adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.464.2000A (acknowledgments)
We gratefully acknowledge funding for Swift at the University of Leicester by the UK Space Agency. AAR's studentship funding is provided by the STFC. We thank the referee for their useful comments and suggestions.
Paper: 2019MNRAS.487.5630H (acknowledgments)
ACKNOWLEDGEMENTS We thank Scott Tremaine for helpful discussion, and comments on the manuscript. We also thank Stephen McMillan and Piet Hut for stimulating discussions, Douglas Heggie, Fred Rasio, Evgeni Grishin, and Chris Hamilton for comments on the manuscript, and the anonymous referee for a helpful report. ASH gratefully acknowledges support from the Institute for Advanced Study, and the Martin A. and Helen Chooljian Membership. JS acknowledges support from the Lyman Spitzer Fellowship.
Paper: 2016A&A...590A..26C (fulltext)
from the Coronene, Ovalene, Anthracene, Tetracene families ( Ricca et al. 2012 ; Peeters et al., in prep.), and the Pyrene family. While most of these PAHs are very compact, we have also included a few non-compact ones, to investigate the effect of the molecular structure on the 3.3/11.2 m ratio. All the PAHs in the selections are shown in Fig. A.2 ). As the UV absorption cross-sections for all PAHs are very similar, the uncertainty produces a vertical shift in Fig. 5 . Figure 5 shows that the intensity ratio is sensitive to the size of the PAHs ( Allamandola et al. 1985 ) and forms a well-defined relationship ( Ricca et al. 2012 ). The slope and shape of the relation is not sensitive to the stellar input spectrum or the extinction in the PDR. However, it can be shifted vertically for different average photon energies or adopted intrinsic strength. 4.2. PAH size With the model (Fig. 5 ), the 11.2/3.3 m ratio can be converted to PAH sizes. The horizontal colored lines in the figure have the same color coding as the 11.2/3.3 m intensity ratio in the maps. In the NW PDR (orange in Fig. 2 ), PAHs have a typical size of 50 carbon atoms. Moving towards the star, the ratio drops (green) and PAHs have average sizes of . In the south PDR the average sizes of the PAHs are slightly larger with respect to the NW PDR, (green) in the PDR surface, and increase towards the left up to (light blue) in Fig. 4 . The results show that the 11.2/3.3 m ratio is mainly sensitive to size and has little dependence on molecular structure. Matrix isolation and gas phase experiments, as well as theoretical density functional theory (DFT) calculations show that the intrinsic band-strength ratio of the C-H stretch to the out-of-plane-bending mode (3.3/11.2 m) is very constant between different PAHs ( Joblin et al. 1994 ; Langhoff 1996 ). However, the ratios derived by these different techniques are different. Further experiments are needed to settle this issue. However, we emphasize that a different adopted band-strength ratio will only produce a vertical shift in Fig. 5 . An uncertainty in the adopted intrinsic strength translates to a systematic uncertainty in quantifying PAH size. If the adopted intrinsic strength is increased by a factor of 2, then a range of –105 would change to a range of –80. The trends, however, remain. Fig. 5 11.2/3.3 m intensity ratio of 27 different PAHs with blue coronene family, green compact, and red non-compact PAHs. The average photon energy absorbed by the PAHs used is 6.5 eV (see Eq. ( 1 )). The ratio of the C-H stretching mode (3.3 m) over the solo out-of-plane bending mode (11.2 m) is extracted from the theoretical spectra from the NASA Ames PAH database. The -axis is linearly proportional to the adopted intrinsic band strength of the 11.2/3.3 m ratio. The colored horizontal lines have the same color coding as used for the 11.2/3.3 m ratio maps in Figs. 2 and 4 . 4.3. PAH size variation in the PDR Figure 6 shows a crosscut across
Paper: 2019A&A...628A..14L (fulltext)
