adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJS..242...16L (fulltext)
is time independent. This is the case for GRB 160625B as presented in the right panel of Figure 8 . Moreover, typically is observed to increase over a pulse (Ryde Pe’er 2009 ); this is the case for most bursts in our sample, with two prominent cases of GRB 140329B and GRB 150330A. To derive the parameters in the rest frame, the selected bursts should have known redshift. A redshift measurement of 2.73 is found for GRB 140206B (Malesani et al. 2014 ), and 1.406 for GRB 160625B (Zhang et al. 2018 ). However, GRB 140329B and GRB 150330A are without known redshift. On the other hand, Iyyani et al. ( 2013 ) investigated how the different values of redshift affect the results and pointed out that, for different values of z , the estimated outflow parameters change within a factor, but the time evolution of the behavior of the parameters remains the same. We therefore assume an average value of GRBs (Bagoly et al. 2006 ), z = 2, for the calculation (see also Iyyani et al. 2013 ). 3.1.3. Lorentz Factor, Γ The coasting values of the Lorentz factor ( r ph > r s ) are derived by (see also Equation (4) in Pe’er et al. 2007 )where Y is the ratio of total fireball energy to the energy emitted in gamma-rays. As depicted in Figure 9 , temporal evolution of the Lorentz factor shows either an approximately constant (e.g., GRB 140206B) or a monotonic decay behavior (e.g., GRB 140329B exhibits a monotonic decline throughout the pulse), or both (initially approximately constant and then decaying faster, e.g., GRB 150330A, GRB 160625B). The best fitting gives power-law indices of 0.01 ± 0.07 for GRB 140206B and −0.51 ±0.03 for GRB 140329B. We fit GRB 150330A and GRB 160625B with a smoothly broken power-law function, which gives best-fitting power-law indices of 0.69 ± 0.52 (before the break) and −0.52 ± 1.44 (after the break) for GRB 150330A and 0.37 ± 0.27 (before the break) and −0.89 ± 0.33 (after the break) for GRB 160625B. Figure 9. Temporal evolution of the Lorentz factor, Γ. Color conventions are the same as in Figure 8 . GRB 140206B presents a constant behavior with best-fitting power-law index 0.01 ± 0.07, while GRB 140329B shows monotonic decay with index −0.51 ± 0.03. GRB 150330A and GRB 160625B exhibit a smoothly broken power-law decay feature, with power-law indices 0.69 ± 0.52 and −0.52 ± 1.44 before and after break for GRB 150330A and 0.37 ± 0.27 and −0.89 ± 0.33 before and after break for GRB 160625B. The global view on the evolution of the Lorentz factor decays with time from a few hundred Y 1/4 down to below 100 Y 1/4 (e.g., Fan Wei 2011 ; Peng et al. 2014 ). The decreasing Lorentz factor may have several implications (Iyyani et al. 2013 ). Since typically is observed to increase over a pulse, the decrease of the values of Γ = Γ( t ) is not surprising by realizing the fact that ; such a decrease must, therefore, be a common characteristic form over individual pulse structures in GRBs (Ryde Pe’er 2009 ). This is because, during the rising
Paper: 2019ApJ...876..105X (fulltext)
of the two mini-BAL outflows in our sample. The Si iv trough is quite narrow and the S iv trough is unblended with other absorptions. In this case, the excellent template fit of the S iv trough is evident by the value f ( χ 2 ) = 0.4%. The S iv * absorption trough is heavily contaminated by a strong, likely Ly α , absorption trough from an intervening system. SDSS J0941+1331 has a multicomponent Si iv absorption trough. Following the procedure outlined in Section 4.2 , we use only the marked velocity interval as the template (see Figure 2 ). The Si iv template fits the S iv and S iv * absorption troughs well with f ( χ 2 ) = 6.8% in our survey. In order to account for possible systematic differences due to the Ly α forest, we compare the derived N ion from template fittings to the pure AOD extractions that includes the Ly α absorption features. In Figure 3 , we show the comparison of the observed S iv 1062.66 Å and S iv * 1072.97 Å absorption troughs. The AOD column densities integrated within the vertical dotted lines are N(S iv ) AOD = 1620 and N(S iv *) AOD = 740 (hereafter, all N ion are in units of 10 12 cm −2 ), while the template fitting results integrated over the same velocity range are N(S iv ) tmpl = 1450 and N(S iv *) tmpl = 540. Note that the AOD method overestimates the N ion because it includes the contributions from Ly α forest absorptions. These values yield a density sensitive ratio, N(S iv *)/N(S iv ), of 0.46 and 0.37 for the AOD method and the template fitting method, respectively. Using the same methodology described in Section 4.2 , the AOD method leads to a decrease of R by 25%. This difference is similar to our errors for the determined R value in Table 4 . Therefore, possible Ly α forest contaminations