adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...868L..11M (fulltext)
power-law decline begins roughly when the jet centroid comes into view. The sharpness of the light curve peak and the immediate transition to t − p decline implies that we are in the regime 20 20 If ( θ v − θ j ) ≲ θ j there will be a long-lived phase during which the light curve decays as t −1 . θ v − θ j ≫ θ j . We denote by t 1 as the time that we see the edge of the jet, namely Γ( t 1 ) ≃ 1/( θ v − θ j ), and t 2 as the time that we see the jet axis, Γ( t 2 ) ≃ 1/ θ v . Now, ignoring sideways spreading of the jet we can approximate (e.g., Sari et al. 1998 ) to obtain //. Here we use the approximation that θ j / θ v is much smaller than unity. Observationally, t 1 occurs sometime during the transition from the t 0.8 rise to the peak of the light curve and t 2 occurs when the decay phase approaches t − p . Using the results from our MCMC analysis, we find that Δ t / t ≲ 1/3 (68% confidence or better, depending on the where t 1 and t 2 lie), indicating that θ v ≳ 8 θ j . To verify this simple estimate we produced light curves from a top-hat jet using semi-analytic code. 21 21 The semi-analytic code (Soderberg et al. 2006 ) takes proper account of all relativistic effects such as Lorentz boost and light arrival time from each point in the jet and it includes lateral spreading of the jet. Following corollary comparison of the code results to BOXFit (van Eerten et al. 2012 ), we use lateral spreading of 30% of the local thermal speed. We verify that varying the spread velocity, including taking no spreading at all, does not change the result significantly. We find that light curves that fit the transition seen from the peak to the decline of the light curve have θ v ≃ 6 θ j . Together with the upper limit on the observing angle from the Laser Interferometer Gravitational-Wave Observatory (LIGO)-Virgo Collaboration ( θ v ≲ 28°; Abbott et al. 2017a ), our constraint implies θ j ≲ 5°. If instead we use the estimate of Γ ≃ 4.1 ± 0.5 close to the peak of the light curve from the very long baseline interferometry (VLBI) measurement (Mooley et al. 2018a ), then we get θ j ≲ 3° and θ v ≃ 15°. 5. Summary and Conclusions In this companion Letter to our polarization and high angular resolution imaging studies, we have presented deep VLA, ATCA, MeerKAT, and uGMRT observations (Section 2 ) of GW170817 post-peak of the light curve, i.e., between 180 and 300 days post-merger. Our spectral analysis does not yield any evidence for the cooling break to have entered the radio band, or of any achromatic steepening in the light curve indicating the transition of the outflow into the non-relativistic regime (Section 3.1 ). We find that the light curve decay is consistent with t −2.2 (Section 3.2 ), which is a classic signature of a jet where the slope of the decay is equal to the power-law energy index p of the synchrotron-emitting electrons. We also find that the transition from the power-law rise to decay ( t 0.8 to t −2.2 ) is fairly sharp. The data on the light
Paper: 2021MNRAS.500.3926B (fulltext)
54 value measured for our sample. Fig. 8 shows the distribution of D 34 and D 54 for our program stars. Figure 8. D 34 and D 54 distribution for our program stars. Among 105 stars for which we estimated D 34 values, 19 (≈20 per cent) show D 34 ≥ 2.7. However, D 54 for our stars mostly range between 0.2 and 1.5 (≈70 per cent). Only 19 stars exhibit D 34 1.0, whereas D 54 > 1.5 in 27 cases. Comparing with literature, we find that Dachs et al. ( 1990 ) reported D 34 to lie within 1.2–3.2 for their sample of 26 southern early-type Be stars. However, D 54 for their case range within 0.4–1.0, clustering near 0.7. On the contrary, Slettebak et al. ( 1992 ) obtained D 34 for 41 CBe stars to range between 2.05 and 5.7, while D 54 being always less than 1.0. Both the previous authors also noticed that on an average, weak H α emission (H α EW −25 Å) is usually characterized by flat Balmer decrements ( D 34 2.0, D 54 > 0.6). Our study could not confirm any such trend that is evident from Table 4 . Our result for D 34 fairly agrees with Slettebak et al. ( 1992 ) and Dachs et al. ( 1990 ), although the range of our D 34 is larger. This is possibly due to the larger number of stars for our sample. We found that 19 of our program stars show D 34 ≥ 2.7, implying that the disc for these stars are optically thick in nature. But our result does not match for D 54 . The different range of D 34 and D 54 values obtained in our study might be due to the fact that we considered A V values for calculating the Balmer decrements in our case. This may not have been done by previous authors. Moreover, we estimated D 34 for all 20 weak emitters also that show H α EW −5 Å. 3.4.2 Epoch-wise variation of <italic>D</italic><sub>34</sub> Apart from