adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...869..176L (acknowledgments)
We thank Profs. Hong-bo Hu and Qiang Yuan for helpful discussions. We appreciate Su-jie Lin for the discussion of the generated cross section of antiproton and provide EPOS 1.99 data. This work is supported by the National Key Research and Development Program of China (No. 2016YFA0400200), the National Natural Science Foundation of China (Nos. 11875264, 11635011, 11761141001, and 11663006). Software: DRAGON (Evoli et al. 2008 , 2017 ) available at https://github.com/cosmicrays .
Paper: 2017A&A...607A.104B (fulltext)
Zeeman splitting is based on the works of Landi Degl’Innocenti (1976) , Schadee (1978) , Rees et al. (1989) , and Larsson et al. (2014) . For each considered Zeeman split spectral line, ZRAD requires the following precalculated quantities: the energy levels and transitions taken from the Leiden Atomic and Molecular DAtabase (LAMDA; Schöier et al. 2005 ) or JPL spectral line catalog ( Pickett et al. 1998 ), Landé factors of the energy levels used, line strengths of the allowed transitions between Zeeman sublevels, and the radius of the gas species used. For the line shape, ZRAD includes the natural, collisional, and Doppler broadening (line shape: Voigt profile) and the magneto-optic effect (line shape: Faraday-Voigt profile; Larsson et al. 2014 ). The Voigt and Faraday-Voigt profiles are obtained from the real and imaginary part of the Faddeeva function, respectively ( Wells 1999 ). In ZRAD, a fast and precise solution of the Faddeeva function is realized with the Faddeeva package ( Johnson 2012 ). 3. Zeeman splitting We consider the Zeeman splitting theory as outlined in Sect. 3 of our previous work ( Brauer et al. 2017 ). In the following, we provide a brief description of the most important physical quantities. By assuming that the Zeeman shift is negligible when compared to the line width , the circularly polarized fraction can be written as a function of the derivative of the net flux as (see Crutcher et al. 1993 ; Brauer et al. 2017 ) whereas the frequency shift owing to Zeeman splitting can be calculated with Here, is the Bohr magneton, the Landé factor, and the total angular or atomic momentum quantum number projected on the magnetic field vector . A single prime denotes the upper level; a double prime denotes the lower level of the line transition. Following Eqs. ( 1 ) and ( 2 ), the magnetic field strength in the LOS direction can be obtained by fitting the derivative of the net flux profile to the circularly polarized fraction of a Zeeman split spectral line. The derived LOS magnetic field strength converges toward a reference magnetic field strength if the assumption of Eq. ( 1 ) is valid and the LOS is optically thin ( Brauer et al. 2017 ). The reference magnetic field strength can be calculated by averaging the LOS magnetic field strength weighted with the spectral line net flux along the LOS to the observer as follows: Here, is the magnetic field strength at a certain position, is the unit vector in the LOS direction, and is the path length along the LOS. 4. Spectral lines of CN at 113 GHz Zeeman observations of CN are usually performed by observing seven of the nine hyperfine transitions at ( Falgarone et al. 2008 ; Pillai et al. 2016 ). However, we limit ourselves to the spectral lines at and since they represent two extreme cases (see Table 1 ; Crutcher et al. 1999 ; Falgarone et al. 2008 ). The line at has the largest Zeeman shift and one of the highest relative sensitivities to compared to the other lines. In contrast, the
Paper: 2015A&A...584A..26B (acknowledgments)
We are very grateful to the referee, Anthony Brown, for a constructive and thorough report that improved the manuscript substantially. We are also very grateful to Frédéric Arenou for enlightening explanations regarding the treatment of H ipparcos parallax measurement errors, and to Michail Vidiassov and David Barnes for their help with R, rgl and 3D PDF figures. We acknowledge the help of the anonymous specialists who provided their assistance on various StackExchange.com forums. H. Bouy is funded by the Ramón y Cajal fellowship program number RYC-2009-04497 and by Spanish Grant AYA2012-38897-C02-01. J. Alves acknowledges travel support from the ESAC Faculty council. This research has made use of the VizieR and Aladin images and catalog access tools and of the SIMBAD database, operated at the CDS, Strasbourg, France. This research has made use of Topcat ( http://www.starlink.ac.uk/topcat/ , Taylor 2005 ). This research made use of Astropy, a community-developed core Python package for Astronomy ( Astropy Collaboration et al. 2013 ). We acknowledge the use of the Legacy Archive for Microwave Background Data Analysis (LAMBDA), part of the High Energy Astrophysics Science Archive Center (HEASARC). HEASARC/LAMBDA is a service of the Astrophysics Science Division at the NASA Goddard Space Flight Center. Using observations obtained with Planck ( http://www.esa.int/Planck ), an ESA science mission with instruments and contributions directly funded by ESA Member States, NASA, and Canada.
