adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.480..751A (fulltext)
arises due to propagating fluctuations in the accretion flow. In this picture, slower fluctuations arise in the outer regions of the flow, and as they propagate inwards, more rapid fluctuations are added. Variability at a given frequency is produced at a characteristic radius, so includes the spectrum produced at that radius, but then it propagates inwards so it is the sum of different spectral components at all radii interior to that where the characteristic frequency of the component is generated. This is shown schematically shown in Fig. 5 , where the sketch is of a gradually stratified flow, with each region contributing a harder spectrum as the radius decreases, rather than the two component model used above. A fundamental assumption of this picture is that smaller radii correspond to harder spectra. We will discuss this in Section 4 . Figure 5. Cartoon picture showing the disc (grey) and regions of the inner accretion flow contributing to each variability component. The cartoon image of Fig. 5 reveals a model-independent way to extract the spectra as a function of radius. By studying the difference between the variability spectra, we can attempt to isolate the spectrum which is characteristic of the radius at which each variability component is produced. As described above, the highest frequencies are expected to be the ‘cleanest’, i.e. only present in the innermost region and thereby modulating the emission from the fewest emission components. This is supported by our result that the spectrum of L 3 is well-fit by a single pure Comptonization component. L 2 has a lower frequency, and it should therefore contain the same contributions as L 3 , as well as emission from larger radii. To isolate the emission in the regions successively further out, we rescaled the L 3 spectrum to match that from L 2 in the highest energy bin (where the contribution is dominated by the innermost region), and then subtracted the L 3 spectrum. This is the maximum contribution from the hard innermost region, so this spectrum is the softest that can be characteristic of the region where L 2 is produced. Similarly, we rescaled and subtracted the spectrum of L 2 from L 1 . The results are shown in Fig. 6 . Figure 6. Spectra of each variability component after subtracting the contribution from higher frequencies (smaller radii). Colours and symbols are the same as in Fig. 2 . While the data points have large uncertainties, some general features can still be distinguished. The spectrum characteristic of the largest radii (red squares) is the softest, and is subtly different to the shape of the soft Comptonization in our two-component fits. The spectrum which can be associated with L 2 (green circles) is appreciably harder than that of L 1 , while that of L 3 is well described by a single hard Comptonization component with high electron temperature and seed photons around a few keV (as above). These results suggest a more complex flow than in our two component model
Paper: 2018ApJ...856...60S (fulltext)
al. 2002 ). In their consideration of the maser data on the turbulence in the flows from the massive YSOs, Strelnitski et al. ( 2002 ) proposed that “the maser hot spots originate at the sites of ultimate dissipation of highly supersonic turbulence.” This assertion finds support in a well-ordered spatio-kinematical pattern in the small-scale water maser features reported in Cepheus A HW2 by Uscanga et al. ( 2003 ), in W75N by Uscanga et al. ( 2005 ), and in W49N by Gwinn ( 1994b ). Observations of Uscanga et al. ( 2005 ) suggested microstructure with a size about 1 au. This structure had a short lifetime supposedly on the order of a month. All information on the known examples of the structures with an “eddy-like” spatio-kinematical pattern does not contain evolutionary information and has a form of snapshots, although the other maser structures in Cepheus A HW2 show persistence on the timescales of years (Torrelles et al. 2001 ). Uscanga et al. ( 2005 ) speculated that these short-lived kinds of spatio-kinematical microstructures are either produced by fluid instabilities within the shocked material or correspond to nearly round cloudlets (turbulent eddies?) in the ambient medium. In the sections below, we discuss a spectroscopic origin for our observations as well as three dynamical phenomena that may explain them. 4.1. Spectroscopic Origin: Hyperfine Splitting The velocity separation of 0.54 km s −1 between the components in Figure 6 is close to the velocity separations of the H 2 O hyperfine splittings of 0.45 km s −1 between the F = 7–6 and F = 6–5 transitions and 0.58 