adsabs/FOCAL huggingface dataset:
As one of the previous studies on the atomic gas in the Perseus region, Stanimirović et al. (2014) calculated the 





 optical depth toward 26 extra-Galactic radio continuum sources (such as quasars). They calculated 





 as a function of velocity toward each radio source by using the method described in Heiles & Troland (2003). The calculated 





 spectra were fitted with a sum of Gaussian functions, and the peak 





 values and the Gaussian FWHMs (full width at half maximum) of each velocity component were derived. However, they found the optically thick 





 gas only toward 





 of lines of sight. We test the results in Stanimirović et al. (2014) and Fukui et al. (2014, 2015), which differ by several factors. To do this, we compare 





 derived in Stanimirović et al. (2014) and that obtained in Section 3.5. Since our 





 corresponds to the average value within given velocity width (





; see Section 3.4), we compare the following two values toward each radio source located in the region we analyzed: (1) the sum of the areas of each Gaussian component of the 





 profiles calculated in Stanimirović et al. (2014), and (2) the products of our 





 and 





. The results are listed in columns 8 and 9 of Table 2. Note that 4C +30.04, 3C 092, and 3C 093.1 are located at the masked area; therefore 





 cannot be calculated by our method toward them. Although there is a rough positive correlation between them, they differ 





 times as a concrete numerical value. The correlation between them is plotted in Figure 16. The solid line indicates the one-to-one relationship (





), and the dashed line is the best-fit regression line through the origin (





). The results in Stanimirović et al. (2014) are systematically smaller than our results, and it is obvious that there is a discrepancy between these two results. This discrepancy can, however, be explained by characteristics of the data used to derive 





 in the present study and Stanimirović et al. (2014).
Citation: Fukui et al.; 2015
Citation function: Differences; Differences
We use the largest sample of FRB 121102, which is observed by GBT at 4–8 GHz (Zhang et al. 2018). This sample contains 21 pulses reported in Gajjar et al. (2018) and 72 pulses identified by machine learning. These pulses were observed within a 6 h observation. They share the same observation conditions and were observed by the same telescope. Therefore, we can put them together to analysis and ignore complex selection effects. Power-law distributions of energy αE = 1.63 ± 0.06 and distributions αT = 1.57 ± 0.13 for these 93 FRB 121102 bursts are shown in Fig. 5. Gourdji et al. (2019) discovered a low-energy sample with 41 bursts for FRB 121102 and found αE ∼ 1.7 if all bursts are included (see their fig. 5). However, if the low-energy bursts are discarded, a steeper αE ∼ 2.8 is found. Wang & Zhang (2019) also found that six samples of FRB 121102 bursts observed by different telescopes at different frequencies show a universal energy distribution with αE around 1.7. Meanwhile, a similar power-law index of energy distribution for non-repeating FRBs is also found (Lu & Piro 2019; Zhang & Wang 2019). The waiting time distribution of FRB 121102 can also be described by a non-stationary Poisson process with mean burst rates $\lambda _{0}=1.23^{+0.80}_{-0.38} \times 10^{-5} \,\rm ms^{-1}$. Zhang et al. (2018) found that the rate of detection is not stationary, and the distribution of waiting time cannot be well fitted using Poissonian distribution for the same sample. For a small sample of waiting times of FRB 121102, Oppermann, Yu & Pen (2018) modelled the distribution of waiting times as Weibull distribution, which can describe non-Poissonian distributions with clustering. It must be noted that the non-stationary Poissonian distribution used in this paper is similar to the Weibull distribution. Because the rate of bursts in a non-stationary Poisson process also varies with time (Wheatland et al. 1998), the mean burst rate is 1.23$^{+0.80}_{-0.38}\times 10^{-2}$ s−1. Using the same data, Zhang et al. (2018) found that the rate is 0.05 s−1 for Poissonian distribution. Using a sparse waiting time sample, Oppermann et al. (2018) derived a mean repetition rate of $5.7^{+3.0}_{-2.0}$ d−1. The large discrepancy between the two rates is that the waiting times used in this paper are derived from 93 bursts in 5 h observation, comparing to 17 bursts in about 74 h observation in Oppermann et al. (2018).
Citation: Zhang & Wang 2019
Citation function: Similarities
The results presented here are based on the assumptions that the CBPs do not interact with the material ejected from their binary star. However, numerical studies have indicated that this material is neither lost isotropically from the binary during the CE phase, nor does it all become unbound. Instead, 1–10% of the ejecta may fall back into a CB disk according to Kashi & Soker (2011), and Passy et al. (2012) suggest that ∼80% of the ejected material may remain gravitationally bound to the binary (also see Kuiper 1941; Shu et al. 1979; and Pejcha et al. 2016 for mass loss outflows through the L2 Lagrange point24