Paper: 2015A&A...579A..82R (acknowledgments)
The authors thank the French CNRS/INSU program PCMI for their partial support of this work. V.W. and F.H. research is funded by the ERC Starting Grant (3DICE, grant agreement 336474). The authors also thank the referee for suggestions which helped to improve this paper.
Paper: 2021ApJ...912L..15T (fulltext)
available.) CBP1 showed a lying J-shaped structure consisting of simple potential loops, while CBP2 looks like a minisigmoid structure composed of two evident sigmoidal strands (see Figures 1 (A)–(C)). A line-of-sight (LOS) magnetogram is displayed in Figure 1 (D) to show the magnetic field in the eruption source region, in which the white (black) patches represent positive (negative) polarities. The purple curve outlines the shape of CBP1, which connects opposite magnetic polarities of P1 (positive) and N1 (negative), while the green and orange curves outline the two strands of CBP2 linking opposite polarities of P2 (positive) to those of N2 (negative). It is noted that a negative magnetic polarity (N) was located in between P1 and N1. The overall eruption process can be divided into three stages: the formation of the first jet (jet1: 11:10 UT—11:30 UT), the interaction of jet1 with CBP2 (11:30 UT—11:37 UT), and the formation of the second jet (jet2: 11:37 UT—12:00 UT). In the following subsections, we discuss the eruption characteristics in association with those stages in more detail. 3.1. Formation of Jet1 and Its Interaction with CBP2 Figure 2 shows the formation of jet1 and its interaction with CBP2, in which the first, second, and third rows are XRT Al_Poly, AIA 211 Å, and AIA 171 Å time sequence images, respectively. In the bottom row, the left panel shows the magnetic flux variations (measured from the HMI LOS magnetograms) within the eruption source region of CBP1, while the right panel shows the corresponding relative intensity curve (measured from XRT Al_Poly, AIA 211 Å, and AIA 171 Å images) within the same region. The left column of Figure 2 shows CBP1 and jet1. The rising of the lying J-shaped CBP1 caused the collimated jet1, and the jet spire showed an obvious westward lateral sweeping motion as indicated by the arrows in Figures 2 (A) and (B). In the meantime, a bright patch around the eastern end of CBP1 appeared, which has a typical inverted-Y (or cusp) shape and can be regarded as the post-flare loop (PFL1) caused by the reconnection between CBP1 and its ambient open field lines (see the upward arrows in Figures 2 (A)–(I) and the inset in Figure 2 (G)). With an overall consideration of the appearance locations of the jet, PFL1, and the magnetic polarities as shown in Figure 1 (D), the magnetic reconnection should occur between CBP1 and the open field lines rooted in the negative magnetic region N, and this can also explain the westward lateral motion of the jet spire. From the EUV and soft X-ray intensity lightcurves in Figure 2 (K), one can identify that the start of jet1 was about 11:18 UT (see the vertical black line in each panel). For the magnetic flux variations (Figure 2 (J)), both the positive and absolute value of the negative magnetic fluxes showed obvious rising (decl.) before (after) the start of the jet. Such a variation pattern of the magnetic fluxes suggests the first emerging and then cancellation of the
Paper: 2019ApJ...872..127M (fulltext)
to study time variations in the outer heliosphere and its interaction with the VLISM over more than half a solar cycle. They found that the globally distributed flux (GDF) and Ribbon evolved differently, with the GDF, which mostly arises from charge exchange in the heliosheath, exhibiting a leveling off and partial recovery of ENAs consistent with the prior output flattening and recovery of the solar wind output. In contrast, the ribbon showed a greater time delay and loss indicating a source of the Ribbon beyond the heliopause, most likely as a secondary ENA source—one of the six original ideas suggested by the IBEX team at the time of the discovery (McComas et al. 2009c ). Since then, a variety of more detailed physical