is estimated from the r -band Petrosian half-light radius R 50,petro and the Petrosian 90% radius R 50,petro following R 50 = R 50,petro /(1 – 8 × 10 −6 ( R 90,petro / R 50,petro ) 8.47 ) (Graham et al. 2005 ). We also update the offset parameters from the literatures. Lunnan et al. ( 2015 ) calculated the projected R off from HST images for SLSNe. Anderson et al. ( 2016 ) reported the projected R off from SNe II to the nearest H ii regions. Kelly Kirshner ( 2012 ) provided the deprojected r off = R off / R 50 for SNe II and SNe Ibc. Although projected offsets and deprojected offsets are statistically consistent, they are different for specific objects. Since most offsets in the literature and catalogs are projected, we use these deprojected offsets only when projected offsets are not available. We also convert them to r off when R 50 is available in our catalog. The sample sizes for SLSNe, SNe Ibc, SNe II, and SNe Ia with host galaxy properties are 107, 900, 4251, and 7221, respectively. To have a consistent z range as the FRB sample, we screen the samples with z 1. This gives sample sizes of 95, 874, 2829, and 7003 for the four types, respectively. Most SNe Ibc, SNe II, and SNe Ia have z 1. The excluded objects are mostly those without redshift information. The sample size for each parameter for each type of stellar transient is presented in Table 2 . 3. Multivariate Comparison We would like to perform a comparative study of the host galaxy properties between FRBs and other stellar transients. Since multiple parameters are involved, multivariate analysis methods are needed. We perform two tests. First, taking all the FRBs as a whole sample, we compare it with other samples using the multivariate Kolmogorov–Smirnov (KS) test. Second, we also compare each individual FRB with other samples to see which type it most likely belongs to. For easy understanding, we use the naive Bayes method to test individual FRBs in the FRB sample to see how they may be consistent with various types of stellar transients. We try to classify LGRBs, SLSNe, SNe Ib/Ic, SNe II, SNe Ia, and SGRBs with their host galaxy properties using the naive Bayes method and then apply the same method to each FRB. Galaxies evolve with redshift, and the FRBs in our sample all have redshift z 1, so to enable a direct comparison, in the following we compare the FRBs with the transients with redshifts z 1 only. 3.1. Multivariate KS Test 3.1.1. Method The classical KS test compares the cumulative distribution function (CDF) of two distributions P ( x ) and P ( x ′), which could be one data sample and one model sample, or two data samples. The largest distance between the two CDFs is defined as D
Paper: 2019MNRAS.484...60M (acknowledgments)
ACKNOWLEDGEMENTS We thank Arif Babul and members of the LoCuSS collaboration for their support and assistance. SLM and GPS acknowledge support from the STFC. AF and AEE acknowledge support from NASA Chandra Grant G06-17116B. AF acknowledges support from a McWilliams Postdoctoral Fellowship. GPS acknowledges support from the Royal Society. CO, DPM, ZA, JEC, and CARMA operations were supported by NSF grant AST-1140019. CPH acknowledges support from PRIN INAF 2014. This paper is dedicated to the memory of Dr. Yu-Ying Zhang.
