adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...586A.140P (fulltext)
could introduce spurious velocities along the line of sight. In order to test this methodology, we use the GALAXY mock catalogue, in two different scenarios. The first scenario is an ideal one, with no mask or selection function: we apply the three approaches on a box populated with our mock catalogue, and compare the recovered radial velocities with the real ones provided by the catalogue. When performing this comparison, we evaluate a 3D grid for the original galaxy radial velocity by assigning to each cell the radial velocity of the galaxy falling nearest to the cell centre, that is, we adopt a nearest particle (NP) method. For different radial bins of the wavevector, we evaluate the correlation coefficient () computed from the cross-power spectrum between the recovered and the original radial velocity fields and their respective auto-spectra: In this expression, stands for , that is, the radially averaged power spectrum of the line of sight velocity modes. The superscripts stand for “original” and “recovered” components, respectively, and the asterisk denotes the complex conjugate operation. Fig. 3 Correlation coefficient of the recovered line-of-sight velocities with the actual ones in our GALAXY mock catalogue. Solid, dotted, and dashed lines refer to the LINEAR, LOG-LINEAR, and LOG-2LPT approaches, respectively. The red lines consider the ideal scenario without any sky mask or selection function, while the black ones are for the same sky mask and selection function present in the real CGC. In Fig. 3 the red lines display the correlation coefficients for the ideal scenario. Solid, dotted, and dashed lines refer to the LINEAR, LOG-LINEAR, and LOG-2LPT approaches, respectively. On large scales (low modes), the three approaches provide correlation coefficients that are very close to unity, while they seem to lose information on small scales in the same way. We note that a direct comparison to the results of K12 is not possible, since, in our case, we do not use the full dark matter particle catalogue, but only a central halo one. After the real mask and selection function obtained from the CGC is applied to the GALAXY mock catalogue, then the recovery of the peculiar velocities from the three adopted approaches worsens considerably. The black lines in Fig. 3 display, in general, much lower correlation levels than the red ones. The LINEAR approach seems to be the one that retains most information on the largest scales, and it out-performs the LOG-LINEAR and the LOG-2LPT methods on all scales. We hence expect the LINEAR approach to be more sensitive to the kSZ effect than the other two methods, particularly on the largest scales. Once the velocity inversion from the CGC density contrast has been performed, we compute the spatial correlation function between the recovered velocities and the kSZ temperature anisotropies, where is estimated as in Eq. ( 4 ), and the ensemble average is obtained after running through all galaxy pairs lying a
Paper: 2019A&A...632A..48B (fulltext)
modes. Simulations suggest that the algorithm should be able to reach one-shot correction resulting in a λ /1000 nm rms WFE with approximately 10 7 photons, and that with one iteration it should be able to correct up to ∼ λ /8 of rms WFE. In Sect. 5 , we demonstrated the principle with the vAPP in SCExAO, both with the internal source and on-sky, by measuring and controlling the 30 lowest Zernike modes and improving the raw contrast between 2.5 and 4 λ / D by a factor ∼2. Furthermore, we showed that the algorithm can correct WFEs of 300 nm and 150 nm rms with the internal source and on-sky, respectively. Although the contrast gains, both with the internal source and on-sky, are moderate, it does demonstrate that the coronagraphic PSFs of the vAPP can be used for wavefront sensing. The FPWFS performance of the SCExAO vAPP can be improved in various ways: (1) better model calibration to increase the contrast gain, (2) measuring more and higher spatial frequency modes to increase the area of correction, and (3) improving the convergence speed of the algorithm. For this demonstration the model was calibrated ad hoc by tuning the pupil undersizing, rotation, and the detector pixel scale by hand. These parameters should be more accurately measured or be part of the fitted parameters. To measure more and higher spatial frequency modes we want to replace the current Zernike mode basis with a pixel-mode