adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...911...42K (fulltext)
to CMB observations at a frequency around 150 GHz and shows a high level of non-Gaussianity in both total intensity and polarization. We therefore choose to apply our algorithm to thermal dust maps, with the goal of obtaining simulations with realistic and nontrivial small-scale structures at subdegree angular resolution. In this section, we describe the methodology used as well as the results obtained. Existing templates of thermal dust emission on the full sky have been obtained by applying component separation algorithms to Planck maps. In this work, we used the ones obtained using the GNILC (Generalized Needlet Internal Linear Combination) method at 353 GHz, which have the advantage of not being contaminated by CIB radiation in total intensity (for a detailed description of the data and the method applied to get this thermal dust template, we refer to Planck Collaboration Int. XLVIII 2016 ). The GNILC dust maps have an angular resolution that varies in the sky and depends on the S/N of the Planck high-frequency maps: in total intensity, the effective beam FWHM ranges from 5′ to about 22′, while in polarization, it varies in the interval 5′–80′ (Planck Collaboration IV 2020 ). 10 10 The GNILC maps are available on the Planck Legacy Archive website, http://pla.esac.esa.int/pla/#maps . Both polarization and intensity maps follow the HEALPix 11 11 https://healpix.sourceforge.io/index.php pixelization scheme (Górski et al. 2005 ) at N side = 2048, i.e., ∼1.7′ pixel resolution. 4.1. Total Intensity We first tested the ability of F or SE to generate realistic small-scale features in total intensity (quantified by the Stokes I parameter). To do so, we needed a set of patches for which real small-scale structures have been observed. As stated above, the GNILC template in total intensity has a variable angular resolution, which is equal to 5′ in regions close to the Galactic plane, thus making them suitable to be used to train the GAN. In order to further select only those regions less contaminated by noise, we built a mask from the Planck HFI 353 GHz I map. In particular, we took into account only that part of the sky where the S/N in the 353 GHz map at the full angular resolution (about 5′) is above a threshold of 8. We smoothed the obtained mask with a Gaussian beam with an FWHM of 1° in order to regularize its borders. We also cut out the inner part of the Galactic plane (with b 10°), which, due to its peculiarity, is not suitable to be used as part of the training set. The resulting mask is shown in Figure 2 and covers a fraction of the sky of about 23%. Figure 2. Sky mask adopted in this work (see Section 4.1 for details). Images used to perform the GAN training have been selected within the purple region. Yellow dots represent the position of the center of the patches shown in Figures 3 – 5 , whereas the green one corresponds to the patch in Figures 6 – 8 . In order to train the GAN, both M LS and images were needed. The M LS patches
Paper: 2020MNRAS.496.4209F (fulltext)
difficult to provide because we have little prior information on it. However, a parsimonious parametric model of the PSF is suited for deconvolution processes (Drummond 1998 ; Markham Conchello 1999 ; Fétick et al. 2019a ). It is thus more efficient to parametrize the PSF using the available information on the observing system. The PSF model developed in Fétick et al. ( 2019b ) is adapted to AO-corrected images as it is able to describe both the coherent core and the turbulent halo of the AO-corrected PSF. Even with the strong prior of a parsimonious PSF parametrization, the joint maximum a posteriori estimator is not ensured to converge towards a satisfactory result. Indeed, the degeneracy of this estimator was demonstrated by Blanco Mugnier ( 2011 ) with a linear combination of PSFs for application to biomedical imaging. In this paper, we show that the joint estimator is also degenerated with a non-linear PSF model, although adapted to astronomical imaging. Because of the drawbacks of the joint estimator, we consider another method, which is the marginal estimator (Berger, Liseo Wolpert 1999 ; Blanc, Mugnier Idier 2003 ; Levin et al. 2009 ). This method consists of integrating – marginalizing – the object out of the problem to estimate only the PSF parameters. This estimator is asymptotically consistent and non-biased (Lehmann 1983 ) when the number of data or the signal-to-noise ratio (S/N) increase. We apply this method to our astronomical case in order to estimate the PSF parameters. The PSF is then used in a conventional (non-blind and non-myopic) deconvolution to estimate the object. In Section 2 , we explain the reasons for our