adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...650A..93C (fulltext)
we present two other spectral extraction methods for the SPI data. B.1. Method 2: Using different event selection In this method, we consider the threshold of the PSD events, as set in the SPI telescope electronics. These are from 400 keV to 2.2 MeV after revolution 1170 and from nearly 650 keV to 2.2 MeV before. In this energy range, we used the PSD events only. This selection has been prescribed in Roques Jourdain (2019) in order to avoid spectral artifacts caused by potential spurious events. These spurious events appear at high energies (> 400 keV) for very bright sources (typically above 1 Crab). They are caused by a displacement of low-energy photons toward high energy, when the source is so bright that many photons can be detected in the same detection electronics time window. The energy reconstruction for the SE chain is then not reliable. This does not happen in the PE chain, which records the same event much more rapidly. B.2. Method 3: Using SPIDAI In this method, we use the SPI Data Analysis Interface 6 (SPIDAI) to reduce the data from SPI. We created a sky model containing Cyg X-1 and Cygnus X-3 and set the source variability to ten scws for Cygnus X-3 and five scws for Cyg X-1 (e.g., Bouchet et al. 2003 ; Cangemi et al. 2021 ). We then created the background model by setting the variability timescale of the normalization of the background pattern to five scws. In order to avoid the effects due to solar flares, radiation belt entries, and other nonthermal incidents, we removed scws for which the reconstructed counts compared to the detector counts give a χ 2 > 1.5. This selection reduces the total number of scws by ∼10%. The shadowgrams were then deconvolved to obtain the source flux, and spectra were extracted between 20 and 2000 keV using 27 logaritmically spaced channels. Given the large number of scws in both states, we extracted six and four different spectra for the LHS and HSS, respectively, each of them was extracted from data of 500–600 scws and corresponds to a distinct period. For each spectrum, we applied a correcting factor of 1/0.85 [above 400 keV or 650 keV according to the considered period, Roques Jourdain 2019 ) to account for the change efficiency. We then computed two average spectra for both states. In the HSS, the normalization of the spectrum has a slight variability over the four different periods which explain the larger error bars that we observe in this state when computing the average spectrum. B.3. Comparison between the methods The right panel on Fig. B.1 shows the broad SPI spectra (20–2000 keV) used in this paper along with the spectra extracted with the aforementioned methods. The left panel on Fig. B.1 shows the same spectra between 400 keV and 700 keV, that is to say the range for which the selection between SEs and PSD events is different from the the method described in this paper and method 2, described in Appendix B.1 . Fig. B.1. Left panel: spectra extracted with three different methods:
Paper: 2016MNRAS.461.3948P (acknowledgments)
The author gratefully acknowledges partial financial support by the Portuguese Fundação para a Ciência e Tecnologia under contract SFRH/BSAB/1420/2014.
