adsabs/FOCAL huggingface dataset:
In addition to confirming the origin of some short GRBs, combining data from observations of GW170817 and GRB 170817A allowed for the inference of basic properties of short GRB jets. These include the isotropic equivalent luminosity of the jet, determined through a redshift measurement made possible by the optical follow-up of the GW localization (Abbott et al. 2017a; Goldstein et al. 2017), and the geometry of the GRB jets (Williams et al. 2018; Farah et al. 2020; Mogushi et al. 2019). The precise mechanism by which the jet is launched is still unknown, although it is typically believed to be either neutrino-driven or magnetically driven (Nakar 2007, but see also Liu et al. 2015 and references therein). Indeed, the scientific debate about the emission profile of the jet and the subsequent gamma-ray production mechanism of GRB 170817A is still ongoing (Hallinan et al. 2017; Kasliwal et al. 2017; Lamb & Kobayashi 2017; Troja et al. 2017; Gottlieb et al. 2018b; Lazzati et al. 2018; Gill & Granot 2018; Mooley et al. 2018; Zhang et al. 2018; Ghirlanda et al. 2019). It is generally believed that there are symmetric polar outflows of highly relativistic material that travel parallel to the total angular momentum of the binary system (Aloy et al. 2005; Kumar & Zhang 2014; Murguia-Berthier et al. 2017). These jets are thought to be collimated and roughly axisymmetric, emitting preferentially in a narrow opening angle due to a combination of outflow geometry and relativistic beaming. The data from extensive multi-wavelength observation campaigns that ran for nearly 20 months following the merger (Fong et al. 2019; Makhathini et al. 2020; Troja et al. 2020) are in agreement with a structured jet model, in which the energy and bulk Lorentz factor gradually decrease with angular distance from the jet symmetry axis (e.g., Dai & Gou 2001; Lipunov et al. 2001; Rossi et al. 2002; Zhang & Mészáros 2002; Ghirlanda et al. 2019; Beniamini et al. 2020). Further, according to one of the models proposed, as the jet drills through the surrounding merger ejecta it inflates a mildly relativistic cocoon due to interactions between the material at the edge of the jet and the ejecta (Lazzati et al. 2017; Gottlieb et al. 2018a). In this case, it is possible that the cocoon alone could produce the gamma-rays observed from GRB 170817A (Gottlieb et al. 2018b). Additional joint detections of GRBs and GWs will significantly aid our understanding of the underlying energetics (Lamb & Kobayashi 2017; Wu & MacFadyen 2018; Burns et al. 2019), jet geometry (Farah et al. 2020; Mogushi et al. 2019; Biscoveanu et al. 2020; Hayes et al. 2020), and jet ignition mechanisms (Veres et al. 2018; Ciolfi et al. 2019; Zhang 2019) of binary neutron star (BNS) coalescences.
Citation: Troja et al. 2017
Citation function: Motivation
Unlike BH–BH mergers, binary neutron star (BNS) merger mechanisms are expected to yield an optical counterparts powered by the radioactive decay of rapid neutron capture process (r-process) elements synthesized in the merger ejecta (Li & Paczyński 1998; Metzger et al. 2010) or by the cooling of shock-heated material around the neutron star (NS; Piro & Kollmeier 2018). On 2017 August 17, the LVC detected the first and best example of a BNS merger: GW170817 (Abbott et al. 2017a). Only two seconds after its detection, a short gamma-ray burst (GRB 170817A) was detected (Connaughton et al. 2017; Goldstein et al. 2017). GRB 170817A was followed by the discovery of the optical counterpart Swope Supernova Survey 17a [SSS17a (AT 2017gfo)] ∼11 h later in the galaxy NGC 4993 (Coulter et al. 2017) and later confirmed by others teams (Abbott et al. 2017c; Arcavi et al. 2017; Covino et al. 2017; Díaz et al. 2017; Drout et al. 2017; Kilpatrick et al. 2017; Lipunov et al. 2017; McCully et al. 2017; Pian et al. 2017; Shappee et al. 2017; Smartt et al. 2017; Soares-Santos et al. 2017; Troja et al. 2017; Utsumi et al. 2017; Valenti et al. 2017). The combination of EM and GW information on the BNS can be used to constrain the mass and the radii of NS (Margalit & Metzger 2017) or their equation of state (Bauswein & Janka 2012; Annala et al. 2018). Also, observations of the optical–infrared (IR) ‘kilonova’ counterpart (Chornock et al. 2017; Cowperthwaite et al. 2017; Drout et al. 2017; Kilpatrick et al. 2017; Nicholl et al. 2017; Shappee et al. 2017; Valenti et al. 2017; Villar et al. 2017) provided the first observational confirmation that NS mergers produce the majority of the r-process elements heavier than iron (Burbidge et al. 1957; Cameron 1957; Kasen et al. 2017; Pian et al. 2017; Metzger 2019). They also permit tests of theoretical kilonova models. For example, Drout et al. (2017) showed that the temperatures cooled from 10 000 to 5100 K in between 12 and 36 h after the event, confirming model predictions. Also, Shappee et al. (2017) used spectra taken 11.76 and 12.72 h after the merger to show that the photosphere was expanding at ∼0.3c.
Citation: Díaz et al. 2017
Citation function: Background; Background
