adsabs/FOCAL huggingface dataset:
The NLA analysis on these new satellite/central disaggregated data vectors are shown in Fig. 15. As expected, the galaxy bias is consistent between the two, and constrained primarily by wgg, which is the same in the two data vectors. By construction the large scale fits to these data are each sensitive to a particular combination of two halo IA power spectra. Specifically $\mathbf {D}^{c}$ is sensitive to ($P^{2h, s}_{\rm GI}$, $P^{2h, ss}_{\rm II}$), and $\mathbf {D}^{s}$ probes ($P^{2h, c}_{\rm GI}$, $P^{2h, cc}_{\rm II}$). Again, we assume that on two halo scales, the satellite/central composition of the density tracer is not relevant. Notably, the amplitude of large-scale central alignments in Fig. 15 is stronger than that of satellites by a factor of ∼2, at the level of a few σ. The subject of satellite alignments has been discussed quite extensively in the literature, and the overall picture fits with our results here. A number of theoretical studies point to satellite IAs being dominated by tidal torque induced radial alignments within their haloes (Faltenbacher et al. 2007; Knebe et al. 2008; Pereira, Bryan & Gill 2008), which scale rapidly with separation, and tend to wash out on very large scales. There is also now evidence from various observations on both cluster and galaxy scales supporting the same picture (Sifón et al. 2015; Singh et al. 2015; Huang et al. 2018). This, again, is consistent with Johnston et al. (2019) and Fortuna et al. (2021), who suggest satellite shapes are effectively random on sufficiently large scales. Centrals, on the other hand, tend to align with the host halo, and so trace the large scale correlations in the background large-scale structure (Catelan et al. 2001; Kiessling et al. 2015). Although not shown in Fig. 15, it is also worth noting that the central galaxies show a clear monotonic increase in IA amplitude with redshift, a trend which is not replicated in satellite galaxies. There is also some evidence to suggest not only that satellites are distributed in a significantly anisotropic way within their haloes (Zentner et al. 2005; Piscionere et al. 2015; Butsky et al. 2016), but also that this anisotropy can have a non-trivial impact on the IA signal, even on large scales (Huang et al. 2016; Samuroff, Mandelbaum & Di Matteo 2020).
Citation: Pereira, Bryan & Gill 2008
Citation function: Background; Background
The most striking characteristic of this event is displayed by the AIA 304 Å movie in which a twisted coherent structure is gradually shown to have underwent a rolling motion during the fadeaway of the brightening (panels (d) and (e)). Interestingly, it is clearly evident from the Hα observations that the twisted structure is a filament, which consists of three sections with an approximate length of about 45 Mm (panel (k)). Taking the magnetic field polarity around the filament into account, it is obvious that the filament roughly separated oppositely polarized magnetic fields, with the western ends rooted in a negative-polarity region and the eastern ends rooted in a positive-polarity region (panel (l)). As a consequence, the axial field component of the filament can be determined as pointing to the right when viewed from its positive-polarity side, and the filament was identified as dextral, which is consistent with the preferential filament pattern in the northern hemisphere (Martin 1998). Careful inspection of the 304 Å observations also shows that the filament probably consists of two sets of mutually intertwined dark threadlike structures (panel (e)), which may naturally explain why it displays three sections in the Hα observations. One set of the dark threadlike structures may include its western and eastern parts (see the twin arrows). They first appeared above the brightening and then separated from each other and lifted up. Finally, they formed a coherent reverse s-shaped structure, with its dipped part blocked by the other sets of dark threadlike structures (panels (b) and (e)), which appeared to be co-spatial with the spread brightening and showed up immediately after the fade away of the brightening (panels (d) and (e)). The dynamic evolution of these dark threadlike structures is also displayed on the Hα images (panels (i)–(k)). The above observations suggested that the newly formed filament is a flux rope with twisted magnetic structures, and its formation is again attributed to reconnections between T1 and T2. Its apparent rolling motion might be thus understood in terms of a change in the orientation of the connecting T1 and T2. All these observations, including the two-sided spread brightenings, the newly formed EUV loop, and the dynamic formation of the flux rope seem to support the previous idea that the formation of the flux rope is the result of a tether-cutting type reconnection (Moore et al. 2001; Liu et al. 2010; Chen et al. 2014) between two sets of sheared arcade cool loops. However, our observation further reveals the formation of a filament by magnetic reconnection among T1 and T2.
