adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...888...90R (fulltext)
0.6–2.1, corresponding to E ( B − V ) = 0.2 to 0.7. However, the dust maps of Green et al. ( 2015 ) and Lallement et al. ( 2019 ) show E ( B – V ) increasing gradually up to 0.1–0.2 at a distance of 1 kpc and remaining flat at larger distances in the direction of G70.0–21.5. The probable explanation is that Boumis et al. ( 2002 ) assumed the recombination value of about 3 for the Balmer decrement, while collisional excitation produces ratios of 5 to 3.7 for temperatures of 20,000–40,000 K (CHIANTI; Del Zanna et al. 2015 ). Thus, for the temperatures estimated in Table 2 , E ( B – V ) could be overestimated by around 0.4–0.5. Variations in the collisional contribution to the Balmer lines would produce variation in the [N ii ] to H α ratios seen in Table 2 . A complication is that the Hectochelle fibers do not cover the entire postshock cooling region. The 15 in fiber diameter corresponds to about 2.2 × 10 16 cm at a distance of 1 kpc. If the ambient density is on the order of 0.2 cm −3 , as in the NW Balmer line filaments of the halo SNR SN 1006 (Ghavamian et al. 2002 ; Bandiera et al. 2019 ), the cooling distance can be larger than the fiber diameter, though the narrow ionization and cooling zone behind the shock would still fit within a fiber. It is probable that the sharp H α filaments seen in the MDM images are just the high-emissivity region just behind the shock, and a fainter, cooler, more diffuse region trails behind. That could account for some of the variation in the strengths of the [O i ], [N ii ], and [S ii ] lines relative to H α in Figure 7 of Fesen et al. ( 2015 ) and Table 3 of Boumis et al. ( 2002 ). To further investigate the line ratios, we ran models with the code described by Raymond ( 1979 ) and Cox Raymond ( 1985 ). We assume a preshock density of 0.1 cm −3 and a magnetic field of 0.5 μ G, and we consider a range of shock speeds from 50 to 90 km s −1 and preshock neutral fractions from 0.05 to 0.6. The density choice is based on the height in the Galactic halo, and the magnetic field has little effect on the postshock region where the strong lines are produced. The preshock neutral fraction is determined not only by the overall Galactic ionizing UV background, but also by the SNR itself. Cox ( 1972a ) showed that an SNR emits a strong burst of ionizing radiation when it enters the shell formation stage, and the recombination time is longer than the SNR evolution timescale. Thus, the preshock gas will be overionized in the case of G70.0–21.5. We truncate the models at distances corresponding to 125. The density could be higher, however, and that makes the cooling region proportionally thinner, so we also consider models truncated at 25 and 50, which correspond to preshock densities of 0.2 and 0.4 cm −3 . In general, a fairly wide range of parameters can match the observed ratio of [N ii ] to H α . However, we reject shock speeds below 60 km s −1 because they predict line widths smaller than we observe. We find that the
Paper: 2016ApJ...833..139A (fulltext)
from the synchrotron emission by accelerated electrons but instead from a thermal process; the low-energy photon index would then relate to the spectral slope in the Rayleigh–Jeans regime of the blackbody component ( α = +1). 5.2. Coarse Time-resolved Spectral Analysis In this section, we describe the spectral evolution with time for the defined time intervals. Values from all fits are shown in Table 1 . As described below, strong spectral evolution is seen in the prompt phase. In the first time interval a , the Band function or a power-law function with exponential cutoff (CUTPL) provides a good fit to the data and there is no statistical difference between Band and CUTPL. This is also consistent with the fact that in the case of fitting the Band function alone the high-energy photon index β is very low ( β −3). Furthermore, when we adopt CUTPL to represent the obtained spectrum there is no need for any additional spectral component, as there is no statistical improvement by adding an extra component such as the blackbody or a power-law function (ΔPGstat ∼ 1) as shown in Table 1 . In the second and third time intervals, b and c , if we choose only the Band function, we find the same residuals as obtained in Section 5.1 (e.g., the low-energy bump). We see that the best-fit model is Band+PL and there is clear statistical improvement with respect to the case of the Band function alone (ΔPGstat = 86 and 157 in intervals b and c , respectively). Compared with the spectrum in the initial interval, the Band component low-energy index becomes harder. Interestingly, in these intervals the obtained low-energy photon indices are too large ( α = +0.2 to +0.4) to be explained by non-thermal synchrotron emission as