adsabs/FOCAL huggingface dataset:
In the present work, we neglect the processes that generate the halo, but this topic deserves some review. Notably, the apparent growth of the halo at the expense of the anti-sunward suprathermal ‘strahl’ population may imply that the halo is locally formed in the inner heliosphere by scattered strahl electrons (e.g. Maksimovic et al. 2005; Štverák et al. 2009). This has led to significant theoretical development, focused on the resonant interaction of electrons with the whistler and fast-magnetosonic whistler (FM/W) modes (e.g. Vocks et al. 2005; Saito & Gary 2007; Vasko et al. 2019; Verscharen et al. 2019b; Micera et al. 2021; Zenteno-Quinteros, Viñas & Moya 2021; Tang, Zank & Kolobov 2022; Vo, Lysak & Cattell 2022). Observations have struggled to confirm these theories. Notably, whistlers are practically absent (occurrence rate 0.1 per cent) during PSP perihelion passes (Cattell et al. 2022). Additionally, the eVDFs sampled by Helios and PSP are stable with respect to the oblique FM/W mode (Jeong et al. 2022a). Theoretical calculations show that at r ≲ 1 AU, the strahl is stable to whistler fluctuations (Horaites et al. 2018b; Schroeder et al. 2021) and should be unaffected by whistler turbulence in the inner heliosphere (Boldyrev & Horaites 2019). High-resolution measurements of the strahl at 1 AU confirm that ‘anomalous diffusion’, e.g. from whistler waves, is not required to explain the strahl angular widths at resolvable energies ≲300 eV Horaites et al. (2018a), Horaites, Boldyrev & Medvedev (2019). Similar results were found from simulations at distances r ≲ 20RS (Jeong et al. 2022b), which showed that near the corona the strahl is adequately described by a combination of Coulomb collisions and expansion effects. This all suggests that a mechanism besides local wave particle scattering may account for the halo’s presence in the inner heliosphere. Such theories have been proposed (e.g. Leubner 2004; Lichko et al. 2017; Che et al. 2019; Horaites et al. 2019; Scudder 2019), though no consensus has emerged.
Citation: Saito & Gary 2007
Citation function: Background
In this paper we extend previous work based on cosmological simulations by analysing the general magnetic field properties and the diffuse radio halo emission in galaxy clusters in the IllustrisTNG project, a set of cosmological magnetohydrodynamics simulations run with the moving-mesh code arepo (Springel 2010) that include a comprehensive module for galaxy formation physics. The main and novel aspect of our work is the analysis of the diffuse radio emission resulting from radio haloes in galaxy clusters (Feretti & Giovannini 1996; Murgia et al. 2009; Vacca et al. 2011; Feretti et al. 2012). We investigate radio emission from clusters by a detailed comparison with observations, trying to match the current observational constraints and to make predictions for the upcoming radio surveys that will be performed with the new generation of radio instruments such as SKA and LOFAR. The analysis of simulated radio haloes gives us a complementary view on the spatial extent and energy content of magnetic fields in galaxy clusters, since the radio emission is proportional to their strength. As such, the study of radio halo scaling relations (Giovannini et al. 2009; Cassano et al. 2013; Zandanel, Pfrommer & Prada 2014) with the total X-ray power and halo mass may yield important information about the amplification mechanisms of magnetic fields in clusters and the level of turbulence in the ICM. The modelling of radio emission makes it also possible to study the transport of charged particles and their re-acceleration to relativistic speeds, and it constrains the probability of detecting extended radio-emitting structures in a statistical sample of realistic simulated clusters. The comparison of the simulated radio emission with actual observations might also be employed as a useful check for the implementation of the galaxy formation physics modules used to perform the simulations, although our modelling of relativistic particles is rather preliminary and might have a non-negligible impact on the results.