Paper: 2018ApJ...855...45S (fulltext)
order of Type 2, 3, and 4 successively. The Type 1 clumps are puzzling, because we would expect that they are the clumps just before the onset of cluster formation in a stage prior to the Type 2 clumps, but their chemical compositions indicate that they are older than the Type 2 clumps. We will discuss the nature and origin of the Type 1 clumps in Section 4.3 . 4.2. Velocity Structures of the Clumps The results shown in Figures 11 – 12 suggest that there are interesting systematic differences among the types of clumps. In the following, we further investigate the velocity structures of the Types 1–3 clumps. Figure 15 shows the distribution of the velocity dispersion σ obs measured with the 13 CO and C 18 O emission lines for the 24 clumps as a function of the projected distance from the cluster center (for Type 2 and Type 3 clumps) or from the clump peak (for Type 1 clumps). Values of σ obs measured with the two emission lines are shown by dots with different colors—gray and pink for the 13 CO and C 18 O emission lines. We found that σ obs at the center of the clumps differs depending on the types of the clumps; the σ obs values of Type 1 (except for clumps 3890a and 3890b), Type 2 (except for 4417b), and Type 3 are mostly ≲0.5 km s −1 , ≳1.0 km s −1 , and ≳0.5 km s −1 , respectively. This suggests that the large velocity dispersion at the clump center is a typical feature of cluster-forming Type 2 and Type 3 clumps. The velocity dispersion is particularly large in the Type 2 clumps, indicating that there must be a lot of motions around the clump center in the beginning of cluster formation. Figure 15. Variation of velocity dispersion ( σ obs ) for each clump as a function of the distance from the cluster center (for the Types 2 and 3 clumps) or the C 18 O emission peak position (for the Type 1 clumps). The different color dots represent the velocity dispersion values measured from the 13 CO (gray) and C 18 O (pink) data. Blue and red squares represent the median values of σ obs in the 0.2 pc bin for the 13 CO and the C 18 O data, respectively. The error bars show the standard deviation of each piece of data in the bins. We also investigated the C 18 O distributions based on the position–velocity (PV) diagrams for all of the clumps. In Figure 16 , we show examples of the PV diagrams measured along the major and minor axes of the clumps, which are displayed in panels (b) and (c) of the figure. As denoted by the white broken circles in panels (b), we found that the PV diagrams of some Type 2 clumps measured along the major axis show double peaks separated by ∼1 km s −1 , and they are symmetrically located with respect to the clump centers. This feature is seen only in three of the seven Type 2 clumps (4417c, 4423a, and 4753b), but not in Type 1 or Type 3 clumps. Figure 16. Distributions of the H 2 column density derived from the C 18 O integrated intensity, observed position–velocity diagram measured along the major axis (cut 1–1′) , and that
Paper: 2020MNRAS.496.1683X (fulltext)
indices in the respective bands (see equation 2 ), for a large control sample of aperiodic quasars. This requires the availability of spectra in both bands, as well as optical and UV time-domain data (ideally sampled in both bands at the same time). Unfortunately, the number of quasars with such data are limited; Charisi et al. ( 2018 ) analysed a small sample of 42 quasars and found that the Doppler signature can arise by chance in (40 per cent) of the cases for the near-UV (far-UV) band. This probability reflects the limited data quality of the control sample (as is also demonstrated from the increase of chance coincidence in the lower quality far-UV data), and represents only an upper limit on how frequently quasars mimic the Doppler colour variations. A larger sample of quasars with better UV + optical is needed to assess this caveat more accurately. 4.6 Constraints from future observations An interesting question to ask is how much the evidence for or against the Doppler model may tighten with continued multiband monitoring of PG1302-102. To address this question, we generated hypothetical data representing future observations over ∼10 yr, covering two additional cycles of periodicity, and computed the p -values of the Doppler model with the extended data. For simplicity, we focused on the V and M 2 bands only. In particular, we assumed that the follow-up monitoring will continue with observations similar to those in Cycles 13 and 14. We mimicked the cadence of the existing Swift observations, by picking consecutive epochs of future observed dates that are 122 d apart (i.e. three epochs per year), and generated a total of 33 new mock observations to cover a baseline of 10 yr. We also approximated the photometric errors for the mock V - and M 2-band data by taking the average of the errors over the last seven Swift observations, which yields ∼0.03 and ∼0.01 mag, respectively. 