adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019MNRAS.490.2648B (fulltext)
found by Colombo et al. ( 2014 ). Their results, furthermore, impose a cautionary warning for the study of the mass–size relationship of our own Galaxy: if we are unable to distinguish whether an observed Galactic cloud is in one or another environment, we might be producing a mass–size relationship that arises from clouds in different physical environments. It is convenient to stress that, although Colombo et al. ( 2014 ) made an effort to avoid line blending in their cloud decomposition, the situation can be quite complex, since even at the smallest scales where coherent structures can be found (i.e. cores, fibres), substantial superposition along the line of sight can occur (Ballesteros-Paredes Mac Low 2002 ; Gammie et al. 2003 ; Shetty et al. 2010 ; Zamora-Avilés et al. 2017 ; Clarke et al. 2018 ). The situation for clouds in external galaxies might deserve a full further study, since the resolution is substantially different in external Galaxies (typically, 30–50 pc; see e.g.Sun et al. 2018 ; Utomo et al. 2018 ), with only one study (Faesi et al. 2018 ) achieving resolutions of ∼10 pc. A first puzzling challenge is to understand, for instance, the origin of mass–size relation with q 2 in the interarm region of M51, since q = 2 is the limit when clouds are defined via thresholds in column density. 4.3 Fractal dimension? Different studies have interpreted the exponent of the power-law mass–size relation as representative of the fractal dimension (e.g. Elmegreen Falgarone 1996 ; Roman-Duval et al. 2010 ; Beattie, Federrath Klessen 2019 ). This approach has two hypotheses: First, the mass can be a proxy for the area. Secondly, the linear size is related also to the area in a quadratic way, since in the observational studies the size is typically considered to be proportional to the square root of the area of the cloud. In principle, the first hypothesis seems plausible: observed molecular clouds exhibit rapidly decaying N -PDFs (Kainulainen et al. 2009 ). The same is found for MCs in numerical simulations (e.g. Vázquez-Semadeni García 2001 ; Tassis et al. 2010 ; Ballesteros-Paredes et al. 2011b ; Kritsuk, Norman Wagner 2011 ). This means that the filling factor of the dense structures is quite small, which in turn, implies that the mass should be proportional to the area. However, for a collection of Galactic clouds, the superposition effects we discussed are not compatible with invoking mass as a proxy for the area. As shown in the present work, the same fact that the exponent in the mass–size relation is larger than 2 means that the mass is not proportional to the area. In addition, invoking the square root of the area as a proxy for the linear size does not tell us what will be the linear size–area correlation, which is precisely what is intended to be measured with the fractal dimension. A more appropriate estimation of the fractal dimension will be that of comparing the actual area of an ensemble of clouds with an independent estimation
Paper: 2016MNRAS.456...47M (fulltext)
of a gas parcel being essentially independent of its collapse history – explaining why the asymptotic T − Δ relation holds so tightly despite the varied histories of cosmological fluid elements. (The tightness of this relation is absolutely striking in calculations that do not allow for shocking but allow for varied collapse histories. See the Zeldovich approximation calculations in Fig. 1 .) We further show that Compton cooling off of the CMB has no effect on the asymptotic slope of T (Δ) and that recombination cooling is just weak enough not to break the relation. This paper is organized as follows. We first enumerate the relevant heating and cooling processes in Section 2 . Next, in Section 3 , we derive an analytic expression for the asymptotic T − Δ relation in the limit of only photoheating and adiabatic heating/cooling. These are the processes considered in previous attempts to understand this relation analytically. Section 4 generalizes our solutions to include all relevant cooling processes. We briefly consider three applications of our results in Section 5 : (1) how quickly temperature fluctuations are erased, (2) the importance of shock heating, and (3) the thermal evolution of collapsing gas clouds. Lastly, Section 6 contrasts our results with previous attempts to understand the intergalactic T − Δ relation. Throughout we use the standard phenomenological parametrization for the temperature of the low-density IGM, where Δ is the baryonic density in units of its cosmic mean, T 0 is the gas kinetic temperature at Δ = 1, and γ − 1 is a power-law index. (Sometimes γ is called the ‘equation of state.’) When necessary, we assume a flat ΛCDM Universe with h = 0.7, Ω m = 0.3, Ω b = 0.045, and Y He = 0.25. Following Hui Gnedin ( 1997 ), we use the Zeldovich approximation to follow the density (necessary for computing the temperature) of fluid elements for certain calculations. Our Zeldovich approximation calculations draw matter elements randomly from the probability distribution of collapse eigenvalues, assuming that the gas traces the dark matter. This probability distribution only depends on the variance of the density field, which we set to 1.2. This choice is equal to the variance of a real space top hat filter containing a Lagrangian mass of 10 10 M ⊙ in our reference cosmology at z = 3 (and approximately the Jeans mass near the cosmic mean density at this redshift). 