Stokes V spectrum profile characteristic of circular polarization generated by the Zeeman effect, but it can arise from scattering of a cloud outside the velocity range of the maser. Non-anti-symmetric Stokes V spectra were observed by Herpin et al. (2006) , but they can also be explained by a velocity gradient along the propagation path of the maser, or the lack of spatial resolution from the single-dish observations. 5.2. H<sub>2</sub>O masers 5.2.1. Simulations The relevant characteristic maser luminosities are given in Table 4 . We list the relevant luminosities for individual hyperfine transitions as well as the blended line. Compared to the SiO maser, radiative interactions remain relatively weak with respect to magnetic interactions up to high maser luminosities. This is due to the much smaller line strength of this maser transition, which means that the Zeeman effect will be the dominating polarizing mechanism up to high maser luminosities, and will thus follow Eq. ( 38 ) up to high maser brightness. Linear polarization will also remain rather low because the isotropic decay will be a dominant de-polarizing entity up to ( T b ΔΩ) sat (Ksr) at about ∼10 10 Ksr. Strong linear polarization is thus only seen for the strongest masers. Table 4. Characteristic maser luminosities for the 22 GHz water maser. For the regime T b ΔΩ ≪ ( T b ΔΩ) mag. sat , the maser circular polarization can be described by Eq. ( 38 ). A LTE analysis of the constant A F F ′ , gives for the individual hyperfine transitions, A 76 = 13.3, A 65 = 8.3, and A 54 = 1.0. A LTE analysis of a completely blended water maser line gives A blend = 8.2. Figure 15 shows the results of our full radiative transfer analysis of the circular polarization constants. Apart from the standard maser line profile, water masers are further broadened by their hyperfine structure. This leads to an overestimation of Δ v L . The dominant Zeeman effect though, comes from a single hyperfine transition. This produces higher Zeeman A F F ′ coefficients with respect to a LTE analysis. We observe that this effect is most pronounced for masers pumped at v th = 0.6 km s −1 , where the hyperfine transitions are minimally mixed. At v th = 2.0 km s −1 , the hyperfine broadening is negligible and the LTE value for the A F F ′ coefficient of the F = 7−6 hyperfine transition is returned for the weakest masers. Fig. 15. The A F F ′ coefficients of an isotropically pumped water maser at B = 20 G as a function of the magnetic field-propagation direction angle cos θ . The panels show different thermal widths v th = (a) 0.6 km s −1 , (b) 1.0 km s −1 , and (c) 2.0 km s −1 . Plots are given for different log( R / g Ω). The LTE solutions (constant over cos θ ) of the different hyperfine sub-transitions are indicated by a dotted line. Paradoxically, the preferred pumping of the hyperfine component with the strongest Zeeman effect has a consequence that the linear polarization deteriorates in the relevant intensity
Paper: 2016A&A...586A.117P (acknowledgments)
Based on observations with the 100-m telescope of the MPIfR (Max-Planck-Institut für Radioastronomie) at Effelsberg. This research made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. A.P. is a member of the International Max Planck Research School (IMPRS) for Astronomy and Astrophysics at the Universities of Bonn and Cologne. C.C.-G. acknowledges support by UNAM-DGAPA-PAPIIT grant number IA101214. We thank the Effelsberg operators for the useful help. We acknowledge Roberto Fanti for useful discussions and suggestions and Jochen Heidt for providing us with spectral redshifts of some of the sources. We thank the anonymous referee for careful revising of the paper and useful comments.
Paper: 2016MNRAS.457.1507C (acknowledgments)
The results of the research presented in this paper have been performed within the scope of the contract ASI/2007/I/082/06/0 with the Italian Space Agency. The authors are particularly thankful to the University of Rome ‘La Sapienza’ and the University of Bologna-Forlì Radio Science teams, for their precious support in the validation and testing of the orbit14 software.
