adsabs/FOCAL huggingface dataset:
The mechanism that produces large spin-up glitches such as those seen in the Vela pulsar (Dodson, Lewis & McCulloch 2007; Palfreyman et al. 2018) and PSR J0537−6910 is thought to be a sudden transfer of angular momentum from a rapidly rotating superfluid that permeates the neutron star inner crust to the rest of the star, which is slowing down from electromagnetic radiation braking (Anderson & Itoh 1975; Haskell & Melatos 2015; Graber, Andersson & Hogg 2017). Link et al. (1999) provide strong support for this theory by first showing that the fractional moment of inertia (Isf/I) of the superfluid angular momentum reservoir is related to a pulsar’s glitch activity via Isf/I ≥ 2τcAg, where I is total moment of inertia and $\tau _{\rm c}=-\nu /2\dot{\nu }$ is pulsar characteristic age. They then show that the glitch activity of pulsars like Vela gives Isf/I ≳ 0.01, and this approximately matches the theoretical fractional moment of inertia of a neutron star’s crust Icrust/I. For PSR J0537−6910, we find 2τcAg = 0.00874, which agrees with that found in previous works (Middleditch et al. 2006; Antonopoulou et al. 2018; Ferdman et al. 2018). More recently, calculations by Andersson et al. (2012) and Chamel (2013) indicate that the above relation is underestimated after accounting for superfluid entrainment and should instead be $I_{\rm sf}/I\ge 2\tau _{\rm c}A_{\rm g}\langle m_{\rm n}^\ast \rangle /m_{\rm n}$, where $\langle m_{\rm n}^\ast \rangle $ and mn are averaged effective neutron mass and neutron mass, respectively (and $\langle m_{\rm n}^\ast \rangle /m_{\rm n}\approx 4.2$; Chamel 2012), and as a result, the angular momentum required by a glitch is more than the crust can provide. One solution proposed by Andersson et al. (2012) is that the superfluid component in the crust extends into the core. Subsequently, Ho et al. (2015, 2017) show that, by combining a pulsar’s glitch activity with a measurement of true age or surface temperature, one can obtain valuable constraints on properties of the superfluid and even measure the mass of the pulsar; for the Ag and estimated age of PSR J0537−6910, the pulsar’s mass turns out to be much higher than the canonical 1.4M⊙. Very recently, Sauls, Chamel & Alpar (2020) suggest superfluid entrainment may not be as strong as found by Chamel (2012, 2017). More work is needed to resolve the issue.
Citation: Middleditch et al. 2006
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Citation: Graber, Andersson & Hogg 2017
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Citation: Link et al. (1999)
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Citation: Andersson et al. (2012)
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Citation: Andersson et al. (2012)
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Citation: Ho et al. (2015
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Citation: Sauls, Chamel & Alpar (2020)
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We can even further extend this analysis and constrain cosmic-ray acceleration efficiency. Using accretion shock models like Pavlidou & Fields (2006), we can estimate particle acceleration energy efficiency by comparing the kinetic power of accreted gas that follows from the model, with the power that goes into SFCR particles that is constrained by the detected neutrino and gamma-ray fluxes. For example, if we assume that SFCRs are dominantly accelerated in shocks that produce gamma-ray and neutrino spectra with index α = 2.2 (corresponding to Mach number ∼5; Bell 1978; Schlickeiser 2010), then from using the latest observed neutrino flux (with index αν = 2.46) as the upper limit, it follows that the energy efficiency of accelerating cosmic rays at these structures in the energy range 1 GeV–1 TeV is ∼40%, that is, we find that ∼40% of energy that goes into shocks gets converted into accelerated particles. This is a slightly higher efficiency compared to results from numerical models of nonlinear diffusive shock acceleration of, for example, Kang & Ryu (2013), who found that shocks of similar strength result in ≳10% acceleration energy efficiency. If, on the other hand, we consider spectral index α = 2 to represent the SFCR spectra (corresponding to Mach number ∼10) and use that to find the upper limit of SFCR flux as maximally allowed by the observed EGRB and neutrino flux with index αν = 2, we find that in this case cosmic-ray acceleration energy efficiency is  1%. This efficiency is about an order of magnitude lower compared to results from the numerical model of Kang & Ryu (2013), who found that shocks of similar strength result in an efficiency of ∼20%. Such extreme changes in efficiency that follow from our analysis are, of course, due to the fact that our constraint comes from the high-energy end that is fixed by observed neutrino fluxes, while most energy in cosmic rays comes from the low-energy end due to the power-law spectra.
Citation: Schlickeiser 2010
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Citation: Kang & Ryu (2013)
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Citation: Kang & Ryu (2013)
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In this work, we analyse a set of high-resolution stellar spectra that were acquired by the High Efficiency and Resolution Multi-Element Spectrograph (HERMES; Barden et al. 2010; Sheinis et al. 2015), a multifibre spectrograph mounted on the 3.9-metre Anglo-Australian Telescope (AAT). The spectrograph has a resolving power of R ${\sim } 28\, 000$ and covers four wavelength ranges (4713–4903, 5648–5873, 6478–6737, and 7585–7887 Å), frequently also referred to as ‘spectral arms’. Observations used in this study have been taken from multiple observing programmes that make use of the HERMES spectrograph: the main GALAH survey (De Silva et al. 2015), the K2-HERMES survey (Wittenmyer et al. 2018), and the TESS-HERMES survey (Sharma et al. 2018). Those observing programmes mostly observe stars at higher Galactic latitudes (|b| > 10°) and employ different selection functions, but share the same observing procedures, reduction, and analysis pipeline (internal version 5.3; Kos et al. 2017). All three programmes are magnitude-limited, with no colour cuts (except the K2-HERMES survey), and observations predominantly fall in the V magnitude range between 12 and 14. This leads to an unbiased sample of mostly southern stars that can be used for different population studies, such as multiple stellar systems in our case. Additionally, stellar atmospheric parameters and individual abundances derived from spectra acquired during different observing programmes are analysed with the same The Cannon procedure (internal version 182112 that uses parallax information to infer log g of a star; Ness et al. 2015; Buder et al. 2018), so they are intercomparable. The Cannon is a data-driven interpolation approach trained on a set of stellar spectra that span the majority of the stellar parameter space (for details, see Buder et al. 2018). Whenever we refer to valid or unflagged stellar parameters in the text, only stars with the quality flag flag_cannon equal to 0 were selected.
Citation: Sharma et al. 2018
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Citation: Barden et al. 2010
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Citation: Buder et al. 2018
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Citation: Buder et al. 2018
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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: Chen et al. 2020
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Citation: Chen et al. 2020
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As we have shown that the Ep–Eγ relation does not significantly evolve with redshift, we can use it to calibrate GRBs. To avoid the circularity problem, the Padé method proposed by Liu & Wei (2014) is applied. The main calibrating procedures are as follows: First, derive the distance-redshift relation of SNe Ia [here we use the Union2.1 (Suzuki et al. 2012) data set] using the Padé approximation of order (3,2), i.e.

