adsabs/FOCAL huggingface dataset:
In addition, the graduated cylindrical shell (GCS) model, which was developed by Thernisien et al. (2006, 2009) and Thernisien (2011), is applied to obtain the three-dimensional parameters of these CMEs. Figure 5 shows the fitting results of these CMEs. Seen from these images, the GCS model can well represent the topology of these CMEs. The last three columns in Table 1 show the fitting results of these CMEs, including the propagation directions, velocities and face-on angular widths. Assuming a constant velocity and considering the influence of the propagation direction and angular width on the prediction of the arrival time suggested by Shen et al. (2014), CME-1 would arrive at the Earth around the time of September 6 22:27 UT. However, previous results show that fast CME would decelerate during their propagation in interplanetary space (e.g., Gopalswamy et al. 2001a, 2005; Vršnak 2001; Vršnak & Žic 2007; Temmer et al. 2011; Lugaz & Kintner 2012, and reference therein). Such deceleration may make the CME-1 arrive at the Earth later than September 6 22:27 UT. Thus, CME-1 is more likely to be the solar source of ICME-1. Seen from the Figure 3, the front edge of the CME-2 is lower than the front edge of CME-1 indicating that CME-2 would arrive at the Earth later than CME-1. Thus, CME-2 was the solar source of the ICME-2. It should be noted that, based on the fitting results of GCS model, CME-2 is faster than CME-1 and their propagation directions are close to each other. Thus, these two CMEs are expected to interact in interplanetary space. Seen from the in situ observations, possible interaction region signatures were detected between these two ICMEs with lower magnetic field, higher velocity, high density, and higher plasma beta. Furthermore, similar analysis shows that CME-3 might arrive at Earth after September 7 19:21 UT. Considering the long duration of Ejecta-4 and the larger angular width of CME-3, we verify that CME-3 is the solar source of ICME-4 and the driver of the second shock. It should be noted that, no obvious Earth-directed CME could be identified as the solar source of Ejecta-3. A possible explanation is that this ejecta structure is formed in the sheath region of ICME-4 during its propagation outward (e.g., Zheng & Hu 2018, and reference therein).
Citation: Temmer et al. 2011
Citation function: Compare/Contrast
As a prototypical Seyfert 2 galaxy with starburst at a distance of 14.4 Mpc (1″ = 72 pc, Bland-Hawthorn et al. 1997), NGC 1068 was observed at radio (Greenhill et al. 1996), millimeter (Schinnerer et al. 2000), infrared (Jaffe et al. 2004), optical (Antonucci & Miller 1985), UV (Antonucci et al. 1994), and X-ray (Kinkhabwala et al. 2002). High spatial resolution CO (1–0) observations show two molecular spiral arms with a diameter of ~40″ and a northern half-bar, while a CO (2–1) map reveals a nuclear ring with two bright knots in the CND region (Schinnerer et al. 2000). The dense gas fraction as traced by HCN (1–0) (Tacconi et al. 1994; Helfer & Blitz 1995) and CS (2–1) (Tacconi et al. 1997; Takano et al. 2014) in the nuclear region is higher than the two arms. Observations of CO (3–2) (Krips et al. 2011; Tsai et al. 2012; García-Burillo et al. 2014) showed that the difference of molecular gas temperatures between the nuclear region and the two arms was not as large as that of densities. Dozens of molecular lines at millimeter wavelength were detected at CND with single-dish observations (Usero et al. 2004; Nakajima et al. 2011, 2013; Aladro et al. 2013). Moreover, several molecules were detected and resolved toward NGC 1068 with interferometers in the past few years (Tosaki et al. 2017; Kelly et al. 2017; Furuya & Taniguchi 2016; Izumi et al. 2016; Imanishi et al. 2016; Nakajima et al. 2015; Viti et al. 2014; Takano et al. 2014; García-Burillo et al. 2014, 2016). The molecular gas in the CND region was denser and hotter than that in the starburst ring, while chemical properties in the two regions were also different (Viti et al. 2014). The highest molecular gas temperature was higher than 150 K, and the gas density was above 105 cm−3 in the CND region (Viti et al. 2014). The distribution of different species of molecules were also different: CO isotopic species, for instance, were enhanced in the starburst ring, while the shock/dust related molecules were enhanced in the CND region (Nakajima et al. 2015). The spatially resolved observations showed that the CND region was a complex dynamical system. For instance, the east and west dots were dominated by a fast shock and a slower shock (Kelly et al. 2017), while the dust torus also showed complex kinematics (García-Burillo et al. 2016). Gas inflow was driven by a past minor merger (Furuya & Taniguchi 2016), while the outflow was AGN driven (García-Burillo et al. 2014). We conducted adeeper survey of millimeter lines toward the CND region of NGC 1068 with the IRAM 30 m telescope, with the goal to quantify the gas properties in the CND. Compared to previous single-dish observations, our data probe weaker transition lines, which could place more constraints on the physical and chemistry properties of the CND.