Paper: 2018A&A...617A..94L (acknowledgments)
Acknowledgements We thank an anonymous referee for several very useful comments that improved our paper. We are grateful to the SOFIA engineering and operations teams for their support which enabled the observations presented here. The research reported here is based largely on observations made with the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is jointly operated by the Universities Space Research Association, Inc. (USRA), under NASA contract NAS2-97001, and the Deutsches SOFIA Institut (DSI) under DLR contract 50 OK 0901. This work was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. USA Government sponsorship acknowledged.
Paper: 2015MNRAS.451..300G (acknowledgments)
We thank Victor De la Luz and Ernesto Aguilar for reading the manuscript and providing important comments. This research is partly supported by grant CIC-UMSNH-4.9.
Paper: 2015ApJ...804..108H (acknowledgments)
We thank the referee and Karin Muglach for helpful comments that led to improvements in the paper, and Brian Dennis, Richard Schwartz, and Kim Tolbert for their continuing help and support with the RHESSI data analysis. G.H. was supported by NASA Heliophysics Guest Investigator and Living with a Star TR T Grants and the RHESSI project. A.F. was supported by the RHESSI project.
Paper: 2018ApJ...867L..27A (fulltext)
the FD energy scale (determined calorimetrically; Abu-Zayyad et al. 2013b ; Tsunesada et al. 2017 ). The systematic uncertainty on the TA energy scale is 21% (Abbasi et al. 2016 ) and its energy and angular resolutions are 15%–20% and 10–15, respectively, depending on the event geometry and energy (Abbasi et al. 2014 ). In this Letter we use data collected by the TA SD array in a 9 yr period from 2008 May to 2017 May with reconstructed energies above 43 EeV, zenith angles less than 55°, and declinations δ > −10° using the same quality cuts as in Abbasi et al. ( 2014 ). This data set comprises 284 events. We neglect the finite angular and energy resolution of TA events, and consider the detector fully efficient, i.e. with a flat response for all showers with energies and zenith angles in the considered range, so that its directional exposure ω TA equals the geometrical one for δ > −10°, which varies with declination but not with right ascension (Sommers 2001 ):where φ TA = +393 is the detector latitude and θ m = 55° is the maximum zenith angle accepted. The energy threshold of E min = 43 EeV used in this analysis corresponds to the Auger energy threshold of 39 EeV, at which the most significant correlation with SBG was found. Here we took into account the 10.4% difference between the energy scales of the two experiments as estimated by a comparison of energy spectra around 5 EeV (Verzi et al. 2017 ; Abu-Zayyad et al. 2018 ). 2.2. Source Catalog Following the Auger analysis (Aab et al. 2018 ), we select the candidate sources from a sample of 63 SBGs outside the Local Group compiled by the Fermi -Large Area Telescope (LAT) Collaboration (Ackermann et al. 2012 ) for the gamma-ray emission search. 36 36 Only four of those objects were actually successfully detected in gamma-rays in that work: NGC 253, M82, NGC 4945, and NGC 1068. Imposing the cut of flux greater than 0.3 Jy at 1.4 GHz leaves 23 objects in the catalog of candidate sources. Their UHECR fluxes were assumed to be proportional to their radio fluxes at 1.4 GHz. These objects are listed in Table 1 . Table 1 Selected Source Candidates from the SBG Catalog used in this Analysis (the same as in Aab et al. 2018 ) Note. The last column shows the relative source contribution weighted with the TA directional exposure ω TA . In the Auger analysis, the effect of energy losses by UHECRs during their propagation was found to be negligible in the SBG model, as most of the anisotropic flux originates from sources within a few Mpc; in this Letter, we neglected the losses for simplicity. 2.3. Test Statistic and Flux Model Let be the unit vector representing a direction in the sky, pointing away from the observer. Given two flux models describing a null hypothesis and an alternative hypothesis, respectively, and the directional exposure of an experiment, the test statistic (hereinafter ) is defined as twice the log-likelihood ratioand being the reconstructed arrival direction of the i -th observed
Paper: 2018A&A...616A.147R (fulltext)