described in Section 5.1 . Together with the small high-energy photon indices ( β ≤ −3), this indicates a narrow spectral component originating in thermal emission from the photosphere. From the theoretical point of view, Beloborodov ( 2010 ) pointed out that synchrotron emission and temporal variability lead to a softer spectrum ( α = +0.4) than the Rayleigh–Jeans index ( α = +1). In addition, a relativistically expanding conical jet structure implies a dispersion in the Doppler factor, which can broaden the photon spectrum (Asano Mészáros 2013 ). Here, we choose the diskpbb model implemented in XSPEC as a modified blackbody model to reproduce the thermal photospheric emission. The diskpbb model represents a multi-temperature blackbody spectrum where the local disk temperature T ( r ) at a distance r from a compact object is proportional to . While this model describes optically thick radiation from a geometrically thin accretion disk, we introduce this model phenomenologically to reproduce our modified blackbody spectrum. If we adopt this model, the photon index in the Rayleigh–Jeans range is represented as 2–2/ p and the diskpbb model can obtain a broader spectrum than a pure blackbody. The diskpbb model plus the power-law function,
Paper: 2020MNRAS.494.5787S (fulltext)
Recent studies have focused on searching for fast radio bursts at GHz frequencies in the first few minutes of a GRB, associated with the plateau phases (Bannister et al. 2012 ), or a deeper search, ∼ Jy sensitivity, for prompt (minute to hours) radio emission from short GRBs (Kaplan et al. 2016 ; Anderson et al. 2018b ). This was followed by a catalogue of 1 d follow-up observations at 15.7 GHz with the Arcminute Microkelvin Imager which increased the number of radio detections of short and long GRBs (Staley et al. 2013 ; Anderson et al. 2018a ). The new detections spanned 0.3–33 d post-burst, meaning that coverage during the prompt emission itself remains largely unexplored. Recently, a rapid response mode was implemented for the LOw Frequency ARray (LOFAR; Van Haarlem et al. 2013 ). LOFAR operates using either its low band antenna (LBA, 10–90 MHz) or with its high band antenna (HBA, 120–240 MHz) and is capable of responding to transient alerts within 5 min of receiving the trigger notice. Rowlinson et al. ( 2019 ) outline the first LOFAR search for prompt radio emission in long GRB 180706A, beginning 4.5 min after the γ-ray trigger. By utilizing the full capability of LOFAR, the observational constraints for coherent radio emission can now reach mJy sensitivities, revolutionizing the search for this emission from many transient events. In this paper, we explore the detectability of low-frequency radio waves associated with X-ray flares, under the assumptions of a Poynting flux model for the prompt emission mechanism in which X-ray flares are a manifestation of prompt non-thermal pulses. We draw a subset of X-ray flares from an established sample, and apply the model to estimate the fraction of low-frequency waves that would have been detectable by LOFAR for a given set of observational conditions. We discuss the optimal strategy to apply to the current LOFAR triggering and data processing, to find the emission from flares expected within this framework. 2 MODEL Low-frequency waves have been postulated to arise at the shock front of a highly magnetized, relativistic wind (generated by the rotational energy of a compact object) interacting with its surroundings (Smolsky Usov 2000 ; Usov Katz 2000 ). In this model, a variable current flowing between the two media either side of the travelling shock front leads to the generation of low-frequency radio waves in the MHz regime. The radio emission should occur at a peak frequency, ν max , in the observed frame of where z is the redshift of the GRB and we adopt an observer frame value of G for the magnetic field in the wind at the radius of low-frequency wave generation (Usov Katz 2000 ), where ε B is the fraction of the total energy in the magnetic field. We note there are several assumptions embedded within equation ( 1 ), such as the properties of the magnetic wind and the deceleration radius (see Usov Katz 2000 , for further details regarding these assumptions). A delay is expected
Paper: 2017ApJ...846L...5G (fulltext)