(17)

\begin{equation}
\mu (z)=\frac{\alpha _0+\alpha _1z+\alpha _2z^2+\alpha _3z^3}{1+\beta _1z+\beta _2z^2},
\end{equation}

where the coefficients (α0, α1, α2, α3, β1, β2) and the corresponding covariance matrix are derived by fitting equation (17) to the Union2.1 data set (see Liu & Wei 2014 for details). Assuming that the low-z GRBs trace the same Hubble diagram to SNe Ia, we can calculate the distance moduli of low-z GRBs directly from equation (17). The uncertainty of μ propagates from the uncertainties of the coefficients (αi, βi). Then the luminosity distance of low-z GRBs can be obtained using the relation

(18)

\begin{equation}
\mu (z)=5\log \frac{d_L(z)}{\rm {Mpc}}+25.
\end{equation}

As dL is known, the collimation-corrected energy can be further calculated from equation (11). Note that there are only 12 low-z GRBs and 12 high-z GRBs available since the others have no measurement of jet opening angle. Then we fit the Ep–Eγ relation (i.e. equation (5)) to the 12 low-z GRBs, which gives the best-fitting parameters

(19)

\begin{eqnarray}
\sigma _{\rm int}=0.161\pm 0.059,\ \ a=50.632\pm 0.062,\ \ b=1.537\pm 0.145.\nonumber\\
\end{eqnarray}