looking into the H α line, the transient nature of CBe star discs can be identified by studying the epoch-wise variation of estimated D 34 values of those stars where repeated observations are done. For our program stars where previous estimations of D 34 are available (at least two), we compared our estimated D 34 values to those obtained by other authors. We found that only 10 of our program stars have at least two sets of observations for D 34 including our estimations. Only one among them (HR 2142) has three observations for D 34 . Fig. 9 demonstrates the variation of D 34 as observed by us and previous authors. It is seen that our value of D 34 is lower than that of previous authors (e.g. Dachs et al. 1990 , Briot 1971 , Rojas Herman 1958 , Burbidge Burbidge 1953 , and Karpov 1934 ) in every case. Figure 9. Variation of D 34 in 10 of our program stars as observed by us and previous authors. The black line indicates the cut-off value 2.7, adopted from Hummer Storey ( 1987 ), which is assumed to distinguish between an optically thin and thick region that prevails in case B condition defined by Baker Menzel ( 1938 ) and later by Brocklehurst ( 1971 ). The epoch-wise variation study of D 34 for 10 of our program stars certainly imply
Paper: 2016A&A...586A.132P (fulltext)
form the previous iteration, resulting in a final cleanest sample containing 224 245 QSOs with (for which we have Ly free photometry in the -, -, and -bands), 135,953 with (where we can use the -band), and 77 633 QSO with , where we can use all the SDSS bands. We have an estimate of the intrinsic colours , and an estimate of the reddening for each QSO that is retained by the redshift constraints. Even though the unobscured QSO intrinsic colours are computed independently for each band pair, we do obtain consistent results across the band pairs, i.e. holds for all the bands , , and , over all the redshifts considered. Working with the column density maps as an estimate of the extinction instead of the gives very similar estimates of , and is independent of any dust modelling, so this means we did not translate potential dust modelling systematics into our QSO estimates. Fig. B.3 QSO magnitude increase per unit of dust extinction as a function of the QSO redshift. We use an extinction curve with . The and band curves are shown in a thinner trace for and , the redshifts at which the intergalactic Ly line can affect the photometry in these bands. In order to compare the estimate with a QSO estimate, we need to derive a QSO extinction from the different colour excess . We proceed as follows. For a given QSO spectrum and extinction curve shape, we can compute the SDSS magnitude increase per dust extinction for . These ratios depend on the assumed extinction curve and QSO spectral shape, and therefore on the QSO redshift. Using the QSO composite spectrum of Vanden Berk et al. (2001) and the extinction curve presented by Fitzpatrick (1999) parametrized via , we compute the ratios : Figure B.3 shows as a function of the QSO redshift , for the different bands and . Even though the QSO intrinsic colours are strong functions of its redshift, the extinction curves are smooth enough that is mostly redshift independent. Using the extinction curves with (which was also used to constrain the optical properties of the grains used in the DL model), for each redshift , we define: and Finally, for each QSO, we define its as the average of the values for all the band pairs that are allowed by its redshift . Appendix C: Impact of the CIB anisotropies and instrumental noise on the parameter estimation Fig. C.1 Comparison of the original and recovered dust mass under CIBA and instrumental noise simulation in the diffuse ISM for a resolution. Colour corresponds to the density of points (see Fig. 5 ). We study the impact of CIB anisotropies (CIBA) and instrumental (stochastic) noise in our mass estimates in the diffuse ISM (where their effect should be the largest). We simulate data by adding CIBA and instrumental noise to DL SEDs, and fit them with the same technique as we use to fit the observed data. The results quantify the deviations of the recovered parameters from the original ones. We start by a family of four DL SEDs with and , a typical , and . We
Paper: 2018ApJ...856..142C (fulltext)