basis ( Paul et al. 2013a ). This should also reduce the effect of unsensed modes on the estimation, when estimating a truncated mode basis ( Paul et al. 2013b ). Improving the convergence speed of the algorithm would result in a higher correction cadence or the correction of a larger mode basis in a similar time. A relatively simple improvement would be to use the multi-processing capabilities in the latest python libraries not yet available on SCExAO, this would enable multi-core processing and give a maximum speed improvement of a factor of 40. Theoretically, the best possible performance we can then expect is a wavefront estimation with sub 1 nm WFE every six seconds for targets down to a m H = 8 (five seconds of integration and one second for estimation). Other coronagraph model improvements include the integral field spectrograph CHARIS, an accurate model of the focal-plane filter and the incoherent background due to residual turbulence effects. This is because science observations with the SCExAO vAPP are done with CHARIS. Preventing detector saturation due to bright fields of the vAPP requires a focal-plane neutral density filter that attenuates the bright fields. Therefore, exposures with the focal-plane filter in place will have a larger dynamic range. This increase in dynamic range will make the incoherent background, which is due to uncorrected atmospheric turbulence, a more prominent feature in the image compared to the current on-sky results. Thus, the incoherent background needs to be included within the coronagraph model as well (
Paper: 2016MNRAS.455.2945M (acknowledgments)
We thank Hector Gil-Marín for useful discussion. DJ thanks Max-Planck Institute for Astrophysics for hospitality. NM acknowledges support from the Science and Technology Facilities Council (ST/L00075X/1).
Paper: 2018A&A...612A...3H (fulltext)
in SNRs. We also investigated the opposite problem assuming that this canonical paradigm is valid and put constraints on the parameter space spanned by the ambient gas density around the shock and electron-to-proton energy fraction. Overall,the derived flux upper limits are not in contradiction with the canonical CR paradigm. Assuming this paradigm holds true, we can constrain typical ambient density values around shell-type SNRs to n ≤ 7 cm −3 and electron-to-proton energy fractions above 10 TeV to ε ep ≤ 5 × 10 −3 . Finally, we compared the presented flux upper limits to the flux measurements of the seven non-interacting shell-type SNRs detected both in the radio and VHE range. We found evidence of correlation between the ratio of VHE γ -ray luminosity to radio luminosity, ( L VHE ∕ L radio ), and source age. This correlation can be explained by invoking the theory of magnetic field amplification at SNR shocks, which accounts for the rapid decrease in radio luminosity by predicting a declining magnetic field strength as the shock slows down with increasing source age. Further development in the SNR population from the observational point of view should be achieved with the next generation instrument: the CTA observatory. In this work we have also estimated the performance of this future observatory to probe the ambient gas density and the SN blast energy parameter space. The results suggest that this instrument will be an important leap forward in the investigation of the Galactic SNRs and will likely be able to confirm or invalidate the CR paradigm. Appendix A Additional material Table A.1 Results overview. Notes. The SNR morphology type is indicated by S for shell-type and C for mixed morphology SNRs. The type of the latter is indicated as thermal (t), plerionic (p), or both (tp). If molecular cloud interaction is probable or certain (information taken from SNRcat), this is indicated by an asterisk. For sources with an observational live time ≲1 h, instrumental effects can impact the result and should be used with care. R ON are the radii of the circular analysis regions. Distance assumptions correspond to the arithmetic mean of the density range limits reported in SNRcat. Table A.2. Literature estimates on source ages, distances, and ambient densities. Notes. Some of the ambient density values are not direct measurements but are derived from fits to the X-ray spectra. In some cases correlations with high density gas clumps are observed or inferred. The corresponding densities inside the clumps are denoted by an asterisk. Table A.3. Spectral parameters of the shell-type SNRs detected in both the radio and VHE bands. Notes. Except for the value for RX J1713-3946, all radio flux density values are taken from Green’s catalogue Green (2014) . Also given are the references to the models applied in Sect. 5 . ( a ) Assuming a systematic flux error of 20%. ( b ) Sum of north-east and south-west regions. ( c ) Value from ( Acero et al. 2009 )
