adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.481.4342N (fulltext)
1, 2), etc., while the (1, 1, 0) mode is not important. We should therefore conclude that magnetic relaxation towards the k = 1 ABC state is more advanced in our k ini = 4 simulation. Figure 5. Energy contribution of the dominant Fourier modes of the B x magnetic field component as function of simulation time ct / L for simulations k2_T5_1024M (left), k2_T6ic_1152P (middle), and k4_T5_1152P (right). The black lines show the total energy of the B x component, represented as . The red lines show the contribution of the initial ‘ABC’ modes: (0, k ini , 0) and (0, 0, k ini ); the green lines show the contribution of the (1, 1, 0) modes; the blue lines show the contribution of the ‘Taylor’ modes (0, 1, 0) and (0, 0, 1); and the grey lines show the residual energy contained in all other modes. Fig. 6 shows the magnetic energy spectra for simulations k2_T5_1024M, k2_T6ic_1152P, and k4_T5_1152P for a range of simulation times. The magnetic energy spectra are dominated by the white noise for for the T5 simulations and for for the k2_T6ic_1152P simulation. The inertial subrange extends over at least one order of magnitude in wavenumber . In all cases, we observe a freely decaying turbulent cascade that can be described roughly as a power law , with significant deviations in either direction. The cascade appears to be most regular for simulation k4_T5_1152P. Figure 6. Magnetic energy spectra calculated for simulations k2_T5_1024M (left), k2_T6ic_1152P (middle), and k4_T5_1152P (right). Line colours indicate the progression of simulation time from deep blue to brown within the time ranges indicated in the bottom-left corner of each panel. The thick dashed lines indicate the power-law slopes of −5/3, −2, −2.5, −3. Fourier decomposition was also attempted for electric field , and for velocity field , however, the corresponding spectra of electric and kinetic energies are dominated by the white noise. 3.4 Particle momentum distribution Fig. 7 presents the time evolution of the particle momentum distributions for all five simulations. For ultra-relativistic electrons with Θ ≫ 1, the dimensionless particle momentum is basically equivalent to the dimensionless particle energy u = γβ = p /( m e c ) ≃ γ. For all simulations, the initial momentum distribution is adopted to be the Maxwell–Jüttner distribution (Section 2). Figure 7. Particle momentum distributions u 2 N ( u ) compared for all simulations. The distributions are normalized to the peak of the initial distribution for each simulation. Line colours indicate the progression of simulation time from deep blue to brown over the range indicated in the top-left corner of each panel. We find evidence for non-thermal particle acceleration for all simulations with k ini = 2. The effective power-law indices p (such that N ( u )∝ u − p ) are estimated roughly by finding a power-law slope that makes a line parallel to the data (see Werner et al. 2016 for more elaborate fitting methods), with a different approach used
Paper: 2020MNRAS.493.3997M (fulltext)
instrument (Pfeiffer, Frank Baumuller 1992 ). The experiment had two goals. The first one was to understand whether the precision of a single astrocomb is confirmed by an independent system, and the second one was to assess zero-point offsets in the wavelength calibration introduced by switching between the two astrocombs. Probst et al. ( 2020 ) analyses the same data set, but with a focus on describing the experimental set-up, astrocomb hardware and its optimization during the campaign before the astrocomb’s deployment in 2015 May. This paper, on the other hand, focuses on data analysis techniques and advanced algorithms that will provide wavelength calibration precision required by the ELT projects discussed above. The paper is divided as follows. In Section 2 , we briefly describe the experimental set-up – the spectrograph and the two astrocombs. Section 3 describes the data set and general properties of the spectra. Section 4 gives details on our automatic algorithm to detect astrocomb lines and obtaining their centres and wavelengths. Wavelength calibration is discussed in Section 5 , where we describe how we deal with instrumental effects that impact significantly on wavelength calibration precision and accuracy, e.g. defects associated with the CCD manufacturing process. In the same section, we describe our findings on the optimal wavelength calibration model for HARPS spectra. We present our findings on precision and consistency in Section 6 , where we also consider and model the contribution of flux dependent velocity shifts. Finally, our results are presented in Section 7 and discussed in Section 8 . 