Citation: Feretti & Giovannini 1996
Citation function: Motivation
The choice of these initial abundances is motivated by the following two scenarios about the history of the midplane material. The first scenario is that the material going into protostellar systems is inherited from the cloud out of which the protoplanetary disk collapsed and formed. This scenario is denoted “inheritance”, and it implies that the material has the same composition as found in dark clouds, especially their ices (see Marboeuf et al. 2014; Mumma & Charnley 2011). The second scenario is the case where the material coming from the dark cloud experiences heating events from the protostar (i.e., accretion bursts or regular stellar irradiation). These heating events are assumed to alter the chemistry in disks significantly (see, e.g., Visser et al. 2015). In the extreme case, the chemistry is reset, meaning that the molecules are assumed to be dissociated into atoms out to R = 30 AU, which can then reform molecules and solids in a condensation sequence, as traditionally assumed for the inner solar nebula (e.g. Grossman 1972). Hence, the scenario considering atomic initial abundances is denoted “reset”. Early chemical models of protoplanetary disks often assumed a set atomic initial abundances (e.g., Willacy et al. 1998; Aikawa et al. 1999; Semenov et al. 2004; Vasyunin et al. 2008; Walsh et al. 2010); however, these early models also did not typically include a comprehensive grain-surface network and focussed solely on the gas-phase chemistry. Some recent physico-chemical models do include surface formation of e.g., H2O ice, via the Eley-Rideal mechanism (e.g., Kamp et al. 2013; Bruderer et al. 2014; Helling et al. 2014). Early models that did include grain-surface chemistry (e.g. Willacy 2007; Walsh et al. 2010) were limited to simple atom-addition stemming from Tielens & Hagen (1982) and Hasegawa & Herbst (1993). Here, a more comprehensive grain-surface network is used which includes radical-radical recombination, atom addition, and also ice processing (Garrod et al. 2008). This work differs from earlier protoplanetary disk models in that we directly compare and quantify the effects of the inheritance versus reset scenarios for a single disk model using a comprehensive gas-grain chemical network.
Citation: Visser et al. 2015
Citation function: Uses; Uses
A pragmatic solution to these problems is to generate and analyse synthetic Milky Way catalogues cast in the observational frame of the survey (Bahcall & Soneira 1980; Robin & Creze 1986; Bienayme, Robin & Creze 1987). ‘Mock catalogues’ of this general type were first used in cosmology in the 2000s (e.g. Cole et al. 2005) and have now become an essential tool for the design and analysis of large galaxy and quasar surveys. Realistic mock catalogues provide assessments of an instrument’s capabilities and biases, tests of statistical modelling techniques applied to realistic representations of observational data, and detailed comparisons between theoretical predictions and observations. Perhaps one of the best known recent attempts is the Besançon model (Robin et al. 2003), which provides a disc (or set of discs) with a set of coeval and isothermal (single velocity dispersion) stellar populations assumed to be in equilibrium, with analytically specified distributions of density, metallicity, and age. This has been the basis of the Gaia Universe Model (GUMS; Robin et al. 2012). However, these models are not dynamically consistent and oversimplify the structure of the Galaxy, particularly the stellar halo that is modelled as a smooth component. An important advance was made by Sharma et al. (2011), who developed the galaxia code for creating mock stellar catalogues either analytically or from phase-space sampling of hybrid semi-analytic-N-body simulations to represent stellar haloes in a cosmological context (Bullock & Johnston 2005; Cooper et al. 2010). Rybizki et al. (2018) have developed a mock catalogue designed specifically for Gaia DR2 based on galaxia. Building on the method of Sharma et al. (2011), Lowing et al. (2015) developed a technique to distribute synthetic stars sampled from a cosmological N-body simulation in such a way as to preserve the phase-space properties of their parent stellar populations. In a separate method, Hunt et al. (2015) introduced the snapdragons code that generates a mock catalogue taking into account Gaia errors and extinction and demonstrated the resulting observable kinematics of stars around a spiral arm in an idealized smoothed particle hydrodynamic simulation set-up in isolation.
Citation: Robin et al. 2012
Citation function: Background; Background; Background
Hu, Holz & Vale (2007b) proposed the idea of using the ratio of CMB convergence to galaxy lensing convergence as a way to measure the distance ratio (distance to surface of last scattering relative to the distance to the source galaxy sample used to estimate the galaxy lensing) and hence constrain the geometry, Ωk and the equation of state of dark energy. The ratio is defined as

(40)

\begin{equation}
\mathcal {R}(z_{\rm l})=\frac{\kappa (z_{\rm l},z_{\ast })}{\kappa (z_{\rm l},z_{\rm s})}=\frac{\Sigma _{\rm c}(z_{\rm l},z_{\rm s})}{\Sigma _{\rm c}(z_{\rm l},z_{\ast })}.