14 Finally, we combined the additional hypothetical data with the original nine Swift points to construct the full new light curves. First, we assumed that the Doppler boost model is, in fact, correct. We simulated 50 random realizations of mock V -and M 2-band data, using the fiducial DB and DRW parameters from DHS15 . We generated continuous DB and DRW light curves for a time period of 10 yr, downsampled these at the forecast future MJDs and added photometric uncertainty. We show an example of a hypothetical light curve in the left most panel of Fig. 11 . The other two panels in Fig. 11 represent different models from the fiducial, which will be explained later in this section. We then computed the corresponding p -values of the mock data, following the method described in Section 2.2 . We generated 1000 mock light curves to assemble the reference CDF, corresponding to the ‘theoretical prediction’ for the new data in the Doppler model (see Fig. 2 ). In this analysis, we added the new mock data points one-by-one, and re-computed the p -values as each of the 1, 2,
Paper: 2021MNRAS.501...24C (fulltext)
magnitude smaller than the size of the thermal disturbance. In all cases, the back torque exerted on the planet by the perturbed disc was evaluated using the softening length ε 2 . We have found that thermal torques thus evaluated do not depend on the softening length, and that there are only minute differences in the perturbation of the density field arising from heat release. All runs with larger mass planets as well as those focused on the study of the cold torque were performed using a softening length ε 2 . 2.3 Code and mesh domain To numerically solve equations ( 1 )–( 5 ), we use the hydrodynamic public code fargo3d 1 (Benítez-Llambay Masset 2016 ) with orbital advection enabled (Masset 2000 ). We have implemented the energy diffusion equation as an additional source step corresponding to the following differential equation: where is given by equation ( 4 ). In our simulations we adopt parameters for the pressure length-scale and for the thermal diffusivity that are typical of those found at a few astronomical units in protoplanetary discs (e.g. Benítez-Llambay et al. 2015 ; Bitsch et al. 2015 ; Lega et al. 2015 ). As a consequence the size of the thermal disturbance λ c is significantly smaller than the pressure length-scale H (Masset 2017 ). This is in contrast with the work of Hankla et al. ( 2020 ), who considered a thermal disturbance comparable in size to the pressure length-scale, which allowed them to satisfy largely the scale separation between the distance x p of the planet to corotation, and λ c , i.e. they have x p ≪ λ c ≲ H , whereas in our case we have x p ≲ λ c ≪ H . Namely, we take the following values in all our runs: where Ω p is the planet’s frequency. At the planet’s location we have Resolving this length-scale over a few zones while simulating a full disc would have a prohibitive computational cost, especially for parameter space explorations such as those presented here. For this reason we restrict our computational domain in the three coordinates. In the r -direction we cover the interval [ r min , r max ] = [0.9 r p , 1.1 r p ], in the θ-direction we use (hence we simulate only one hemisphere of the disc and use reflecting boundary conditions at the mid-plane). Finally our azimuthal extent is . The cell numbers in each direction are ( N r , N θ , N φ ) = (880, 64, 1536). Recast in pressure length-scale our box size is (4 H, H , 7 H ), while its resolution is (2.3 × 10 −4 r p , 7.8 × 10 −4 r p , 2.3 × 10 −4 r p ). To avoid reflections on the radial boundaries of our computational box, we use damping boundary conditions as in de Val-Borro et al. ( 2006 ), the width of the inner damping ring being 3 × 10 −2 r p and that of the outer ring being 3.5 × 10 −2 r p , the damping time-scale at the edge of each damping ring being 1/20th of the local orbital period. The size of the box considered is too small to properly capture the Lindblad and corotation torques. However, when our focus is on the heating torque, we
Paper: 2017ApJ...846...42K (acknowledgments)
The authors thank the anonymous referee and the AAS Journal statistician for useful comments, Łukasz Stawarz and Fabio R. Herpich for fruitful discussions, and Rafael Pacheco Cardoso and Graciana Brum for their help in the morphological classification. This work was carried out within the framework of the Polish National Science Centre grant UMO-2013/09/B/ST9/00026. Grażyna Stasińska was partially supported by the National Research Centre, Poland, DEC-2013/08/M/ST9/00664, within the framework of the HECOLS International Associated Laboratory. D.K.W. was partly supported by the Polish National Science Centre grant UMO-2013/09/B/ST9/00599. G.S. and N.V.A. acknowledge the support from the CAPES CSF–PVE project 88881.068116/2014-01. The Sloan Digital Sky Survey is a joint project of The University of Chicago, Fermilab, the Institute for Advanced Study, the Japan Participation Group, the Johns Hopkins University, the Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy, the Max-Planck-Institute for Astrophysics, New Mexico State University, Princeton University, the United States Naval Observatory, and the University of Washington. Funding for the project has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Aeronautics and Space Administration, the National Science Foundation, the U.S. Department of Energy, the Japanese Monbukagakusho, and the Max Planck Society.