members. These stars were classified as [Fe/H] ∼ −3.0 from measurements of the Ca ii K line. It appears that the strong carbon enhancement of these Sculptor members biased the Ca ii K metallicities in lower-resolution spectra (see Section 5.2 ). For each star in Table 5 , we derived a Mg abundance from the 5172.7 and 5183.6 Å lines if the S/N was sufficiently high. Then, we compared the derived [Mg/Fe] ratio of these stars to the expected [Mg/Fe] ratio for dwarf galaxy stars in their metallicity regime. We would expect to see systematically higher [Mg/Fe] values if the Ca ii K–based metallicities were biased lower, such as in the case of stars with high A(C) values. We consider two examples: stars 10_7_442 and 10_8_1226 have carbon abundances close to the CEMP threshold and Mg line equivalent widths in the linear regime of the curve of growth (reduced equivalent widths ≲−4.45). For these two stars, we measure [Mg/Fe] values of 0.23 and 0.17, respectively. These [Mg/Fe] ratios are roughly at the lower end of the regime of what is expected for dwarf galaxy stars at these metallicities. This suggests that we are not strongly underestimating our [Fe/H] measurements for stars that are near the CEMP threshold. If we include stars from Table 5 with Mg line equivalent width measurements in the nonlinear regime of the curve of growth at face value and adopt the M2FS metallicities and carbon abundances when available, the average [Mg/Fe] of stars with [C/Fe] > 0.50 is 0.43. This [Mg/Fe] ratio is also in the regime of expected values. As mentioned, if the metallicities were substantially underestimated, we would expect to get much larger [Mg/Fe] values. For comparison, all the CEMP-s candidates have [Mg/Fe] ≳ 1.0 if we take the KP-based [Fe/H] measurements at face value. While these Mg abundance estimates may have large uncertainties (up to ∼0.4 dex, as is expected for data of this quality), they suggest that we are not strongly biased in our metallicity estimates for stars without copious carbon enhancement. We also compared our observed MagE spectra to MIKE spectra of CS22892-52 ([Fe/H] = −3.16; T eff = 4690 K) and HD122563 ([Fe/H] = −2.93; T eff = 4500 K) that had been degraded to match the resolution of the MagE data. Measurements of these standard stars are from Roederer et al. ( 2014 ). We find that the strengths of the Mg b lines observed with MagE appear to be roughly consistent with what is expected from our Ca ii K derived metallicities. Thus, only stars with very strong carbon enhancement are incorrectly selected into our M2FS sample. These stars are overwhelmingly likely to have their carbon abundance elevated by accretion from a binary companion (see Figure 10 ) and should already be excluded in a calculation of the CEMP fraction. This confirms that our selection is not biased in favor of CEMP-no stars. Below a fiducial metallicity of [Fe/H] ∼ −3.0 and after excluding CEMP-s, Ba-strong, and CH-strong stars, we can reasonably assume that
Paper: 2016ApJ...819..119L (fulltext)
ones before we can unambiguously identify the assembly bias signature in the universe. As discussed in Section 5.1 , the best candidate appears to be , followed by and the mean age (see also the proxy discussed in Lim et al. 2016 ). These in principle could be obtained from high-quality spectra with VESPA, as well as the aforementioned codes, and will be subjects of our future investigation. Inspiration could also come from the analysis of Miyatake et al. ( 2015 ), who have recently claimed a detection of strong assembly bias using galaxy clusters. Their proxy for halo formation time is , the mean projected separation of member galaxies from the cluster center. In principle, it is possible to adopt a similar proxy for galaxy scale halos, although the number of satellites is much smaller, and the membership determination is less certain. We conclude by noting that it is also imperative to better understand theoretically the origin of assembly bias across the mass spectrum of halos. If the cause at low-mass scales is the backsplash halos, then instead of identifying the best formation time indicator, one should look for ways to observationally distinguish such galactic systems, or to take such population fully into account when comparing with theoretical models. Regarding the high-mass end, since it is likely that the physical origin of assembly bias in high-mass halos is believed to be quite different than for low-mass halos (Dalal et al. 2008 ), the detection presented by Miyatake et al. ( 2015 ) is not necessarily inconsistent with the non-detection reported here. It is clear that the assembly bias phenomenon is far richer and complicated than expected, and warrants further investigations, such as exploring better proxies for halo formation time, and the scale dependence of assembly bias (Section 2.4.1 ). 6. SUMMARY As the assembly bias is a robust prediction of the CDM theory of structure formation, establishing it observationally would not only further vindicate this extremely successful theory, but also shed light on the baryonic physics of galaxy formation. In this exploratory study of the detection of assembly bias, we have shown (with the aid of weak gravitational lensing) that some previous claims of detection may be simply due to differences in halo mass of the galaxy samples, rather than a real manifestation of assembly bias (Section 3 ). We have then investigated a couple of ways of constructing galaxy samples with similar mean halo masses, thus facilitating a direct search for assembly bias in the real data (Section 4 ). We focus on the halo mass scale of , where assembly bias is expected to be large, and construct early- and late-forming halo samples by making use of a central galaxy catalog ( Y07 ), under the assumption that the SFH of central galaxies reflects the formation history of underlying halos (which is well supported by a state-of-the-art galaxy formation model; Section 5.1 ). Satellite galaxies living in massive halos
