adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...652A..20L (fulltext)
the ion density in the “strong gradient” simulation at , the LHDI fluctuations are highlighted in the unstable plane (yellow) and in a cut that is perpendicular to the ambient magnetic field. The initialization of the simulations ensures pressure balance by means of the Vlasov equilibrium proposed in Alpers (1969) , Pu et al. (1981) , which shapes a plasma boundary with density and magnetic field asymmetries, uniform temperature, with no electric field and no velocity nor magnetic field shear. Hereafter, we refer to the side I (resp. side II ) of this boundary as the high (resp. low) density side. The expressions for the initialization profiles are: Here, T = T i + T e is the uniform temperature, PBC is the constant of pressure balance (set to have the plasma beta β I = 10/3 and β II = 1/12), and w is a constant that defines the width of the layer – set to 0.98 (resp. 0.5) in the “strong” (resp. “weak”) gradient case. We use a density and magnetic field asymmetry of n I / n II = 10 and B I / B II = 0.5. The shape of these initialization profiles is shown in the left panel of Fig. 1 . In the following, all quantities are normalized to ion quantities in side I : the ion gyrofrequency, ; the ion skin depth, ; and the Alfvén speed . The numerical box dimensions are: L x = L y = 2 π and L z = 20 π , with a number of cells, namely, N x = N y = 288 and N z = 1472. The box is elongated (by a factor of in the magnetic field direction in order to reliably include the narrow cone angle over which unstable LHDI modes grow. The timestep used in the simulations is d t = 3.4 × 10 −4 to satisfy the CFL stability condition. The simulations are ran for several tens of the ion gyroperiods until electron acceleration saturates. We use 100 macro-particles per cell and a second order spline interpolation for the macro-particles. We use a reduced ion-to-electron mass ratio m i / m e = 100 to make simulations computationally feasible while maintaining a sufficient scale separation between ions and electrons. The implications of using such a reduced mass ratio are discussed in Sect. 4 . We use an ion-to-electron temperature ratio T i / T e = 1, and a plasma-to-cyclotron frequency ratio of . The magnitude of the density asymmetry used in these simulations encompasses the typical parameters observed in small planetary magnetospheres such as Mercury ( Gershman et al. 2015 ). Two different simulations are investigated using two different layer widths: (i) a steeper boundary case (with the inverse gradient scale length ε n = 1, defined in Eq. ( 7 )), hereafter called a “strong gradient” simulation; and (ii) a smoother boundary case (with ε n = 0.5), hereafter called the “weak gradient” simulation. These parameters are chosen to ensure that the amplitude of the LHDI fluctuations is well above the PIC noise level and also to saturate through the two different mechanisms introduced in Sect. 2.1 : ion trapping or current relaxation. 3. Evolution of LHDI and associated
Paper: 2019MNRAS.487.1662L (acknowledgments)
ACKNOWLEDGEMENTS The authors thank the referee for her/his useful comments and suggestions, which helped improving the quality of the paper. The authors acknowledge financial support from the Italian Space Agency (ASI) under the contracts ASI-INAF I/037/12/0 and ASI-INAF n.2017-14-H.0.
Paper: 2018MNRAS.476..617P (fulltext)
can lead to an almost infinite amount of parameter space to consider (Bate, Clarke McCaughrean 1998 ; Parker Meyer 2012 ; Jaffa, Whitworth Lomax 2017 ). We therefore restrict our comparison to either box fractals as defined by Goodwin Whitworth ( 2004 ); Cartwright Whitworth ( 2004 ) or centrally concentrated clusters with different radial density profiles (Cartwright Whitworth 2004 ; Cartwright 2009 ). 3.2 The mass segregation ratio, Λ<sub>MSR</sub> MSTs are often used to quantify the relative spatial distribution of the most massive stars in a star-forming region (Allison et al. 2009 ; Parker Goodwin 2015 ), but the method can be applied to any distribution of points with assigned masses (or indeed any other scalar property), and we will apply it to the dense cores in Orion B. For the data set we use in this paper, the ‘mass segregation ratio’ (Λ MSR ) is defined as the ratio between the average MST pathlength of 10 randomly chosen cores in a star-forming region and that of the 10 most massive cores: As described in Allison et al. ( 2009 ) and Parker et al. ( 2011a ), we define the lower (upper) uncertainty as the MST length, which lies 1/6 (5/6) of the way through an ordered list of all the random lengths (corresponding to a 66 per cent deviation from the median value, 〈 l average 〉). This determination prevents a single outlying object from heavily influencing the uncertainty, which could be an issue if using the Gaussian dispersion as the uncertainty estimator. If Λ MSR > 1, then the most massive cores are more spatially concentrated than the average cores, and we designate this as significant if the lower error bar also exceeds unity (see also Alfaro González 2016 ; González Alfaro 2017 ). Parker Goodwin ( 2015 ) show that Λ MSR can sometimes be too sensitive in that it sometimes finds that random fluctuations in low-number distributions lead to mass segregation according to our definition. Therefore, if Λ MSR is calculated to be less than 2, then we also do not consider this to be a significant deviation from a random distribution. 