2 In addition, we also use a 20 Mpc, 512 3 gas and 512 3 dark matter particle simulation that was run with the gadget -3 smooth particle hydrodynamic code (Springel 2005 ). Reionization occurs instantaneously in this simulation at z = 9, heating all gas to 20 000 K, and the subsequent heating and cooling takes rates applicable for primordial gas if only the H i and He i are kept ionized with an ultraviolet background that is flat in specific intensity [erg s −1 Hz −1 cm −2 sr −1 ]. 2 RELEVANT HEATING AND COOLING PROCESSES The evolution of the temperature of a Lagrangian
Paper: 2017ApJS..228...22L (fulltext)
For instance, a source with ∼4 Jy in VLBI and ∼1 Jy in single-dish flux densities has been known as a variable, J0238+1636, which had strong flux variations of around 5 Jy from 2007 to 2010. 6 6 F-GAMMA project: http://www3.mpifr-bonn.mpg.de/div/vlbi/fgamma/fgamma.html . In addition, a source with ∼0.3 Jy (VLBI) and ∼3 Jy (single dish) is J1849+6705. This has also been known as a variable and shows year-scale variations (see footnote 8). Therefore, these variables were not considered for the fit in K band. At SI ≥ 3, on the other hand, the linear fit results were calculated with all sources because the number of sources was small to fit. The slope for the compact sources is 1.07 ± 0.065, while that of extended sources is 0.66 ± 0.006 in K band. We infer that most of the radiation from the compact sources (SI 3) comes from the compact core region, whereas the extended sources (SI ≥ 3) have missing flux. In the Q band, because there are few common sources, the weighted linear fit was applied to all 76 sources. We expect that most of the radiation (∼80%) comes from the compact component, although about 20% of the flux density in the Q band is missing. Figure 6. Top: flux–flux relationship of 232 common sources in K band between single-dish observations from the KVNCS and VLBI observations from (Charlot et al. 2010 ) according to the structure index (SI). The error bar of each source indicates the 1 σ measurement error. Regions (1) and (2) are separated by the median flux densities (the first dotted line) of the KVNCS in K band. Regions (2) and (3) are separated by 1 Jy (the second dotted line) arbitrarily. Red (SI 3) and blue (SI ≥ 3) lines show the weighted linear fit results obtained using the data in (2) and those from all extended sources, respectively (black line: proportional case). Bottom: flux–flux relationship of 76 common sources in the Q band. The error bar of each source indicates the 1 σ measurement error. The green line shows the weighted linear fit result obtained using the all data, regardless of the SI (black line: proportional case). 4.3. Spectral Index Distributions Table 3 shows the statistics of α for 1533 sources in K band and 553 sources in the Q band, and 513 sources simultaneously detected in the K and Q bands. As expected, many of our sources have flat spectra in both the S and X and the K and Q bands. The distributions of the spectral indices of the S and X ( α SX ), X and K ( α XK ), X and Q ( α XQ ), and the K and Q ( α KQ ) bands are shown in Figure 7 . The top panel shows the distributions of α SX and α XK for 1533 sources and α XQ for 553 sources. The distribution of α XK becomes broader than that of α SX , but ∼88% of the α SX values and ∼70% of the α XK values belong to the flat-spectrum ranges. In addition, the distribution of α XQ is similar, and we expect that these are relatively bright sources. In the bottom panel, the distribution of α XK is broader than the other distributions and shifts toward
Paper: 2016ApJ...828...47P (fulltext)