km s −1 between F = 6–5 and F = 5–4. However, there are several problems with this spectroscopic hypothesis. First, the components of the double-peaked spectrum in Figure 6 are not spatially coincident, so they would have to be associated with different hyperfine components. Second, the F = 7–6 hyperfine transition has the lowest frequency (and F = 5–4 the highest) of the three strong transitions (Kukolich 1969 ), while the strength order is F = 7–6, F = 6–5, then F = 5–4 (Deguchi Watson 1986 ). We would therefore expect the strongest peak at the lowest frequency (most positive Doppler shift) in our spectrum, but the opposite is seen in Figure 6 . Moreover, all three of the hyperfine transitions introduced above have comparable line strengths (see Figure 1 of Deguchi Watson 1986 ), so a triplet spectrum would be expected rather than a doublet in the case of hyperfine intensity anomalies. We think this explanation is unlikely because it would require some complicated combination of hyperfine-specific pumping and/or competitive gain effects to generate the observed spectrum. 4.2. Keplerian Rotation For the first dynamical interpretation, the maser hot spots (see Figure 9 ) might be amplifying along chords (i.e., filaments) in the plane of a Keplerian disk, orbiting a protostellar or protoplanetary object, viewed approximately edge-on. In this case, the length of the filaments
Paper: 2017A&A...608A..84C (fulltext)
2. Data sets and Mg <sc>ii</sc> absorber detection 2.1. X-shooter spectral samples The GRB afterglow spectra were collected in multiple semesters between 2009 and 2016 taking advantage of the X-shooter guaranteed program and subsequent open time proposals. With a uniform set of selection criteria for target-of-opportunity follow-up adopting the criteria for the low-resolution study of Fynbo et al. (2009) , that is, mainly avoiding regions with high Galactic extinction and bursts near to the Sun, the total number of obtained afterglow spectra is 116 until October, 2016. All spectra were obtained with VLT/X-shooter, which provides intermediate spectral resolutions of measured from UV to near-IR wavelengths. The S/N of the spectra vary as some afterglow spectra were obtained several hours to a few days after the GRB trigger, when only host spectral signatures and no characteristic power-law afterglow signature was visible. Removing these from the sample leaves 81 afterglow spectra, again with varying S/N between 1 and 50 per pixel for different bursts and wavelength coverage (see Selsing et al., in prep., for details). The data were reduced with the ESO VLT/X-shooter pipeline ( Modigliani et al. 2010 ) managed by Reflex ( Freudling et al. 2013 ), and with our own post-processing steps to improve the flux calibration and rejection of bad pixels (Selsing et al., in prep.) with reduction scripts made available online 2 . The redshifts of the GRBs lie in the range . Turning to the quasar sample, the XQ-100 survey is a VLT/X-shooter ESO Large Programme that targeted 100 quasars at with a S/N higher than 20 per pixel over the entire wavelength range (see Lopez et al. 2016 , for details). The quasars were selected without prior knowledge of the presence of strong absorbers along our line of sight. The quasars and a large majority of GRB afterglows were observed with X-shooter slit widths of 1.0, 0.9, and 0.9 arcsec for the UVB, VIS, and NIR-arms, respectively, resulting in similar spectral resolutions between the two samples. The redshift distributions of the GRB and quasar samples are shown in Fig. 1 . By construction the quasars have a quite distinct distribution as high-redshift objects were selected, whereas there are no intrinsic constraints for the GRB redshift selection. To match the redshift distribution of GRBs we could include other quasars observed with X-shooter, using archive data. However, these quasars might have been selected on the basis of the presence or lack of any kind of intervening and intrinsic absorption line systems, so we do not expand the quasar sample beyond the XQ-100 data. Fig. 1 Redshift distributions for the two background source samples (XQ-100 and XSGRB). The 81 GRB afterglows and 100 quasars have median redshifts of and , respectively. 2.2. Additional data sets Since the earlier discrepancy on the incidence of Mg ii absorbers relied on the statistics of GRB absorbers obtained to that date, we also examined another
Paper: 2016MNRAS.456..909A (acknowledgments)
JDC thanks J. Peacock, A. Pontzen and J. Whitehouse for suggestions, and especially M. White for numerous discussions. We thank N. Dalal, V. Desjacques, F. Leclercq, M. Neyrinck and the anonymous referee for helpful comments on the draft.
