adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.502.4247A (fulltext)
the analytical solution and the real solution that increases with time. This effect was observed for all the solutions studied for this work. Figure 3. Long-term error evolution in the radial distance, the latitude, and the longitude for a frozen sun-synchronous orbit. 6.3.2 Example: Molniya orbit As an example of this formulation to eccentric orbits, we select a typical Molniya orbit with initial elements: a = 26 600.0 km, e = 0.74, , , , and . Figs 4 and 5 present the results of applying this formulation to this particular initial conditions. From these figures, it can be seen that, even for very eccentric orbits, the formulation along with the perturbation theory used provides a very good approximation to the problem even for a first-order solution. In particular, the order of magnitude of the error in latitude and longitude remains the same as in the near-circular case, while the radial distance experiences an increase in its error due to the larger variation of this variable during the dynamic. Therefore, this example shows that this formulation can be applied without any problem even to high eccentric orbits. Figure 4. Evolution of radial distance, latitude, and longitude for a Molniya orbit. Figure 5. Evolution of error in radial distance, latitude, and longitude for a Molniya orbit. Moreover, and as done in the previous example, we can study the performance of a long-term propagation of this orbit. Fig. 6 shows the evolution of the error in radial distance, latitude, and longitude of the orbit as a function of the number of orbital periods that the orbit is propagated. In this figure, it can be observed the increase in error as the propagation time increases due to the approximation performed in the frequencies of the motion using this first-order solution. Nevertheless, even with this increase in error, there is an angular error smaller than , and a radial error of 0.5 km after 100 orbital revolutions. These results represent a relative error of 10 −5 after this long propagation when compared to absolute value of their variables, which shows the good performance of the methodology even for high eccentric orbits. Additionally, and if we compare this result with the long-term errors obtained from the sun-synchronous orbit (Fig. 3 ), we observe a decrease in the absolute accuracy due to the higher variation of the variables. Nevertheless, the relative error is still in the same order of magnitude. Figure 6. Long-term error evolution in the radial distance, the latitude, and the longitude for a Molniya orbit. 6.3.3 Example: hyperbolic orbit For the final example, we select an hyperbolic orbit to show that this formulation can also be successfully applied to this kind of trajectories. To this end, an orbit with the following initial orbital elements is used: a = −35 000.0 km, e = 1.2, , , , and . Figs 7 and 8 show the solution for these initial conditions. Note that, since the orbit is hyperbolic, only a section of the orbit was
Paper: 2019MNRAS.490.3426D (acknowledgments)
ACKNOWLEDGEMENTS We thank an anonymous referee for providing a thorough and insightful report, which greatly improved the quality of this manuscript. AD thanks Azi Fattahi for help with simulation data, and Russell Smith for useful discussions regarding stellar population models. We thank the Auriga team for allowing us to use their data in Fig. 12 . AD is supported by a Royal Society University Research Fellowship. AD also acknowledges the support from the STFC grant ST/P000541/1. JLS acknowledge the support of the Leverhulme and Newton Trusts. 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. We acknowledge the Gaia Project Scientist Support Team and the DPAC. This work used the DiRAC Data Centric system at Durham University, operated by ICC on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk ). This equipment was funded by BIS National E-infrastructure capital grant ST/K00042X/1, STFC capital grant ST/H008519/1, STFC DiRAC Operations grant ST/K003267/1, and Durham University. DiRAC is part of the National E-Infrastructure. AD thanks the staff at the Durham University Day Nursery who play a key role in enabling research like this to happen.