affect our derived R only moderately. Figure 3. Comparison of the normalized flux of S iv 1062.66 Å(blue) and S iv * 1072.97 Å(red) absorption troughs for SDSS J0941+1331 in velocity space. The vertical dotted lines are the range of Si iv template corresponding to Figure 2 . For SDSS J1135+1615, the Si iv absorption troughs are saturated and rather flat. We choose the two strongest local maxima to construct the Si iv template (see Figure 2 ). Fitting the S iv and S iv * absorption troughs with this template shows that the S iv * absorption trough is deeper than that of S iv . Including the many narrow Ly α troughs in the N ion extractions increase the N(S iv ) and N(S iv *) by 16% and 14%, respectively, and only marginally affects the derived R (6%). 5. Photoionization Analysis In order to derive the physical conditions of the outflow, we run the spectral synthesis code Cloudy (version c17.00, Ferland et al. 2017 ) to generate a grid of photoionization simulations. Cloudy self-consistently solves the photoionization and thermal equations necessary to model the physical conditions in the gas. The absorbers are modeled as plane-parallel, thin slabs of constant density gas with solar metallicity. They are irradiated by
Paper: 2017ApJ...849...75H (fulltext)
12 . Panel (a) shows that current sheet disruption takes place earlier with a lower growth rate in Run H5, where the initial noise level is three orders of magnitude higher than that of Run S5. Because current sheet disruption marks the onset of fast reconnection, the onset also occurs earlier in Run H5. However, as can be seen from panel (b), the reconnection rates after nonlinear saturation are essentially the same for the two runs. Therefore, we can conclude that reconnection rate after saturation is nearly unaffected by the noise level and the condition at the onset. This conclusion is also in line with the earlier study by Huang Bhattacharjee ( 2010 ), where the dependence of the averaged reconnection rate on the external forcing amplitude is found to be fairly weak. Figure 12. Comparison of time histories of (a) the linear growth rate and (b) the reconnection rate for Run S5 (with an initial noise level ) and Run H5 (). The initial noise level affects the disruption time and the linear growth rate at disruption, but the reconnection rate after nonlinear saturation remains approximately the same. From an observational point of view, the period between current sheet disruption and nonlinear saturation is especially interesting. During this period, the outflow profile changes from a nearly monotonic Sweet–Parker-like profile to a highly fluctuating profile, as shown in Figure 13 (a). Accordingly, the histogram of the outflow profile changes from double-peak-shaped to triangle-shaped, as shown in Figure 13 (b). A triangle-shaped histogram is a natural consequence of the outflow profile being highly fluctuating. To understand this, we consider an outflow profile of the form , where and the first term mimics the Sweet–Parker-like outflow profile, and the second term mimics short-wavelength fluctuations. If the fluctuation is much smaller than the background, i.e., , the histogram is double-peak-shaped. Conversely, if the fluctuation is on the order of the background, i.e., , which is the case in the saturated phase, then the histogram is triangle-shaped. The transition from a double-peak-shaped histogram to triangle-shaped histogram can lead to observable effects through spectroscopy of emission spectral line profiles, because the observed spectral line profile is a convolution of the Doppler-shifted line profile along the line-of-sight direction. For reconnection events with outflows along the line-of-sight direction, the histogram of the outflow profile can be viewed as a proxy for the shape of the line profile. A triangle-shaped line profile has been reported in IRIS (Interface Region Imaging Spectrograph (De Pontieu et al. 2014 )) observations of the Si iv line during transition region explosive events (Innes et al. 2015 ). With the high-cadence mode of the IRIS spectrograph, it is possible to observe transitions from a double-peak-shaped line profile to a triangle-shaped line profile in transition region explosive events (L.-J. Guo
Paper: 2019A&A...622A..63G (fulltext)