By directly extrapolating the Ep–Eγ relation to high-z GRBs, we can inversely obtain the collimation-corrected energy for 12 high-z GRBs from equation (5). Finally, calculate the luminosity distance of high-z GRBs from equation (11), and then the distance moduli from equation (18). The uncertainty of distance moduli propagates from the uncertainties of Eγ, Sbolo and Fbeam, i.e. (Schaefer 2007),

(20)

\begin{equation}
\sigma _{\mu }^2=\left(\frac{5}{2\ln 10}\right)^2\left[(\ln 10)^2\sigma _{\log E_{\gamma }}^2 + \frac{\sigma _{S_{\rm bolo}}^2}{S_{\rm bolo}^2} + \frac{\sigma _{F_{\rm beam}}^2}{F_{\rm beam}^2}\right],
\end{equation}

where

(21)

\begin{eqnarray}
\sigma _{\log E_{\gamma }}^2=\sigma _a^2 + \left(\sigma _b\log \frac{E_{p,i}}{300\ {\rm keV}}\right)^2 + \left(\frac{b}{\ln 10}\frac{\sigma _{E_{p,i}}}{E_{p,i}}\right)^2 + \sigma _{\rm int}^2.\nonumber\\
\end{eqnarray}
Citation: Liu & Wei (2014)
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Citation: Liu & Wei 2014
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Several outcomes from the IllustrisTNG simulations have validated the model against observational constraints, making them suitable for the tasks at hand. Among them we highlight: the shape and width of the red sequence and the blue cloud of z = 0 galaxies (Nelson et al. 2018a), the existence and locus of the star formation main sequence (MS) at low redshifts (Donnari et al. 2019), the distribution of stellar mass across galaxy populations at z ≲ 4 (Pillepich et al. 2018), the galaxy size–mass relation for star-forming and quiescent galaxies at 0 ≤ z ≤ 2 (Genel et al. 2018), the evolution of the galaxy mass–metallicity relation (Torrey et al. 2018), and quantitatively consistent optical morphologies in comparison to Pan-STARRS data (Rodriguez-Gomez et al. 2019) as far as galaxy properties are concerned. These come in addition to observationally-consistent results in relation to the properties of massive hosts and their intra-halo gas: e.g. the X-ray signals of the hot gaseous atmospheres (Davies et al. 2020; Truong et al. 2020), the amount and distribution of highly ionized Oxygen around galaxies (Nelson et al. 2018b), and the distributions of metals in the ICM at low redshifts (Vogelsberger et al. 2018). Importantly, the new physical mechanisms included in the TNG model have been shown to return galaxy populations whose star formation activity is in better agreement with observations than previous calculations like Illustris (Genel et al. 2014; Vogelsberger et al. 2014; Sijacki et al. 2015), in galaxy colours (Weinberger et al. 2018; Nelson et al. 2018a), atomic and molecular gas content of satellite galaxies (Stevens et al. 2019), and quenched fractions of central and satellite galaxies taken together with no distinction (z ≲ 2; Donnari et al. 2019). In a companion paper (Donnari et al. 2020), we discuss in detail the level of agreement between TNG results and observations when centrals and group and cluster satellites are considered separately and show that the quenched fractions of TNG galaxies are overall consistent with observational constraints and hence the results of this paper trustworthy.
Citation: Truong et al. 2020
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Citation: Donnari et al. 2020
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As discussed in Section 1, to identify SMGs in the simulation, it is ideal to compute individual simulated galaxies’ submm flux densities via dust radiative transfer. The mass and spatial resolution of Illustris and IllustrisTNG are insufficient to resolve the subkpc structure of the ISM, and owing to both the subgrid equation-of-state treatment of stellar feedback and resolution, such simulations tend to feature overly puffy discs and thus underestimate the attenuation (Trayford et al. 2017). Consequently, we do not perform radiative transfer directly. Instead, we use the following relation based on the results of performing radiative transfer on higher resolution hydrodynamical simulations of idealized isolated discs and mergers (Hayward et al. 2011, 2013a):
(1)$$\begin{eqnarray*}
\frac{S_{850}}{\left(\text{mJy}\right)} = 0.81 \left(\frac{\text{SFR}}{100 \, \left(\text{M}_\odot \, \text{yr}^{-1}\right)} \right)^{0.43} \left(\frac{M_{\rm dust}}{10^8 \, \left(\text{M}_\odot \right)} \right)^{0.54},
\end{eqnarray*}$$where SFR is the ‘instantaneous’ SFR associated with the star-forming gas cells (for consistency with Hayward et al. 2011) and Mdust is the dust mass. When applying the relation, following Hayward et al. (2013b), we incorporate a scatter of 0.13 dex (independent of SFR and dust mass) assuming a Gaussian distribution (Hayward et al. 2011). The submm flux densities predicted using this relation also agree well with the results of performing dust radiative transfer directly on cosmological zoom simulations (Liang et al. 2018; Cochrane et al. 2019) and a semi-analytical model ( Benson, private communication). Moreover, by combining the above with the observed SFR–M⋆ and Mdust(M⋆, z) relations, one can derive an S850–M⋆ relation (Hayward 2012; Hayward et al. 2013a) that agrees reasonably well with that observed for SMGs (Davies et al. 2013). Although given sufficient resolution and unlimited computing time, it would be preferred to perform radiative transfer directly, our approach has the advantage of trivial computational expense, and it may actually be more accurate than direct radiative transfer given the resolution of state-of-the-art large-volume simulations such as Illustris and IllustrisTNG .
Citation: Hayward et al. 2011
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Citation: Hayward et al. 2011
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Citation: Hayward et al. 2011
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Evolution of the SMD (top) and SFRD (bottom) along the cosmic history (see top axis for the corresponding redshift). For these plots, we assumed all three BBG candidates without ALMA detection to be real passive galaxies at z ∼ 6. In the top panel, the SMD of our BBG sample at z ∼ 6 (red circle) is shown in conjunction with those of star-forming (cyan symbols) and passive (magenta symbols) galaxies at lower redshifts from the literature (M13: Muzzin et al. 2013; D14: Duncan et al. 2014; S14: Straatman et al. 2014; G15: Grazian et al. 2015; S16: Song et al. 2016; D17: Davidzon et al. 2017; B19: Bhatawdekar et al. 2019; K19: Kikuchihara et al. 2019). The vertical error bar associated with our BBG data corresponds to a 1σ uncertainty propagated from the Poisson error (Gehrels 1986) for the BBG number and the SED fitting uncertainty for the stellar mass. The horizontal error bar shows the redshift range expected from our BBG color selection. In the bottom panel, the red shaded region corresponds to the SFRD expected from the progenitors of the z ∼ 6 BBGs at a 99.7% confidence level (3σ). The SFRD measurements at 