Paper: 2019MNRAS.483..840B (fulltext)
angles and distances of events with joint γ-ray and GW detections (red crosses) from a simulation of SJPL model with θ 0 = 0.1 and δ = 4.5. Top and bottom panels show results for an effective GW horizon distance of 107 and 220 Mpc, respectively. The median distance of future joint detection events is ≈150 Mpc and correspondingly the median GRB luminosity is 7 × 10 48 ergs −1 ≫ L GRB170817 . The fact that GRB170817 is an atypical event of the sample of expected joint prompt and GW detections is also illustrated in Fig. 4 , where we present the expected L γ − θ obs distributions of joint detections for two models of prompt emission given a detection horizon of 220Mpc. For both models, the properties of GRB170817 (blue crosses) are not representative of the sample. Only and of the simulated events (jointly detected in γ-rays and GWs) in the CL and SJPL models, respectively, is found to have 0.25 ≤ θ obs ≤ 0.45 and 5 × 10 46 ≤ L γ ≤ 4.8 × 10 47 erg s −1 . The shape of the observed two-dimensional distributions is driven by the emission model, since the emission is typically dominated by material that is radiating within its 1/Γ beaming cone from the observer. A differentiation between the underlying emission models would require a sufficiently large number of joint GW and GRB detections (see also Section 5 ). Figure 3. Distances and observation angles for mergers detected in GW alone (yellow circles) and for mergers with joint GW and γ-ray detections (red crosses). We present the distribution with a detection horizon of 107 Mpc (top), corresponding to the effective detection horizon for GW170817 or 220 Mpc (bottom), suitable for future events. The inferred values for GRB170817 are shown with a blue symbol. The maximum distance at which it could have been observed in γ-rays is depicted by the vertical dashed line. Results are shown for an SJPL model with θ 0 = 0.1 and δ = 4.5. Figure 4. Two-dimensional distributions of observation angles and γ-ray luminosities for different structure models assuming a joint prompt GRB and GW detection. The location of GRB170817 on these plots is marked with blue crosses that denote the 1σ error in luminosity (Zhang et al. 2018 ) and observation angle (Mooley et al. 2018b ). 4 THE COCOON EMISSION We estimate here the thermal energy stored in the cocoon, the fraction of it radiated in γ-rays at breakout and the cooling emission signature arising when the thermal cocoon eventually becomes optically transparent. The latter corresponds to a ∼ hours long ultraviolet (UV) signal, that may be detectable, provided sufficiently rapid localization of the NS merger. The peak thermal energy stored in the cocoon (as a function of time) is found by Duffell et al. ( 2018 ). As mentioned above, here we assume that the that the injected opening angle is equal to the opening angle after breakout. Rewriting equations (10) and (21) in Duffell et al. ( 2018 ) in a slightly different form, we obtain: where erg and E j is the beaming
Paper: 2016MNRAS.462.2138F (acknowledgments)
The author appreciates the referee's valuable advices to improve the quality of the present paper.
Paper: 2017MNRAS.466.1733B (fulltext)
inclination, i , must be specified during the starspot modelling and line profile recovery. The stellar radius cannot generally be measured directly from observation, which means that i is not generally known. We performed a 2 search for a range of stellar axial inclinations, i rec , to determine the effect of an incorrectly determined axial inclination. A minimum 2 is always attained at the simulated combination of v sin i and P rot , irrespective of adopted axial inclination for 30 i rec 90. These parameters are therefore effectively independent of the choice of i rec . However, for the trial i rec values, only 2percent r.m.s. variation in was seen, with no discernible trend in 2 versus i rec in the 3090 range above the scatter. Thus, although the simulated inclination of i 60 cannot be recovered for v sin i = 5km s 1 , this does not preclude identification of v sin i and P rot , which are correctly recovered at all trial i rec values. A robustly determined rotation period, P rot and v sin i still enable i to be estimated for an assumed stellar radius. The typical 10percent uncertainty for M dwarf estimates of R (Stassun etal. 2012 ; Williams etal. 2015 ) will dominate uncertainty in the axial inclination determination. For instance, uncertainties of 10percent in sin i yield , , , and (upper limits at 75 and 90 arise from requiring sin i 1). For RV sim data using i 60 and N obs = 60, Fig. 3 (right) shows the FAP for RV corr data points using i 3090 (in 15 steps). In fact, there is a tendency towards improved FAPs when using higher inclinations (i.e. i rec = 75 and 90) for recovery. This is likely related to the lack of resolution elements, when instrumental resolution and stellar v sin i are of comparable magnitude. 4 SENSITIVITIES As every object and data set combination is unique, a full exploration of parameter space is beyond the scope of this paper. Nevertheless, a more complete picture of the range of sensitivities can be gained by exploring the S/N and v sin i values that are expected empirically. Fig. 5 shows the reduction in starspot noise achieved, = sim / corr (where sim and corr are the r.m.s. for the RV sim and RV corr data points), as a function of v sin i for the simulated S/N ratios. The lowest values of are found for Model 2, where 1. Again, this is due to an inability to recover the small spots, and in some combinations, the noise may be marginally increased by5percent. The procedure is most efficient for v sin i 5 km s 1 , where a maximum 8.6 is achieved. At v sin i 1km s 1 , the efficiency is generally lower, as fitting distortions below the instrumental resolution is less effective. For v sin i 10km s 1 , although starspot distortions are modelled well, the broader lines due to higher v sin i limit the efficiency of the procedure by increasing the RV uncertainties. Figure 5. Starspot noise reduction factor, , as a function of v sin i for S/N = 2000, 5000 and 10000 (bottom to top). For projected equatorial rotation
Paper: 2017MNRAS.464.2396L (acknowledgments)
We thank to Dr John Pye for very useful comments. KB is grateful the Paul Scherrer Institute and ETH Zurich, Switzerland, where some of the early work for this paper was done. TL is supported by an Science and Technology Facilities Council studentship. PW is supported by an Science and Technology Facilities Council consolidated grant (ST/L000733/1).