We used the theoretical models by Eggleton et al. (2008), Charbonnel & Lagarde (2010), and Lagarde et al. (2012) for the comparison. They provide quantitative values representing the first dredge-up, thermohaline (TH), and thermohaline and rotation (TH+V) induced mixing. Eggleton et al. (2008) estimated the mixing speed with their formula for the diffusion coefficient and found that a range of three orders of magnitude in their free parameter can lead to the observed levels of 12C /13C. Their predicted value of the 12C /13C ratio for a solar-metallicity 2 M⊙ star at the RGB tip is 17. A more recent model of the thermohaline-induced mixing by Charbonnel & Lagarde (2010) lists for the same stars a higher value of about 20. The model of Charbonnel & Lagarde (2010) of thermohaline instability induced mixing and the model by Eggleton et al. (2008) are both based on the ideas of Eggleton et al. (2006) and Ulrich (1972). It includes developments by Charbonnel & Zahn (2007). Eggleton et al. (2006) found a mean molecular weight (μ) inversion in their 1 M⊙ stellar evolution model, which occurred after the so-called luminosity bump on the RGB, when the hydrogen-burning shell reaches the chemically homogeneous part of the envelope. The μ-inversion is produced by the reaction \hbox{$^3{\rm He(}^3{\rm He}, 2p)^4{\rm He}$}He(33He,2p)4He, as predicted in Ulrich (1972). It does not occur earlier because the magnitude of the μ-inversion is low and negligible compared to a stabilising μ-stratification. Charbonnel & Zahn (2007) computed stellar evolution models including the ideas of Kippenhahn et al. (1980), who extended Ulrich’s equations to the case of a non-perfect gas. Charbonnel & Zahn (2007) also introduced a double diffusive instability (i.e. thermohaline convection) and showed its importance in the chemical evolution of red giants. This mixing connects the convective envelope with the external wing of the hydrogen-burning shell and induces surface abundance modifications in evolved stars (Charbonnel & Lagarde 2010).
Citation: Eggleton et al. (2006)
Citation function: Background
Cluster cosmology requires jointly modelling the physical parameters describing the evolution of the intra-cluster medium (ICM) alongwith the impact of selection procedure. While the first self-consistent methods have followed a backward modelling of the recovered cosmology-dependent, mass function (e.g. Vikhlinin et al. 2009), more recent studies moved to a forward approach whose likelihood includes physical quantities such as luminosity, temperature, or gas fraction (e.g. Mantz et al. 2014, 2015). Cluster number counts from Sunyaev-Z’eldovich surveys are routinely modelled in terms of the signal-to-noise ratio (S/N) or the Compton parameter of the detections, which can be related to the cluster mass via scaling relations from X-ray, lensing, or velocities (e.g. Vanderlinde et al. 2010; Hasselfield et al. 2013; Benson et al. 2013; Bocquet et al. 2015; Planck Collaboration XXIV 2016). In this context, we are developing a cosmological analysis method (ASpiX) based on X-ray cluster number counts that does not explicitly rely either on cluster mass determinations or physical quantities. This method consists in the modelling of the multidimensional distribution of a set of directly measurable X-ray clusters quantities, namely: count rates (CRs), hardness ratios (HRs), and apparent size (rc), which are all cosmology independent. This method is particularly suited to rather shallow survey-type data, when the number of collected X-ray photons is too low to enable detailed spectral and morphological analyses. Thanks to its modularity, the ASpiX method considerably eases the process by simultaneously fitting in the observed parameter space, the effect of cosmology, selection, and cluster physics. Depending on the volume surveyed, that is, the number of clusters involved in the analysis, the number of parameters that may be fitted can increase from a few to 15 or more, including in particular scatter and evolution in the scaling relations. This method cannot rival approaches including deep pointed X-ray observations along with ancillary data from other wavebands and, fundamentally, faces the same uncertainties as to the observable-mass transformation. However, the method allows the inclusion of the vast majority of the detected clusters even when only a few tens of photons are available. Furthermore, when cosmological simulations are produced at a significantly high rate, the method will allow us to totally bypass any mass estimate or scaling-relation related formalism; instead, it will solely rely on the simulations by comparing the observed and simulated parameter distributions (Pierre et al. 2017). In the end, neitherassumptions based on the hydrostatic equilibrium nor any modelling of the mass function will be necessary.
Citation: Bocquet et al. 2015
Citation function: Background
While using the Padé(2,1) approximation, we checked the influence of the quasar relation on the cosmological constraints. We first provide the constraints considering a free quasar relation. Then combining that with the following two special cases, the effect of the slope parameter γ is studied. One case is the fitting parameters q0 and j0 in the case of fixed parameters δ, γ, and β. The other is to refit q0 and j0 by changing the setting of the fixed parameter γ within a 1σ error (0.005). The fixed parameters δ, γ, and β are given in terms of the best fitting results from 1598 quasars in a flat ΛCDM model. By substituting Eqs. (25) into (26), and replacing dL with Eq. (6), we obtained the best fits, that is 