Paper: 2019MNRAS.482.1043C (fulltext)
the surface brightness of the SZE signature is independent of the cluster redshift, which together with the expected temperature and density evolution of the cluster ICM at fixed virial mass and the changing solid angle of clusters with redshift lead an SZE signal-to noise-selected sample to be approximately mass-limited (Haiman, Mohr Holder 2001 ; Holder, Haiman Mohr 2001 ). Because we plan to carry out a dynamical analysis, the cluster sample we analyse includes only those SPT systems with spectroscopic follow-up. This spectroscopic subsample is not a signal-to-noise-selected sample, but rather is the largest sample of SPT-selected clusters we could assemble for the analysis. We plot the sample of 110 clusters in the space of redshift versus mass in Fig. 1 , together with the full distribution of the SPT–SZ sample with the latest available redshifts (Bayliss et al. 2016 ; Khullar et al. 2018 ; Strazzullo et al. 2018 , Bocquet et al., in preparation) . Note that in comparison to the SPT–SZ cosmology sample (de Haan et al. 2016 ), the median redshift and mass for our sample is 0.56 and 7.26 × 10 14 M ⊙ as compared to 0.55 and 6.08 × 10 14 M ⊙ . Approximately 6 per cent of the sample lies at > 1 as compared to ∼9 per cent of the full SPT–SZ ξ > 5 sample (with updated redshifts from Bocquet et al., in preparation). Thus, in comparison to the complete SPT–SZ sample, the subset of those clusters we study here have somewhat higher masses and are somewhat under-represented at high redshift. Full information on the cluster sample is provided in Table 1 . From left to right, the columns correspond to the SPT cluster designation, the total number of passive galaxy redshifts available, the number of passive galaxy member redshifts, the cluster redshift, the SZE-based mass , and the corresponding virial radius (described in Section 3.3.1 ). Figure 1. Masses versus redshifts of the cluster sample. The coloured dots are coded according to the number of member galaxies in each cluster (see colour bar on right). Overplotted in grey is the full distribution of the SPT–SZ sample. Table 1. Cluster spectroscopic sample: we present the cluster name, total number of passive galaxies in the spectroscopic sample N tot , members used in our analysis after interloper rejection N mem , cluster redshift clus , and cluster mass and corresponding virial radius from Bocquet et al. ( 2015 ). 3.2 Spectroscopic sample We use spectroscopic follow-up including data taken using the Gemini Multi Object Spectrograph (Hook et al. 2004 ) on Gemini South, the Focal Reducer and low dispersion Spectrograph (Appenzeller et al. 1998 ) on VLT Antu, the Inamori Magellan Areal Camera and Spectrograph (Dressler et al. 2006 ) on Magellan Baade, and the Low Dispersion Survey Spectrograph (Allington-Smith et al. 1994 ) on Magellan Clay. We combine the data sets presented in Ruel et al. ( 2014 ) and Bayliss et al. ( 2016 ) with the one described in Sifón et al. ( 2013 ), obtaining a sample of
Paper: 2015MNRAS.449..191X (acknowledgments)
We thank the referee for his/her helpful comments. This work is supported by the Natural Science Foundation of China (grants 11078014, 11233006 and 11220101002).
Paper: 2019MNRAS.490.5712G (fulltext)
Rayleigh tail of the dust blackbody spectrum, we do not expect a significant shift in the sources probed or in the polarization of each source between 95 and 150 GHz. Supporting this position, Bonavera et al. ( 2017b ) found the DSFG 〈 p 〉 to not vary with frequency from 143 to 353 GHz. For a reasonable mask flux threshold of 2 mJy, which is achievable by existing experiments like SPT-3G, the total polarized foreground power is less than the EE power spectrum out to l ≲ 5700 (l ≲ 4700) and the BB lensing power spectrum out to l ≲ 5300 (l ≲ 3600) at 95 (150) GHz. Figure 5. CMB polarization surveys will be minimally impacted by polarized source power after masking radio and dusty galaxies above reasonable flux thresholds. The predicted AGN power at 95 GHz (left-hand panel) and 150 GHz (right-hand