certain criteria. As an independent check the scatter in the DM was investigated among the models with a reduced χ 2 less than twice the minimum value. If there were five or more such models the dispersion (actually 1.48 ⋅ MAD) around the median was determined and compared with the formal error. This exercise revealed no systematic effects and the errors estimated in such a way are consistent with the formal errors. 6.2. Comparing dEBs and TRGB with Cepheid and RR Lyrae distances In Sect. 5.1 the TRGB distances are compared with the distances to 14 dEBs. One can also compare the TRGB distances with other independent distance estimates, as we did in Sects. 5.2 – 5.4 . We therefore took an identical approach as in Sects. 5.2 and 5.3 and determined the median DM and reddening value of CCs (in the LMC), and RRL (in the SMC) in the direction of the dEBs. The results are listed in Table 3 which first repeats the DM and reddening derived in the literature for the dEBs and the TRGB distance (based on the second-order derivative method) from Table 1 . Columns 5 and 6 give the DM and reddening values based on the CCs and RRL in those fields. Table 3. Comparison of distances for the dEBs. Notes. Columns 1–4 are taken from Table 1 . They indicate the name of the system, the DM and reddening based on the works listed in Col. 4 of Table 1 , and the TRGB distance using that reddening. Columns 5 and 6 list the DM and reddening of CCs (for the LMC objects) and RRL (for the SMC objects) in the direction of the EBs, and Col. 7 lists the TRGB distance using the reddening in Col. 6. Similarly, Columns 8 and 9 give the DM and reddening in Rubele et al. (2018) in the direction of the EBs, and Col. 10 lists the TRGB distance using the reddening in Col. 9. Finally, Col. 11 lists the reddening derived from Haschke et al. (2011) in the direction of the EBs, and Col. 12 lists the TRGB distance using that reddening. It is evident that the reddening estimates are smaller than adopted in the dEB analysis. In the SMC this is the case for all five objects. Although the differences are within the respective error bars it appears to be a systematic effect. In the LMC this is the case for eight out of nine objects but the differences appear to be smaller on average than for the SMC. To test the effect of reddening, the TRGB distance was derived using the E ( B − V ) from col. 6, and the results are listed in col. 7. It is clear that the effect on the DM is roughly inversely proportional to a change in E ( B − V ). Based on the definition of the sharpened magnitude, the absolute calibration equation (Eq. ( 3 )) and the reddening coefficients one expects a relation ΔDM/Δ E ( B − V )= − 1.1. The overall effect is noticeable however. The weighted mean DM of the nine LMC dEBs is shifted from 18.483 ± 0.006 mag to 18.523 ± 0.005 mag, and that of the five SMC binaries is shifted from 19.023 ± 0.007 mag to 19.051 ± 0.009 mag. In a similar way we used the data of Rubele et al. and took
Paper: 2019ApJ...870..123E (fulltext)
than those within the UV/optical, and Figure 3 (top) indicates a diversity of X-ray/optical lags, with the X-rays leading the UV in three cases and lagging in the fourth. This poses a severe problem for the reprocessing model, for which no simple solution is currently apparent. Swift is the workhorse without which IDRM cannot (currently) be successfully performed on AGN, as it is the only fully operational observatory that provides coverage throughout the X-ray, UV, and optical regimes necessary for this experiment. However, it is also very helpful to expand coverage to longer wavelengths and higher spectral resolution than that afforded by the UVOT alone. We note that simultaneous ground-based optical monitoring has been performed on all four of these targets, and the first data set (on NGC 5548) has already been published (Fausnaugh et al. 2016 ). Reduction and analysis of optical photometry and spectroscopy on the other three is ongoing and will be published shortly. This should provide further constraints on the accretion disk fits (especially the overall size) and possibly on the effects of line and diffuse emission from the BLR. Note as well that HST can also play a crucial role, providing much higher spectral resolution that can allow direct detection and modeling of the BLR continuum component, and extending the UV coverage to wavelengths as short as ∼1150 Å. That has been used to resolve the excess lag around the Balmer jump in one of these targets (NGC 4593, Cackett et al. 2018 ), and it is expected to play a prominent role in future campaigns. These unprecedented data also reveal the need for improvements in our time-series analysis tools. The uncertainties on lags output by JAVELIN (Zu et al. 2011 ) are on average a factor of ∼2.5 smaller than the same quantity output by the ICCF FR/RSS technique (Peterson et al. 1998 ). The precise cause of this discrepancy has not been determined, but given the fact that the older ICCF FR/RSS technique is more conservative in its assumptions and results, it and not JAVELIN was utilized to derive the results reported above. More generally, there is no interband lag tool currently in use that allows separation of two distinct interband lag signals, as seems to be the case in the UV/optical, where short lags from the accretion disk and longer lags from the BLR appear to be present. Ultimately these techniques will need to be developed and directly compared so as to determine which are more reliable and suitable for studying AGN interband lags, but that is beyond the the scope of the current paper. These results also highlight the need for theoretical progress, especially in understanding the X-ray emission component(s) and relation to the disk and BLR that emit at lower energies. These observations present severe problems for the “lamp-post” reprocessing model. Can it be modified or must it be discarded? If it is the latter then what will take its place? The current set of reduced data are