3.3 The local surface density ratio, Σ<sub>LDR</sub> We calculate the relative local surface density of the most massive cores compared to lower mass cores using the local surface density ratio, Σ LDR (Maschberger Clarke 2011 ; Küpper et al. 2011 ; Parker et al. 2014 ). We first determine the local surface density around each core, Σ, as where r N is the distance to the N th nearest neighbour, N (Casertano Hut 1985 ). We adopt N = 10 throughout this work. We divide the median Σ for the 10 most massive cores, , by the median value for all the cores to define a ‘local density ratio’, Σ LDR (Parker et al. 2014 ): If Σ LDR > 1, then the most massive cores are in areas of higher local surface density than the average core, the significance of which is quantified by a Kolmogorov–Smirnov (KS) test on the cumulative distribution of the cores, ranked by their local surface densities Σ. We reject the hypothesis that
Paper: 2019MNRAS.482.5250B (acknowledgments)
ACKNOWLEDGEMENTS We would like to thank R. Ekers for useful comments and discussion. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO), through project number CE110001020. The Australia Telescope Compact Array is part of the Australia Telescope, which is funded by the Commonwealth of Australia for operation as a National Facility managed by CSIRO. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The figures in this work made use of astropy , a community-developed core python package for astronomy (Astropy Collaboration 2013 ). SPO acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG) under grant BR2026/23.
Paper: 2018A&A...614A..69R (acknowledgments)
Acknowledgements We acknowledge support from the STFC consolidated grant 173869-01. Support from a Marie Curie International Research Staff Exchange Scheme Radiosun PEOPLE-2011-IRSES-295272 RadioSun project is greatly appreciated. This work benefited from the Royal Society grant RG130642. This paper is based (in part) on data obtained with the International LOFAR Telescope (ILT) under project code LC3-012 and LC4-016. LOFAR ( van Haarlem et al. 2013 ) is the Low Frequency Array designed and constructed by ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources: CNRS-INSU, Observatoire de Paris and Université d’Orléans, France; BMBF, MIWF-NRW, MPG, Germany; Science Foundation Ireland (SFI), Department of Business, Enterprise and Innovation (DBEI), Ireland; NWO, The Netherlands; The Science and Technology Facilities Council, UK. We thank the staff of ASTRON, in particular Richard Fallows, for assistance in the set-up and testing for the solar LOFAR observations used.
Paper: 2015A&A...578A.123R (acknowledgments)
The Fermi /LAT Collaboration acknowledges the generous support of a number of agencies and institutes that have supported the Fermi /LAT Collaboration. These include the National Aeronautics and Space Administration and the Department of Energy in the United States, the Commissariat à l’Énergie Atomique and the Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France, the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare in Italy, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), High Energy Accelerator Research Organization (KEK) and Japan Aerospace Exploration Agency (JAXA) in Japan, and the K. A. Wallenberg Foundation, the Swedish Research Council and the Swedish National Space Board in Sweden. This research made use of data obtained with the Global Millimeter VLBI Array (GMVA), which consists of telescopes operated by the MPIfR, IRAM, Onsala, Metsahovi, Yebes, and the VLBA. The data were correlated at the correlator of the MPIfR in Bonn, Germany. The VLBA is an instrument of the National Radio Astronomy Observatory, a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. This study makes use of 43 GHz VLBA data from the VLBA-BU Blazar Monitoring Program (VLBA-BU-BLAZAR; http://www.bu.edu/blazars/VLBAproject.html ), funded by NASA through the Fermi Guest Investigator Program. The VLBA is an instrument of the National Radio Astronomy Observatory. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated by Associated Universities, Inc. The Submillimeter Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. BR is thankful to Svetlana Jorstad, Benoit Lott, Biagina Boccardi, and Shoko Koyama for useful discussion and comments on the paper. We thank the referee for constructive comments.