Paper: 2018ApJ...862L..10E (fulltext)
of 27 or 23 kpc. Figure 3. Left: radial profiles of the (blue) and continuum (red) emission. Error bars with colors matching the points indicate the error in the weighted mean, and larger gray error bars indicate the dispersion of individual spaxels in the image. Only points with >3 σ detections are shown. Gray triangles show the radial profile of a quasar observed on the same nights as BX418, and the dotted gray line shows the best-fit Moffat profile to the quasar data. Blue lines indicate exponential fits as described in the text. Right: growth curve for the total flux as a function of radius. The horizontal blue line shows the total flux measured as described in the text. We also show the azimuthally averaged profile of a quasar that was observed on the same nights as BX418, with exposures alternating with those of BX418 (gray triangles, where the peak value has been scaled to match the peak continuum surface brightness of BX418). The radial profile of the quasar is reasonably well-fit with a Moffat profile with FWHM = 096 (dotted gray line), and we use this profile as a model of the PSF. As this model is a nearly perfect match to the continuum profile of BX418, we conclude that the continuum emission is not resolved; given the half-light radius of 1.5 kpc from HST imaging (Erb et al. 2010 ), this is not surprising. We then fit the binned surface profile with an exponential function convolved with the PSF (solid blue line), finding a best-fit scale length of 2.58 ± 0.02 kpc; however, this model fits only the inner portion of the extended halo, significantly underestimating the surface brightness at radii greater than 18 (15 kpc). Fitting an exponential function to only the points beyond this radius (dashed blue line), we find a scale length of 6.2 ± 1.1 kpc. This scale length of 6.2 kpc lies at the low end of the scale lengths found for stacked images at z > 2, which range from ∼7 to ∼25 kpc (Steidel et al. 2011 ; Momose et al. 2014 , 2016 ); these measurements are also made at radii ≳2′′. It is also within the range of halo scale lengths found for individual galaxies detected by the Multi Unit Spectroscopic Explorer (MUSE; Leclercq et al. 2017 ). In the right panel of Figure 3 we show the growth curve for the flux as a function of radius, with the total flux measured as described above indicated by the horizontal blue line. This curve indicates that half of the observed emission lies within a radius of 7.1 kpc (08), and the flux reaches 95% of its total value at a radius of 23 kpc (28). 3.2. The Velocity Structure of Extended <inline-formula> <tex-math> </tex-math> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="apjlaacff6ieqn68.gif"></inline-graphic> </inline-formula> Emission Examination of the profiles of individual spaxels within the extended halo reveals significant differences in the blue-to-red peak ratio and velocity separation in different regions. We show two examples in Figure 4 . In the left and right
Paper: 2016MNRAS.462.1111D (acknowledgments)
MDP thanks Lara Nava and Daisuke Kawata for helpful discussions and references, Alice Breeveld for her helping hand, and his friends Marco D'Alessandro, John Moore, Ernesto Amato, Peter Rockhill, Peter Včasný, Alexander J. Zech, and Pierluigi Cox for their encouragement. AR acknowledge support from PRIN-INAF 2012/13 and from Premiale LBT 2013. BG acknowledges financial support of the NASA through the NASA Award NNX13AD28A and the NASA Award NNX15AP95A. Part of this work is under the auspice of the FIGARONet collaborative network, supported by the Agence Nationale de la Recherche, programme ANR-14-CE33. DAK acknowledges support by TLS Tautenburg in form of a research stipend. DM acknowledges support from Ida as well as financial support from Instrument Center for Danish Astrophysics (IDA). GS acknowledge the financial support of the Italian Ministry of Education, University and Research (MIUR) through grant FIRB 2012 RBFR12PM1F. MDP and MJP acknowledge support from the UK Space Agency. SRO acknowledges the support of the Spanish Ministry, Project Number AYA2012-39727-C03-01. ZC gratefully acknowledges financial support from the Icelandic Research Fund (IRF). This work made use of data supplied by the UK Swift Science Data Centre at the University of Leicester, and by the Chandra Data Archive.