choice of a parametric PSF model adapted to AO-corrected images. The first approach would be to use this parametric model to perform joint estimation. In Section 3 , we show that the deconvolution based on a joint estimation of the PSF and the object does not converge towards the correct solution in spite of the parametric PSF model introduced. Thus, we consider the marginalized approach in Section 4 . We study the effect of the S/N and the supervision level on the PSF estimation and image deconvolution. We show that this method can be adapted for blind deconvolution, with an example using astronomical images from the Spectro-Polarimetric High-contrast Exoplanet Research (SPHERE)/Zurich Imaging Polarimeter (Zimpol) on the Very Large Telescope (Beuzit et al. 2019 ). Finally, we conclude in Section 5 . 2 PSF AND IMAGING MODELS 2.1 PSF model Because ofhighly variable atmospheric conditions and the complexity of the AO correction, the PSF must be estimated for each observation. The simple method of estimating a PSF on a bright star is not possible for many observational cases, especially for observation of rsesolved objects in a small field of view. The PSF must be estimated with another method in order to be able to perform post-processing techniques on the image. This estimation might be based on telemetry or on
Paper: 2016A&A...585A..64S (fulltext)
and also separately on the blue ([3600, 5500] Å) and red ([5500, 7200] Å) regions. The cyan contours overlaid correspond to 1 to 5. The three maps are similar, and the location of the solution is consistent within 1. The elongated shape of the contours, in particular, on the vs. [Fe/H] plane reflects the degeneracy between the parameters. For good data and normal stars, the maps as well as the parameters do not depend on the fitted wavelength range. Figure 6 shows the maps for G 156-031, a star at the edge of the parameter space populated by the MILES stars. The values increase sharply on the low side, resulting in a dissymmetry that is potentially at the origin of a bias. We discuss below the stars labelled in Fig. 1 and listed in Table 6 . Fig. 5 maps for HD 61913 with different fitting wavelength ranges. First with the complete wavelength range used (36007400 Å), second 36005500 Å, and third 55007200 Å. The red crosses indicate the minimum, and the cyan crosses the compiled parameters. The inner contour in cyan correspond to the external errors, and the other contours correspond to the 2, 3, 4 ... levels. Table 6 Remarkable stars labelled in Fig. 1 . (1) V1855 Ori = MS 0515.4-0710 (MILES 175)is a K0 or K2 type star. V1855 Ori and MS0515.4-0710 currently appear as two distinct sources in SIM-BAD, but their cross-identification with a unique sourceleaves no doubt, and allows us to complete our compilation. Favata et al. (1997) and Ce-narro et al. (2007) determined[4570, 3.50, 0.16] using the Strömgren photometry of Moraleet al. (1996) and the calibration re-lation of Alonso et al. (1996) . Xing (2010) gives [en-tity!#x2009!]= 4908 K and = 4.45 dex,computed from the colour and calibrations of Casagrande et al. (2006) . Biazzo et al. (2012) give [5100, 4.2, 0.05], based on high-resolution spectroscopic observations. Martin et al. (1994) adopted = 5150 K from their own relation between the spectral type and the temperature. In our compilation we adopt the average parameters [4860, 4.05, 0.10]. The parameters obtained after fitting the blue range, [], and the red range, [], differ significantly, as is often the case for variable stars. Our adopted measurement is not discrepant with the analysis of Biazzo et al. (2012) and therefore we trust our measurements. (2) HD 113285 (MILES 459) is a M8 III type pulsating variable star. This star belongs to the LICK/IDS library ( Worthey et al. 1994 ). Gorgas et al. (1999) determined = 2924 K by extrapolating the versus spectral type relation of Ridgway et al. (1980) . Gorlova et al. (2003) obtained = K. The parameter was derived from the spectral type and was obtained from , luminosity, and mass. McDonald et al. (2012) derived = 2602 K using spectral energy distribution (SED) fitting, neglecting Galactic extinction and assuming a solar composition. The authors PVK adopted [], and in our compilation we adopt the average of the available measurements [], where = 0.0 dex is a reasonable guess for this type
Paper: 2016ApJ...823..116Z (acknowledgments)
This work was supported by the Ministry of Science and Technology of China (grant no. 2013CB837900), the National Science Foundation of China (grant nos. 11125313, 11203017, 11433002, 61271349, and 61371147), the Chinese Academy of Sciences (grant no. KJZD-EW-T01), and the Science and Technology Commission of Shanghai Municipality (grant no. 11DZ2260700).