Paper: 2016ApJ...833..177S (fulltext)
DM measurement (see below). To our knowledge, this is the first time frequency-dependent profile evolution (not related to scattering) has been observed in an FRB. Figure 3. Burst time series plotted in four sub-bands for bursts 8, 10, and 13. The frequency range used for each sub-band is indicated above each time series. Frequency-dependent profile evolution is evident (see Section 3 ). 3.2. Temporal and Flux Properties For each burst we measured the peak flux, fluence, and burst width (FWHM). Before measuring these properties we normalized each burst time series using the radiometer equation where the noise level is given bywhere T sys is the system temperature of the receiver, 20 K for GBT 2 GHz observations and 30 K for Arecibo L-wide; G is the gain of the telescope, 2 K/Jy for GBT and 10 K/Jy for Arecibo; B is the observing bandwidth; and t int is the width of a time-series bin. The peak flux is the highest 1.3 ms-wide bin in this normalized time series. The fluence is the sum of this normalized time series. To measure the width we fit the burst with a Gaussian model. The peak time is the best-fit mean in the Gaussian model. In Table 3 we show the above measured properties for each burst. The GUPPI bursts have peak flux densities in the range 0.02–0.09 Jy at 2 GHz assuming the bursts are detected on axis. These are similar to the range of peak flux densities found for the ALFA-detected bursts presented by Spitler et al. ( 2016 ), 0.02–0.3 Jy. The PUPPI-detected burst had a peak flux density of 0.03 Jy, again assuming a perfectly on-axis detection, similar to the faintest bursts seen by Spitler et al. ( 2016 ). Table 3 Burst Properties Note. Bursts 12–16 were detected at 2 GHz at GBT and burst 17 was detected at 1.4 GHz at Arecibo. The errors quoted on DM are, in order, statistical and systematic. Burst peak times are corrected to the solar system barycenter and referenced to infinite frequency. 3.3. Dispersion Properties We measured the DM of each burst except burst 15, which was detected over too narrow a frequency range to make a reliable DM measurement. For each burst, time series were generated in sub-bands by averaging over blocks of frequency channels. The number of sub-bands depended on the S/N of the burst, and sub-bands with too little signal (S/N 2) or too contaminated by RFI were excluded. Times of arrival (TOAs) for each frequency sub-band were calculated using a Gaussian template. The width of the Gaussian template was chosen to match the burst width measured from the whole band, as described above. TEMPO2 19 19 http://www.atnf.csiro.au/research/pulsar/tempo2/ (Hobbs et al. 2006 ) was used to find the best-fit DM from the TOAs. The results are given in Table 3 . Several of these bursts (13 and 14) have formal DM measurements that are larger than the mean by a statistically significant margin. We argue that this reflects unmodeled frequency-dependent profile evolution (see Figure 3 ) and not a measurement of time-variable DM.
Paper: 2020MNRAS.491.1709N (fulltext)
snapshots (see Table 3 ) – this is different from dark matter particles whose number count is constant across redshift and simulations. Figure 9. The MST constructed on halo catalogues derived from the HZ (blue), LZ (orange), and LN (green) N -body simulations. From left to right are the MST statistics: degree ( d ), edge length ( l ), branch length ( b ), and branch shape ( s ). They are plotted from top to bottom according to snapshots at redshift 1, 0.5, and 0. Corresponding shaded areas show the jackknife uncertainties in the measurements. The distribution of the MST statistics are indistinguishable from each other at all redshifts, demonstrating that we should expect to see similar lines of degeneracy as power spectrum. Note . To mitigate this issue, for each redshift we match the number of haloes to the lowest number found in the simulations (thus always matching the number of haloes found in the LZ simulations). For those with more haloes, we simply select the most massive haloes. In Fig. 9 , we find no real noticeable difference in the statistics suggesting the degeneracies of the MST may be similar to that found for P ( k ). 6 COMPARING THE SENSITIVITY TO COSMOLOGY OF POWER SPECTRUM, BISPECTRUM, AND THE MINIMUM SPANNING TREE In this section, we compare the sensitivities to cosmological parameters of power spectrum P ( k ), bispectrum B ( k 1 , k 2 , k 3 ), and MST, measured on the same halo catalogues, to establish whether the MST can improve parameter constraints. Specifically, we compare the constraints on A s , Ω m , and ∑ m ν for 10 sets of mock simulations. To obtain reliable posterior distributions for the three methods and their joint constraints, we would normally run a Markov Chain Monte Carlo (MCMC) using an analytic expression for the data vector. However, the MST statistics cannot be obtained analytically and hence have to be obtained from simulations. P ( k ), B ( k 1 , k 2 , k 3 ), and MST are therefore estimated from a grid of simulations in parameter space. To limit the noise in the estimates of the theory we take the mean of five simulations rather than just one at each point in parameter space. Additionally, since our simulation grid is rather sparse we use Gaussian process (GP) regression to interpolate the data vector. Finally, we use a corrected likelihood function (see Sellentin Heavens 2016 ; Jeffrey Abdalla 2018 ) which accounts for the use of an estimated covariance matrix. 6.1 COLA simulation suites A suite of COLA (Tassev, Zaldarriaga Eisenstein 2013 ) simulations were constructed using the mg-picola software (Winther et al. 2017 , an extension to l-picola by Howlett, Manera Percival 2015 ) which, among other things, can model the effects of massive neutrinos (Wright, Winther Koyama 2017 ). This allowed us to generate N -body-like simulations relatively cheaply (in terms of computation time), albeit by sacrificing accuracy at small scales. All simulations are run in boxes of lengths 250 , with 256 3