\end{equation}

Similar distance ratio tests have also been proposed using galaxy or galaxy cluster lensing alone, in both strong lensing (e.g. Link & Pierce 1998; Golse, Kneib & Soucail 2002) and weak lensing regimes (e.g. Jain & Taylor 2003; Bernstein & Jain 2004). Several studies have already measured the distance ratios (e.g. Taylor et al. 2012; Diego et al. 2015; Kitching et al. 2015; Caminha et al. 2016, and references therein), though they are afflicted by several systematics such as, uncertainties in modelling cluster profiles and cosmic variance in the case of multiple strong lens systems, and photometric redshift uncertainties as well as imaging systematics that cause a redshift-dependent shear calibration in the case of weak lensing. The small redshift baseline also limits the cosmological applications of these measurements using optical weak lensing alone (see discussion in Hu et al. 2007b; Weinberg et al. 2013). Using CMB lensing in cosmographic measurements is advantageous in several ways. First, the source redshift for the CMB (redshift of surface of last scattering) is well known, so one of the two redshift slices being compared has no redshift uncertainty. The long redshift baseline between CMB and galaxy lensing sources also improves the sensitivity of $\mathcal {R}$ to cosmological parameters (Hu et al. 2007b). However, using CMB lensing with galaxy lensing makes $\mathcal {R}$ become more sensitive to some of the systematics in galaxy lensing (for example, multiplicative bias), and $\mathcal {R}$ can also be used as test for the presence of such systematics.
Citation: Taylor et al. 2012
Citation function: Background; Compare/Contrast
The SFR surface densities of ΣSFR = (750 ± 440) and (1800 ± 700) M⊙ yr−1 kpc−2 found above for GN10 and the central region of AzTEC-3 are even higher than the values found for some other compact starbursts like ADFS-27 (z = 5.66; 430 ± 90 M⊙ yr−1 kpc−2) and HFLS3 (z = 6.34; 480 ± 30 M⊙ yr−1 kpc−2; Riechers et al. 2013, 2017). The source-averaged value of >(500 ± 160) M⊙ yr−1 kpc−2 for AzTEC-3 is comparable to these sources. Like these systems, GN10 and AzTEC-3 thus show the properties expected for so-called “maximum starbursts”. At face value, the peak ΣSFR in AzTEC-3 may slightly exceed the expected Eddington limit for starburst disks that are supported by radiation pressure (e.g., Thompson et al. 2005; Andrews & Thompson 2011), but it is potentially consistent under the assumption of a more complex source geometry. On the other hand, the high ΣSFR value of GN10 could be boosted by an obscured AGN contribution to the dust heating. GN10 exhibits strong 0.5–8 keV X-ray emission.43

43
AzTEC-3 is not detected at X-ray wavelengths.
 Using Equation (15) of Ranalli et al. (2003), its observed (absorption-corrected)44

44
From fitting a Galactic absorption plus power-law model, an effective photon index Γ = 





 was found for GN10, but it was not possible to simultaneously fit the absorbing column NH and Γ due to the limited photon counts. The data are consistent with no intrinsic absorption within the uncertainties, such that the absorption-corrected LX could be considered an upper limit (Laird et al. 2010) or, alternatively, a lower limit, in the case that it is heavily absorbed.
 rest-frame 2–10 keV X-ray luminosity of LX = 5.6(12.5) × 1042 erg s−1 (Laird et al. 2010) corresponds to an SFRX of 1100(2500) M⊙ yr−1. Given its SFRIR = 





 M⊙ yr−1, its LX remains consistent with intense star formation, but a contribution from a modestly luminous obscured AGN cannot be ruled out, depending on the (relatively uncertain) absorption correction required. This would also be consistent with a possible excess mid-infrared emission due to an obscured AGN, which may be favored by some of the SED fits.