and white to strongest continuum flux). The actual phases of observation are indicated by arrows on the right hand side. The similar line of C iv λ 1550.774 appears offset to higher velocity. Both features are formed mainly in the wind of the Be star, but shell variations are present on the high velocity side of the core. Figure 3.12. Al ii λ1670. The top panel shows the individual spectra of Al ii λ 1670.7867 plotted as a function of radial velocity and orbital phase (such that the continuum is aligned with the phase of observation). The spectral flux depth relative to the continuum is indicated by the scale bar in upper right. The lower panel shows the same interpolated in orbital phase and portrayed as a gray scale image (black corresponding to deepest absorption and white to strongest continuum flux). The actual phases of observation are indicated by arrows on the right hand side. Figure 3.13. Al iii λ1854. The top panel shows the individual spectra of Al iii λ 1854.7164 plotted as a function of radial velocity and orbital phase (such that the continuum is aligned with the phase of observation). The spectral flux depth relative to the continuum is indicated by the scale bar in upper right. The lower panel shows the same interpolated in orbital phase and portrayed as a gray scale image (black corresponding to deepest absorption and white to strongest continuum flux). The actual phases of observation are indicated by arrows on the right hand side. Figure 3.14. Al iii λ1862. The top panel shows the individual spectra of Al iii λ 1862.7895 plotted as a function of radial velocity and orbital phase (such that the continuum is aligned with the phase of observation). Thespectral flux depth relative to the continuum is indicated by the scale bar in upper right. The lower panel shows the same interpolated in orbital phase and portrayed as a gray scale image (black corresponding to deepest absorption and white to strongest continuum flux). The actual phases of observation are indicated by arrows on the right hand side. Figure 3.15. Fe iii λ1895. The top panel shows the individual spectra of Fe iii λ 1895.46 plotted as a function of radial velocity and orbital phase (such that the continuum is aligned with the phase of observation). The spectral flux depth relative to the continuum is indicated by the scale bar in upper right. The lower panel shows the same interpolated in orbital phase and portrayed as a gray scale image (black corresponding to deepest absorption and white to strongest continuum flux). The actual phases of observation are indicated by arrows on the right hand side. The lines of Fe iii λ λ 1895.46, 1896.814 also appear in the figure. Figure 3.16. Mg ii λ2796. The top panel shows the individual spectra of Mg ii λ 2796.352 plotted as a function of radial velocity and orbital phase (such that the continuum is aligned with the phase of observation). The spectral flux depth relative to the continuum is indicated by the scale bar in
Paper: 2021MNRAS.501..261A (fulltext)
on pauses in X-ray bursts studied with RXTE , suggest that there is no link between the detection of double-peaked profiles and the detection of a pause during the rise. However, our interpretation would be incorrect if the pauses are short and weak manifestations of a peak in the X-ray burst profile, meaning that the X-ray burst in SAX J1808.4–3658 can be considered a triple-peaked X-ray burst, and B1 in MAXI J1807 can be considered a double-peaked burst. in’t Zand et al. ( 2003 ) reported on an unusual Type-I X-ray burst from GRS 1747-312 2 observed with RXTE whilst the system was apparently in quiescence. The 2–10 keV light curve profile exhibits a pause-like feature in the rise of the X-ray burst, while the full 2–60 keV light curve did not show a pause. The pause in the 2–10 keV light curve is considerably longer than those seen in S1 and B1 ( ∼20 s), and, judging from Fig. 2 a of in’t Zand et al. ( 2003 ), the pause occurs at roughly 70 per cent of the peak count rate. The X-ray burst also had an unusually strong PRE that was not at the start of the X-ray burst, as is expected. It is unclear whether this is a longer manifestation of the pause phenomena we see in B1 and S1, but it shows that the presence of pause-like features can depend on the energy band used. Independently of whether the pauses are related to the multipeaked phenomena discussed above, our results, together with those presented by Bult et al. ( 2019 ), show that short pauses in the rise of X-ray burst light curves occur in at least two sources, suggesting that these types of pauses might be present in the X-ray light curves of Type-I bursts from other NS-LMXBs. We only know of two sources (the accreting millisecond X-ray pulsar SAX J1808.4–3658 and MAXI J1807), each with one reported X-ray burst with a clear pause during the rise. This small sample size prevents us from a detailed study on the relation between pauses and X-ray burst characteristics/properties, and naturally it is unclear whether the pauses seen in B1 and S1 are caused by the same mechanism. However, under the assumption that we are seeing manifestations of the same phenomena in B1 and S1, even with a sample of two we can conclude that pauses in the X-ray bursts are possible in both pulsating and non-pulsating systems, suggesting that the strength of the magnetic field might not play an important role, unless the lack of pulsations in MAXI J1807 is due to an alignment between rotation and magnetic axes (Lamb et al. 2009a , b ). In addition, the occurrence of the pause does not appear to depend on the composition of the burning material either, or whether the X-ray burst reaches PRE. To our knowledge, standard X-ray burst models do not predict pauses in the X-ray burst rise. However, we note that Keek, Heger in’t Zand ( 2012 ) finds X-ray bursts with pauses in their rise when modelling X-ray bursts that occur shortly after a superburst. In the hot, He-rich atmospheres following a superburst, X-ray bursts