processes for various types of secondary sources have been proposed (Chalov et al. 2010 ; Heerikhuisen et al. 2010 ; Schwadron McComas 2013 ; Isenberg 2014 ; Giacalone Jokipii 2015 ; Zirnstein et al. 2018a ). As also pointed out by McComas et al. ( 2017 ), in the second half of 2014 the dynamic pressure increased by ∼50% from ∼1.6 to ∼2.4 nPa and remained that way for at least the following couple of years. These authors discussed how such a persistent increase in the solar wind output and dynamic pressure should have a continuing major impact on evolution of the structure of the outer heliosphere and the returned ENAs. Specifically, they predicted that the ENA response should be (1) seen first from the direction of maximum heliosheath pressure ∼20° south of the upwind direction (McComas Schwadron 2014 ), (2) observed first in the highest energy ENAs, which travel back fastest and then extending to lower energies over time, and (3) followed a couple years later by changes from the Ribbon and back along the flanks of the heliosphere. IBEX observations generally bore out these predictions and McComas et al. ( 2018b ) showed that this large increase in solar wind dynamic pressure observed in the second half of 2014 at 1 au had begun to modify the heliosheath and produce enhanced higher energy ENA emissions observed near Earth by IBEX starting in late 2016. As predicted, the enhanced emissions were centered south of the upwind direction and were seen first in the higher energy ENAs. McComas et al. ( 2018b ) also combined the observations with MHD simulations to show that the heliosphere was re-expanding from this persistent pressure enhancement after at least a decade of diminished size. McComas et al. ( 2018b ) further connected the IBEX observations with simulations to show that this pressure enhancement also very likely produced the largest enhancement in heliosheath dynamic pressure seen at Voyager 2, which was observed in late 2015 (Richardson et al. 2017 ). The roughly one year between the Voyager 2 observation and pressure enhancement beginning at 1 au in late 2014 indicated that Voyager 2 observed a transient pressure wave in the heliosheath, which was launched by the supersonic solar wind reaching the termination
Paper: 2020MNRAS.497.2730G (fulltext)
is ∼92.5 per cent, and improves as the offset distance becomes closer to zero. Ching et al. ( 2017 ) provided optical identifications using data from Sloan Digital Sky Survey (SDSS) of 19 179 of radio sources from FIRST using over an area of 800 deg 2 . They obtain a reliability of 93.5 per cent using a rule-based methodology with visualization inspection of complex FIRST sources. We generalize this analysis to all collated groups and investigate any second-order effects as functions of collated number of FIRST components, maximum distance ( D ) and curliness ( Q ). The R was evaluated across a range of separations (spaced from 0 to 10 arcsec in steps of 1 arcsec) after dividing our collated groups into meaningful subsets. For the subsets in the D and Q dimensions, we established boundaries at the 20th, 40th, 60th, 80th, 85th, 90th, and 95th percentiles, where finer bins were used in regions of the parameter space with rapid variations identified after inspection of the distribution. Otherwise, we created boundaries based on the number of associated FIRST components in each collated group. We interpolate between R measures across these dimensions to create a set of reliability curves, which we present as in Fig. 5 . Broadly speaking, these curves show that as measures of complexity increase, judged either by the number of collated FIRST components or Q , the infrared predicted position becomes less reliable. At a threshold of 3.4 arcsec, these R are above 80 per cent with significant spreads of upwards of ∼12 per cent between the simple and complex groups. However, the reliability curves in Fig. 5(c) suggest groups across all D have about the same degree of reliability. We use these interpolated reliability curves to provide a measure of reliability for all collated groups in our catalogue data