24
Mass loss through L2 results in several possible outcomes, e.g., isotropic or equatorial wind, CB disk; for details, see Table 1 and Figures 12 and 13 of Pejcha et al. (2016).
). Either of these scenarios would significantly complicate the dynamical evolution of the system as the CBP could accrete material and gain mass and also experience migration similar to that during planetary formation.25

25
There are, however, two potential benefits of the former in terms of detection: a more luminous planet would be more amenable to direct imaging efforts, and a more massive planet would cause stronger ETVs.
 Such accretion of material of a different specific angular momentum will change the orbital evolution of the planet, as will gravitational interaction with the bulk gas in an accretion-favorable environment such as a CB disk. Additionally, interactions between CBPs and the CE ejecta of close binary stars (including ejection or destruction of the planets) may also play an important role in the elusive mechanisms responsible for the shaping, morphology, and chemistry of planetary nebulae (see Bear & Soker 2016 for triple star origin of asymmetric planetary nebulae; for recent reviews see Zijlstra 2014 and Jones 2015, and references therein). We note that the CE-triggered dynamical disturbances discussed here occur on timescales of ∼1 year and are thus “instantaneous” compared to any subsequent planet migration, which occurs on much longer timescales.
Citation: Kuiper 1941
Citation function: Compare/Contrast
We present these results with one very important caveat. Accurately determining metallicities at different redshifts is of key importance to studying the evolution of the MZR. In the local universe, relationships between strong emission line ratios and metallicity can be calibrated to âdirectâ electron temperature-determined metallicities from measuring auroral lines such as [Oâiii]Â Î»4363 (Pettini & Pagel 2004; Pilyugin & Thuan 2005) or photoionization models of star-forming regions (Zaritsky et al. 1994; Kewley & Dopita 2002; Kobulnicky & Kewley 2004; Tremonti et al. 2004). At redshifts above z â¼ 1, it is nearly impossible to detect weak auroral lines for directly determining metallicity (but see Yuan & Kewley 2009; Rigby et al. 2011; Brammer et al. 2012a; Christensen et al. 2012; Maseda et al. 2014). Creating photoionization models that suitably represent high-redshift star-forming regions requires knowledge of physical parameters which have been poorly constrained up to this point. Thus, it is unknown if local metallicity calibrations hold at high redshifts. Figure 6 shows a comparison between metallicities determined using the O3N2 indicator and the N2 indicator for both local SDSS galaxies (grey points) and MOSDEF z â¼ 2.3 galaxies (black points). The black dashed line indicates a one-to-one relationship. If local calibrations do indeed hold at high redshifts, then the relationship between metallicities determined from different indicators should not evolve with redshift. It is clear that the z â¼ 2.3 galaxies are offset below the local galaxies. The dotted line is the best-fit line of slope unity to the individual z â¼ 2.3 galaxies, yielding an offset of â0.1 dex from a one-to-one correspondence, over twice that displayed by the SDSS sample. Steidel et al. (2014) found an offset slightly larger than this at z â¼ 2.3. This offset demonstrates that the two metallicity indicators are not evolving in the same way with redshift, and shows the need of metallicity calibrations appropriate for high-redshift galaxies.
Citation: Yuan & Kewley 2009
Citation function: Compare/Contrast
Citation: Zaritsky et al. 1994
Citation function: Background; Background
Finally, we attempted to estimate the total obscured star formation within the Mpc scale environments of ELAN, which can be achieved by assuming that the excess number of submillimeter sources are all associated with the respective ELAN. To compute the SFR densities we first seek a proper conversion from S850 to SFRs. We utilized a sample of ALMA-identified SMGs in the UKIDSS-UDS field, which has been studied in detail with proper SED fittings (Dudzevičiūtė et al. 2020). Similarly to our observations, the sample of Dudzevičiūtė et al. (2020) was drawn from a flux-limited sample produced by the SCUBA-2 Cosmology Legacy Survey (Geach et al. 2017). Their results should therefore be representative, on average, of SCUBA-2 sources uncovered in other fields. These authors found a linear correlation of log10[SFR(M⊙ yr−1)] = (0.42 ± 0.06) × log10[S870(mJy)] + (2.19 ± 0.03) for their SMGs, which span a flux range of ∼1 − 10 mJy, appropriate for the SCUBA-2 sources discovered in our target ELAN fields. We then computed the total SFR densities by integrating over a given S850 range in which the SFR contribution at a given S850 is the product of the excess number density of the submillimeter sources and their