Paper: 2017ApJ...834..193K (acknowledgments)
We thank Brian Svoboda for helpful comments and suggestions. JCT and SK acknowledge an NRAO/SOS grant and NSF grant AST1411527. PC acknowledges the financial support of the European Research Council (ERC; project PALs 320620). This paper makes use of the following ALMA data: ADS/JAO.ALMA#2013.1.00806.S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. Facility: ALMA .
Paper: 2019MNRAS.487.1160B (acknowledgments)
ACKNOWLEDGEMENTS The authors would like to especially thank Catherine Watkinson for her contributions to an earlier draft of this work. We would also like to thank Daniel Mortlock, Conor O’Riordan, Brad Greig, Andrei Mesinger, Emma Chapman, Roberto Trotta, Alan Heavens, Suman Majumdar, and the anonymous referee for their helpful conversations and suggestions. We acknowledge use of linux os, c, c ++, fortran 90, gnu, gsl,overleaf, github, python , and the python packages numpy, scipy, cornerplot, matplotlib . T. Binnie thanks Science Technology Facilities Council (STFC) for their studentship funding. J. R. Pritchard is pleased to acknowledge support from the European Research Council under ERC grant number 638743-FIRSTDAWN.
Paper: 2018A&A...609A..35P (acknowledgments)
This work was supported by NASA grant NNX16AC07G through the XMM-Newton Guest Observing Program, and through grants for HST program number 14054 from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. The research at the Technion is supported by the I-CORE program of the Planning and Budgeting Committee (grant number 1937/12). E.B. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 655324. SRON is supported financially by NWO, The Netherlands Organization for Scientific Research. N.A. is grateful for a visiting-professor fellowship at the Technion, granted by the Lady Davis Trust. S.B. and M.C. acknowledge financial support from the Italian Space Agency under grant ASI-INAF I/037/12/0. B.D.M. acknowledges support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 665778 via the Polish National Science Center grant Polonez UMO-2016/21/P/ST9/04025. L.D.G. acknoweledges support from the Swiss National Science Foundation. G.P. acknowledges support by the Bundesministerium für Wirtschaft und Technologie/Deutsches Zentrum für Luft- und Raumfahrt (BMWI/DLR, FKZ 50 OR 1408 and FKZ 50 OR 1604) and the Max Planck Society. P.O.P. acknowledges support from CNES and from PNHE of CNRS/INSU.