Paper: 2018ApJ...866..133D (fulltext)
of the system or the effects of anisotropic line emission, are subsumed into the constant f . Thus, f is expected to be different for every individual AGN, but should be approximately constant for every emission line in a given AGN assuming similar geometries and dynamics of the line-emitting gas. In every case where the lags from multiple emission lines can be measured in a single object, it is found that , as expected from Equation ( 1 ), suggesting that this is the case (Peterson Wandel 1999 , 2000 ; Kollatschny 2003 ; Peterson et al. 2004 ; Bentz et al. 2010b ). It is worth reminding the reader that the scale factor f depends on which parameter is used to characterize the emission-line width, as we discuss in Section 4 . The scaling factor f can be determined for an AGN if there is an independent measurement of the black hole mass. Unfortunately, there are few cases where the black hole radius of influence is large enough that either stellar or gas dynamical modeling can also be used. At the present time, there are stellar dynamical masses (Davies et al. 2006 ; Onken et al. 2014 ) and gas dynamical masses (Hicks Malkan 2008 ) for NGC 3227 and NGC 4151; these are useful for comparison purposes, but it would be unwise to attempt to calibrate the entire reverberation-based mass scale on only two objects. Instead, one of the well-known correlations between central black hole mass and properties of the host galaxies can be used. The first of these to be used to calibrate the AGN black hole mass scale was the correlation between black hole mass and host galaxy luminosity (Magorrian et al. 1998 ) by Laor ( 1998 ). More recently, calibration of the AGN black hole mass scale has been based on the strong correlation between the black hole mass and the velocity dispersion of host-galaxy bulge, the M BH – σ * relationship, which applies to both quiescent (Ferrarese Merritt 2000 ; Gebhardt et al. 2000a ; Tremaine et al. 2002 ; Gültekin et al. 2009 ; McConnell et al. 2011 ; McConnell Ma 2013 ) and active galaxies (Gebhardt et al. 2000b ; Ferrarese et al. 2001 ; Nelson et al. 2004 ; Onken et al. 2004 ; Dasyra et al. 2007 ; Woo et al. 2010 , 2015 ; Graham et al. 2011 ; Park et al. 2012 ; Grier et al. 2013a ; Batiste et al. 2017 ). With σ * measurements now available for ∼30 AGNs from the reverberation-mapping database (Woo et al. 2015 ), an ensemble average can be computed by comparing the predicted masses from the M BH – σ * relationship with the observed VPs, using the line dispersion to characterize the line width. Using this prescription, black hole masses have been measured for ∼60 AGNs using reverberation mapping (see Bentz Katz 2015 for an up-to-date compilation). An important result from reverberation mapping is the observed “ R – L ” relationship between the size of the BLR and the AGN luminosity (Wandel et al. 1999 ; Kaspi et al. 2000 , 2005 ; Bentz et al. 2006b , 2009a , 2013 ). This R – L relationship allows us to bypass
Paper: 2018ApJ...860L..24H (fulltext)
period (10,000–14,000 days in the MHD case), the large-scale magnetic field does not show any polarity reversal. We note that the polarity reversal is seen around 4500 and 8000 days. Figure 1. 3D volume rendering of the perturbation of the specific entropy multiplied by the background density for the (a) HD and (b) MHD cases are shown. A corresponding movie for panel (b) is available online. The animation continues for seven seconds covering 40 days of the global convection. The turbulent nature of the thermal convection is clearly seen in the animation. (An animation of this figure is available.) Figures 2 (a) and (c) show the streamline of the mass flux of the meridional flow for the HD and MHD cases, respectively, where includes the radial and latitudinal components of the velocity. The meridional flow in the HD case (Figure 2 (a)) shows a north-south aligned feature around the tangential cylinder. The overall flow pattern is counterclockwise (clockwise) in the