Paper: 2019A&A...625A.142E (acknowledgments)
Acknowledgements This work is based on data collected under the NGTS project at the ESO La Silla Paranal Observatory. The NGTS facility is operated by the consortium institutes with support from the UK Science and Technology Facilities Council (STFC) project ST/M001962/1. This paper uses observations made at the South African Astronomical Observatory (SAAO). The contributions at the University of Warwick by P.J.W., R.G.W., D.L.P., D.J.A., B.T.G. and T.L. have been supported by STFC through consolidated grants ST/L000733/1 and ST/P000495/1. Contributions at the University of Geneva by D.B., F.B., B.C., L.M., and S.U. were carried out within the framework of the National Centre for Competence in Research “PlanetS” supported by the Swiss National Science Foundation (SNSF). The contributions at the University of Leicester by M.R.G. and M.R.B. have been supported by STFC through consolidated grant ST/N000757/1. C.A.W. acknowledges support from the STFC grant ST/P000312/1. E.G. gratefully acknowledges support from Winton Philanthropies in the form of a Winton Exoplanet Fellowship. M.N.G. is supported by the STFC award reference 1490409 as well as the Isaac Newton Studentship. J.S.J. acknowledges support by Fondecyt grant 1161218 and partial support by CATA-Basal (PB06, CONICYT). D.J.A. gratefully acknowledges support from the STFC via an Ernest Rutherford Fellowship (ST/R00384X/1). P.E., A.C., and H.R. acknowledge the support of the DFG priority program SPP 1992 “Exploring the Diversity of Extrasolar Planets” (RA 714/13-1). L.D. acknowledges support from the Gruber Foundation Fellowship. The research leading to these results has received funding from the European Research Council under the FP/2007-2013 ERC Grant Agreement number 336480 and from the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation. This work was also partially supported by a grant from the Simons Foundation (PI. Queloz, ID 327127). This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://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. PyRAF is a product of the Space Telescope Science Institute, which is operated by AURA for NASA. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.
Paper: 2019ApJ...886..105Z (fulltext)
We see that the Strang CTU algorithm achieves second order for most variables (as expected), with some quantities converging to almost third order (for smooth flows, the PPM algorithm approaches third-order accuracy in space), while having difficulty with . For SDC-2, we see second-order convergence in all the variables, including . Finally, for SDC-4, all of the variables converge at rates of ∼3.8–3.9, demonstrating the fourth-order accuracy expected for the method. This test shows that the SDC algorithm can achieve fourth-order convergence for reactive hydrodynamics problems with astrophysical networks. Figure 7. Thermodynamic, dynamic, and nuclear state at t = 0.06 s for the reacting hydrodynamics test run with SDC-4 using 128 2 cells. Table 6 Convergence ( L 1 Norm) for the Reacting Convergence Problem with the Strang CTU Solver Table 7 Convergence ( L 1 Norm) for the Reacting Convergence Problem with the SDC-2 Solver Table 8 Convergence ( L 1 Norm) for the Reacting Convergence Problem with the SDC-4 Solver 5.6. Burning Buoyant Bubble Our final convergence test problem considers a hydrostatic atmosphere with a temperature perturbuation. 11 11 This problem setup is available in Castro as Exec/reacting _ tests/bubble _ convergence . Buoyancy causes the perturbation to rise (and eventually roll up in the nonlinear phase). The presence of reactions prevents the bubble from fizzling out, keeping it buoyant via the heat deposition. In addition to looking at the convergence rate of the numerical solutions, we also consider how well we maintain hydrostatic equilibrium in an undisturbed hydrostatic atmosphere. We create an initial atmospheric model that is isentropic and in hydrostatic equilibrium by integrating the system where the relation ρ ( p , s ) is provided by our equation of state. We take gravity, g , to be constant and the composition to be uniform throughout the atmosphere (pure helium, with the other nuclei mass fractions set to the small value 10 −8 ). To integrate this system, we specify the conditions at the base of the atmosphere, which we take to be the lower domain boundary (not the center of the bottom-most cell). We specify and , and get and through the general stellar equation of state. We integrate this system using fourth-order Runge–Kutta, using a step size of Δ x /2 to get from the bottom of the domain to the first cell center, and then a step size of Δ x to integrate to each of the remaining cell centers vertically in the domain. The initial conditions are then converted to cell averages using the same transformation discussed earlier in the paper. Note that the hydrostatic model is generated specifically for the resolution of the problem, and as such, the initial atmosphere converges with fourth-order accuracy. For the boundary conditions, we use periodic conditions on the sides and reflecting boundary conditions at the top and bottom. Table 9 lists the problem setup parameters. Table 9 Hydrostatic Atmosphere