Citation: Kelly et al. 2017
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Citation: Kelly et al. 2017
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C-statistic (e.g. Jang & Miller 1997) is the most commonly used and the one-way analysis of variance (ANOVA; de Diego 2010) the most powerful test for verifying the presence of variability in a DLC. However, we did not employ either of these tests because, de Diego (2010) has questioned the validity of the C-test by arguing that the C-statistics does not have a Gaussian distribution and the commonly used critical value of 2.567 is too conservative. On the other hand, the ANOVA test requires a rather large number of data points in the DLC, so as to have several points within each sub-group used for the analysis. This is not feasible for our DLCs which typically have no more than about 30–45 data points. Therefore, we have instead used the F-test which is based on the ratio of variances, F = variance(observed)/variance(expected) (de Diego 2010; Villforth, Koekemoer & Grogin 2010), with its two versions : (i) the standard F-test (hereafter Fη-test, Goyal et al. 2012) and (ii) scaled F-test (hereafter Fκ-test, Joshi et al. 2011). The Fκ-test is preferred when the magnitude difference between the object and comparison stars is large (Joshi et al. 2011). Onward Paper II, we have only been using the Fη-test because our objects are generally quite comparable in brightness to their available comparison stars. An additional gain from the use of the Fη-test is that we can directly compare our INOV results with those deduced for other major AGN classes (Goyal et al. 2013). An important point to keep in mind while applying the statistical tests is that the photometric errors on individual data points in a given DLC, as returned by the algorithms in the iraf and daophot softwares are normally underestimated by the factor η which ranges between 1.3 and 1.75, as estimated in independent studies (e.g. Gopal-Krishna, Sagar & Wiita 1995; Garcia et al. 1999; Sagar et al. 2004; Stalin et al. 2004a; Bachev, Strigachev & Semkov 2005). Recently, using a large sample, Goyal et al. (2013) estimated the best-fitting value of η to be 1.5, which is adopted here. Thus, the Fη statistics can be expressed as


\begin{equation*}
F_{1}^{\eta } = \frac{\sigma ^{2}_{({\rm q-s1})}}{ \eta ^2 \langle \sigma _{{\rm q-s1}}^2 \rangle }, \hspace{5.69046pt} F_{2}^{\eta } = \frac{\sigma ^{2}_{({\rm q-s2})}}{ \eta ^2 \langle \sigma _{{\rm q-s2}}^2 \rangle }, \hspace{5.69046pt} F_{{\rm s1-s2}}^{\eta } = \frac{\sigma ^{2}_{({\rm s1-s2})}}{ \eta ^2 \langle \sigma _{{\rm s1-s2}}^2 \rangle },\end{equation*}

 where $\sigma ^{2}_{({\rm q-s1})}$, $\sigma ^{2}_{({\rm q-s2})}$ and $\sigma ^{2}_{({\rm s1-s2})}$ are the variances of the ‘quasar–star1’, ‘quasar–star2’ and ‘star1–star2’ DLCs and $\langle \sigma _{{\rm q-s1}}^2 \rangle =\sum _{\boldsymbol {i}=0}^{N}\sigma ^2_{i,{\rm err}}({\rm q-s1})/N$, $\langle \sigma _{{\rm q-s2}}^2 \rangle$ and $\langle \sigma _{{\rm s1-s2}}^2 \rangle$ are the mean square (formal) rms errors of the individual data points in the ‘quasar–star1’, ‘quasar–star2’ and ‘star1–star2’ DLCs, respectively. η is the scaling factor (= 1.5) as mentioned above.