Citation: Martin 1998
Citation function: Compare/Contrast
Studies of the pulse characteristics of X-ray pulsars offer vital information about their nature and the geometry of the binary system. The shape of the pulse profile offers insight into the mode of accretion inflows, the pulsar luminosity, the geometry of the accretion column and the magnetic field configuration (Parmar et al. 1989a). Therefore, such studies offer an understanding of pulsar systems and the process of mass accretion in the presence of the strong magnetic field of a neutron star. Detailed studies of pulse characteristics were conducted during outburst activities of some of pulsars such as EXO 2030+375 (Parmar et al. 1989a), Cepheus X-4 (Koyama et al. 1991; Mihara et al. 1991; Mukerjee & Agrawal et al. 2000), XTE J1946+274 (Paul et al. 2001), etc., which offered valuable information and understanding of the physical processes responsible for the observed properties of these pulsars. Spectroscopic studies, however, reveal information about the environment surrounding a pulsar and the underlying mechanism responsible for energy generation in these systems. Detailed studies of cyclotron absorption features, if present in the pulsar spectrum, not only enable us to determine its surface magnetic field, but they also offer an insight into the line-producing region, its structure, and the geometry of the accretion column (Staubert et al. 2019). Therefore, detailed studies of cyclotron absorption features have been pursued by researchers since their discovery (Truemper et al. 1978), as they are an important diagnostic probe for pulsars. Cyclotron absorption features were detected in the spectrum of many Be binaries covering a wide range of energies, starting from a lower energy of ∼10 keV (Jun et al. 2012; DeCesar et al. 2013) to a higher energy at ∼100 keV (La Barbara et al. 2001). Detailed studies of cyclotron line sources and their properties were reported by Staubert et al. (2019) and Maitra (2017). The Be binaries, particularly because of their transient nature, offer us opportunities for detailed study of some of these interesting properties as a function of changes in source luminosity and in time. The studies of cyclotron line energy with respect to its pulse phase, source luminosity, and time, showed wide variations for some sources, such as Vela X-1, Cen X-3, and Her X-1 (Staubert et al. 2019). These interesting properties help us to systematically investigate and understand their underlying physical properties.
Citation: Koyama et al. 1991
Citation function: Background; Background; Background
Since 2018 the Parker Solar Probe (PSP) mission is collecting solar wind plasma and magnetic field data through the inner heliosphere, reaching the closest distance to the Sun ever reached by any previous mission (Fox et al. 2016; Kasper et al. 2021). Thanks to the PSP journey around the Sun (it has completed 11 orbits) a different picture has been drawn for the near-Sun solar wind with respect to the near-Earth one (Bale et al. 2019; Kasper et al. 2019; Chhiber et al. 2020; Malaspina et al. 2020; Bandyopadhyay et al. 2022; Zank et al. 2022). Different near-Sun phenomena have been frequently encountered, with the emergence of magnetic field flips, i.e., the so-called switchbacks (Dudok de Wit et al. 2020; Zank et al. 2020), kinetic-scale current sheets (Lotekar et al. 2022), and a scale-invariant population of current sheets between ion and electron inertial scales (Chhiber et al. 2021). Going away from the Sun (from 0.17 to 0.8 au), evidence of radial evolution of different properties of solar wind turbulence (Chen et al. 2020) as the spectral slope of the inertial range (from −3/2 close to the Sun to −5/3, at distances larger than 0.4 au), an increase of the outer scale of turbulence, a decrease of the Alfvénic flux, and a decrease of the imbalance between outward (z
+) and inward (z