Paper: 2016ApJ...820...51P (fulltext)
weighting (red) used in Ali et al. ( 2015 ), which excises fringe rates at risk for contamination by crosstalk, and applies a Blackman–Harris window in the time domain to generate a compact time-domain convolution kernel with regions weighted below 5% of the peak response suppressed. Also illustrated are the weightings for matching the YY to the XX polarization beam (blue dashed) and for reducing polarization leakage (blue solid) described in Section 4.2 , as well as a weighting that balances the needs of sensitivity, polarization leakage, crosstalk removal, and off-axis foregrounds (green). Bottom: the time-domain convolution kernel corresponding to the red curve in the top panel. Real and imaginary components are illustrated in cyan and magenta, respectively, with the absolute amplitude illustrated in brown. The next step in implementing the fringe-rate filter is translating the analytic filter profile in fringe-rate space into a time-domain kernel that can be used to convolve the simulated time series of visibilities. In effect, we implement the fringe-rate filter as a finite impulse response (FIR) filter. The convolution kernel corresponding to this FIR filter is shown in the lower panel of Figure 3 . Applying the fringe-rate filter as an FIR filter in the time domain, as opposed to directly multiplying the desired filter to Fourier-transformed visibilities, has the advantage that flagged or missing data can be naturally excluded from the filter by neglecting FIR taps (coefficient multiplies) that target the missing data. The summed output of the FIR filter are then renormalized to account for the missing samples. Another advantage of the FIR implementation of the fringe-rate filter is the potential for windowing the time-domain filter profile. While time-domain windowing causes further deviations from the ideal fringe-rate filter profile, it can be used to produce a more compact time-domain kernel. Reducing the number of time-domain samples used in the FIR filter improves the computational efficiency of the filter, helps limit the number of samples potentially corrupted by spurious systematics such as radio frequency interference, and reduces boundary effects. In Ali et al. ( 2015 ), a 12 hr data set was filtered in this manner, with boundary effects limited to an hour on each end. The final analysis proceeded on 8.3 hr selected from within the region not affected by boundary effects. 4. APPLICATIONS In Section 3 , we discussed how a fringe-rate filter can be implemented in practice once a particular form for the filter is selected. In this section, we optimize the selection of filters (or equivalently, of fringe-rate weights) for various applications in low-frequency radio interferometry. The key to this optimization will be the insight from Section 2 , namely that the effect of fringe-rate filtering can be regarded as both a time integration and a modification of the spatial response of the primary beam on a per-baseline basis.
Paper: 2016ApJ...826...20G (acknowledgments)
This work was supported under NASA contract NNG08FD60C and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by NASA. Additional funding for this work was also provided under NASA grants NNX12AJ36G and NNX14AG07G. S.K. acknowledges funding from the Swiss National Science Foundation (200021-140308). A.J.M.’s participation was supported by NASA Earth and Space Science Fellowship award NNX13AM41H. Part of this work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. A.C. was supported by NASA grants NNX15AK26G and NNX14AN84G. I.G.H. is supported by a Royal Society University Research Fellowship. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research made use of the NuSTAR Data Analysis Software (NuSTARDAS), jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). This research made extensive use of the IDL Astronomy Library ( http://idlastro.gsfc.nasa.gov/ ) and of the SolarSoft IDL distribution (SSW). Additional figures were produced using the Veusz plotting package (© 2003–2015 Jeremy Sanders). Facility: NuSTAR .