2 EXPERIMENTAL SET-UP 2.1 The HARPS instrument The High Accuracy Radial velocity Planet Searcher (HARPS; Mayor et al. 2003 ) was built for extreme stability and precision and is one of the most stable astronomical spectrographs in existence. HARPS is a fibre fed, high-resolution ( R = λ/Δλ = 115 000), R4 grism cross-dispersed echelle spectrograph installed on the 3.6m telescope at ESO’s La Silla Observatory and the first instrument to be equipped with an astrocomb for regular operations (Probst et al. 2016 ). The light of the two input fibres is dispersed into 72 echelle orders on the detector, simultaneously covering the wavelength range between 378 and 691 nm. The spectrograph is enclosed in a thermal and pressure controlled vacuum vessel, with long-term temperature variations at the 0.01 K level and operating pressure below . Several thermal and mechanical effects can slightly shift the positions of the spectrum on the detector with time, an unavoidable effect that is eliminated through simultaneous referencing: drifts in the science fibre are tracked by simultaneously observing a spectrum rich in velocity information content (e.g. ThAr, an astrocomb, or a Fabry–Pérot etalon) in the secondary fibre (Baranne et al. 1996 ). Each fibre has a static double scrambler. A servo controller (‘secondary guiding’) ensures that the object image is
Paper: 2018MNRAS.478.2056T (acknowledgments)
ACKNOWLEDGEMENTS We thank N. Kylafis for useful comments and discussions. We also thank the anonymous referee for useful comments that helped improved this paper. The software used in this work was in part developed by the DOE NNSA-ASC OASCR Flash Center at the University of Chicago. For post-processing our results we partly used yt analysis toolkit (Turk et al. 2011 ). KT and AT acknowledge support by FP7 through Marie Curie Career Integration Grant PCIG-GA-2011-293531 ‘SFOnset’. AT and KT would like to acknowledge partial support from the EU FP7 Grant a39353*PIRSES-GA-2012-31578 ‘EuroCal’. Usage of the Metropolis HPC Facility at the Crete Center for Quantum Complexity and Nanotechnology of the University of Crete, supported by the European Union Seventh Framework Programme (FP7-REGPOT-2012-2013-1) under grant agreement no. 316165, is alsoacknowledged.
Paper: 2020ApJ...889..189S (acknowledgments)
We thank Gabriel Brammer for insightful discussions related to this work. This paper is based on observations made with the NASA/ESA Hubble Space Telescope . The Space Telescope Science Institute (STScI) is operated by the Association of Universities for Research in Astronomy, Inc. (AURA) under NASA contract NAS 5-26555. ACS was developed under NASA contract NAS 5-32864. The Spitzer Space Telescope is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. These observations are associated with program GO-14096. Archival data are associated with programs GO-9270, GO-12166, GO-12477, and GO-12253. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). This research made use of Photutils, an Astropy package for detection and photometry of astronomical sources (Bradley et al. 2019 ). This research made use of Astropy, a community-developed core Python package for Astronomy (The Astropy Collaboration et al. 2018 ). This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. F.A.-S. acknowledges support from Chandra grant G03-14131X. M.B. and V.S. acknowledge support by NASA through ADAP grant 80NSSC18K0945, NASA/ HST through HST -GO-14096, HST -GO-13666 and two awards issued by Spitzer/JPL/Caltech associated with SRELICS_DEEP and SRELICS programs.
Paper: 2019MNRAS.482.4174F (acknowledgments)
ACKNOWLEDGEMENTS MWF and PAW gratefully acknowledge the receipt of research grants from the National Research Foundation (NRF) of South Africa. MWF thanks Dr. L. Balona for some help with the data for the early years.