products for each of these dimensions. For groups outside the range of these reliability curves, we set the R = 0.5 across all dimensions. Of the 24 reliability curves in Fig. 5 , 6 have fewer than 20 genuine matches with separations 1 arcsec and are the most susceptible to small number statistical effects. Since increasing separations accumulate counts from smaller separations (i.e. bins are not exclusive), the number of genuine matches very quickly grows so that all other curves and separation bins have at least 20 genuine matches. Figure 5. Panel (a): the reliability curves of the cross-matched infrared predicted positions against AllWISE as a function of number of collated FIRST components. Panel (b): the reliability curves of the cross-matched infrared predicted positions made against AllWISE as a function of the parameter. Boundaries of the were spaced to contain roughly the same number of groups to maximize the clarity of the different reliability trends. All groups included in this figure contain three or more FIRST components. Panel (c): the same as panel (b) except as a function of D . All groups included in this figure contain two or
Paper: 2018MNRAS.477.1472B (acknowledgments)
ACKNOWLEDGEMENTS The authors wish to thank Kathryn Johnston and Douglas Boubert for stimulating discussions that helped to improve this paper. VB is grateful to Natàlia Mora-Sitjà for the careful proofreading of the manuscript. The authors thank the anonymous referee for thorough reading of the paper. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 308024. AJDe is supported by a Royal Society University Research Fellowship. AJDe also acknowledges support from the STFC grant ST/P000451/1. NWE thanks the Center for Computational Astrophysics for hospitality during a working visit. The authors gratefully acknowledge support by CONICYT/RCUK’s PCI program through grant DPI20140066 (‘Newton Funds’). MC is additionally supported by the Ministry for the Economy, Development, and Tourism's Millennium Science Initiative through grant IC 120009, awarded to the Millennium Institute of Astrophysics (MAS); by Proyecto Basal PFB-06/2007; and by FONDECYT grant #1171273.
Paper: 2018MNRAS.476.2432G (fulltext)
than the RM of the low- z DLA sample. Such a probability indicates no significant effect. Any additional RM contribution from the plasma of the intragroup medium (see Section 3.7 ) must be low, indicating either low electron density, a weak magnetic field, or field reversals along the line of sight. The most likely scenarios are either that there is a (1 + z ) 2 dilution in the RM of background quasars, or that the Galactic RM foreground varies substantially between samples. There is no evidence for a (1 + z ) 2 dilution in the RM of the DLAs themselves, which do not show a marked difference from the control samples. Previous attempts to observe the former scenario of an evolving background quasar, e.g. Hammond, Robishaw Gaensler ( 2012 ), find that any (1 + z ) 2 dilution in RM due to cosmological expansion is weak out to z ∼ 3.5 (see also Xu Han 2014 ). If our data do therefore show an evolution in the background quasars, it may be due to a low-redshift selection effect on, for example SDSS quasars relative to high-redshift SDSS quasars, or possibly a low-redshift evolution in the magnetoionic environment surrounding the quasars themselves. To constrain the latter scenario of a varying Galactic RM foreground between samples, we use the best available model from Oppermann et al. ( 2015 ). A plot of the distribution of Galactic RMs (GRMs) towards each source is shown in Fig. 9 . There is again no difference between specific samples. We calculate a Bayesian probability of 61.5 per cent that the GRM towards J1243+4043 is greater than the GRM towards the low- z DLA sample based on 21-cm absorption measurements. This is again consistent with no effect. The combination of no difference in RM, together with no difference in GRM, means that we are consequently unable to draw conclusions about the coherent magnetic fields in the DLA in J1243+4043 itself. The only difference