panel) for the 〈 p 2 〉 measured in this work is shown by the solid red (yellow) line for a 50 (2) mJy masking flux threshold in temperature. The filled area represents the 1σ uncertainties on 〈 p 2 〉 (no uncertainty in the source distribution has been included). The dashed blue line and the filled blue area show the mean and the 1σ upper limit on the predicted DSFG polarized power, respectively. We have assumed that the fractional polarization of the DSFGs remains constant from the value measured at 150 GHz down to 95 GHz. The total polarized foreground power will be less than the CMB EE power spectrum out to l ≲ 5700 (l ≲ 4700) and CMB lensing BB power spectrum out to l ≲ 5300 (l ≲ 3600) at 95 (150) GHz. The green arrow in the right-hand panel shows the 95 per cent CL upper limit from the recent SPTpol EE power spectrum measurement (Henning et al. 2018 ). The polarization fraction measurements in this work support the expectation that extragalactic foregrounds will be fractionally smaller for CMB polarization than temperature measurements, thus allowing more modes to be used in polarization analyses. The equivalent angular multipole for temperature is l ∼ 4000 and l ∼ 3100 at 95 and 150 GHz, respectively – more modes will be available to cosmological studies in polarization than temperature. Note that the results are not particularly sensitive to the flux cut since the polarized foreground power envelope is being driven by DSFGs. The DSFG intensity power varies slowly with mask threshold since most DSFGs are fainter than 2 mJy. It is also worth noting that better measurements of the DSFG 〈 p 2 〉 are likely to make the AGN power, and thus masking threshold, somewhat more important. We have reasons to believe that the polarization of DSFGs is lower than AGN, but 1σ limits in this work are higher (at 150 GHz) for DSFGs due to the limited number of DSFGs in the sample. The inferred power from the measured 〈 p 2 〉 values in this work are compatible with current observational constraints. Using the measured EE bandpowers from the SPTpol survey at 150 GHz, Henning et al. ( 2018 ) places a 95 per cent confidence level upper limit of on polarized point
Paper: 2015MNRAS.452.1779S (acknowledgments)
The authors would like to thank Antonio Bibiano and the anonymous referee for their insightful comments. GMS is supported by a Swinburne University SUPRA postgraduate scholarship. DC acknowledges the receipt of a QEII Fellowship from the Australian Research Council. The Millennium and milli-Millennium Simulations used in this paper were carried out by the Virgo Supercomputing Consortium at the Computing Centre of the Max Planck Society. The data bases used in this paper and the web application providing online access to them were constructed as part of the activities of the German Astrophysical Virtual Observatory (GAVO).
Paper: 2017ApJ...846..113N (acknowledgments)
We thank B. Lembège for valuable discussions. The numerical simulations were performed on the Fujitsu PRIMERGY CX400 at Kyushu University. M.N. is supported by “Boursier du Gouvernement Français.” S.M. is supported by a Grant-in-Aid for Scientific Research (C) 25400479 from JSPS. We are also grateful to ISSI (Bern) for their support of the team “Physics of the injection of particle acceleration at astrophysical, heliospheric, and laboratory collisionless shocks.”
Paper: 2019ApJ...882...42B (fulltext)
of all neighboring sources in 120 radius fitting to the IRAC frame a higher-resolution image (the HST detection mosaic) convolved with a kernel reconstructed from the empirical IRAC PSFs and the PSF of the prior image. Aperture photometry (with a diameter of 18) is then performed after removing the models of the neighboring objects. A correction based on the PSF and on the light profile of the source under analysis is finally applied to obtain total fluxes. Table 4 gives the observed IRAC fluxes. As with the HST data, the photometry has been corrected for Galactic extinction (Schlafly Finkbeiner 2011 ), although this extinction does not affect the measurements, given