Citation: Andrews & Thompson 2011
Citation function: Compare/Contrast; Compare/Contrast
The atmospheric parameters of white dwarfs, the effective temperature (Teff) and surface gravity (log g), play a fundamental role in the study of post-main-sequence stellar evolution (see e.g., Kalirai, Marigo & Tremblay 2014; Rolland, Bergeron & Fontaine 2018), evolved planetary systems (Koester, Gänsicke & Farihi 2014), Galactic formation history (Tremblay et al. 2014), and the calibration of instruments (Bohlin, Gordon & Tremblay 2014). For decades, the most precise method to determine Teff and log g has been the spectroscopic fitting of the Balmer lines in hydrogen-atmosphere DA white dwarfs (Bergeron, Saffer & Liebert 1992; Finley, Koester & Basri 1997), and the He i lines in helium-dominated DB stars (Beauchamp et al. 1999; Voss et al. 2007; Bergeron et al. 2011). In contrast, trigonometric parallax measurements of stellar remnants or their companions can also be used to characterize Teff and stellar radii from photometric analyses (Koester, Schulz & Weidemann 1979; Bergeron, Leggett & Ruiz 2001). The surface gravity can then be inferred by using the white dwarf mass–radius relation (see e.g., Fontaine, Brassard & Bergeron 2001), with a small dependence on the assumed internal stratification (Romero et al. 2012). The main advantage of the photometric technique is that it can be used to fit cool DC white dwarfs with no optical transitions as well as metal- or carbon-rich remnants where spectroscopic log g determinations are difficult (Dufour, Bergeron & Fontaine 2005; Dufour et al. 2007b). One shortcoming is that for $T_{\rm eff} \gtrapprox 12\, 000$ K, optical colours of all stellar remnants get rather insensitive to the temperature, requiring very precise photometry for that determination (Carrasco et al. 2014). Until now, the main limitation of the photometric analyses has been that precise parallax measurements were available for only hundreds of white dwarfs (Bédard, Bergeron & Fontaine 2017), compared to the ≈ 30 000 objects (Kleinman et al. 2013; Kepler et al. 2015, 2016; Gentile Fusillo et al. 2018) with spectroscopy from the Sloan Digital Sky Survey (SDSS; Abazajian et al. 2009).
Citation: Bergeron et al. 2011
Citation function: Background; Background
Pulsar timing experiments (Sazhin 1978; Detweiler 1979) allow us to explore the low-frequency (∼1–100 nHz) part of the gravitational-wave (GW) spectrum. By measuring deviations from the expected arrival times of radio pulses from an array of millisecond pulsars, we can search for a variety of GW signals and their sources. The most promising sources in the nanohertz part of the GW spectrum are supermassive binary black holes (SMBHBs) that form via the mergers of massive galaxies. Orbiting SMBHBs produce a stochastic GW background (GWB; Lommen & Backer 2001; Jaffe & Backer 2003; Volonteri et al. 2003; Wyithe & Loeb 2003; Enoki et al. 2004; Sesana et al. 2008; McWilliams et al. 2012; Sesana 2013; Ravi et al. 2015; Rosado et al. 2015; Kelley et al. 2016; Sesana et al. 2016; Dvorkin & Barausse 2017; Kelley et al. 2017; Bonetti et al. 2018; Ryu et al. 2018), individual periodic signals or continuous waves (CWs; Sesana et al. 2009; Sesana & Vecchio 2010; Mingarelli et al. 2012; Roedig & Sesana 2012; Ravi et al. 2012, 2015; Rosado et al. 2015; Schutz & Ma 2016; Mingarelli et al. 2017; Kelley et al. 2018), and transient GW bursts (van Haasteren & Levin 2010; Cordes & Jenet 2012; Ravi et al. 2015; Madison et al. 2017; Islo et al. 2019; Bécsy & Cornish 2021). We expect to detect the GWB first, followed by detection of individual SMBHBs (Siemens et al. 2013; Rosado et al. 2015; Taylor et al. 2016; Mingarelli et al. 2017) that stand out above the GWB. Detection of GWs from SMBHBs will yield insights into galaxy mergers and evolution not possible through any other means. Other potential sources in the nanohertz band include cosmic strings (Damour & Vilenkin 2000, 2001; Berezinsky et al. 2004; Damour & Vilenkin 2005; Siemens et al. 2006, 2007; Ölmez et al. 2010; Sanidas et al. 2013; Blanco-Pillado et al. 2018; Chang & Cui 2021; Ghayour et al. 2021; Gorghetto et al. 2021; Wu et al. 2021a; Blanco-Pillado et al. 2021; Lin 2021; Chiang & Lu 2021; Lazarides et al. 2021; Chakrabortty et al. 2021; Ellis & Lewicki 2021), phase transitions in the early universe (Witten 1984; Caprini et al. 2010; Addazi et al. 2021; Arzoumanian et al. 2021; Di Bari et al.2021; Borah et al. 2021; Nakai et al. 2021; Brandenburg et al.2021; Neronov et al. 2021), and relic GWs from inflation (Starobinskiǐ 1979; Allen 1988; Lazarides et al. 2021; Ashoorioon et al. 2021; Yi & Zhu 2021; Li et al. 2021; Poletti 2021; Vagnozzi 2021; Sharma 2021), all of which would provide unique insights into high-energy and early-universe physics.