Paper: 2017MNRAS.467.2552C (acknowledgments)
Acknowledgments This research was funded through a VICI grant of NWO (the Netherlands Organization for Scientific Research) and an A-ERC grant 291141 CHEMPLAN. Financial support by NOVA (the Netherlands Research School for Astronomy) and the Royal Netherlands Academy of Arts and Sciences (KNAW) through a professor prize are acknowledged. The described work has had much advantage from collaborations within the framework of the FP7 ITN LASSIE consortium (GA238258). SI acknowledges the Royal Society for financial support. We thank D. M. Paardekooper and V. Taquet for discussions on the laser interference measurements and gas-phase formation of COMs, respectively. We also acknowledge Rachel James for careful proof reading.
Paper: 2020ApJ...897..161K (fulltext)
additionally with 10 6 , 10 6.5 , 10 7.5 , and 10 8 particles for convergence tests, plus 12 miscellaneous tests with 10 7 particles detailed in Section 3.3 . To be precise, these stated particle numbers refer to the number of particles per Earth mass (the bare target plus impactor mass is 1.02 M ⊕ ). Thus, the numerical resolution stays the same for simulations with different-mass planets. For example, a “10 7 ” simulation that includes a 0.1 M ⊕ atmosphere actually contains a total of ∼1.12 × 10 7 particles. For most of the suite we focus on the 10 −2 M ⊕ atmosphere. Figure 2 summarizes the parameters for each simulation. Note that the v c = 0.75 v esc scenarios would require some third body to have slowed down the impactor during its approach to below the mutual escape speed. This is unlikely in the case of primary impactors falling in to Earth in our solar system but is a useful test for the consequences of a highly grazing impact resulting in a large bound fragment that will re-impact at a later time. It also lets us compare with other models, which predict little erosion in this regime. Figure 2. Suite of simulation scenarios, arranged by their speed and impact parameter at contact (see Figure 1 and Appendix A ). As shown in the legend, the nested marker colors indicate the mass of the atmosphere (in Earth masses) for each simulation, while the line angles indicate the number of particles per Earth mass. At the high-speed end, given Earth’s position in the solar system, 5 v esc is around the highest typical velocity that might be expected for an impact (Raymond et al. 2009 ). For context, Earth’s orbital speed around the Sun is about 3 v esc . The suite’s extension to 8 v esc both allows us to test the extreme end of the parameter space and is a regime that could be more common in other planetary systems, for example, with a more massive star or a target planet deeper in the star’s potential well. Furthermore, in studies like that by Inamdar Schlichting ( 2015 ) where erosion is estimated as a function of the impactor’s momentum, using very high velocities will allow us to test the degeneracy between impactor mass and speed across a wide range of momenta in future suites with different impactor masses. For the relatively small impactor mass used here, even 8 v esc is not predicted by Inamdar Schlichting ( 2015 ) to remove more than 3/4 of the atmosphere. The simulations are run using SWIFT with a simple “vanilla” form of SPH plus the Balsara ( 1995 ) switch for the artificial viscosity as described in Kegerreis et al. ( 2019 ) to a simulation time of 100,000 s (roughly 28 hr) in a cubic box of side 80 R ⊕ to allow the tracking of ejecta. Any particles that leave the box are removed from the simulation. Throughout the first 10 hr we record snapshots every 100 s, for high time resolution during the impact and its immediate aftermath. To reduce data storage requirements, we then output snapshots every 1000 s for the remainder. 2.3.