Paper: 2016A&A...595A...5M (fulltext)
there are some 1015 defining sources outside the central distribution that should be examined individually for possible contamination by the host galaxy or other anomalies. The lower left plot in Fig. 7 extending to mas shows more detail near the centre, but the distribution remains remarkably symmetric even at the highest resolution. The lower right plot in Fig. 7 is a close-up view of the very central region within mas in each coordinate and shows again the predominance of defining sources and the significant fraction of sources in the very inner zone at a few 0.1 mas from the centre. The very small bias in declination is now visible with more sources in the negative values of . The proportion of defining sources (blue squares) increases with resolution, but in all three plots the optical and radio positions are statistically in very good agreement for all three categories of sources despite the larger uncertainties in the radio data for the non-defining and especially the VCS-only sources. 4.1.2. Angular separations Table 1 gives the overall statistics of the angular separation () between the optical and radio positions, subdivided according to the type of ICRF source. The table shows that 94% of the optical positions are within 10 mas of the radio position of the associated ICRF2 source. This is quite remarkable given the limited time coverage and the numerous limitations of the astrometric solutions for Gaia DR1 as explained in Lindegren et al. (2016) . For the defining sources the percentage is even higher (98%). There is in fact a very consistent pattern in that the fraction of separations within a given limit () is always higher for the defining sources than for the non-VCS sources, which in turn have a higher fraction than the VCS-only sources. Since the distinction between the three categories is irrelevant for Gaia (cf. Fig. 4 ), this confirms the difference in the quality of the radio positions expressed by the quoted uncertainties in ICRF2. Table 1 Number of ICRF sources with angular separations between the optical and radio positions. Notes. In brackets are the percentages of sources in the group below the given limits. Fig. 8 Distribution of the angular distances between the Gaia solution and the ICRF2 positions. Left: the 2054 solutions closer than 10 mas to their radio position. Right: same for the 258 defining sources. Figure 8 shows the distribution of separations in graphical form. The difference between the whole set and the set of defining sources is even more conspicuous than for the coordinate differences. The median is 1.16 mas in one case and only 0.61 mas in the other. As mentioned earlier this is primarily due to the very different quality in the ICRF2 between the defining sources and the rest of the catalogue and confirms a well-known feature of the ICRF2 with fully independent observations. 4.2. Normalised differences In this section we discuss the positional differences scaled by their standard uncertainties. We
Paper: 2019A&A...622A.192E (acknowledgments)
Acknowledgements We would like to thank the referee for many thoughtful comments which helped to improve this paper. This work issupported by the Swiss National Science Foundation through the grant number 200020 - 162630. We acknowledge the financial support from the Programme National de Planétologie (PNP) of CNRS-INSU co-funded by the CNES, and by the Agence Nationale de la Recherche (ANR-14-CE33-0018). J.O. acknowledges financial support from the ICM (Iniciativa Científica Milenio) via the Núcleo Milenio de Formación Planetaria grant, from the Universidad de Valparaíso, and from Fondecyt (grant 1180395). J.M. acknowledges support from the NASA through the NASA Hubble Fellowship grant HST-HF2-51414.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555.
Paper: 2017MNRAS.468.2897D (acknowledgments)
Acknowledgments The majority of the work of MD was performed at Caltech and supported through a Jansky Fellowship of the National Radio Astronomy Observatory. MD also greatly appreciates earlier support from UCLA, and is indebted to Lech Walesa, the leader of Solidarnosc , for his key role in the fall of communism in Europe, which made it possible to conduct this study. DJ appreciates support from NASAs Origins programme. The data presented herein were obtained at the CSO, supported through NSF grant AST-0540882 at the time of the observations. The authors thank the telescope staff and I-Ting Ho for support, Nicolas Biver for comments and for providing published CO production rates of comet Hale-Bopp, and an anonymous referee for helpful suggestions.