between the data sets is the distributions of the high- versus low-redshift RMs, which display no subsequent difference in the GRMs. This is consistent with our earlier interpretation of either an uncontrolled selection effect or an evolution of the quasar magnetoionic environment at low redshifts. Figure 9. The distribution of GRMs for high- z DLAs (top panel) and low- z 21-cm absorbing QGPs (bottom panel). The ‘control’ refers to sight lines with no DLA ( top ) and no 21-cm absorption ( bottom ), respectively. In terms of J1243+4043, the magnetic field properties do not allow us to distinguish between different scenarios in order to place strong constraints on this source. Future broad-band spectropolarimetric observations of this source will enable improved tests that can attempt to determine where this sight line fits within the greater source population. 3.7 H <sc>i</sc> emission from the associated galaxy group: the environment of the foreground galaxy In the WSRT data, we serendipitously detect H i 21-cm emission associated with other members of the galaxy group. The group
Paper: 2018ApJ...867...23H (fulltext)
we have optimistically ignored flux and noise from unsubtracted host-galaxy light that will be there as a consequence of not having a galaxy template at the time of classification. Even if a spectrum of the host galaxy does exist (e.g., from a ground-based spectrograph), the exact galaxy SED at the position of the SN will not be accurately measured. The SN Ia efficiency for a given selection cut (see Section 3.1 ) is shown in Figure 6 . Efficiency is defined as the number of SNe correctly identified as Type Ia and passing each cut, divided by the number of SNe Ia that pass Cut 0. This calculation is per 0.1 redshift bin. SNe that pass Cut 4 have short-, medium-, and deep-exposure IFC-S spectra, in addition to the six short follow-up spectra. Figure 6. SDT and SDT* (left and right panels, respectively) SN Ia selection efficiency as a function of redshift. Efficiency is defined as the number of SNe correctly identified as Type Ia within each cut, divided by the number of SNe Ia that pass Cut 0 (see Section 3.1 ). The efficiency is calculated per 0.1 redshift bin. The gold squares, green diamonds, blue triangles, and black circles represent the efficiency of SNe Ia that pass Cuts 1, 2, 3, and 4 respectively. Lines connect data from the same tier of the survey. The large drop in efficiency from the first to the second cut at z 0.8 for the SDT strategy is caused primarily by the SDT requirement that the SN flux rises between each epoch. Comparison of the two strategies suggests that the looser selection criteria of the SDT* strategy is significantly more efficient than that of the SDT. The efficiency is low at particular redshifts, 0.3 z ≤ 0.4 and 0.7 z ≤ 0.8. This efficiency gap is partially the result of the survey design producing insufficient SN discoveries at the high- z end of each tier. However, photometric selection criteria that require the color to be consistent with an SN Ia at their host-galaxy redshift, and that the flux rise between epochs, are the main contributors to the low efficiency. For the shallow imaging tier, which covers 0.1 ≤ z 0.4, these criteria are problematic due to the tier’s short 13 s exposure, which results in noisy light curves and an undetectable rise value. The large reduction in the number of SNe between Cuts 1 and 2 is also caused by the required increase in flux between epochs. In many cases, statistical noise causes a candidate to appear to fade between two successive epochs. To reduce this selection artifact, this criterion is loosened via the iterative examination of a range of measured rise values (including negative values) for the simulated SNe Ia as a function of redshift for each tier, and a looser cut is applied. The requirements on the discovery colors ( Y − J for shallow, J − H for medium and deep) are also tightened, excluding some of the most extreme SNe Ia and significantly reducing the number of CC SNe at each step. The effect of these improved selection criteria is to increase the SN