Paper: 2020ApJ...902..104L (fulltext)
Long Baseline Array (VLBA) images at 43 GHz (Agudo et al. 2011 ). A quasi-stationary feature within 2 mas of the core associated with a bent jet, was analyzed using Global mm-VLBI Array (GMVA) observations at 86 GHz (Hodgson et al. 2017 ). The spectral index showed different behavior between the core and stationary component at millimeter wavelengths. The spectral index of the core at 15–43 GHz and 43–86 GHz was inverted. When the core was flaring, the spectral index changed to become steep (Hodgson et al. 2017 ). In this paper, we report results from simultaneous multiple frequency VLBI observations of OJ 287 using the KVN at 22, 43, 86, and 129 GHz, from 2013 January until 2016 December. The paper is organized as follows. In Section 2 , we present the description of the observations for both the KVN data and all archival data used in this paper. Sections 3 and 4 describe results and various analyses of the flux density variability at multiple frequencies, respectively, and Section 5 includes a discussion on the results and analysis. Finally, we summarize this paper in Section 6 . We use luminosity distance D L = 1630 Mpc at a redshift of z = 0.306, assuming H =68 , , and = 0.302 (Spergel et al. 2015 ; Hodgson et al. 2016 ). Spectral index α is defined as . 2. Observations and Data Reduction 2.1. KVN Observations OJ 287 was observed by the KVN, which is a 500 km scale VLBI network consisting of three 21 m radio telescopes located in Seoul (KVN Yonsei), Ulsan (KVN Ulsan), and Jeju (KVN Tamna), Korea. The observations were conducted at 22, 43, 86, and 129 GHz bands simultaneously with a mean cadence of roughly a month since 2012 December except for maintenance seasons from June to August. The angular resolution of the KVN is 6 mas at 22 GHz, 3 mas at 43 GHz, 1.5 mas at 86 GHz, and 1 mas at 129 GHz. We used data obtained in 31 epochs in the period from 2013 January 16 (MJD 56308) until 2016 December 28 (MJD 57750). In each epoch, the source was observed with up to eight 5 minute scans per 24 hr. The observations were recorded in left circular polarization mode at a recording rate of 1 Gbps. The observing frequencies are 21.700–21.764, 43.400–43.464, 86.800–86.864, and 129.300–129.364 GHz, with a total bandwidth of 256 MHz (thus 64 MHz in each band). Detailed descriptions of the iMOGABA observations are reported in Lee et al. ( 2016b , 2017a ). 2.2. Data Calibration The obtained data were processed with the DiFX software correlator in Daejeon, Korea (Deller et al. 2007 , 2011 ), and the data was calibrated following the standard procedures (phase and amplitude calibration, fringe fitting, and bandpass correction) in the AIPS package from the National Radio Astronomy Observatory (NRAO). For the calibration, the KVN pipeline was used (Hodgson et al. 2016 ). The visibility phases in the 22 GHz band were transferred to those in the higher frequency bands (i.e., 43, 86, and 129 GHz bands) using the pipeline in order to increase the coherence time
Paper: 2021ApJ...908..100T (fulltext)
of the CCH/HC 5 N ratio between the observed values and the model calculations (Taniguchi et al. 2019a ). The red and black curves show the modeled CCH/HC 5 N ratio and temperature, respectively, in (a) the slow warm-up period model with ζ = 1.3 × 10 −17 s −1 , (b) the fast warm-up period (5 × 10 4 yr) model with ζ = 1.3 × 10 −17 s −1 , and (c) the fast warm-up period model with ζ = 4.0 × 10 −14 s −1 . The orange line indicates the observed value in G28.28–0.36 and the yellow range covers the observed values in the three MYSOs including the standard deviation. The blue line in panel (a) indicates the observed value in L1527 and the blue range covers the error in L1527. Figure 6. Production fraction of formation pathways of HC 3 N during the warm-up period (Taniguchi et al. 2019a ). JHC 3 N denotes molecules on the dust surface. As discussed in Section 4.2 , the observed CCH/HC 5 N ratios in MYSOs agree with the model at a temperature of ∼85 K. This temperature corresponds to 1.08 × 10 6 yr, 16 16 This age does not mean a physical age of an MYSO, because we derive the age from the chemical network simulations and the age will change by the assumed