Paper: 2019ApJ...883...77S (fulltext)
± 71, 604 ± 21, and 569 ± 22, respectively, consistent with the adopted value given the size of the 95% CIs. Plots of this experiment’s results are not shown owing to their similarity to Figure 6 , with the exception that the θ p plots for the and data show more scatter and larger 95% CIs than the results shown in Figure 6 for the data. The conclusion of the first experiment is that different data are an unlikely explanation for the differences between the adopted value of θ o and the previous estimates. The second experiment fits ellipses to isophotes in sky and galaxy coordinates using the method of J87 to determine whether the results for this method depend on the coordinate system used. Both and for the NIR data are used for determining whether the results for this experiment depend on whether or not the data are filtered for 180° symmetry. Different initial values for the position angles in sky coordinates, φ oi , and phase angles in galaxy coordinates, θ oi , are used for determining whether the results for this experiment depend on these initial values. Initial values of i = 0.23 are used for sky coordinates, and i = 0.10 for galaxy coordinates. These i are adopted from the mean values found by M95 . The ellipse centers are assumed to be the kinematic centers adopted in this paper. The results are shown in Figure 26 . Figure 26. Results for ellipses fit to isophotes. Panel (a) shows the results for sky coordinates. Panel (b) shows the results for galaxy coordinates. The data type and initial values for the different markers are indicated in each panel. The red filled circles and green filled triangles are slightly offset from the center of each ring of data for clarity. The cyan shaded region in panel (b) shows the 95% CI for the adopted value of θ o . The results for sky coordinates shown in panel (a) of Figure 26 are in excellent agreement with the results shown in Figure 2 of M95 . The mean of the φ o results for the data is 927 ± 19 when φ oi = 931 and 931 ± 22 when φ oi = 568. The mean of the φ o results for the data is 925 ± 35 when φ oi = 931. These are all consistent with the mean of the previous estimates given the size of the 95% CIs. The results for galaxy coordinates are shown in panel (b) of Figure 26 . The mean of the θ o results for the data is 534 ± 54 when θ oi = 206 and 547 ± 62 when θ oi = 598. The mean of the θ o results for the data is θ o = 551 ± 42 when θ oi = 206. These are all consistent with the adopted value of θ o given the size of the 95% CIs. The results for the second experiment are inconsequential to the goal of the three experiments, so plots of them are not shown. The results for in sky coordinates consistently differ from the initial values by only a few percent and converge to a mean of ≈0.23. The results for in galaxy coordinates converge to a mean of 0.16 for the data when θ oi = 206, 0.17 for the data when θ oi = 598, and 0.14 for the data when θ oi = 206. There are two conclusions from the
Paper: 2017ApJ...836..144S (fulltext)
the horizontal and vertical components to contribute equally to the line-of-sight field when ∼ 1/6 radian, which is about 9° and almost one day from central meridian. This may explain why it is difficult to extract the canopy fluctuations from the background at locations more than 2–3 days west of the central meridian. 3.3. Inward Motions To study the much slower motions with speeds km s −1 , we again use running differences, but with much longer lags ∼20–30 min. The differences remove the saturated fields near the inner edge of the penumbra. The longer time lags allow the slowly moving features to change their positions by greater amounts and therefore provide stronger signals for their detection in the difference images. Figure 8 compares a jmap of the line-of-sight field for AR12218 (top) with a running difference jmap of this field (middle) and a running difference jmap of 6173 Å continuum intensity (bottom), all with slit orientations of φ = 358°. The difference jmaps were obtained using 22.6 min lags. Short strips of unsubtracted continuum jmaps are shown at the right to indicate the location of the sunspot