−) propagating components (Chen et al. 2020) has been provided. Although the near-Sun solar wind shares different properties with the near-Earth one (Allen et al. 2020; Cuesta et al. 2022), significant differences have been also found in the variance of magnetic fluctuations (about 2 orders of magnitude) and in the compressive component of inertial range turbulence. In a similar way, Alberti et al. (2020) first reported a breakdown of the scaling properties of the energy transfer rate, likely related to the breaking of the phase-coherence of inertial range fluctuations. These findings, also highlighted by Telloni et al. (2021) and Alberti et al. (2022) analyzing a radial alignment between PSP and Solar Orbiter, and PSP and BepiColombo, respectively, have been interpreted as an increase in the efficiency of the nonlinear energy cascade mechanism when moving away from the Sun. More recently, by investigating the helical content of turbulence Alberti et al. (2022) highlighted a damping of magnetic helicity over the inertial range between 0.17 and 0.6 au suggesting that the solar wind develops into turbulence by a concurrent effect of large-scale convection of helicity and creation/annihilation of helical wave structures. All these features shed new light onto the radial evolution of solar wind turbulence that urges to be considered in expanding models of the solar wind (Verdini et al. 2019; Grappin et al. 2021), and also to reproduce and investigate the role of proton heating and anisotropy of magnetic field fluctuations (Hellinger et al. 2015).
Citation: Chhiber et al. 2021
Citation function: Background; Background
Several qLMXBs have been identified in GCs and in the Galactic field (for some examples, see table 4 in Guillot et al. 2009 and references therein). While LMXBs in the field were detected following the onset of a bright accretion outburst, most qLMXBs in GCs, including all those with the highest flux at Earth, have not shown accretion activity.3 Most of these sources have only been spectrally identified based on their similarities to field LMXBs, observed during quiescence (e.g. Cen X-4 or Aql X-1). Previous works have confirmed that H-atmosphere models accurately describe the spectra of qLMXBs, with radii in the range 10–15 km, as expected for NSs, either from single sources (e.g. Heinke et al. 2006a; Webb & Barret 2007; Guillot, Rutledge & Brown 2011; Heinke et al. 2014; Bogdanov et al. 2016), or from statistical analyses of multiple qLMXBs (e.g. Guillot et al. 2013; Guillot & Rutledge 2014; Lattimer & Steiner 2014; Guillot 2016; Steiner et al. 2018). However, in some cases the accreted material may not be hydrogen, but helium (e.g. Servillat et al. 2012; Catuneanu et al. 2013; Heinke et al. 2014). One way to circumvent this is to identify the nature of the donor star, i.e. to determine the nature of the material transferred on to the NS (e.g. with the detection of an H α emission line, presumably originating in a faint accretion disc, Haggard et al. 2004). The possibility of helium (or heavier element) atmospheres is well-founded on the existence of ultracompact X-ray binaries (UCXB), with white dwarfs or helium-dominated donors4 (e.g. Zurek et al. 2009; Altamirano et al. 2010; Sanna et al. 2017; Cadelano et al. 2019). In fact, around 1/3 of the LMXBs in GCs with constraints on the companion nature, possess a white dwarf donor (Bahramian et al. 2014). Since NS He-atmosphere models have harder spectra than H-atmosphere models, using the incorrect composition for the observed thermal emission can result in biases of the inferred radii (Servillat et al. 2012; Heinke et al. 2014).
Citation: Heinke et al. 2014
Citation function: Compare/Contrast
We overplot the observed number of member galaxies and the host stellar mass in Figure 11(a) with colored symbols. Colors indicate the five groups of Δm
12. The circles and diamonds indicate the LV galaxy groups and SAGA survey sample, respectively. The LV sample, most of which is from the compilation by Carlsten et al. (2021), consists of 13 galaxy groups of which satellite membership is determined by measuring their distances using either the TRGB or SBF method: the MW (McConnachie 2012, for compiled data), M31 (McConnachie 2012; Martin et al. 2016; McConnachie et al. 2018, for compiled data), the Local Group (including the MW and M31 subgroups and quasi-isolated outlying members classified by McConnachie 2012), NGC 2403 (Carlin et al. 2021), NGC 4258 (Kim et al. 2011; Spencer et al. 2014; Carlsten et al. 2021), NGC 4631 (Tanaka et al. 2017; Carlsten et al. 2021), M51 (Carlsten et al. 2021), M101 (Bennet et al. 2017; Danieli et al. 2017; Carlsten et al. 2019b; Bennet et al. 2019), M94 (Smercina et al. 2018), NGC 1023 (Trentham & Tully 2009; Carlsten et al. 2021), M104 (Javanmardi et al. 2016; Carlsten et al. 2021), M81 (Chiboucas et al. 2009, 2013), and NGC 5128 (Crnojević et al. 2014; Müller et al. 2017; Crnojević et al. 2019; Müller et al. 2019). The other galaxy groups that are in the Carlsten et al. (2021) compilation but not this study are excluded because either the spatial coverage is too low (less than 20% of the projected virial area is covered) or the SBF S/N is too low to confirm satellites as faint as M