 are collected from the literature (MD14: Madau & Dickinson 2014; O13: Oesch et al. 2013; O14: Oesch et al. 2014; F15: Finkelstein et al. 2015a; M16: McLeod et al. 2016; B16: Bouwens et al. 2016; I18: Ishigaki et al. 2018; O18: Oesch et al. 2018; B19: Bhatawdekar et al. 2019). All of them at 





 are estimated by integrating the UVLFs down to MUV = −17 mag. The SFRD estimated at z ∼ 17 from an observed global 21 cm absorption trough (M18: Madau 2018; Bowman et al. 2018) is also shown in yellow. The functional fit to the MD14 data, which is proportional to (1 + z)−2.9 at high-z (Madau & Dickinson 2014), is superposed by the solid line. Two other power-law functions supporting an accelerated evolution at z ≳ 8 (





 Oesch et al. 2014) and a smooth evolution from lower redshift (





 Finkelstein et al. 2015a) are shown by dotted–dashed and dotted lines, respectively. The SFRD derived assuming a universal relation among the halo mass, SFR, and dark matter accretion rate (Harikane et al. 2018) is also superposed by the gray shade in its 1σ uncertainty. All of the SMD and SFRD measurements from the literature are corrected for the stellar IMF and the cosmological model to match those in this work.
Citation: Bhatawdekar et al. 2019
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Citation: Bhatawdekar et al. 2019
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I denote by λi and ϖi the mean longitude and longitude of periastron of planet i (in astrocentric coordinates), respectively. The actions canonically conjugated to the angles λi and −ϖi are the circular angular momentum Λi and the angular momentum deficit (AMD, see Laskar 2000) Di, respectively. These actions are defined as follows \begin{eqnarray} \Lambda_i &amp;=&amp; \beta_i\sqrt{\mu_i a_i},\\  D_i &amp;=&amp; \Lambda_i-G_i = \Lambda_i \left(1-\sqrt{1-e_i^2}\right), \end{eqnarray}Λi=βiμiai,Di=Λi−Gi=Λi1−1−ei2,where \hbox{$G_i=\Lambda_i\sqrt{1-e_i^2}$}Gi=Λi1−ei2 is the angular momentum of planet i, βi = mim0/ (m0 + mi), \hbox{$\mu_i = \G (m_0+m_i)$}μi = 𝒢(m0 + mi). At low eccentricities the deficit of angular momentum Di is proportional to \hbox{$e_i^2$}ei2. The Hamiltonian (Eq. (3)) can be expressed using these action-angle coordinates (6)\begin{equation} \label{eq:hamLaD}  \H = - \sum_{i=1}^n \frac{\mu_i^2\beta_i^3}{2\Lambda_i^2}  + \sum_{1\leq i<j\leq n} \H_{i,j}(\Lambda_i,\Lambda_j,D_i,D_j,\lambda_i,\lambda_j,\varpi_i,\varpi_j), \end{equation}ℋ=−∑i=1nμi2βi32Λi2+∑1≤ij≤nℋi,j(Λi,Λj,Di,Dj,λi,λj,ϖi,ϖj),where the first sum is the Keplerian part, which depends only on Λi (or equivalently ai), and ℋi,j is the perturbation between the planets i and j, which depends on the eight coordinates associated to i and j. I follow the method described in Laskar & Robutel (1995) to compute Hi,j as a power series of the eccentricities (or equivalently of \hbox{$\sqrt{D_i}$}Di and \hbox{$\sqrt{D_j}$}Dj), and a Fourrier series of the angles, where the coefficients are functions of Λi and Λj (i.e., of the semi-major axes). For a system that is close to the resonance or resonant, the semi-major axes remain close to the nominal resonant values (Kepler’s third law) (7)\begin{equation} \label{eq:nominalsma}  \frac{a_i}{a_j} \approx \frac{a_{i,0}}{a_{j,0}} = \left(\frac{k_{i,j}}{k_{j,i}}\right)^{2/3} \left(\frac{\mu_i}{\mu_j}\right)^{1/3}\cdot \end{equation}aiaj≈ai,0aj,0=ki,jkj,i2/3μiμj1/3·I introduce (8)\begin{equation} \Delta\Lambda_i = \Lambda_i - \Lambda_{i,0}, \end{equation}ΔΛi=Λi−Λi,0,where (9)\begin{equation} \Lambda_{i,0} = \beta_i\sqrt{\mu_i a_{i,0}}, \end{equation}Λi,0=βiμiai,0,and expand the Keplerian part at degree 2, and the perturbative part at degree 0 in ΔΛi(10)\begin{eqnarray} \label{eq:hamdLaD}  \H &amp;=&amp; \sum_{i=1}^n n_{i,0} \Delta\Lambda_i  - \frac{3}{2}\frac{n_{i,0}}{\Lambda_{i,0}}\Delta\Lambda_i^2\nonumber\\  &amp;&amp;\quad+ \sum_{1\leq i<j\leq n} \H_{i,j}(D_i,D_j,\lambda_i,\lambda_j,\varpi_i,\varpi_j), \end{eqnarray}ℋ=∑i=1nni,0ΔΛi−32ni,0Λi,0ΔΛi2 +∑1≤ij≤nℋi,j(Di,Dj,λi,λj,ϖi,ϖj),where ni,0 is the nominal mean motion of planet i, such that (11)\begin{equation} \frac{n_{i,0}}{n_{j,0}} = \frac{k_{j,i}}{k_{i,j}}\cdot \end{equation}ni,0nj,0=kj,iki,j·The perturbative part does not depend on Λi anymore, but is simply evaluated at Λi,0.
Citation: Laskar & Robutel (1995)
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