umbra and penumbra. Also, yellow arrows are oriented at slopes corresponding to 0.5 km s −1 and placed in rows to provide a speed reference. Figure 8. φ = 358° jmaps of the line-of-sight field (top), its running difference (middle), and the continuum running difference (bottom) for AR12218. The difference jmaps show inward tracks (short arrows) along the edge of the masked umbra. Yellow lines (middle panel) separate inflows from outflows whose curved tracks resemble the legs of the quasi-parabolic track of a nearby supergranule (arrow in top panel). The middle panel shows numerous tracks extending inward and outward on both sides of the sunspot. Some of the inward tracks are marked by short arrows. These tracks have speeds comparable to the 0.5 km s −1 reference arrows, and often decelerate at rates ∼0.1 m s −2 as they move toward the gray band that masks the sunspot umbra. In the bottom panel, ingoing tracks of continuum intensity (occasionally indicated by short arrows) are visible along the edge of the masked umbra. We suppose that these continuum tracks are produced by penumbral grain, which also move inward with speeds ∼0.5 km s −1 (Muller 1973 , 1976 ; Zirin Wang 1989 , 1991 ; Lites et al. 1998 ; Sobotka et al. 1999 ; Sobotka Sütterlin 2001 ; Georgakilas et al. 2002 ). The inward tracks of the magnetic field are usually longer than the continuum tracks and show more curvature. This may be due to the greater height of formation of the Fe I 6173 Å line than of the 6173 Å continuum. In the middle panel many tracks curve outward from the mid-penumbra, decelerating as they approach the network region of positive-polarity (white) flux near R / R SG = −2. These outward tracks occasionally join inward tracks to form in/out pairs similar to the parabolic profile of diverging motion in the neighboring supergranule (indicated by the
Paper: 2019MNRAS.482.2484B (acknowledgments)
ACKNOWLEDGEMENTS TM acknowledges the support of the Australian Research Council through grant FT150100099. DLK acknowledges support by NSF grant AST-1412421. This scientific work makes use of the Murchison Radio-astronomy Observatory, operated by CSIRO. We acknowledge the Wajarri Yamatji people as the traditional owners of the Observatory site. Support for the operation of the MWA is provided by the Australian Government (NCRIS), under a contract to Curtin University administered by Astronomy Australia Limited. We acknowledge the Pawsey Supercomputing Centre that is supported by the Western Australian and Australian Governments. The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This research has made use of material from the Bordeaux VLBI Image Database (BVID). This work was supported by the Centre for All-sky Astrophysics (CAASTRO), an Australian Research Council Centre of Excellence (grant CE110001020).
Paper: 2018MNRAS.477.1015G (acknowledgments)
ACKNOWLEDGEMENTS We thank the referee Romeel Davé for his insightful comments which helped us improve the paper. MWG would like to thank Janine Pforr for useful discussions. CCP acknowledges support from the Leverhulme Trust Research Project Grant RPG-2013-418 and from a previous grant from the UK Science and Technology Facilities Council (STFC; grant ST/J001341/1). GAMA is a joint European-Australasian project based around a spectroscopic campaign using the Anglo-Australian Telescope. The GAMA input catalogue is based on data taken from the SDSS and the UKIRT Infrared Deep Sky Survey. Complementary imaging of the GAMA regions is being obtained by a number of independent survey programs including GALEX MIS, VST KiDS, VISTA VIKING, WISE, Herschel –ATLAS, GMRT, and ASKAP providing UV to radio coverage. GAMA is funded by the STFC (UK), the ARC (Australia), the AAO, and the participating institutions. The GAMA website is: http://www.gama-survey.org . GALEX is a NASA Small Explorer, launched in 2003 April. We gratefully acknowledge NASA’s support for construction, operation, and science analysis for the GALEX mission, developed in cooperation with the Centre National d'Etudes Spatiales of France and the Korean Ministry of Science and Technology. We thank the KiDS Consortium for making public the weak-lensing catalogues that were used in this analysis at this website http://kids.strw.leidenuniv.nl/DR3/lensing.php This research is based (in part) on data products from observations made with European Southern Observatory (ESO) Telescopes at the La Silla Paranal Observatory under programme IDs 177.A-3016, 177.A-3017, and 177.A-3018.