corresponding SFR based on the conversion. For each field, by considering a flux range of S850 = 1 − 20 mJy and assuming a sphere with an equivalent circular radius of the corresponding effective area, we obtain SFR densities of 1100 ± 500, 1100 ± 500, 2300 ± 1100, and 1400 ± 1100 M⊙ yr−1 Mpc−3 for Fabulous, Slug, Jackpot, and MAMMOTH-1 ELAN, and a weighted average SFR density of ΣSFR = 1200 ± 300 M⊙ yr−1 Mpc−3. We plot the results in Fig. 5, showing that they are consistent with those found in the Mpc-scale environments of other quasar samples or proto-clusters at z ∼ 2 − 3 (Clements et al. 2014; Dannerbauer et al. 2014; Kato et al. 2016). This result suggests that the star formation activities around ELANe are at a similar level of other dense systems in this redshift range, or, in other words, at a factor of about 300 greater than the cosmic mean.
Citation: Dudzevičiūtė et al. 2020; Dudzevičiūtė et al. (2020)
Citation function: Uses; Similarities; Similarities
Most of our knowledge about reconnection comes from effectively 2D reconnection experiments, and only recently have efforts to understand 3D magnetic reconnection been pursued (Priest 2011; Pontin 2011; Shepherd & Cassak 2012; Janvier et al. 2014), revealing a much wider range of dynamics. Very little is known about the actual properties of the solar plasma during the reconnection process on the Sun. Many authors have studied the global and local reconnection rates and timescales of solar reconnection during solar flares based on analysis of the ribbon motions in the chromosphere (Fletcher & Hudson 2001; Qiu et al. 2002, 2010; Isobe et al. 2005; Jing et al. 2005; Saba et al. 2006; Miklenic et al. 2007; Xie et al. 2009). Generally, they use measurements of the magnetic flux at the photosphere, the velocity of the ribbons parallel to the PIL, and the 2D approximation of the standard flare model to determine the reconnected flux and the electric field and Poynting flux at the reconnection site. In a future paper, we plan to test the validity of this approximation. However, these studies are purely based on the ribbon dynamics and are not able to capture the dynamics of the 3D coronal magnetic field that is actually involved in the reconnection process. For example, to calculate the energy release rate from reconnection, one needs to combine observations with knowledge of the location and size (i.e., length—the width is generally below the resolution) of the reconnection current sheet, which can only be provided by data-constrained magnetic field models and extrapolations or by data-driven MHD simulations with the appropriate NLFFF initial conditions (and is still only an approximation). Aulanier et al. (2000, 2012) and Schrijver et al. (2011) utilize idealized MHD simulations to interpret the appearance of the flare ribbons. Although such studies show a qualitative similarity of the QSLs and flare ribbons and are valuable for determining the basic topology of the region, no actual estimates of the reconnection parameters can be obtained, which could be improved by the use of data-constrained and data-driven magnetic field modeling.
Citation: Qiu et al.; 2010
Citation function: Background
The recovery of the sky signal from these huge, noisy time streams, a process called map-making, represents one of the most important steps in CMB data analysis and, if the detector noise properties and scanning strategy are known, map-making becomes a linear inverse problem. The generalized least-squares (GLS) equation provides an unbiased solution to map-making for an arbitrary choice of weights given by a symmetric and positive definite matrix (Tegmark 1997a). Moreover, if we consider the inverse covariance of the time domain noise as the weights, the GLS estimate is also a minimum variance and a maximum likelihood solution to the problem. However, computation of the solution in such a case may require either an explicit factorisation of a huge, dense matrix (Tegmark 1997a; Borrill 1999; Stompor et al. 2002) or an application of some iterative procedure (Wright 1996; Oh et al. 1999; Doré et al. 2001; de Gasperis et al. 2005; Cantalupo et al. 2010). These latte typically involve several matrix-vector multiplications at each iteration step. What makes the map-making problem particularly challenging are the sizes of the current and forthcoming CMB data sets which are directly related to the number of floating point operations (flops) needed to achieve the solution and to the memory requirements due to the sizes of the arrays required for it. Both these factors set the requirements on computational resources and indeed many current CMB data analysis pipelines opt for massively parallel computing platforms. However, even in such circumstances, efficient algorithms are necessary to ensure that the analysis can indeed be performed. The computational complexity of the algorithms involving an explicit matrix inversion is 