r
 > −12.3 mag. The SAGA survey sample (Mao et al. 2021) consists of galaxy groups with 36 MW-like host galaxies. The MW-like host galaxies are selected by their K-band magnitudes (−24.6  M

K
  −23 mag). The SAGA survey covered more than 80% of the projected R

h
 = 300 kpc area and confirmed satellite memberships from their redshifts. It is considered complete to satellites brighter than M

r
 = − 12.3 mag. Note that while the spatial coverage of the LV sample significantly differs from galaxy to galaxy, and most of them cover less than the virial area, the SAGA survey sample has a relatively consistent spatial coverage.
Citation: Carlsten et al. 2019b
Citation function: Uses; Uses; Uses
The most general third-order statistics is the three-point correlation function (hereafter referred to as 3PCF), which is defined in configurations space. Alternatively, one can study its Fourier space counterpart, the bispectrum. These two statistics contain, in principle, the same information. However, their analyses implicate different limitations and challenges, which can affect the physical interpretation of the results. A main advantage of the bispectrum is that an analysis in Fourier space allows for a clear exclusion of high-frequency modes in the density fluctuations, which are difficult to interpret theoretically due to their highly non-linear evolution. In configuration space, these high-frequency modes contribute to the 3PCF, in principle, at all scales. In practice, one therefore needs to restrict the analysis to large scales, where their contribution is negligible, lavishing a lot of valuable data. Another advantage of the bispectrum is that its covariance is diagonal for Gaussian density fluctuations. This approximation works well, even for evolved density fields, while deviations from Gaussianity can also be taken into account (Scoccimarro 2000; Sefusatti et al. 2006; Chan & Blot 2017). The covariance of the 3PCF, on the other hand, is not diagonal, even for Gaussian fluctuations, which makes the modelling more difficult (Srednicki 1993; Slepian & Eisenstein 2015; Byun et al. 2017; Gualdi et al. 2017). An additional difference in the analysis of the bispectrum and the 3PCF lies in the fact that the computation of the latter is more expensive. However, this aspect can be tackled by employing advanced algorithms and appropriate computational resources, as done in this work (see also, Barriga & Gaztañaga 2002; McBride et al. 2011a; Jarvis 2015; Slepian & Eisenstein 2015, and references therein). Besides its disadvantages, there are some arguments that speak for the 3PCF. One of them is the fact that the amplitude of the 3PCF (but not its errors) is not affected by shot-noise, whereas the latter affects the bispectrum amplitude at all scales and hence needs to be modelled for correcting the measurements. In addition, an analysis in configuration space has the advantage that complicated survey masks can be easily taken into account in the analysis of observational data, while in Fourier space such masks impose complicated effects on the measured bispectrum, which are difficult to model (e.g. Scoccimarro 2000). A more general consideration is that it is easier to interpret effects such as redshift space distortions or baryon acoustic oscillations (BAOs) on the statistics in configuration space, since that is where the physical processes that cause these effects happen. Studies of third-order correlations in the literature usually focus on either Fourier or configuration space (e.g. McBride et al. 2011b; Marín et al. 2013; Gil-Marín et al. 2015). However, it is worthwhile studying both statistics and cross-check the results, since their different advantages and disadvantages are quite complementary.
Citation: Scoccimarro 2000
Citation function: Compare/Contrast
We used the code detail (Butler & Giddings 1985) based on the method of accelerated Λ iteration (Rybicki & Hummer 1991). The detail opacity package was updated by Przybilla et al. (2011) by including bound–free opacities of neutral and ionized species. The calculated departure coefficients, bi = nNLTE/





, were used by the code synthV_NLTE (Ryabchikova et al. 2016) to calculate the synthetic NLTE line profiles. Here, nNLTE and nLTE are the statistical equilibrium and thermal (Saha Boltzmann) number densities, respectively. The binmag code11