the lack of precision in the IRAC data. Table 4 Candidate Super Eight Spitzer IRAC Magnitudes Notes. The 1 σ uncertainties are quoted for the non-detection upper limits. a There are no IRAC observations for Super8-6. The IRAC uncertainties were measured in the same manner as described in Stefanon et al. ( 2017 ). Briefly, the rms was calculated for the pixels in the apertures using the residual frames (science frame with all objects subtracted). Systematic errors from the kernel reconstruction are also taken into account and the final result was scaled using the aperture correction. We present the cutouts from the available HST bands in Figure 1 – 8 , as well as the Spitzer IRAC cutouts for the objects with available data. Figure 1. HST and Spitzer cutouts for Super8-1. The cutouts are 5′′ × 5′′ and centered on the candidate galaxy. The central circles have a radius of 09, matching the IRAC aperture, and are included to guide the eye. For the IRAC cutouts, both the original images (top) and images after neighbor subtraction (bottom) are shown. The bottom right figure gives the photometry and fit SED for Super8-1. The observed photometry is shown with black open squares, while the fit is shown with dark pink open circles. The fit spectrum is shown with a solid pink line. The upper limits indicate the 1 σ uncertainties. The probability distributions from the redshift fitting performed using EAZY (pink) and BPZ (green) are given in the lower rightmost panel. Figure 2. Same as Figure 1 but for Super8-2. Figure 3. Same as Figure 1 but for Super8-3. Figure 4. Same as Figure 1 but for Super8-4. Figure 5. Same as Figure 1 but for Super8-5. Figure 6. Same as Figure 1 but for Super8-6. Figure 7. Same as Figure 1 but for Super8-7. Figure 8. Same as Figure 1 but for Super8-8. 4. Photometric Redshift Fitting We performed photometric redshift fitting using the EAZY fitting code (Brammer et al. 2008 ). This algorithm can quickly fit linear combinations of spectral templates that are based on semianalytic models (SAMs), rather than spectroscopic samples. Brinchmann et al. ( 2017 ) determined that the least-biased EAZY template for high-redshift galaxy fitting is the eazy _ v1.3 template. This template is based on the original EAZY template that was developed from synthetic galaxy photometry using
Paper: 2017ApJ...848...80M (acknowledgments)
This work was supported under NASA Contract No. 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 the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research has 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).
Paper: 2018MNRAS.474.3152D (acknowledgments)
Acknowledgements We thank Julien Devriendt, Bhuvnesh Jain, Vinu Vikram and Risa Wechsler for helpful discussions during the course of this work, and Tessa Baker, Gary Mamon, Stacy McGaugh and an anonymous referee for comments on the manuscript. HD received support from the Balzan foundation via New College, Oxford, the U.S. Department of Energy under contract number DE-AC02-76SF00515, and St John's College, Oxford. PGF is supported by ERC H2020 693024 GravityLS project, the Beecroft Trust and STFC. GL acknowledges financial support from ‘Programme National de Cosmologie and Galaxies’ (PNCG) of CNRS/INSU, France. GL is supported by ANR grant ANR-16-CE23-0002. This work made use of the Dark Sky simulations, which were produced using an INCITE 2014 allocation on the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory. We thank the Dark Sky collaboration for creating and providing access to this simulation, and Sam Skillman and Yao-Yuan Mao for running rockstar and consistent trees on it, respectively. Additional computation was performed at SLAC National Accelerator Laboratory. This research was supported by the DFG cluster of excellence ‘Origin and Structure of the Universe’ ( www.universe-cluster.de ). This work was partly made in the ILP LABEX (under reference ANR-10-LABX-63) supported by French state funds managed by the ANR within the Investissements d'Avenir programme under reference ANR-11-IDEX-0004-02.