Paper: 2016ApJ...829....1M (fulltext)
beam = 058 × 050, PA = 81°; shown in the lower left corner). Contour levels are −3, 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21 times the rms of the map, 19.3 μ Jy beam −1 . (b) Naturally weighted VLA image of the 3.0 cm emission obtained by combining data from A and B configurations (synthesized beam = 039 × 036, PA = 58°; shown in the lower left corner). Contour levels are −3, 3, 5, 7, 9, 11, 13 and 15 times the rms of the map, 2.1 μ Jy beam −1 . An image of the 3.0 cm emission is presented in Figure 2 (b). The emission shows a tripolar structure, suggesting a combination of dust emission from a disk and free–free emission from a monopolar (one-sided) radio jet perpendicular to it. This kind of morphology, with two perpendicular structures, has already been observed in other sources, such as HH 111 (Rodríguez et al. 2014 ) and HH 80–81 (Carrasco-González et al. 2012 ). In both cases, the emission was separated into two components by fitting two Gaussians to it. In the case of GM Aur, we estimate from the SED fit a dust contribution at 3.0 cm of ∼11 μ Jy, which represents only one-eighth of the detected emission at 3.0 cm. In order to check whether this flux density can account for the disk emission of GM Aur at 3.0 cm, we obtained an image of the estimated dust emission at 3.0 cm by scaling the 7 mm image with the dust spectral index obtained from the SED fit. We then subtracted this scaled image to the 3.0 cm image, which was previously convolved to have the same angular resolution as the 7 mm observations. The resulting image still showed significant emission from the disk. By fitting two Gaussians to the remaining 3.0 cm emission, we separated the source into a component with the same PA as the GM Aur disk ( S ν = 31 ± 3 μ Jy), and another one perpendicular to it ( S ν = 45 ± 3 μ Jy) 9 9 We note that none of the fit parameters were fixed and yet two perpendicular components were obtained. (Figure 3 ). We interpret these two components to be tracing the free–free emission from a photoevaporating disk and a radio jet, respectively (see Section 4 ). Figure 3. Superposition of the two components of the free–free emission at 3.0 cm obtained from our two-Gaussian fit (black and white contours) over the 7 mm emission of the transitional disk of GM Aur shown in Figure 2 (a) (color scale). We propose that the disk component (black contours) is produced by the photoevaporative winds arising from the disk, while the perpendicular component (white contours) is tracing the free–free emission from an accretion-driven radio jet. The 3.0 cm image was previously convolved to the same angular resolution (058 × 050, PA = 81°; shown in the lower left corner) as the 7 mm image. Contour levels are −3, 3, 4, 5, 6, 7, and 9 times the rms of the convolved 3.0 cm image, 2.8 μ Jy beam −1 . We also combined our new 5 cm observations with archival data (Table 1 ) to get a sensitive image (rms ≃ 2.5 μ Jy; beam = 053 × 036, PA = −74°). The image shows a compact
Paper: 2016A&A...587A..98W (fulltext)
Our images also show that the extended Ly emission is typically quite symmetric (albeit with interesting deviations in some objects) – information which is necessarily erased in any stacking process. It will be intriguing to compare the Ly halo maps obtained by MUSE with simulated views from (possibly competing) model predictions. In the context of our simple phenomenological model, two main parameters govern the overall appearance of a Ly halo at : the halo scale length, typically 10 times larger than the scale length of the central continuum source, and the fraction of the total Ly radiation emitted by the halo, typically between 40% and 90%. These two quantities are essentially uncorrelated in our sample, which may indicate that they are physically determined at different scales. The size of a halo would then mainly depend on the spatial distribution and kinematic structure of the circumgalactic gas, independently of how much Ly it emits. On the other hand, the halo luminosity fraction will depend on the origin of the Ly radiation, but also on the importance of resonant scattering of Ly photons from inside the galaxy into the halo. Energetically, our measurements are consistent with the idea that most of the observed Ly radiation is produced within the UV-bright star-forming regions of the galaxies and then scattered outwards. However, additional processes for powering the Ly emission in the extended haloes are not excluded, and more theoretical and modelling work will be required to explore the relative importance of different mechanisms. Additional clues will come from spatially resolved spectroscopy of Ly haloes, which MUSE will enable us to perform at least for the brighter sources (or in strongly lensed objects where the magnification helps to reach even higher spatial resolution; see Patrício et al. 2016 for a first case study using MUSE). By a remarkable coincidence, individual Ly haloes of nearby () galaxies have just now become a subject of detailed scrutiny through the LARS project ( Hayes et al. 2013 ; Östlin et al. 2014 ; Hayes et al. 2014 ; Guaita et al. 2015 ). While the LARS galaxies can be resolved in much greater detail than our faint HDFS sources, the overall comparison of high- and low-redshift Ly halo properties shows some similarities, but also intriguing differences. In particular, the huge discrepancy between the sizes of Ly-emitting regions around and galaxies (Fig. 12 ) suggests that the nature of the circumgalactic medium has changed drastically from the epoch of galaxy formation to the present-day Universe. For the first time, MUSE has allowed us to assess the level of diversity in the Ly halo properties of high-redshift galaxies. While admittedly our sample is too small to go very far in this direction, much more can be expected in the future, as MUSE has just commenced operation. Furthermore, the MUSE data need to be complemented by observations with other instruments and in other wavelengths. In this respect
Paper: 2019A&A...622L...5V (acknowledgments)
The presented research was made possible through funding from the Russian Scientific Foundation, project number 16-12-00071.