Paper: 2019MNRAS.485..550R (fulltext)
δ m ( a i ) = 5 × 10 −5 . Additionally, we insert the initial conditions as follows (see also Batista Pace 2013 ; Malekjani et al. 2017 ) Using these initial values one can be confident that matter perturbations always remain in the linear regime. After finding the evolution of fluctuations (δ m , δ d ), we can calculate the growth rate of large-scale structures in the presence of oscillating DE models considered in this work. The growth rate function can be written as The value of σ 8 (), the matter fluctuation amplitude on scales of 8 Mpch −1 , can also be written as By multiplication of these two functions, one can calculate the value of f ()σ 8 () to compare it with f σ 8 observational data. The growth rate data set were obtained from redshift-space distortions (RSDs) from different galaxy surveys. These data points, when measured using RSDs, are degenerate with the Alcock–Paczynski (AP) effect (Alcock Paczynski 1979 ). Assuming an incorrect cosmological model for the coordinate transformation from redshift space to comoving space leads to residual geometric distortions known as the RSDs. These distortions are induced by the fact that measured distances along and perpendicular to the line of sight are fundamentally different. Measuring the ratio of galaxy clustering in the radial and transverse directions provides a probe of AP effect. However, given high precision clustering measurements over a wide range of scales, this degeneracy can be broken since RSD and AP have different scale dependences (Reid et al. 2012 ). In order to overcome the RSD problem, several methods have been used in the literature to apply the AP test to the large-scale structure (Song Percival 2009 ; Samushia, Percival Raccanelli 2012 ; Blake et al. 2012 ; Blake et al. 2013 ; Hudson Turnbull 2013 ; Feix, Nusser Branchini 2015 ; Howlett et al. 2015 ; Chuang et al. 2016 ; Huterer et al. 2017 ). Since we obtain our growth rate data from the above references; therefore, we can claim that the AP effect has been considered for the growth rate data which we used in our analysis. Nowadays there are more than 30 data points of recent f σ 8 measurements from different surveys. The information in some of these data points overlaps significantly with other data points in this collection. Some of them are updated version of previous measurements either with enhancements in the volume of the survey, during its scheduled run or with different methodologies by various groups. Therefore, the collection of these data points should not be used in its entirety. Therefore, in our analysis we use the ‘Gold-2017’ compilation of robust and independent f σ 8 measurements from different surveys constructed by Nesseris, Pantazis Perivolaropoulos ( 2017 ). We apply these data points to put constraints on model parameters. The likelihood function becomes where the statistical vector p is {Ω DM0 , Ω b0 , h , 0 , A, B , σ 8 } and σ 8 is the present value of σ 8 (). The best-fitting parameters and
Paper: 2017MNRAS.464.4895G (fulltext)
X-ray emitting e + e − pairs are fast cooling, which allows us to write their detailed energy balance. This results in a lower limit on , which is the ratio of the magnetar's long-term mean energy output in outflows (quiescent MHD wind + sporadic outbursts), , to its spin-down luminosity. This clearly implies that the MWN is not powered predominantly by the magnetar's spin-down-powered wind, and an alternative dominant energy source is required, most likely the decay of the magnetar's magnetic field. We show that the decay of its dipole field alone is not enough, and a significantly larger (by a factor of ∼10 2 –10 3 ) energy reservoir is needed. The most plausible candidate is the magnetar's internal magnetic field, which has to be ≳ 10 − 30 times larger than its dipole field. Finally, by assuming a maximum allowed initial internal field strength (on theoretical grounds) of B int, max ∼ 10 16 –10 16.5 G we obtain an upper limit on g ≲ 5 × (10 2 –10 3 ). In Section 7 we consider the possible origin of the GeV/TeV emission observed by Fermi /LAT and H.E.S.S. In Section 7.1 we consider the possibility of a hadronic origin for the GeV/TeV emission. While inverse-Compton emission by electrons in the MWN is energetically very challenging, we find a much more plausible and energetically reasonable alternative to be the decay of neutral pions that are produced by the interaction of cosmic rays (CR) accelerated in the shock driven by the SNR into the external medium, with nuclei in the nearby giant molecular cloud (GMC). In Section 7.2 , we consider the possibility of inverse-Compton emission by relativistic electrons in the MWN that upscatter seed photons from the NIR Galactic background and the CMB. For Galactic NIR seed photons we find it extremely difficult to account for the observed GeV/TeV luminosity. For CMB seed photons it is also very difficult to account for the GeV/TeV luminosity, and moreover, together with the power-law MWN electron energy distribution (as reflected by their X-ray synchrotron emission) one would expect a much broader spectral peak at TeV energies, which is hard to reconcile with the Fermi /LAT and H.E.S.S. observations. Therefore, we find this option implausible. Finally, we also consider emission from relativistic non-thermal bremsstrahlung from electrons accelerated at the SNR forward shock. Our conclusions are discussed in Section 8 . Our main findings are that the MWN is powered predominantly by outflows from the magnetar, whose main energy source is most likely the decay of its strong internal magnetic field. These conclusions became possible because of the discovery of this first MWN. The outflows from the magnetar accumulate inside the MWN, which therefore serves as a calorimeter that helps us study the system's history. 2 IMPLICATIONS OF THE ASSOCIATION WITH THE SNR The location of Swift J1834 at the very centre of the SNR W41 strongly supports their association. Moreover, it can also constrain the SGR's proper
Paper: 2018MNRAS.477.2716K (acknowledgments)
ACKNOWLEDGEMENTS MRK acknowledges support from the Australian Research Council’s Discovery Projects funding scheme (project DP160100695). BB is supported by the NASA Einstein Postdoctoral Fellowship and an Institute for Theory and Computation Fellowship. JCF is supported by an Institute for Theory and Computation Fellowship. We thank S. Oh for helpful discussions of bar surveys, and the referee, A. Dekel, for helpful comments.