Paper: 2016ApJ...826..140P (fulltext)
the CO abundance model and the spatial resolution of the synthetic observations. We keep the velocity resolution fixed, to match that of the observations, because our main focus is the comparison of the simulation with the Outer Galaxy Survey. We will study the effect of varying both the spatial and the velocity resolution in a separate work. To look for possible trends related to the spatial resolution also in the observations, we divide the observed clouds into six slightly overlapping distance intervals, corresponding to the spatial resolution intervals [ x i /1.25, x i × 1.25], with x i = 0.5 × 2 i /2 pc, i = 0, ..., 5 (see Table 1 ). The number of observed clouds, N i , in the x i intervals is N 0, ...5 = 212, 584, 1936, 1816, 360, and 88. The third interval is the most populated one and has an average resolution of 1.04 pc, nearly identical to that of our fiducial model. This procedure of dividing the observed clouds into distance intervals is not exactly the same as reobserving with different resolutions clouds at a fixed distance, as in the synthetic observations. In the real observations, clouds are selected from maps containing objects with a rather large range of distances. Thus, the following comparison of the dependence on resolution of simulated and observed clouds should be considered of a qualitative nature. We first consider the dependence of the slopes of the mass and size distributions, β M and β R , respectively, on the CO abundance model and on the resolution, shown in Figures 8 and 9 . The squares show the mean spatial resolution and mean slope derived from the observations in each distance interval (the lowest-resolution bin is not shown because the power-law fit to the histograms has too large uncertainty due to the small number of clouds). The shaded areas give the 1 σ uncertainty in the estimated slope and the standard deviation of the spatial resolution values (the statistical uncertainty in the resolution, from the cloud distance uncertainty, may be even larger). Figure 8. Power-law exponents of the fits to the mass distributions vs. spatial resolution. Each plot shows a different model for the assumed relative abundance of CO, with the error bars giving the 1 σ uncertainty of the estimated slope. The square symbols are the values from the different distance intervals of the observational sample (see text), and the shaded areas show the standard deviation of the mean spatial resolution within each interval and the estimated 1 σ uncertainty of β M . The lowest-resolution interval of the observations and the lowest-resolution value of model V3 are not shown due to their excessive uncertainty. Figure 9. Power-law exponents of the fits to the size distributions vs. spatial resolution. As in the previous figure, each plot shows a different model for the assumed relative abundance of CO, with the error bars giving the 1 σ uncertainty of the estimated slope. The square symbols are the values from the different distance
Paper: 2016MNRAS.456.1627M (fulltext)
from PFS are expected to be less competitive against existing cosmological probes. Notice that, observations by SDSS galaxy surveys in the low to moderate redshift range 0.15 z 0.67 is already being used to set interesting constraints on any departure from GR, e.g. Bel et al. ( 2015 ) obtained at the 2σ level. We would also like to stress that our results are based on all-sky analysis and we use an f sky scaling to take into account the effect of partial sky coverage. Surveys that cover a small fraction of sky such as PFS will have to deal with strong mode–mode coupling that drives the covariance matrix to near singularity. A detailed analysis will be presented elsewhere. Our constraints are derived for a spectroscopic survey. Inclusion of photometric redshift error will degrade the discriminating power of the survey. The comparison of the linear and non-linear curves in Figs 12 and 13 suggests that the non-linear contributions can make a significant effect. However, in the non-linear regime the covariance matrix should include the bispectrum and the trispectrum of the density field, which would reduce the χ 2 . Therefore, we can expect that a fully non-linear analysis, with a better modelling of the covariance matrix (e.g. from numerical simulations) would lower down the non-linear curves and make them closer to the linear result. However, because the departures from the ΛCDM power spectrum increase on weakly non-linear scales, non-linearities should still improve the discriminatory power of the analysis of 3D clustering as compared with the linear result. Here we follow a conservative approach as we estimate the constraints on the MG parameters by using the linear-theory χ 2 . We have studied the constraints in both f ( R ) and Dilaton models with and without RSD. We find that RSD only lead to a small broadening of the constraints. Below galaxy number density of the surveys rapidly lose their ability to discriminate. On the other hand, increasing the number density beyond does not lead to drastic improvements of the results. Our constraints are based on lower order tangential modes l ≤ 25. We find that the information contents in different l are highly degenerate. In the range 0.01 ≤ k ≤ 0.2 h Mpc −1 , we find that for more than three to four (logarithmic) bins in k the covariance matrix can become singular. Finally, our results are based on a uniform distribution of noise (constant ) while real surveys may have more complicated variations in the average galaxy number density which may depend on the radial and angular coordinates in the sky. 6 DISCUSSION AND FUTURE PROSPECTS In this paper we have studied the possibility of constraining MG theories using galaxy clustering. We have studied two different MG models: f ( R ) theories and Dilaton theories. In both cases we have used a specific parametrization and computed the χ 2 as a criterion for constraining the departure from the ΛCDM models. We used the sFB transforms and the