The GBM targeted search pipeline (Blackburn et al. 2015 ), which operates using a seed time and an optional seed sky map, was updated in preparation for the second observing run of Advanced LIGO (O2) to produce an overall lower false-alarm rate (FAR) and to be more sensitive to sGRB-like signals (Goldstein et al. 2016 ). The improvements implemented include replacing the “hard” spectral template with a more physically and observationally representative cutoff power-law spectrum, increasing the data temporal resolution, improving the background estimation, and utilizing localization information in the ranking statistic. 2.1.2. GBM Results The GBM was observing ∼82% of the LIGO sky map at the time of the LIGO trigger, and no onboard GBM trigger was recorded close in time to GW170104. The nearest triggers were more than 13 hr preceding and more than 10 hr following GW170104, and both of these triggers were due to steep non-astrophysical rate increases near the boundary of the SAA just before the detectors were turned off for passage. The untargeted search did not find any untriggered sGRB candidate on January 4. The nearest candidate preceding GW170104 was more than 15 hr earlier, and the nearest candidate following GW170104 was about 8 days later. The targeted search was run over a 1 minute window centered at t GW , searching for any signal on timescales from 0.256 s, increasing by factors of two up to 8.192 s (six timescales). As reported in Burns et al. ( 2017 ), no credible significant candidate was found. The most significant result from the search was on the longest timescale with the “soft” spectral template, which the search found to be marginally consistent with part of the LIGO sky map. Further inspection of the count-rate light curves for each detector reveal a slow-rising bump that begins ∼100 s before t GW and has a duration of ∼300 s. This bump, which is strongest in the GBM Na i channel 1 (12–25 keV) and does not appear above channel 2 (50 keV), is consistent with a low-level increase in magnetospheric activity as Fermi proceeds through its orbit. Using the GBM, we can set an upper bound on the impulsive flux expected from an sGRB-like signal. The current technique is to estimate the background in the same way that the targeted search does (unbinned Poisson maximum likelihood over a sliding window) and calculate the 3 σ upper bound in the count rate as three times the standard deviation of the background count rate. The count-rate upper bound can then be converted to a flux upper bound by assuming a photon model, folding it through the detector response and fitting the amplitude to determine the associated flux upper bound. We note that this technique produces a conservative upper limit because it utilizes a single GBM detector rather than all detectors over the entire LIGO map, which would be significantly more computationally challenging. We evaluate the limits in this way over the
Paper: 2016MNRAS.459.3812M (fulltext)
discrepancies are caused by differences in how ρ SFR is calculated. A good example of this problem can be illustrated by comparing our estimate of ρ SFR at z ≃ 10 with the results of Oesch et al. ( 2014 ). The headline result from Oesch et al. ( 2014 ) is that log 10 (ρ SFR ) = −3.25 ± 0.35 at z ≃ 10 (lower data point in Fig. 9 ), which is in poor agreement with the value of derived here. However, it is worth noting that the value of log 10 (ρ SFR ) = −3.25 ± 0.35 was calculated by summing the contribution of the five z ≃ 10 candidates found by Oesch et al. ( 2014 ), rather than integrating their estimated z ≃ 10 LF to a consistent UV luminosity limit. Interestingly, Oesch et al. ( 2014 ) also calculate an alternative value of log 10 (ρ SFR ) = −2.80 ± 0.11 (upper data point in Fig. 9 ), based on assuming luminosity evolution of the LF (as adopted here) and integrating to our adopted limit of M 1500 = −17.7. This estimate of ρ SFR is in excellent agreement with both our new determination at z ≃ 10, and our best-fitting linear ρ SFR – z relation. Finally, it is interesting to compare our new observational estimate of the high-redshift evolution of ρ UV with the predictions of recently published models of galaxy evolution. Our data points are plotted against the predictions of seven such models in Fig. 10 . It is important to acknowledge that some of these predictions are at least partly based on hydrodynamic simulations, while others are semi-analytic models, some of which have been effectively tuned to reproduce the UV LF at moderately high redshift (e.g. z ≃ 6). Thus, in a sense, this comparison is not completely fair, and cannot be simply regarded as specifically favouring one form of model over another. Nevertheless, some important general trends can be deduced. In particular, the Illustris (Genel et al. 2014 ) and Munich (Henriques et al. 2015 ) models, while clearly capable of reproducing many (often quite complex) aspects of galaxy evolution, both clearly underpredict ρ UV at redshifts higher than z ≃ 7. We stress that this discrepancy is already clear by z ≃ 8 (where the measured value of ρ UV is well established) and is simply confirmed and strengthened by our new results at z ≃ 9 and ≃ 10. The challenge, then, for such models is to be able to produce star formation activity which extends to higher redshifts than currently predicted. Indeed, the only model shown here that systematically overpredicts ρ UV is that of Behroozi Silk ( 2015 ). We note that the overprediction of raw observed ρ UV at redshifts z ≃ 5–6 by the Illustris and Khochfar et al. simulations is not really a fundamental problem, as both of these models do not include the impact of dust obscuration. Figure 10. Our new measurement of the evolution of the UV luminosity density (ρ UV ) and inferred star-formation rate density (ρ SFR ) at z > 5, compared with the predictions of a range of models and simulations from the literature. The model predictions are as follows:
Paper: 2019MNRAS.486..608L (fulltext)
cloud temperature is assumed to be 5000 K, the column density, , will be enhanced for a given cloud mass. Therefore, for the initial mass function with α = −1 and adopted in this work, the model will produce more systems and overproduce the gas covering fraction for such systems. On the other hand, by increasing the temperature of clouds to be 20000 K, all the gas clouds become highly ionized with . Such a scenario cannot produce any high column density systems as observed. This suggests that the adopted cloud temperature with 10 4 K is perhaps reasonable for reproducing the gas properties of high gas absorbers. Survival of clouds – One of the key assumptions in our model is that the clouds are not destroyed by the hydrodynamic instability. Although a number of studies have shown that various mechanisms can suppress the effect of hydrodynamical instability (e.g. Vietri et al. 1997 ; McCourt et al. 2015 ; Armillotta et al. 2017 ; Gronke Oh 2018 ), it is still unclear how these mechanisms operate in reality. This problem is related to the problem of entraining the cool gas along with a hot wind (e.g. Zhang et al. 2017 ). However, we argue that the existence of cool gas and the evidence of contribution from outflows in the haloes (e.g. Bordoloi et al. 2011 ; Lan Mo 2018 ) suggest that either the cool gas outflows must survive over the hydrodynamic instability, as we assume in our model, or cool gas is redistributed in the haloes by some other mechanisms associated with outflows (e.g. Thompson et al. 2016 ; Lochhaas et al. 2018 ). Gas properties at large scales – Our model has difficulties in explaining systems with beyond 100 kpc (e.g. Zhu et al. 2014 ; Huang et al. 2016 ; Lan Mo 2018 ) due to the low volume density of the hot ambient gas as shown in Fig. 2 . It is, therefore, likely that some of the systems at large distances are produced by neighbouring haloes due to spatial clustering of haloes. Such ‘two-halo’ term is not included in our current model, but should be modelled. 5 SUMMARY We develop a flexible semi-analytic framework to explore the physical properties of the cool pressure-confined circumgalactic clouds with mass from 10 to . We take into account the effects of gravity and interaction between hot gas and cool gas and model the trajectory, the lifetime, and the observed properties of the cool clouds with cloudy simulations. The ensemble properties of the cool CGM are explored by populating clouds following various mass functions in the haloes with three origins, outflows, inflows, and in situ formation. With this framework, we investigate how different mechanisms affect the observed properties of the cool CGM. Our results are summarized as follows: The pressure-confined cool gas clouds have with sizes about 10–100 pc similar to the properties of strong metal absorbers, sub-DLAs, and DLAs observed towards background spectra. We illustrate the effect of hot gas on the motion and lifetime of cool gas clouds; the ram pressure from the
Paper: 2020MNRAS.494.2809M (fulltext)
Photoionization is assumed to be negligible (Kozma Fransson 1998 ). Ionization balance is achieved by equating the rate of impact ionization with the recombination rate for each ion. Within each ion, level populations are computed solving the rate equations for each level under the assumption of thermal balance, i.e. equating the non-thermal heating rate and the rate of cooling via line emission. The assumption is made that the nebula is optically thin, and radiation transport in not performed. The emissivity in the various lines is used to compute the emerging spectrum. The code can be run as a simple one-zone code, in which case the line profile is a parabola with characteristic width determined by the speed of expansion of the outer boundary of the nebula (which is input to the code). Alternatively, a stratified version can use a one-dimensional explosion model (i.e. a radially dependent density/abundance profile), in which case energy deposition, ionization balance, level populations, and emissivity are computed for each shell. Each shell then contributes its own emissivity. Line profiles are built by summing truncated parabolae bounded by each shell’s