O
(


N


p


3


)


$ \textstyle\mathcal O(\mathbf N_\mathrm p^3) $


 flops, where Np is the number of pixels in the map, and therefore they are only suitable for cases where the estimated sky maps do not involve many sky pixels. Nonetheless, whenever feasible, the direct approaches can yield high-precision, unbiased estimates of the sky signal (e.g. Poletti et al. 2017, for a recent example). The next generations of the ground experiments, CMB-Stage III (Arnold et al. 2014; Henderson et al. 2016; Benson et al. 2014) and IV (Abazajian et al. 2016), however, are expected to observe significant fractions of the entire sky with high resolution, thus resulting in maps with Np ≃ 𝒪(106), rendering the direct approaches prohibitive even for the largest forthcoming supercomputers.
Citation: Cantalupo et al. 2010
Citation function: Compare/Contrast; Compare/Contrast; Compare/Contrast
Diffuse interstellar bands (DIBs) are absorption features seen in the spectra of many astronomical objects in the visible and infrared wavelength regions. The total number of these features exceeds 400. These bands are observed in absorption in star light crossing translucent clouds and the carriers of only three such features have been proposed; none of them certain. The origin of the DIBs is as puzzling as since their first detection, more than 90 years ago. The central wavelengths of DIBs are not readily identified with any known atomic or molecular spectral lines. The profiles of DIBs are not just Gaussians as shown for the first time by Westerlund & Krełowski (1988) which may suggest their molecular carriers. Moreover, precisely determined DIB profiles (Galazutdinov, LoCurto & Krelowski 2008b), showing a specific substructure pattern each, should facilitate their carrier's identification. The better-substantiated hypothesis is that DIBs arise from absorption produced by polycyclic aromatic hydrocarbons (PAHs), although Cox (2011) and Salama & Ehrenfreund (2014) doubt this statement and no specific PAH has been identified until recently (Salama et al. 2011). The other possibility is that carbon chain molecules, similar to those observed in star-forming regions, may carry DIBs. but this idea also has no clear proof (Motylewski et al. 2000). Two DIBs, observed in the near-infrared, were tentatively ascribed to the bucky ball (C$_{60}^{+}$) molecule (e.g. Foing & Ehrenfreund 1994; Campbell et al. 2015). Nevertheless there is an indication that this identification was proposed but not finally proven. Recently it has been demonstrated that DIB profiles (at least some of them) change in unison with the rotational temperature of simple homonuclear molecules C2 and C3 (Kaźmierczak et al. 2009). The relation between temperatures of simple carbon chains and those of DIB carriers may suggest that there is a common chemical history and that the DIB carrier may be polyatomic centrosymmetric (non-polar) molecules as well. This is likely why the earlier attempts to match the electronic spectra of known polar molecules (identified in microwave region of wavelengths) and DIBs failed (Motylewski et al. 2000). In a certain sense, this phenomenon still remains an enigma for astrophysicists. For recent reviews see Cami & Cox (2014).
Citation: Galazutdinov, LoCurto & Krelowski 2008b
Citation function: Uses; Uses