Paper: 2021MNRAS.502.3113M (fulltext)
3.3 . 3.3 Expectation–maximization algorithm 3.3.1 Overview The measurements, , can be used to solve for τ c and τ s in , N c and N s in , and σ c and σ s in in equations ( 16 )–( 22 ) using any number of maximum-likelihood techniques. In this paper, we primarily discuss the expectation–maximization algorithm (Dempster et al. 1977 ; Shumway Stoffer 1982 ), which naturally extends to situations where observations are missing or Δ t is not constant (Shumway Stoffer 1982 ; Digalakis et al. 1993 ; Gibson Ninness 2005 ). The method cleanly separates process noise in the physical variables, characterized by , and measurement noise , characterized by . Two other common techniques include using Newton’s method to maximize the log-likelihood, log L , and the score vector method (Durbin Koopman 2012 ). The former method relies on numerical estimates of the inverse of the Hessian matrix, while the latter method calculates the gradient of log L with respect to the parameters and steps towards a maximum of log L (Durbin Koopman 2012 ). In this paper, we estimate the elements of , , and , as opposed to τ c , τ s , N c , N s , σ c , and σ s . It is possible to extend this method to directly estimating the parameters in equations ( 1 ) and ( 2 ) using ‘grey-box estimation’ (Ljung 1986 , 2013 ; Kristensen, Madsen Jørgensen 2004 ). 3.3.2 Algorithm and its logical basis The expectation–maximization algorithm is used to find stationary points of the posterior distribution in the absence of a direct observation of the states, . As in the previous section, we use as compact notation for the elements of , , and . We use the product rule to separate the posterior in terms of the states The first term on the right-hand side of equation ( 27 ) is a multivariate Gaussian with a straightforward interpretation, which we discuss in Appendix B . We will use this term to find stationary points of . To start, we treat as a random variable and take an expectation value of both sides, assuming some fixed value for the parameters, , leaving with The second term on the right-hand side of equation ( 28 ) is maximized at (Dempster et al. 1977 ; Gelman et al. 2013 ), while the first term on the right-hand side can be maximized analytically by a new choice of , which increases the marginal posterior distribution on the left-hand side of equation ( 28 ). This lends itself to the following iterative algorithm (where p now defines how many times we have iterated): Start with a guess of . Calculate (‘E-step’) using current estimate of the parameters . Maximize from step (ii) over (‘M-step’). Set to the maximum. Repeat steps (ii) and (iii) for a fixed number of iterations or until converges. In Appendix B , we discuss the application of this method to our specific case. We first present the full form of . We then discuss the E-step and M-step, along with the implementation used in carrying out the simulations discussed in Section 4 . In Appendix C , we show the detailed formulae used
Paper: 2020MNRAS.497.3097M (fulltext)
and disregarded signals below this cutoff as manifestations of polarization bias (see e.g. George, Stil Keller 2012 ). The FD and p values of our target sources were obtained by fitting a second-order polynomial to the highest peak in of each source (e.g. Heald et al. 2009 ; Mao et al. 2010 ; Betti et al. 2019 ). Specifically, we fitted to the seven data points nearest to the highest peak, with the FD uncertainty calculated by (e.g. Mao et al. 2010 ; Iacobelli, Haverkorn Katgert 2013 ). We did not correct for the Ricean polarization bias since we do not expect it to have significant effects for our case with S/N > 6 (Wardle Kronberg 1974 ; George et al. 2012 ). All these results from our new VLA observations (FD new and p new ), along with their counterparts in the original NVSS catalogue ( p NVSS ), the Taylor et al. ( 2009 ) catalogue (FD TSS09 and p TSS09 ), and Van Eck et al. ( 2011 ) observations (FD VE11 ) are listed in Tables 1 and 2 for on- and off-axis targets, respectively. We found that three on-axis and seven off-axis sources are unpolarized in our new observations (i.e. p new below our 6σ cut-off). The 1σ values in p new of these