Paper: 2020ApJS..251....7F (fulltext)
and best detections. Table D20 StackModelFitExp: Contains the Exponential Fit Parameters to Extended Sources Note. See the StackObjectThin table for a discussion of the primary, secondary, and best detections. Table D21 StackModelFitDeV: Contains the de Vaucouleurs ( 1948 ) Fit Parameters to Extended Sources Note. See the StackObjectThin table for a discussion of the primary, secondary, and best detections. Table D22 StackModelFitSer: Contains the Sérsic ( 1963 ) Fit Parameters to Extended Sources Note. See the StackObjectThin table for a discussion of the primary, secondary, and best detections (Sérsic 1963 ). Table D23 StackApFlxExGalUnc: Contains the Unconvolved Fluxes within the SDSS R3 ( r = 103), R4 ( r = 176), R5 ( r = 300), R6 ( r = 463), R7 ( r = 743), R8 ( r = 1142), R9 ( r = 1820), R10 ( r = 2820), and R11 ( r = 4421) Apertures (Stoughton et al. 2002 ) for Extended Sources Note. These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See the StackObjectThin table for a discussion of the primary, secondary, and best detections. Table D24 StackApFlxExGalCon6: Contains the Fluxes within the SDSS R3 ( r = 103), R4 ( r = 176), R5 ( r = 300), R6 ( r = 463), R7 ( r = 743), R8 ( r = 1142), R9 ( r = 1820), R10 ( r = 2820), and R11 ( r = 4421) Apertures (Stoughton et al. 2002 ) for Extended Sources after the Images Have Been Convolved to a Target of 6 Sky Pixels (15) Note. These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See the StackObjectThin table for a discussion of primary, secondary, and best detections. Table D25 StackApFlxExGalCon8: Contains the Fluxes within the SDSS R3 ( r = 103), R4 ( r = 176), R5 ( r = 300), R6 ( r = 463), R7 ( r = 743), R8 ( r = 1142), R9 ( r = 1820), R10 ( r = 2820), and R11 ( r = 4421) Apertures (Stoughton et al. 2002 ) for Extended Sources after the Images Have Been Convolved to a Target of 8 Sky Pixels (20) Note. These measurements are only provided for objects in the extragalactic sky, i.e., they are not provided for objects in the Galactic plane because they are not useful in crowded areas. See the StackObjectThin table for a discussion of the primary, secondary, and best detections. Table D26 StackPetrosian: Contains the Petrosian ( 1976 ) Magnitudes and Radii for Extended Sources Note. See the StackObjectThin table for a discussion of the primary, secondary, and best detections. Table D27 StackToImage: Contains the Mapping of Which Input Images Were Used to Construct a Particular Stack Table D28 StackToFrame: Contains the Mapping of Input Frames Used to Construct a Particular stack along with Processing Statistics Table D29 StackDetEffMeta: Contains the Detection Efficiency Information for a Given Stacked Image Note. Provides the number of
Paper: 2016MNRAS.459..342M (acknowledgments)
We thank the referee for helpful comments. SPIRE has been developed by a consortium of institutes led by Cardiff Univ. (UK) and including: Univ. Lethbridge (Canada); NAOC (China); CEA, LAM (France); IFSI, Univ. Padua (Italy); IAC (Spain); Stockholm Observatory (Sweden); Imperial College London, RAL, UCL-MSSL, UKATC, Univ. Sussex (UK); and Caltech, JPL, NHSC, Univ. Colorado (USA). This development has been supported by national funding agencies: CSA (Canada); NAOC (China); CEA, CNES, CNRS (France); ASI (Italy);MCINN (Spain); SNSB (Sweden); STFC, UKSA (UK); and NASA (USA). PACS has been developed by a consortium of institutes led by MPE (Germany) and including UVIE (Austria); KU Leuven, CSL, IMEC (Belgium); CEA, LAM (France); MPIA (Germany); INAFIFSI/ OAA/OAP/OAT, LENS, SISSA (Italy); IAC (Spain). This development has been supported by the funding agencies BMVIT (Austria), ESA-PRODEX (Belgium), CEA/CNES (France), DLR (Germany), ASI/INAF (Italy), and CICYT/MCYT (Spain). This work has received support from the European Research Council under the European Union's Seventh Framework Programme (ERC Advanced Grant Agreement no. 291294, ‘ORISTARS') and from the French National Research Agency (Grant no. ANR-11-BS56-0010 – ‘STARFICH'). This research has made use of the SIMBAD data base, operated at CDS, Strasbourg, France. It has also 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.