A third approach to assessing the central star formation is based on the presence of poly-aromatic hydrocarbons (PAHs), which are known to trace young stars, but over a wider range of ages (∼100 Myr) than the ionised line emission. However, there is a debate about whether the PAHs are excited by stars or by the AGN itself, or whether small PAHs are destroyed by the hard radiation from an AGN (Siebenmorgen et al. 2004; Smith et al. 2007; Sales et al. 2010). This may depend on which PAH feature one considers: Diamond-Stanic & Rieke (2010) found that in AGNs on kpc scales the 6.2, 7.7, and 8.6 μm features were suppressed, while the 11.3 μm feature was not. Partly for this reason, recent studies of PAHs close to AGNs have focussed on the feature at 11.3 μm. Esquej et al. (2014) detected PAHs on subarcsec scales in about half of their sample of 29 nearby Seyfert galaxies. They argued that the high column densities in the torus around the AGN would provide shielding that enables PAHs to survive, and the implied central star formation rate density was much higher than in the circum-nuclear region. A similar conclusion about PAH survival was reached by Esparza-Arredondo et al. (2018) for their sample of 19 AGNs, based on the lack of relation between the X-ray luminosity and a central PAH deficit – while PAHs were detected in most AGNs, the equivalent width was lower in the centre. They attributed this to low star formation rates in that region. On the other hand, while Jensen et al. (2017) also detected PAHs in a sample of 13 AGNs, they argued that the similarity and slope of the radial profiles from tens to hundreds of parsecs point towards an origin in a single compact source of excitation. In order to shed more light on the excitation of PAHs in this context, Alonso-Herrero et al. (2020) compared PAH line ratios in 22 AGNs to models of PAH excitation and analysed this in the context of the measured molecular gas content. They concluded that PAHs can be shielded from the hard AGN radiation.
Citation: Esquej et al. (2014)
Citation function: Background; Compare/Contrast; Similarities
Discovered in 1977 from its bright H α emission (Stephenson & Sanduleak 1977), SS433’s defining characteristics are undoubtedly the helical motion of highly collimated jets of plasma launched from its innermost regions, and mass-loaded, non-polar outflows (Fabian & Rees 1979; Margon et al. 1979) which together inflate the surrounding W50 supernova remnant. Knots in SS433’s jet can be resolved at radio frequencies using very long baseline interferometry (VLBI) and indicate the presence of highly relativistic electrons (Vermeulen et al. 1987), while the baryon content is revealed by emission lines ranging from H and He lines in the optical through to highly ionized Fe lines in the X-rays (Kotani et al. 1994; Marshall et al. 2013). The Doppler shifts of the lines indicate precession of the accreting system with a period of ≈ 162 d, also seen in optical (He ii) emission lines originating from the non-polar wind (Fabrika 1997). Both the jets and winds carry a large kinetic luminosity (>1038 erg s−1, e.g. Marshall et al. 2002), which requires extraction of energy via accretion on to a compact object. While the nature of the compact object in SS433 remains somewhat unknown (although dynamical arguments suggest the presence of a black hole – Blundell, Bowler & Schmidtobreick 2008), the rate of mass transfer from the companion star, as inferred from the IR excess (Shkovskii 1981; Fuchs et al. 2006), is thought to be ∼1 × 10−4 M⊙ yr−1, orders of magnitude in excess of the Eddington limit for any plausible stellar remnant (>300 times the Eddington mass accretion rate for a typical stellar mass black hole of around 10 M⊙). Classical theory and radiation magnetohydrodynamic (RMHD) simulations agree that such ‘super-critical’ rates of accretion will lead to a radiatively supported, large scale height (H/R ≈ 1, where H is the height of the disc at distance R from the compact object) accretion disc with powerful winds launched from the surface at mildly relativistic speeds (Shakura & Sunyaev 1973; Poutanen et al. 2007; Ohsuga & Mineshige. 2011; Takeuchi et al. 2013; Jiang et al. 2014; Sadowski et al. 2014).
Citation: Marshall et al. 2013
Citation function: Background
Citation: Kotani et al. 1994
Citation function: Background; Background