Paper: 2018A&A...611L...6P (acknowledgments)
Acknowledgements We would like to thank Rob Rutten for the alignment of SST and AIA data for the June 19 dataset. This work was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement No. 291058, and by the Research Council of Norway, project number 250810, and through its Centres of Excellence scheme, project number 262622. B.D.P. gratefully acknowledges support from NASA grants NNX16AG90G and NNG09FA40C (IRIS). IRIS is a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre. The Swedish 1-m Solar Telescope is operated on the island of La Palma by the Institute for Solar Physics of Stockholm University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias. To visualise, connect, and interpret the data we made extensive use of CRISPEX ( Vissers Rouppe van der Voort 2012 ).
Paper: 2016A&A...586A.138P (fulltext)
scales, might be interpreted in terms of a decrease of with increasing column density ( Matthews et al. 2001 ; Whittet et al. 2008 ). A leading theory for the process of dust grain alignment involves radiative torques by the incident radiation ( Lazarian Hoang 2007 ; Hoang Lazarian 2009 ; Andersson 2015 ). A critical parameter for this mechanism is the ratio between the dust grain size and the radiation wavelength. As the dust column density increases, only the longer wavelength radiation penetrates the cloud and the alignment decreases. Grains within a cloud (without embedded sources) should have lower than those at the periphery of the same cloud. There is evidence for this from near-infrared interstellar polarization and submillimetre polarization along lines of sight through starless cores ( Jones et al. 2015 ), albeit on smaller scales and higher column densities than considered here. If inside the cloud is very low, the observed polarized intensity would arise from the dust in the outer layers, tracing the magnetic field in the “skin” of the cloud. Then the observed orientation of is not necessarily correlated with the column density structure, which is seen in total intensity, or with the magnetic field deep in the cloud. Soler et al. (2013) presented the results of HRO analysis on a series of synthetic observations produced using models of how might decrease with increasing density. They showed that with a steep decrease there is no visible correlation between the inferred magnetic field orientation and the high- structure, corresponding to nearly flat HROs. The HRO analysis of MCs carried out here reveals a correlation between the polarization orientation and the column density structure. This suggests that the dust polarized emission samples the magnetic field structure homogeneously on the scales being probed at the resolution of the Planck observations or, alternatively, that the field deep within high- structures has the same orientation of the field in the skin. 6. Conclusions We have presented a study of the relative orientation of the magnetic field projected on the plane of the sky (), as inferred from the Planck dust polarized thermal emission, with respect to structures detected in gas column density (). The relative orientation study was performed by using the histogram of relative orientations (HRO), a novel statistical tool for characterizing extended polarization maps. With the unprecedented statistics of polarization observations in extended maps obtained by Planck , we analyze the HRO in regions with different column densities within ten nearby molecular clouds (MCs) and two test fields. In most of the regions analysed we find that the relative orientation between and structures changes systematically with from being parallel in the lowest column density areas to perpendicular in the highest column density areas. The switch occurs at . This change in relative orientation is particularly significant given that projection
Paper: 2017MNRAS.465.4817S (acknowledgments)
Acknowledgments This research is mainly supported by the University of Padova and by the ERC Consolidator Grant funding scheme (project STARKEY, G.A. n. 615604). The LUNA experiment was supported by INFN, DFG (BE 4100-2/1), NAVI (HGF VH-VI-417) and OTKA (K101328). We thank Maria Lugaro for helpful comments and discussion.