Ω
m

=
0
.

65

−
0.19


+
0.16




$ \Omega_{m} = 0.65^{+0.16}_{-0.19} $


, δ = 0.23  ±  0.01, γ = 0.62  ±  0.01, and β = 7.60  ±  0.28. This result is consistent with previous research within a 1σ error (Lusso & Risaliti 2016; Melia 2019; Salvestrini et al. 2019; Khadka & Ratra 2020a, 2022; Liu et al. 2020; Wei & Melia 2020). Here, it is worth noting that the 1σ errors of the γ best fit from previous research are both larger than 0.01. Figure 4 shows the fitting results with a free quasar relation. The best fits are δ = 0.23  ±  0.01, γ = 0.64  ±  0.01, 



β
=
7
.

17

−
0.27


+
0.28




$ \beta = 7.17^{+0.28}_{-0.27} $


, q0 = −0.67  ±  0.04, and 




j
0

=
2
.

43

−
0.46


+
0.51




$ j_{0} = 2.43^{+0.51}_{-0.46} $


. This result is in line with the previous results obtained by the logarithmic polynomials. The corresponding q0 − j0 projection is also plotted in Fig. 5 as a gray contour. In addition, we also show the confidence contours of the other two special cases. (1) When we chose δ = 0.23, γ = 0.62, and β = 7.60 (best fits from 1598 quasars in a flat ΛCDM model), the results are q0 = −0.82  ±  0.05 and 




j
0

=
4
.