inner and outer boundary velocity. In both cases, the effects of line blending are automatically taken into account in the computation of the emitted spectrum. Our code has been used for a number of SNe Ia (e.g. Mazzali et al. 2011 ; Mazzali Hachinger 2012 ) and Ib/c (e.g. Mazzali et al. 2007b ), and it can yield a description of the inner layers of the SN ejecta. When used in combination with a description of the outer layers, which can be obtained through a study of the early spectra, the mapping of the inner ejecta that results from nebular studies can be used to obtain a full description of the density and abundance distribution in an SN (e.g. Mazzali et al. 2015 ; Ashall et al. 2016 , 2018 ). One aspect our code attempts to treat is clumping of the ejecta. The simple assumption is made that all ejecta reside in clumps, which occupy a fraction of the total volume, as defined by a filling factor. The density in the clumps is then increased following the decrease in volume, and the composition of the clumps is set to be the same as that of the radial ejecta shell they are in, i.e. all clumps are homogeneously and microscopically mixed. The clumps are still assumed to be in thermal equilibrium. These are of course major simplifications, but in the inner layers of an SN Ia only few elements are present, so it is not unreasonable. Strong clumping has been shown to be very useful in order to reproduce the low ionization seen in late-time spectra of SNe Ic (e.g. Mazzali et al. 2001b ), where it may be thought of as a proxy for the non-spherically symmetric distribution of the ejecta. Clumping is used later in this paper to improve our synthetic spectra, especially in the final epoch. 4 SPECTRAL MODELS AND THE EVOLVING PROPERTIES OF SN 2014J We started our analysis by deriving some basic
Paper: 2019MNRAS.489.3116A (fulltext)
for the BH in each system and could be supernova (SN), direct collapse (DC), or unsure (U). We have stated that just for the central predicted systemic radial velocity. Refer to Sections 7.1 and 7.4 for the explanation of the suggested birth pathway. a This work. b della Valle et al. ( 1994 ). c Cadolle Bel et al. ( 2007 ). d Gaia Collaboration ( 2018 ). e Kirsten et al. ( 2014 ). 5.2 Systemic radial velocity estimates We have four systems with poorly constrained systemic radial velocities ( Swift J1753.5−0127, MAXI J1820+070, VLA J2130+12, and GRS 1716−249). Using the best distance estimates for these systems, we project them on to the Galactic plane. We estimate the expected systemic radial velocity () of the system at this projected distance on to the Galactic plane and assuming that the system is undergoing pure Galactic rotation about the Galactic Centre. We note that this estimated value of systemic radial velocity is not a true indicator of the systemic radial velocity of the system as it might have received kicks, and thus is probably not following a pure Galactic rotation about the Galactic Centre. Thus for each system, we estimate five probable systemic radial velocity Gaussian distributions with medians of , 50 , and 100 (see Table 5 ). We limit our assumed distributions of the systemic radial velocities based on the fact that out of the systems for which systemic radial velocity is measured, the values lie between −142 ± 1.5 for GRO J1655−40 (Shahbaz et al. 1999 ) and 107 ± 2.9 for SAX J1819.3−2525 (Orosz et al. 2001 ). 6 ANALYSIS – POTENTIAL KICK VELOCITY As explained in Section 1.3 , peculiar velocity of a system when it crosses the Galactic plane is a better probe to understand the kick a BHXB received when the BH was born. Since all the parameters, namely proper motion, systemic radial velocity, and parallax (or distance), have error bars associated with them, it is crucial to propagate these errors appropriately to estimate the Galactocentric orbits of the systems. The age of most BHXBs is not known, which makes integrating the Galactocentric orbits back to the time of birth of the BH uncertain. We thus developed an MC methodology that accounts for the errors on the measured quantities and determines the peculiar velocity every time the system crosses the Galactic plane. The code integrates the Galactocentric orbit of every system in our sample back for 10 Gyr and records the velocity of the system at every plane crossing. Instead of a delta function for the measurement, it involves using Gaussian distributions of the measured parameters with reported uncertainties as standard deviation. Random values are picked from these Gaussian distributions as inputs to galpy (Bovy 2014 ) to create instances of Galactocentric orbits for ∼5000 random draws to make sure we have sampled the input distribution properly. We assume a Galactic potential given by the galpy model MWPotential2014 (see Bovy 2014 ). The Galactocentric orbits are