Paper: 2018ApJ...859...37G (fulltext)
the literature. Gray symbols show Local Group dwarf galaxies from Kirby et al. ( 2013 ), and gray dashed and dotted lines represent the median and 16th and 84th percentiles of the metallicity distributions. Cyan dashed and dotted lines show the median and 16th and 84th percentiles of the metallicity distributions for various types of galaxies in Gallazzi et al. ( 2005 ). Green symbols show the stellar mass–metallicity relation for early-type galaxies binned in stellar mass (Conroy et al. 2014 ). Top right: relation between velocity dispersion and [Fe/H] for DF 44, compared with early-type galaxies binned in velocity dispersion (Conroy et al. 2014 ), and Local Group dwarf galaxies (Kirby et al. 2013 ). Bottom left: relation between metallicity and the logarithmic ratio of stellar mass to dynamical mass for DF 44, Local Group dwarf galaxies (Kirby et al. 2013 ) and early-type galaxies binned in velocity dispersion (Conroy et al. 2014 ). Previous work concluded that some UDGs, including DF 44, may live in more massive dark matter halos than would be expected for their luminosities (e.g., M vir > 10 11 M ⊙ , van Dokkum et al. 2016 ). In addition, it is well-known that the stellar properties of early-type galaxies are tightly related to their dynamical masses (e.g., Gallazzi et al. 2006 ; Graves et al. 2009 ). Therefore, we also look into the relation between their stellar properties and gravitational potential well depth. The top right panel of Figure 12 shows the relation between stellar velocity dispersion ( σ ) and metallicities. We compare DF 44 with dwarf galaxies (Kirby et al. 2013 ), massive early-type galaxies binned in velocity dispersion (Conroy et al. 2014 ). We also compare to 12 elliptical and S0 galaxies in the Coma cluster (Trager et al. 2008 ). Their iron abundances are calculated based on their metallicities and the enhanced element abundances ([Z/H] and [E/Fe]) in Trager et al. ( 2008 ) and Equation (3) in Trager et al. ( 2000 ). DF 44 stands out in this plot in the sense that it has lower metallicity for its σ compared to other galaxies. We also know that the stellar mass of DF 44 is unusually low for its σ and since it is the (massive) stars that produce metals, it is perhaps not surprising that DF 44 has low metallicity for its σ . We explore this point further in the bottom-left panel of Figure 12 , where we plot [Fe/H] as a function of the logarithmic ratio of stellar mass to dynamical mass. This ratio is proportional to the integrated star formation efficiency. In this diagram, one can imagine diagonal tracks where a galaxy in a given halo evolves along the track as it converts baryons into stars. Tracks associated with more massive halos will be offset vertically in this diagram, as a given integrated star formation efficiency (a given stellar-to-dynamical mass ratio) will result in a higher stellar metallicity for galaxies in deeper potential wells, as such systems will be able to retain a greater fraction of the
Paper: 2018ApJ...863...89S (fulltext)
et al. ( 2012 ) analysis does not apply. This result is obviously related to the apparent first-infall status of many of these galaxies. Among the galaxies for which the binding energy is well behaved, the Rocha et al. ( 2012 ) relation suggests infall times of ∼7 Gyr for Tri II (also see Carlin et al. 2017 ), Segue 2, and Willman 1; ∼8 Gyr for Draco II and Tuc III; and >12 Gyr for Ret II. Ret II has a higher binding energy than any of the Via Lactea 2 subhalos included in the Rocha et al. sample. 6.5. Implications for the Mass of the Milky Way As discussed in previous sections and shown in Figure 6 , a number of the target galaxies of this study appear to be very loosely bound and may be on their first orbital passage around the Milky Way, which would be an unexpected result. I showed in Section 5 that this problem can be alleviated if the mass of the Milky Way is larger than assumed in the default galpy MWPotential2014 model. Relatively large Milky Way masses have been advocated by some authors (e.g., Boylan-Kolchin et al. 2013 ), but if the mass of the Galaxy is large, that will tend to exacerbate issues such as the missing satellite problem (Klypin et al. 1999 ; Moore et al. 1999 ) and the too big to fail problem (Boylan-Kolchin et al. 2011 , 2012 ). The alternative explanation is that many of the ultra-faint satellites are on their first orbit because they formed in a Magellanic group (D’Onghia Lake 2008 ) that is now infalling into the Milky Way for the first time (Besla et al. 2007 ; Kallivayalil et al. 2013 ). However, if many of the currently known satellites were originally located around the LMC, that also has adverse consequences for the missing satellite problem, because the number of dwarfs formed around the Milky Way would be even smaller. Improved estimates of the Milky Way’s mass based on dynamical modeling of the kinematics of the satellite population provided here and by Gaia Collaboration et al. ( 2018b ) will be valuable for attempting to resolve this dilemma. 