northern (southern) hemisphere. In the MHD case (Figure 2 (c)), we see a clockwise (counterclockwise) meridional flow cell around the base of the convection zone in the northern (southern) hemisphere. Figures 2 (b) and (d) show the angular velocity for the HD and MHD cases, respectively. Because of the strong Lorentz force of the dynamo-generated magnetic field, the shear of the differential rotation, i.e., ΔΩ, decreased in the MHD case (see the difference in the color bar). In addition, the polar region is accelerated as well as the equator in the HD case. This feature is frequently seen in hydrodynamic calculations (Miesch et al. 2000 ; Gastine et al. 2012 ). The most important difference between the HD and MHD case concerns the Taylor–Proudman balance. In the HD case, the contour lines of the angular velocity are almost parallel to the rotational axis (Figure 2 (b)). This indicates that the HD case is in the Taylor–Proudman state. In contrast, the angular velocity in the MHD case shows significant deviation from the Taylor–Proudman state. This indicates that some terms in Equation ( 1 ) are balanced with the Coriolis force term. Figure 2. Panels (a) and (c) show the streamline of the meridional flow for the HD and MHD cases, respectively. The solid and dashed lines show the clockwise and counterclockwise flows, respectively. The angular velocity, Ω/(2 π ), is shown for the HD and MHD cases in panels (b) and (d), respectively, where Ω = v φ / λ + Ω 0 . To investigate the force balance on the meridional plane, we transpose Equation ( 1 ) as where is ignored, because the calculation is already in a statistically steady state. Figure 3 shows the balance in the MHD case. Minor contributions from the advection term ADV and the Lorentz force MAG around the top boundary are seen in Figures 3 (b) and (d), but the Coriolis force (COR, Figure 3 (a)) is mainly balanced with the baroclinic term, i.e., the entropy gradient (BAR, Figure 3 (c)). Although the difference in the differential
Paper: 2017ApJ...840...46I (fulltext)
NVSS object. We restrict our samples to the SDSS–NVSS quasars with a matching radius of 15 arcsec in order to consider the selection effect by the matching radius. This sub-sample contains 6081 radio-loud objects. The relation between and with the matching radius of 15 arcsec is given aswith a scatter of 0.54. The Spearman rank correlation gives , with a p -value of . Therefore, the correlation between disk luminosity and jet power still statistically exists. The jet production efficiency is given as . 5.4. Effect of the Radio-loudness Cut At , radio-loud quasars are also referred to as radio-intermediate quasars (Falcke et al. 1996 ). Most of them are likely to be FR-I type radio quasars. Figure 9 shows the correlation between and of the SDSS–NVSS detected quasars but having . The dashed line represents . 3083 quasars are included. The regression line, shown by the solid line, is given aswith a scatter of 0.38. The Spearman rank correlation gives , with a p -value of . There is still a positive correlation between disk luminosity and jet power in radio quasars. Based on the jet production efficiency arguments above, here we have . By selecting sources at , a slightly higher jet production efficiency is expected but is still consistent with that at at the 1 σ level. Figure 9. Same as Figure 4 , but at . Figure 10 shows the estimated jet production efficiency as a function of the limiting radio-loudness. As we select high jet power objects by setting high , the jet production efficiency gradually increases with . However, even at , is still at an order of 0.1. Figure 10. Jet production efficiency as a function of the limiting radio-loudness The 1 σ statistical uncertainty of the data is also shown. 