99

−
0.63


+
0.69




$ j_{0} = 4.99^{+0.69}_{-0.63} $


, which is represented by the red contours in Fig. 5. There exists more than a 4σ tension with the flat ΛCDM. (2) In only changing γ = 0.625, the new result is q0 = −0.58  ±  0.03 and 




j
0

=
1
.

19

−
0.31


+
0.34




$ j_{0} = 1.19^{+0.34}_{-0.31} $


 which is described by the blue contours of Fig. 5. We find that the γ value changes from 0.62 to 0.625 (within a 1σ error of 0.01), and the 4σ tension disappears. This result is consistent with the ΛCDM model within a 1σ level. We find that the quasar relation can obviously affect the cosmographic constraint, especially the slope parameter γ.
Citation: Liu et al. 2020
Citation function: Similarities; Differences
Many low-luminosity active galactic nuclei (LLAGN) display prominent jets and compact cores that are sources of highly nonthermal continuum radio emission (see, e.g., Heeschen 1970; Wrobel & Heeschen 1991). The observational signatures of the compact cores have been reproduced using models that produce self-absorbed synchrotron emission in the jet (Falcke & Biermann 1995; Falcke et al. 2004) or in a magnetized accretion flow (Narayan et al. 1998; Yuan et al. 2003; Broderick & Loeb 2006; Moscibrodzka et al. 2009; Dexter et al. 2009; see also Falcke et al. 2001). This radiation is emitted by relativistic electrons gyrating around magnetic field lines. In the optically thin limit, the emission is significantly polarized (Jones & Hardee 1979), an effect that has been observed in higher-luminosity AGN sources (Gabuzda et al. 1996; Gabuzda & Cawthorne 2000; Lyutikov et al. 2005). The polarized emission from an accreting AGN can therefore yield information about the magnetic-field morphology of the source, which may be crucial to the evolution of the accretion flow of the AGN. The Event Horizon Telescope (EHT) is a worldwide millimeter-wavelength array capable of resolving the black-hole shadow (Goddi et al. 2017; Event Horizon Telescope Collaboration 2019); this is a characteristic feature of the radio-frequency emission from optically thin AGN at the scale of the event horizon (Falcke et al. 2000; Broderick & Narayan 2006), although the black-hole shadow may be obscured or exaggerated in certain accretion scenarios (see Gralla et al. 2019 and Narayan et al. 2019). The EHT can also determine the polarization state of such emission: Johnson et al. (2015) report 1.3 mm observations (230 GHz) that indicate partially ordered magnetic fields within a region of about six Schwarzschild radii around the event horizon of Sagittarius A* (Sgr A*), the supermassive black hole in the center of the Milky Way. Bower et al. (2003) reported stable long-term behavior and short-term variability in Sgr A* rotation measure, implying a complex inner region (within 10 Schwarzschild radii) in which both emission and propagation effects are important to the observed polarization. Hada et al. (2016) studied the central black hole in the galaxy M 87, and observed a bright feature with (linear) polarization degree of 0.2 at 86 GHz at the jet base. Observations in infrared by Gravity Collaboration (2018) were consistent with a model in which a relativistic “hot spot” of material, orbiting near the innermost stable circular orbit (ISCO) of Sgr A* in a poloidal magnetic field, emits polarized synchrotron radiation.
Citation: Lyutikov et al. 2005
Citation function: Background; Background
Citation: Jones & Hardee 1979
Citation function: Background; Similarities
We futher examine the distributions of the radial number fraction of satellites with different colors in different LSE in Figure 5 to study the role central galaxies play in SCA. It can be seen, in general, that the intersections of these lines for each subsample are located between 