in Eq. (12) , each locally reflected vector I , and hence each local reflection matrix R 1 , is rotated such that the reference plane is no longer the local meridian plane, but the planetary scattering plane. The local rotation angle β depends on the local viewing angle θ and the location of surface area d O on the planet. The geometric albedo A G of the planet with radius r at distance d is given by where π F 0 (0°, λ ) is the reflected total flux at wavelength λ and phase angle α equal to 0°. In P Y M IE DAP, the integration in Eq. (12) is replaced by a summation over locally reflected Stokes vectors. In order to do so, we divide the planetary disk on the sky in pixels, and compute the locally reflected Stokes vector at the center of each pixel. A pixel contributes to the disk signal when its center is within the disk radius. The integration is then given by with N the number of illuminated and visible pixels on the planetary disk, and with subscript n indicating that μ, μ 0 , φ − φ 0 , and β depend on the location of the pixel on the planet. In addition, μ 0 , φ − φ 0 , and β at a given location of the pixel on the planet depend on the planet phase angle α ( Appendix B provides relations that can be used to derive these angles). In the summation, d O n refers to the area of the pixel as measured on the surface of the planet. Although not explicitly indicated in Eqs. (14) and (12) , R 1 will usually also depend on the location (of a pixel) on the planet. Typical horizontally inhomogeneities would be the surface coverage and altitude, and the atmospheric composition and structure. The obvious horizontal variations on Earth are of course the oceans and the continents, and in the atmosphere, the clouds. Horizontal inhomogeneities can be taken into account by using different local reflection vectors R 1 across the planet (in that case, R 1 in Eq. (14) would include a subscript n ). Given a model planet and a planetary phase angle α , the steps to evaluate Eq. (14) are the following: Divide the planet into pixels small enough to be able to assume that the planet properties across each pixel are horizontally homogeneous, and to be able to follow the limb and the terminator of the planet. Calculate for (the center of) each pixel the angles μ (i.e., cos θ ), φ , and the rotational angle β . These angles are independent of the location of the sun or star with respect to the planet. Calculate for the given phase angle , and for (the center of) each pixel, μ 0 (i.e., cos θ 0 ) and φ 0 . These angles depend on the location of the sun or star with respect to the planet. Calculate for (the center of) each pixel the column vector R 1 of the locally reflected light using the appropriate database file (see Sect. 5 ). Perform the summation described by Eq. (14) . The pixels can be defined on the planet, for example, using a latitude and longitude grid, in which case μ d O , the projected area of the pixel (see Eq. (12) ), that is, the pixel area as “seen”
Paper: 2016ApJ...829..110B (fulltext)
choose a unique for each particle upon emission. We assume the field changes on a length scale , where λ is a model parameter, and at the beginning of each time step we calculate the timescale for the particle to experience a significant change in :where z is a random number between 0 and 1. If is less than the time for a particle to leave a zone or end the grid time step, the magnetic “scattering” action is selected, the particle position and energy are updated, and a new field direction is chosen at random. The re-orientation preserves energy and the magnitude of the momentum in the cmf. This is reasonable because these discrete scatters are standing in for smoother and more gradual field gradients. However, it also leads, on average, to energy losses in the center-of-explosion frame, which decrease thermalization efficiency. This can be thought of as the particles transferring their energy to the bulk kinetic energy of the ejecta rather than the thermal background. It is a small effect. The propagation of a particle along field lines depends on total velocity and Particles are emitted on the grid with a random direction defined by the unit vector , so μ is . In the case of radial fields, μ evolves to preserve the magnetic adiabatic invariant . This evolution encourages outward motion. Inward-moving particles encounter ever-stronger fields, and decrease their inward velocity in response, eventually “mirroring” off the field and beginning to travel outward. Particles streaming outward move into weaker magnetic fields, so