7. Summary and Conclusions I have presented Gaia DR2-based proper motions, space velocities, and Galactic orbits for 17 ultra-faint satellites of the Milky Way. I cross-match lists of member stars that are confirmed via spectroscopy or variable star pulsations with the Gaia DR2 catalog to determine the motion of each system across the sky. Thanks to the exquisite astrometric performance of Gaia , even with as few as ∼3 member stars brighter than g = 20 per satellite, the proper motions are determined with a typical precision of ∼0.1 mas yr −1 per coordinate. At a median distance of 35 kpc, the uncertainties on the tangential velocities are therefore ∼25 km s −1 . I find good agreement with the proper-motion measurements reported by Gaia Collaboration et al. ( 2018b ) for Boötes I and Leo I, as well as with HST measurements of Leo I (Sohn et al. 2013 ) and Ω Centauri (Libralato et al. 2018 ). The tangential velocities of the sample of ultra-faint dwarfs analyzed
Paper: 2019A&A...621A..57T (fulltext)
around 150 MHz 6 , the fluence limit at a 5 sigma level at zenith is 2.5 Jy ms for core stations or 1.5 Jy ms if all Dutch stations can be combined fully coherently. At that threshold, more than half of the known FRBs up to a DM of 500 pc cm −3 would be bright enough to be detected. This may be the most promising route to observe FRBs with LOFAR, since it would be possible to detect flat spectrum FRBs at 150 MHz. 6. Conclusions The FRATS project searches in real-time for millisecond-scale radio transients with the LOFAR radio telescope. Some known transients of this kind are FRBs and RRATs. The focus is to obtain an optimal sky coverage by using commensal observations and a large field of view. An innovative addition is the use of ring buffers that hold the last seconds of data from each individual antenna which ultimately allows for post-event beam-forming and imaging. This increases the positional accuracy, potentially to the full arc second resolution. This is important for pinpointing the location of FRBs. The potential of LOFAR is demonstrated by two different results. The first demonstration is the successful commensal observing from the Pilot C survey, where beamformed data were produced to search for millisecond-scale radio transients, during regular interferometric observations. Five pulsars were discovered in a 24-h scan. The second is the identification of a transient as a type II solar radio burst by localizing it in a side lobe. This could only be done by using the buffered data to create an all sky image. Besides these demonstrations we showed the results from the pilot surveys A and B, which searched up to a DM of ∼110 pc cm −3 and 500 pc cm −3 , respectively. While known pulsars were rediscovered, no unknown single-pulse sources have been detected. We have derived limits on the local FRB rates from these surveys. The first pilot survey (A) had an effective observing time of 68.51 beam h, observed from 119 to 151 MHz, the lowest frequency range on which FRBs have been searched. Taking into account the pointing direction and noise level for each observation, this sets a limit on the occurrence of FRBs of less than 1500 events per sky per day above a threshold of 1.6 kJy ms below a DM of ∼110 pc cm −3 . Pilot study B had an effective observing time of 91.2 beam h in the same band. This sets limits at 1400 events per sky per day or 134 events per Gpc 3 per day at a fluence threshold of 6.0 kJy ms for an 8-ms pulse. The expected rate is one per beam per 16 days, for a uniform extragalactic distribution, provided the source flux density follows a steep spectrum of ∼ ν −3 . In the case where the source population as a function of redshift follows the star formation rate, the rate drops to one per beam every 58 days. If the spectrum is less steep and/or a significant fraction of FRBs are broadened by scattering beyond our detection limits, a longer integration time is required. To be able to obtain the required observing time, we