warm-up timescales. as shown in panel (a) of Figure 5 . Just before this time ( t 1.07 × 10 6 yr), the main formation pathway of HC 3 N is the reaction between C 2 H 2 and CN (panel (a) of Figure 6 ). This is consistent with the reaction proposed based on the 13 C isotopic fractionation in G28.28–0.36. The electron recombination reaction of HC 3 NH + is dominant in the model with the high cosmic-ray ionization rate during the whole warm-up period (panel (b) of Figure 6 ). Thus, the 13 C isotopic fractionation of HC 3 N in G12.89+0.49 and G16.86–2.16 can be explained by this high cosmic-ray ionization rate model. These results suggest that the cosmic-ray ionization rates in G12.89+0.49 and G16.86–2.16 may be higher than that in G28.28–0.36 or physical structures in G12.89+0.49 and G16.86–2.16 allow cosmic rays to penetrate into regions where carbon-chain species are formed. The observed CCH/HC 5 N ratios in MYSOs agree with the age of (4.7–4.86)×10 5 yr as indicated in panel (c) of Figure 5 . In summary, the observed CCH/HC 5 N ratios around MYSOs can be reproduced in the chemical network simulations at a given temperature and time. Taking the 13 C isotopic fractionation of HC 3 N into consideration, G12.89+0.49 and G16.86–2.16 may prefer the model with the high cosmic-ray ionization rate ( ζ = 4 × 10 −14 s −1 ), while G28.28–0.36 agrees with the model with the standard cosmic-ray ionization rate ( ζ = 1.3 × 10 −17 s −1 ). These results imply that carbon-chain chemistry in G12.89+0.49 and G16.86–2.16 may resemble that in the OMC-2 FIR 4 young intermediate-mass protoclusters (Fontani et al. 2017 ; Favre et al. 2018 ), but that in G28.28–0.36 is different from the other MYSOs and OMC-2 FIR 4. The clear 13 C isotopic fractionation of HC 3 N in G28.28–0.36 supports its bottom-up formation during the
Paper: 2021A&A...650A..88Z (fulltext)
for each of the late-phase loops, the energy input in late-phase loops reaches ∼7.7 × 10 29 erg. For the main flaring loops (as mentioned in Sect. 3.1 ), both of the two-stage energy release processes contribute to the heating. Hence, the heating duration of the main flaring loops is double compared with the late-phase loops. Assuming that the amplitude of heating rate is equal to H 0 , the energy input in the main flaring loops is estimated to be (1.5−3.8) × 10 29 erg. Based on the EBTEL simulation, the energy input in the post-flare loops amounts to (9.2−11.5) × 10 29 erg. 3.6. Nonthermal energy in flare-accelerated electrons Under the thick-target assumption ( Fletcher et al. 2013 ), HXR emissions at the footpoints are produced by collisional bremsstrahlung during the passage of flare-accelerated nonthermal electrons through a dense target, where the electrons are stopped by Coulomb collisions and lose energy in the chromosphere. The localized plasma can be heated up to ∼10 MK accompanied by chromospheric evaporation and condensation ( Tian et al. 2014 ; Li et al. 2015 ; Zhang et al. 2016a , b ; Zhou et al. 2020 ). The distribution of injected nonthermal electrons above a low-energy cutoff ( E c ) is assumed to be in the form of single power law with a spectral index of δ , , where A 0 is a normalized parameter of the total electron flux (in units of 10 35 electrons s −1 ). In Fig. 6 , three characteristic HXR spectra made from detector 9 of RHESSI during the first stage of energy release are displayed. In each panel, the fitted thermal component is drawn with a red line, while the nonthermal component is drawn with a blue line. It is obvious that the isothermal temperatures from RHESSI are close to those derived from GOES. Fig. 6. Three characteristic HXR spectra with an integration time of 20 s (black lines) and the fitted thermal (red lines) and nonthermal (blue lines) components. T e and EM of the thermal component are labeled. The low-energy cutoff ( E c ), spectral index ( δ ) of nonthermal electrons, and of the residuals are also labeled. The delivered energy by nonthermal electrons above E c is calculated by integrating the injected power during 12:40−12:46 UT, where A nt is the electron injection area, E H is set to be 30 MeV ( Warmuth Mann 2016a ). The nonthermal energy in flare-accelerated electrons during the first stage is estimated to be ∼1.74 × 10 30 erg. Considering that the HXR observation from RHESSI was unavailable during the second stage of energy release around 12:47:30 UT, it is impossible to calculate the nonthermal energy of electrons from RHESSI during the second stage. In Fig. 2 b, the second peak of GOES derivative accounts for ∼1/4 of the first peak, implying that energy input during the second stage accounts for ∼1/4 of the first stage. In this way, the nonthermal energy in the flare-accelerated electrons during the second stage is crudely estimated to be 0.44 × 10 30 erg. Therefore, the total nonthermal energy