 to 





, and with a mean value of 





. Satellites (especially red satellites) prefer to reside in the inner part of groups and the number fraction decreases with the increasing radius. Such evolutionary behavior is consistent with “strangulation” wherein cold gas that could be used for star formation is depleted as satellites are accreted (e.g., Kauffmann et al. 2004; Kang et al. 2005; Luo et al. 2016). The shapes of each curve (the maximum value, the decrease gradient) not only depend on the color of central and satellite galaxies but also depend on the LSE. In the inner part of halos, the fraction of satellites is lower in the more dense LSEs. In the outer parts, it is the opposite, namely, in knots there is a higher fraction of satellites. It must be pointed out is that, in a given environment and for a given central color, the fraction of blue satellites is always higher than red satellites in outer regions. For wall environments, about 25% ∼ 30% satellites reside at the low values of 





, and the fraction decreases very quickly. Additionally, in the inner parts, the fraction of satellites of red centrals (red dashed line in the top left panel of Figure 5) is ∼5% higher than those in blue centrals (blue dashed line in the top left panel of Figure 5). The radial distributions of satellites in filaments are almost absent with the different colors of both central and satellite galaxies. However, in a knot environment, blue centrals have ∼3% more satellites than red centrals at low values of projected radius. These radial satellite distributions with different colors in different LSE indicate that, red central galaxies have merged more satellites than blue central galaxies in the knot LSE, which means satellites with a red central galaxy may be more affected by the interaction with central galaxies and represent a better SCA, especially in the inner region.
Citation: Luo et al. 2016
Citation function: Similarities
In the currently accepted model of star formation, most stars form in families of star clusters within giant molecular clouds (GMCs). However, clusters and their families can be quite diverse, and there is no consensus yet on a general model for cluster formation. An additional complication is that once stars are born, they destroy most of the evidence relating to the initial stages of their formation. In this sense, determining the early stages of cluster formation in GMCs may be key to understanding many distinct and complex processes involved in such a phenomenon. The Pipe Nebula, a nearby (d = 130 pc) GMC with a total mass of 7.9 × 103 M⊙ (Lada, Lombardi & Alves 2010) and very little star formation activity (Brooke et al. 2007; Forbrich et al. 2009, 2010) has played a workhorse role in a number of previous studies (e.g. Alves, Lombardi & Lada 2007; Muench et al. 2007; Alves, Franco & Girart 2008; Lada et al. 2008; Rathborne et al. 2009; Román-Zúñiga, Lada & Alves 2009; Román-Zúñiga et al. 2010; Peretto et al. 2012; Frau et al. 2015; Hasenberger et al. 2018). Many of these works have the common goal of determining how GMCs are organized in the stages that precede their collapse and the consequent conversion of gas into stars. Interestingly, despite the fact that it is almost starless, the column density peaks1 in the Pipe appear to show a clear imprint of clustering, as well as segregation by density and mass (e.g. Román-Zúñiga et al. 2010; Alfaro & Román-Zúñiga 2018, hereafter RAL10 and AR18, respectively). The Pipe was also the first cloud where a ‘core mass function’ (see Alves et al. 2007; Rathborne et al. 2009) was constructed, leading to many subsequent studies that have discussed what appears to be a clear homology to its stellar counterpart (e.g. Alves et al. 2007; Goodwin et al. 2008; Swift & Williams 2008; Kainulainen et al. 2009). These findings are suggestive of a primordial organization of clouds towards the formation of groups and clusters, evident in the properties of density peaks.
Citation: Lada et al. 2008
Citation function: Background; Background; Background