their pitch angle increases and they stream out even faster. We include magnetic beaming and mirroring only for radial fields, where changes to the pitch angle facilitate particle escape and can be meaningful for thermalization. Particles in toroidal fields are confined to one region of velocity space; changes in their pitch angle cannot promote outward motion and so cannot affect thermalization. While particles in random fields could experience beaming and mirroring, they may also undergo pitch angle scattering—interactions with small fluctuations in the field that can change pitch angle, and would act to isotropize μ , counter to beaming and mirroring. We assume the two effects balance out, and hold pitch angle constant for particles in random and toroidal fields. A.3.2. Fission Fragment Transport As discussed in Section 2.2 , at late times, fission fragments may have Larmor radii comparable to the magnetic field coherence length. This could affect the transport, especially for disordered fields, where field lines in close proximity may have very different orientations. In such a system, the guiding center approximation breaks down, and the motion of the particle must be resolved. We estimate the importance of this effect by modeling fission fragment transport in a tangled field as a random walk of the fragment itself (as opposed to its guiding center). In this simple scheme, fission fragments travel in straight lines and
Paper: 2018ApJ...862...28L (fulltext)
the SED fitting used to determine spectral types for companion stars in HMXB candidates. In Section 5 we discuss our results in the context of previous studies, and in Section 6 we provide a brief summary of our results. We assume a Galactic column density, , and a photon index, Γ = 1.7 (Stiele et al. 2011 ), to convert count rates into absorbed energy flux. We assume a distance of 776 kpc to M31 (Dalcanton et al. 2012 ) for luminosity calculations. 2. Data In this study we employ data from NuSTAR , Chandra , and HST . We now describe each data set in more detail below. For an overview of the area observed by each telescope, see Figure 1 . Figure 1. Near-UV image of M31 from the Galaxy Evolution Explorer ( GALEX ; left; Gil de Paz et al. 2009 ) and three-color X-ray image of Chandra Field A (right), see Section 2.2 for more information on Field A data reduction. Magenta regions outline the area observed by PHAT. Green outlines the NuSTAR observed region, and cyan outlines the area observed by Chandra , with solid lines indicating ChandraPHAT observations and dashed lines indicating Field A observations. In the UV image, the 64 sources observed by NuSTAR and Chandra that fall within the PHAT footprint are marked with white crosses, and a 5′ circle outlines the densest inner bulge region of M31. In the X-ray image, all sources detected by wavdetect within Field A are marked with white ellipses (including sources that do not match NuSTAR sources or are outside the PHAT footprint, and thus are not presented in our sample in Table 1 ). 2.1. <italic>NuSTAR</italic> Data NuSTAR source catalogs and source classifications come from Wik et al. (2018, in preparation). Observations were taken between February and October 2015 covering the area outlined in green in Figure 1 with an average exposure time of ∼400 ks, and they were reduced using the nupipeline software. Sources previously observed with Chandra were used for astrometric alignment. For detailed information on data reduction, source detection, and source classification, see Wik et al. (2018, in preparation), who present the entire NuSTAR M31 survey. The NuSTAR observations cover the nucleus and inner disk regions of M31 at an energy range of 4–25 keV using the 4–6 keV, 6–12 keV, and 12–25 keV energy bands. The completeness of the NuSTAR observations starts to fall off at a luminosity of ∼3 × 10 36 erg s −1 and reaches zero at ∼2 × 10 36 erg s −1 . 2.2. <italic>Chandra</italic> Data Chandra data used in this study are comprised of two data sets: the ChandraPHAT data, a Chandra Large Project survey by B. Williams et al. (2018, submitted), and one additional Chandra field (obsid 18046, P.I. Hornschemeier), hereby referenced as Field A, that was reduced for this paper. The ChandraPHAT data set is comprised of seven Chandra pointings, each with a depth of about 50 ks. The Field A data consist of one Chandra pointing with a depth of 25 ks. The ChandraPHAT observations were taken in 2015 October and the