sources are reported within parentheses in the two tables. In total, we derived the FD values of 194 polarized EGSs, with 168 and 26 on- and off-axis targets, respectively. The sky distribution of our EGS FD is plotted in Fig. 1 (top panel). Figure 1. EGS FD values from (top) our new VLA observations and (bottom) Van Eck et al. ( 2011 ) observations, both plotted as colour dots. The background grey-scale map represents the WHAMSS Hα map (Haffner et al. 2003 , 2010 ). The typical uncertainty of our new FD is about , while that of the Van Eck et al. ( 2011 ) is about . The orange circle in the top panel outlines a prominent H ii structure, G26.5, and EGSs within this circle were not considered in our analysis (see Section 5.2 ). Table 1. RM-Synthesis results for on-axis targets. Notes: FD new and p new from this work; FD VE11 from Van Eck et al. ( 2011 ); FD TSS09 and p TSS09 from Taylor et al. ( 2009 ); and p NVSS from Condon et al. ( 1998 ). ⊙ Situated behind the prominent H ii structure G26.5. † Suffers from n π-ambiguity in Taylor et al. ( 2009 ). Table 2. RM-Synthesis results for off-axis targets. Notes: None of these sources are listed in Taylor et al. ( 2009 ) or Van Eck et al. ( 2011 ). FD new and p new from this work; and p NVSS from Condon et al. ( 1998 ). ⊙ Situated behind the prominent H ii structure G26.5. In order to assess the residual leakage signal level present in our data, we formed Faraday spectra of our unpolarized on-axis leakage calibrator (J1407+2827) using data from each of the seven observing blocks. We find that the highest peak out of the seven Faraday spectra is at a value of p = 0.024 ± 0.007 per cent, much lower than that of all of our polarized target sources. Note that the weak polarization signal we see from J1407+2827 here is likely due to polarization bias stemming
Paper: 2018A&A...614A..20D (fulltext)
of the maser emission, the spots could be bright at different times, and thus we consider the fit with nonzero positional offset to be more robust. Second, we performed an astrometric fit to the mean position of the shock front and derive a trigonometric parallax of π = 6.6 ± 0.6 mas, corresponding to a distance of pc. We show the parallax sinusoid from this fit in Fig. 6 , and give the resulting parameters in Table 5 . The values of the parallax derived from the fits to the three main spots are in good agreement with that obtained from the fit to the R.A. motion of the shock front. Table 5 Parameters obtained from the astrometric fits. The systematic errors that were added quadratically are also presented. The correlation coefficient matrices for all fits are shown in Appendix A . From these matrices we noticed a high (anti-) correlation between the parallax and the other fitted parameters in all the fits, as is expected given the short timescale covered by the observations. To check the reliability of our results, in view of these high correlations, we performed a series of Monte Carlo simulations which are presented in Appendix B . The result from these simulations is that, given the statistical and systematic errors in our measurements, the true astrometric parameters can be recovered from our fits even when the observations cover a period of only 3–7 months. The worst case is the individual fit to spot 1; this spot was only detected in four epochs and, consequently, the uncertainties on the fitted parameters are significantly larger. 4 Discussion 4.1 Distance to I16293 As we mentioned in the introduction, Imai et al. (2007) obtained a distance of 178 pc to I16293 by measuring the trigonometric parallax of water masers associated with this YSO. Imai et al. (2007) mentioned that the most severe factor causing their modest astrometric accuracy was the temporal variation in the brightness structure of the maser spots they detected. If water masers have short lifetimes of a few months, as is the case with water masers in I16293, it is possible to misidentify maser spots even from one epoch to the next. Taking into account this possibility, these authors performed several fits by using different combinations of observed positions. By excluding the oddest positions, they obtained an upper limit to the trigonometric parallax of 7.1 mas (corresponding to a lower limit in the distance of d = 141 pc). In the following, we discuss the differences between our analysis and that of Imai et al. (2007) . Fig. 6 Parallax fit to the mean positions of the shock front. Top: parallax sinusoid from the best fit to these data (blue line). The red squares are the mean positions of the shock front. In both cases, we have subtracted the proper motion and the RA position at JD 2453731.31 (the mean epoch from our observations) from the best fit. Bottom : residuals in right ascension. The first difference comes from their criterion for the selection of maser