Paper: 2016MNRAS.456.3866C (acknowledgments)
This work was supported in part by computational resources provided by the Australian Government through the National Computational Merit Allocation Scheme and the Pawsey Supercomputing Centre (projects g61 and ew6).
Paper: 2016MNRAS.461L..21G (fulltext)
the emitting electrons through the inverse Compton mechanisms and that weakens their radio emission. However, the regions of the jet producing the 1.4 GHz radio flux (≳7 GHz rest frame) are not affected by the ‘quenching’ of the radio due to the CMB radiation. This is because they have a magnetic energy density much larger than the CMB one, even taking into account the Γ 2 enhancement due to the relativistic motion of the emitting plasma. At z = 4, the CMB energy density is U CMB ∼ 2.6 × 10 −10 erg cm −3 . In the comoving frame, this is enhanced by a factor ∼Γ 2 , thus reaching U ′ = 2.6 × 10 −8 (Γ/10) 2 erg cm −3 . Most of the observed radiation from the jet is produced in a compact region, where the magnetic field is around 3 G, and the observed self-absorption frequency is ν t ∼ 3 × 10 12 Hz (rest frame, see e.g. Ghisellini Tavecchio 2015 ). In the case of a flat radio spectrum, the self-absorption frequency scales as R −1 , where R is the distance from the black hole. This is the same dependence of the dominant component of the magnetic field B . Therefore in the region self-absorbing at 7 GHz (rest frame) B should be ∼6 mG, and its energy density U B = B 2 /(8π) ∼ 1.4 × 10 − 6 . Since , there is no ‘quenching’ of the synchrotron emission of the jet. 4.1 The proposed scenario To solve the tension between predicted and observed sources, we propose a scenario that follows the ideas put forward by Fabian ( 1999 ). At redshifts larger than ∼4, jetted sources hosting a black hole with mass M ≳ 10 9 M ⊙ 1 are completely (i.e. 4π) surrounded with obscuring material (see the cartoon in Fig. 1 ). Only the jet can pierce through this material and break out. Observers looking down the jet can see the nuclear emission from the accretion disc and the broad emission lines. For observers looking with viewing angles even only slightly larger than θ j ≈ 1/Γ, the optical emission (including broad lines) is absorbed, the flux is fainter, and the source cannot enter the SDSS Catalog. The absorbed radiation is reradiated in the infrared. Even if the hotspots or the lobes were indeed emitting a radio flux above the 1 mJy level at the observed 1.4 GHz frequency, there would be no source in the SDSS to match with if the jet is only slightly misaligned. The quasi-spherical dusty structure (hereafter ‘obscuring’ or ‘dark bubble’) can cover the nuclear region until the accretion disc radiation pressure blows it away. This can occur at a threshold luminosity . 5 DISCUSSION Let us assume that the obscuring bubbles exist not only in jetted sources, but are common to all high-redshift quasars, including radio-quiet ones. The evolution in time of the obscuring bubbles and the central black hole mass could however be different in jetted and non-jetted sources. In fact, the presence of a jet could affect the accretion efficiency η d , defined as : part of the dissipation of the gravitational energy could amplify the magnetic field instrumental to launch the jet. In other