Paper: 2018MNRAS.481.3548S (fulltext)
spectra, the instrumental differences in spectral observations render the SEDs globally more homogeneous. To compare these to models, we made use of the VO SED Analyzer (VOSA; Bayo et al. 2008 ) which provides χ 2 and Bayesian fitting to an array of models and templates. For this data, we adopted the χ 2 fitting to BT-Settl models (Allard, Homeier Freytag 2012 ) with the following limits: 700 T eff 4000 K, and . We also limited the absorption in the V band to 0.001 and turned off the excess fitting option as the non-black body distribution of the spectra of these objects was causing the excess fitting procedures to ignore the mid-IR magnitudes. As an example in Fig. 5 , we plot the VOSA fit of 0227-1624 to the BT-Settl model spectra. 0227-1624 is a 16th -band magnitude L1/L0.5 object at 20 pc with a 8 per cent error on the distance for which we have magnitudes in 11 bands. The model slightly underestimates the near- and mid-infrared bands but follows well for the optical bands. Reasons for this over luminosity are beyond the scope of this paper as it will require a more in-depth study of the models and the fitting of VOSA. Figure 5. An example VOSA χ 2 fitting of the 0227-1624 spectral energy distribution to BT-Settl models. The optical to J bands appear to fit well but the model underestimates the flux in the other near and mid-infrared bands reasons for which are discussed in Section 4.4 . The Gaia G observation is plotted in orange because we manually removed it from the fit. We find the nominal G passband tends to over estimate the flux of the L and T dwarfs which can be seen in Fig. 5 . This systematic excess is also reflected in the model (the blue points) as this uses the G passband for the transformation, however, we felt this known systematic error made using the Gaia magnitude for the 38 objects in the Gaia DR1 to constrain the fits premature. The problem of the G passband is noted in the Gaia documentation, 2 and there is an empirical correction proposed(Maíz Apellániz 2017 ). In Fig. 6 , we plot the RAD1 radius from the VOSA fits to BT-Settl models against the near-infrared spectral types. RAD1 is the ‘scaling radius’, i.e. the radius required to fit the observations to the model based on the parallactic distance. From the PARSEC sample, we removed all objects which are known or suspect unresolved binary systems and known or candidate moving group objects (see Section 5.3 ) leaving 60 objects. We removed the moving group objects as these are in general young and we wanted to be sure that our sample was dominated by objects older than 1 Gyr. In Fig. 6 , the targets plotted as diamonds are literature objects and the PARSEC sample are plotted as asterisks. The radius of objects with ages greater than 1 Gyr in this spectral range is predicted to be a minimum at the spectral type that corresponds to the hydrogen burning limit (e.g. Chabrier et al. 2009 ; Burrows, Heng Nampaisarn 2011 ). Objects with earlier spectral types than this
Paper: 2015A&A...573A.106G (acknowledgments)
We thank Xiaohui Sun for providing the Urumqi 6 cm Stokes I image prior to its availability on the survey website. The paper benefitted from discussions with Jonathan Tan, Neal Evans, and Diederik Kruijssen. We thank our referee Jeff Mangum for a helpful and rapid referee report. We are grateful to the editor, Malcolm Walmsley, for additional comments and for catching many small but significant errors. E.R. is supported by a Discovery Grant from NSERC of Canada. This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France. The original description of the VizieR service was published in ( Ochsenbein et al. 2000 ). This publication makes use of molecular line data from the Boston University-FCRAO Galactic Ring Survey (GRS). The GRS is a joint project of Boston University and Five College Radio Astronomy Observatory, funded by the National Science Foundation under grants AST-9800334, AST-0098562, AST-0100793, AST-0228993, AST-0507657. This research has made use of the NASA/ IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Paper: 2019MNRAS.486.2435M (acknowledgments)
ACKNOWLEDGEMENTS I would like to thank the anonymous referee and editors for their useful suggestions, which helped to improve this article. This work has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under Project 176011: ‘Dynamics and kinematics of celestial bodies and systems’.