Paper: 2017ApJ...839..118V (acknowledgments)
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 127229 to C.J.V. This material is also based on work partially supported by the National Science Foundation under Grant No. AST-1616513 to R.J.R. and Grant Nos. AST-0908126 and AST-1615594 to R.A.M.W. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 679627). We would like to thank the anonymous referee for insightful and helpful comments that led to the overall improvement of this work.
Paper: 2020ApJ...889...36C (fulltext)
not particularly affect the results. Missed associations in this case are due the fact that the light curve iPTF 17cw is not well sampled at early times. Our discussion in Section 4.1.1 clarifies this point. In the case of sources with redshift randomly distributed between 0.01 and 0.1 our results are, as expected, in between the two previously described cases. Specifically, 92% of all simulated sources are detected, and 87% of the detected sources are uniquely and correctly associated. In particular, 100% of the targets simulated from the templates of SN 1998bw and SN 2009bb, 69% of SN 2006aj, and 99% of iPTF 17cw are detected. Of those detected sources, 100% of the targets simulated from the templates of SN 1998bw and SN 2009bb, 48% of SN 2006aj, and 88% of iPTF 17cw are uniquely and correctly associated. For completeness, in Figure 2 we also plot the the efficiency of the optimized observing campaign as a function of total time delay for SN 2006aj-like and PTF17cw-like targets, with redshifts randomly distributed between 0.01 and 0.1. The total time delay is calculated as the sum of the delay between the explosion and the optical detection, and the delay between the optical detection and the first radio observation (which defines the urgency of the radio observing campaign; see Section 3.3 ). In this figure the efficiency (fraction of unique and correct associations) is calculated relative to the number of simulated sources that are detected in each delay bin. We note that for SN 1998bw-like and SN 2009bb-like targets, the efficiency is 100% regardless of the total time delay (and thus, regardless of the adopted observing urgency strategy in the radio). In the case of SN 2006aj-like targets, we completely miss those that are observed with total delays ≳22 days, when SN 2006aj was undetected. Figure 2. Fraction of targets that are uniquely and correctly associated as a function of the total delay between the SN explosion and the first radio observation for sources with z = 0.01–0.1. 4.1.1. Relevance of Early-time Radio Observations for Relativistic SNe In this section we assess the importance of early-time radio observations for correctly and uniquely associating relativistic SNe via an optimized radio follow-up campaign. To this end, we extrapolate the template light curves of SN 2009bb and iPTF 17cw using the early-time behavior of the much better sampled radio light curves of SN 1998bw and SN 2006aj, respectively. We choose to use SN 1998bw to extrapolate the light curve of SN 2009bb, and SN 2006aj to extrapolate the light curve of iPTF 17cw, because they are the most similar to each other. In fact, both SN 1998bw and SN 2009bb were nearby explosions that resulted in a very bright radio signal. On the other hand, both SN 2006aj and iPTF 17cw were very dim and faded away very rapidly. We repeat the simulations and optimization procedure described in the previous section including these early-time extrapolations. Since all sources