Along with the flare related changes, we also examine possible orbital variations by dividing the overall quiescent spectra in periods dubbed as “Quiet 1” and “Quiet 2” (the time bins for the spectra are as shown in Fig. 1) and model the pn, MOS and RGS X-ray spectra. We specifically determine the temperatures, emission measures and abundances relative to solar values (Grevesse & Sauval 1998) with simultaneous iterative global XSPEC fits to the combination of EPIC and RGS (RGS+PN or RGS+MOS) spectra with variable-APEC (VAPEC; Smith et al. 2001) plasma models. As is often observed (Güdel et al. 2001; Robrade & Schmitt 2005; Lalitha et al. 2013), we require multi-temperature components to achieve an adequate description of the observed coronal spectra. We use combinations of two, three and four temperature components and find that a three temperature component model leads to an adequate description of the data. We fit each of these spectra in the full 0.2–10 keV energy range. For fitting the RGS spectra, the temperature and the abundances of elements like carbon, nitrogen, oxygen, neon and iron are allowed to vary freely and independently, however, the abundances are fixed among the different APEC temperature components. For fitting EPIC-MOS or pn spectra we allow the magnesium, sulphur, and silicon abundances to vary along with the oxygen, neon and iron abundance. However, the carbon and nitrogen abundances are fixed to values obtained from the RGS, which is more sensitive to strong individual lines of these elements. In Table 1, we summarise the results of this fitting procedure along with the 90% confidence range errors. Table 1 shows that the quiescent state is characterised by dominant plasma components at ~3, ~7.5 and ~20 MK, while during the flare the coronal temperature bins increase to ~3.5, ~12 and ~32 MK. During the flare, a pronounced enhancement of the emission measure at 2.8 keV is present, indicating the rise of emission measure at a higher temperature. The quiescent bins (quiet 1 and quiet 2) does not show any significant difference in the coronal properties when compared to the overall quiescent time-bin; suggesting no changes in coronal properties with orbital variation.
Citation: Robrade & Schmitt 2005
Citation function: Similarities; Similarities
Various observational techniques, such as measuring the Gunn–Peterson optical depth from QSO spectra or the prevalence of Ly α emission in high-redshift galaxies, have placed very tight constraints on the volume filling fraction of neutral hydrogen in the intergalactic medium (IGM) towards the end of reionization at z∼ 6 (Becker et al. 2001; Fan et al. 2006; Totani et al. 2006; McQuinn et al. 2007; McQuinn et al. 2008; Ota et al. 2008; Ouchi et al. 2010; Bolton et al. 2011; Mortlock et al. 2011; Ono et al. 2012; Becker & Bolton 2013; Chornock et al. 2013; Robertson et al. 2013; Schroeder, Mesinger & Haiman 2013; Caruana et al. 2014; Pentericci et al. 2014; Schenker et al. 2014; Tilvi et al. 2014; McGreer, Mesinger & D’Odorico 2015; Mesinger et al. 2015; Mitra, Choudhury & Ferrara 2015, 2018; Sobacchi & Mesinger 2015; Greig & Mesinger 2017). While the timing of the end of reionization is rather well constrained by observations and modelling (e.g. Fan et al. 2006; Choudhury et al. 2015) as is the photoionization rate of neutral hydrogen in the post-reionization Universe (Bolton & Haehnelt 2007; Calverley et al. 2011; Wyithe & Bolton 2011; Becker & Bolton 2013), much remains uncertain about the onset and extent of the process. This uncertainty is primarily driven by the current lack of understanding of which sources reionized the Universe and the difficulty of observing these systems deep into the epoch of reionization. Various classes of objects have been proposed as the sources of reionization including dwarf galaxies, mini-haloes, massive galaxies, active galactic nuclei, accretion shocks, globular clusters, stellar mass black holes, and dark matter annihilation and decay (Couchman & Rees 1986; Shapiro & Giroux 1987; Haiman & Loeb 1998; Madau, Haardt & Rees 1999; Ricotti 2002; Madau et al. 2004; Ricotti & Ostriker 2004; Mapelli, Ferrara & Pierpaoli 2006; Dopita et al. 2011; Mirabel et al. 2011; Katz & Ricotti 2013, 2014; Madau & Haardt 2015).
Citation: Greig & Mesinger 2017
Citation function: Background