Citation: Sesana et al. 2016
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Citation: Rosado et al. 2015
Citation function: Background
Compact objects, such as, neutron stars and black holes (BHs) are the end products of massive stars. The gravitational pull of BHs is so high that no particle or radiation can escape from them. They both can be detected only by the electromagnetic radiation emitted by the accreted matter falling on them. Most of the black hole candidates (BHCs) in our Galaxy are in close binaries with companion stars which act as donors. Wind material from the companion star or matter accreting via Roche lobe overflow falls towards the black hole due to its intense gravitational pull and starts swirling around it to form accretion disc due to the presence of angular momentum. The observed electromagnetic radiation from a black hole binary is from the accretion flow itself. Some of the black hole candidates in low-mass X-ray binaries are transients in nature, which occasionally undergo outbursts. The increased X-ray flux in these transient X-ray binaries shows variability in the temporal and spectral states. Several works have already been done on this and many papers are available in the literature (see e.g. Tomsick et al. 2000; McClintock & Remillard 2006; Debnath et al. 2008; Nandi et al. 2012; Debnath, Chakrabarti & Nandi 2013; Rao 2013) to explain the variation of spectral and temporal properties of these objects during their active X-ray outburst phases. It is also reported by some authors that these objects transit through several spectral states (e.g. hard state (HS), hard-intermediate state (HIMS), soft-intermediate state (SIMS), soft state (SS)) during their active outburst phases (see Debnath et al. 2013 and references therein). Depending upon the observed spectral states, Debnath et al. (2017) classified these transient BH binaries into two types: classical or type-I (all four states are observable) and harder or type-II (softer states are missing). Low- and high-frequency quasi-periodic oscillations (QPOs) are generally observable in the power density spectra (PDS) of these sources (see Remillard & McClintock 2006 for a review). Low-frequency QPOs are commonly observed in hard and intermediate spectral states. Generally, it has been observed that during the rising HS and HIMS, frequency of these QPOs monotonically increases with time (day) and during the declining phase, the opposite nature is observed. In SIMS, these QPOs are observed sporadically.
Citation: Tomsick et al. 2000
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Critical luminosity (Lcrit) is the luminosity above which a state transition from subcritical to supercritical takes place. The subcritical state (LX Lcrit) is known to be the low luminosity state whereas the supercritical state is high luminosity state (LX > Lcrit) (Becker et al. 2012). The critical luminosity is crucial to determine whether the radiation pressure of the emitting plasma is capable of decelerating the accretion flow (Basko & Sunyaev 1976; Becker et al. 2012). The luminosity during the 2020 giant outburst reached a record high, which was significantly higher than the critical luminosity (Reig & Nespoli 2013). The source entered a supercritical regime from a subcritical regime during the outburst. In the supercritical regime, radiation pressure is high enough to stop the accreting matter at a distance above the neutron star, forming a radiation-dominated shock (Basko & Sunyaev 1976; Becker et al. 2012). For the subcritical regime, accreted material reaches the neutron star surface through nuclear collisions with atmospheric protons or through Coulomb collision with thermal electrons (Harding 1994). These accretion regimes can also be probed by noting changes in the cyclotron line energies, pulse profiles, and changes in the spectral shape (Parmar, White, & Stella 1989; Reig & Nespoli 2013). During the transition from the subcritical to the supercritical regime, sources show two different branches in their hardness–intensity diagram (HID) which are known as horizontal branch (HB) and diagonal branch (DB) (Reig & Nespoli 2013). The HB implies the low-luminosity state of the source, which is represented by spectral changes and high X-ray variability. The DB corresponds to the high-luminosity state that appears when the X-ray luminosity is above the critical limit. The classification HB and DB depends on HID patterns that the source follows. The HB pattern is generally observed in the subcritical regime and the DB pattern is observed in the supercritical regime (Reig & Nespoli 2013).
Citation: Reig & Nespoli 2013
Citation function: Background