5.5. Comparison with Blazar Studies The relation between the disk and jet has been also investigated using blazar spectral fitting studies (e.g., Celotti Ghisellini 2008 ; Ghisellini et al. 2014 ; Inoue Tanaka 2016 ). Differing from our radio quasar studies, blazar studies enable us to compare accretion with jet outflow at the same epoch. Multi-wavelength observations from the radio band to gamma-rays allow us to study overall SED and physical parameters of jets via spectral fitting. A spectrum of a blazar is composed of two emission components: the low-energy component is synchrotron radiation and the other one is an inverse Compton component targeting synchrotron radiation (e.g., Jones et al. 1974 ; Maraschi et al. 1992 ) and/or an external radiation field (e.g., Dermer Schlickeiser 1993 ; Sikora et al. 1994 ). Generally, blazar spectral fitting studies predict large jet production efficiencies (e.g., Celotti Ghisellini 2008 ; Ghisellini et al. 2014 ; Inoue Tanaka 2016 ), while our results show . Such a difference has also been recently reported by Pjanka et al. ( 2016 ) using published blazar samples. Here, the jet power estimates based on blazar spectral fittings strongly rely on the assumptions on the pair fraction and the electron minimum
Paper: 2019A&A...630A..61B (fulltext)
several tens of percent at high optical depths. An accurate calculation of the dichroic optical depth requires the full solution of the intensity profile along the path. In the general case of a dichroic medium, the radiative transfer equation becomes a set of four coupled first-order differential equations with varying coefficients, and a closed expression for the dichroic optical depth cannot be derived. However, the exact solution corresponding to a medium with uniform grain alignment can be used to find the full solution in an elegant way without any further numerical integration. There is no benefit in using τ ext , τ tot , or any other approximation instead of the dichroic optical depth to calculate the next interaction location in a Monte Carlo radiative transfer simulation. Our results have implications for Monte Carlo radiative transfer coders that wish to incorporate the attenuation by elongated dust grains. If scattering polarisation by spherical grains already adds some complexity to Monte Carlo radiative transfer codes, dealing with non-spherical grains increases this complexity to a new level. Compared to spherical grains, the scattering process is significantly more complex. The scattering properties of spherical grains are fully described by just the albedo and the scattering phase function; for elongated grains, a full 4 × 4 scattering matrix comes into play ( Mishchenko et al. 2000 ), and the random determination of a new propagation direction after a scattering event is not trivial ( Wolf et al. 2002 ; Whitney Wolff 2002 ; Lucas 2003 ). A related complexity concerns the amount of data that needs to be stored and accessed. Each of the elements of the extinction matrix and the scattering matrix is not only dependent on grain material, size, and wavelength, but also on shape and incidence angle. Moreover, each element of the scattering matrix needs to be discretised on the unit sphere. Finally, the process of dichroic extinction adds yet another level of complexity, and the results in this paper show that this also affects the random generation of the next interaction location. So far, only a limited number of Monte Carlo radiative transfer codes have attempted to actually calculate dichroic attenuation by non-spherical aligned grains 4 . Wolf et al. (2002) were the first to include non-spherical aligned grains in their Monte Carlo radiative transfer calculations. They presented multiple light scattering calculations and demonstrated that the incorporation of elongated grains is important to explain the circular polarisation of light. They discussed the concepts of dichroism and birefringence, but they did not include these effects in the simulations they presented. Whitney Wolff (2002) presented a Monte Carlo code that models the effects of scattering and dichroic absorption by aligned grains in circumstellar environments. While their code is presented for general geometries, they only discussed models with a uniform grain