11

http://www.astro.uu.se/oleg/binmag.html

 (Kochukhov 2018) was used for automatic spectral line fitting and comparison with the observed spectrum. The typical uncertainties in the fitting procedure with the observed profile are less than 0.02 dex for weak lines and 0.03 dex for strong lines. For consistency with our NLTE studies of C i–C ii (Alexeeva et al. 2016), Ti i–Ti ii (Sitnova et al. 2016), and Ca i–Ca ii (Sitnova et al. 2018), here we use exactly the same model atmospheres for 21 Peg, HD 22136, π Cet, and ι Her, calculated with the code LLmodels (Shulyak et al. 2004), as in the earlier papers. For the remaining stars, the calculations were performed using plane-parallel (1D), chemically homogeneous model atmospheres from the Kurucz’s grid12

12

http://www.oact.inaf.it/castelli/castelli/grids.html

 (Castelli & Kurucz 2003). Table 1 lists the atomic data for the Ne i and Ne ii lines used in the present line-formation analysis. For Ne i lines belonging to the 2p53p–2p53s transition array, we adopted data of atomic oscillator strengths from the experimental measurements deduced using a neon-filled hollow cathode lamp in conjunction with two spectrographs (Piracha et al. 2015). For 2p53d–2p53p transitions at 8377 and 8495 Å, we also used experimental transition probabilities measured with the shock tube (Doherty 1962) and renormalized by the NIST group (Wiese et al. 1966). These values of transition probabilities are supported by the accurate theoretical calculations of Seaton (1998a). For the remaining Ne i lines and for the Ne ii lines, the oscillator strengths were taken from the NIST Handbook of Chemistry and Physics (Fuhr & Wiese 1998—Ne i) and from Kurucz’s website. Kurucz’s theoretical calculations agree very well with the theoretical calculations by Breit–Pauli R-matrix method used in the earlier papers on neon abundance analyses (see Section 3.4). Stark collisional data were adopted from the Kurucz’s website.
Citation: Sitnova et al. 2018
Citation function: Compare/Contrast; Compare/Contrast; Compare/Contrast; Compare/Contrast
Almost all the efforts to reveal the geometry of GW170817 have been based on model fitting to the EM afterglow light curve3

3
Dhawan et al. (2020) suggested a method to estimate the viewing angle using the macronova/kilonova signal. However, while the afterglow light curve is based on rather clear physics the macronova/kilonova light curve involves uncertainties in all aspects, ranging from the composition of the ejecta to its three-dimensional distribution, unknown opacities of the relevant heavy elements and complicated radiation transfer calculations (see Nakar 2020 for a discussion of the various uncertainties). Thus, a concern arises that due to these numerous uncertainties it might be extremely difficult to control the systematics that may arise using this method.
 (Alexander et al. 2017, 2018; Haggard et al. 2017; Margutti et al. 2017; Troja et al. 2017, 2019; D’Avanzo et al. 2018; Dobie et al. 2018; Gill & Granot 2018; Granot et al. 2018b; Lazzati et al. 2018; Lyman et al. 2018; Margutti et al. 2018; Mooley et al. 2018a; Troja et al. 2018; Fong et al. 2019; Hajela et al. 2019; Lamb et al. 2019; Wu & MacFadyen 2019; Ryan et al. 2020; Takahashi & Ioka 2020). All recent studies agree that GW170817 have launched a relativistic jet that broke out of the ejecta successfully and then interacted with the circum-merger medium to produce the afterglow. They all find that the jet had an angular structure with a narrow highly energetic core, with a jet opening θj, which is observed from a viewing angle θobs ≫ θj. Each of these studies obtained a different estimate of θobs, and θj (see Section 5 and Figure 3). The viewing angles found by different studies are in the range of 14°–38° and the jet opening angle is in the range of 25–8°. The typical errors quoted in these studies are a few degrees on the viewing angle and a fraction of a degree on the jet opening angle. Thus, the various estimates are often highly inconsistent with each other. An exception is the work of Mooley et al. (2018b) who used the motion of the radio image as measured in VLBI observations (see also Ghirlanda et al. 2019; Hotokezaka et al. 2019) in addition to the light curve to constrain the system geometry.
Citation: Margutti et al. 2017
Citation function: Background