Citation: Joshi et al. 2011
Citation function: Compare/Contrast
Several laboratory experiments have been performed to determine the mechanical properties of possible building blocks of comets. In particular, Güttler et al. (2009), Schräpler et al. (2015), Lorek et al. (2016), and Katsuragi & Blum (2017) investigated how the compressibility of ice layers and different dust structures created by random ballistic deposition as well as pebble sub-structures (with scales of centimeters, millimeters, and 0.1 mm) depend on the volume filling factor. The respective results are summarized in Fig. 6. Remote observations lead to multiple independent estimates for the volume filling factor. Kofman et al. (2015) derived values of 0.15–0.25, Pätzold et al. (2016) estimated 0.25–0.30, and a study by Fulle et al. (2016) resulted in 0.21–0.37. The corresponding range is depicted in Fig. 6 by the horizontal bar shaded in blue. As thevolume filling factor is known, the laboratory measurements can be used to determine the most likely material of the surface. For the observed volume filling factor, dust and ice layers have a compressive strength above 103 Pa. A surface made up of such dust or ice layers is therefore inconsistent with the results of Groussin et al. (2015) or the compressive strength derived as part of this work. In contrast, a surface consisting of layers of dust and ice aggregates could explain all observations, except for the MPa range derived by Spohn et al. (2015). This is strong evidence for the presence of aggregate layers on the surface of 67P, that is, a surface composed of sub-decimeter-sized aggregates, as inferred from the gravitational collapse scenario. This conclusion can be drawn without precise knowledge of the actual value of the compressive strength, as the upper limit derived as part of this work and by Groussin et al. (2015), even considering all possible errors, is well below 103 Pa for different parts of 67P. The presence of such aggregate layers can only be explained by a formation dominated by relatively low-velocity collisions. Hierarchical agglomeration would cause a significantly higher compressive strength as a result of impact-compaction of the pebbles and possible break-up of aggregates (Blum 2018). The derived mechanical properties combined with the available laboratory models therefore suggest that the gentle collapse of aggregate ensembles played a major role in the formation ofcomets like 67P.
Citation: Kofman et al. (2015)
Citation function: Uses; Uses
The process of planet formation is strongly dependent on the stellar birth environment. The majority of stars exist in clusters or associations within their first few Myr of evolution (Lada & Lada 2003; Longmore et al. 2014; Krumholz, McKee & Bland-Hawthorn 2019), during which time they also host protoplanetary discs (PPDs – e.g. Haisch, Lada & Lada 2001; Ribas et al. 2014). Multiple feedback mechanisms influence disc evolution. In sufficiently dense environments, star–disc encounters can truncate the disc and induce increased accretion rates (Clarke & Pringle 1993; Ostriker 1994; Hall, Clarke & Pringle 1996; Pfalzner et al. 2005a; Olczak, Pfalzner & Spurzem 2006; Pfalzner, Olczak & Eckart 2006; de Juan Ovelar et al. 2012; Breslau et al. 2014; Rosotti et al. 2014; Winter et al. 2018a). Recent studies indicate that in the solar neighbourhood such interactions only have a significant effect in the early stages of cluster evolution due to enhanced stellar multiplicity and substructure, and therefore set initial conditions rather than destruction time-scales (Bate 2018; Winter et al. 2018b; Winter, Booth & Clarke 2018c). However, in regions with massive stars, external photoevaporation by far-ultraviolet (FUV) and extreme-ultraviolet (EUV) photons can rapidly disperse PPDs (Johnstone, Hollenbach & Bally 1998; Störzer & Hollenbach 1999; Armitage 2000; Clarke 2007; Fatuzzo & Adams 2008; Adams 2010; Facchini, Clarke & Bisbas 2016; Ansdell et al. 2017; Haworth et al. 2018b; Winter et al. 2018b). Additionally, before the dispersal of the parent giant molecular cloud (GMC), ram pressure stripping can truncate PPDs (Wijnen et al. 2017a) or additional material can be accreted (Moeckel & Throop 2009; Scicluna et al. 2014), leading to the destruction and reforming of discs during the embedded phase (Bate 2018). If a PPD is destroyed quickly by feedback in dense stellar environments, planets may be unable to form, depending on the efficiency of the formation mechanisms (Youdin & Goodman 2005; Johansen & Lambrechts 2017; Ormel, Liu & Schoonenberg 2017; Haworth et al. 2018a). Given the apparent ubiquity of grouped star formation, quantifying the destruction time-scales for PPDs due to neighbour feedback is of great relevance for understanding the demographics of PPDs and exoplanetary systems.
Citation: Rosotti et al. 2014
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Studies of SEP events are important for different reasons. On one hand, solar eruptive events are well-observed processes of energetic-particle acceleration (Vainio & Afanasiev 2018), which can be studied in detail using a multi-messenger approach, complementing particle data with observations in different wavelengths (e.g., Plainaki et al. 2014; Cliver 2016; Kocharov et al. 2017). For this purpose, the peak flux intensity and detailed temporal variability of the particle flux are important as signatures of the acceleration process in the solar corona and the interplanetary medium (e.g., Desai & Giacalone 2016; Kong et al. 2017). Accordingly, numerous studies were focused on peak fluxes of SEPs and corresponding acceleration and transport processes (e.g., Kouloumvakos et al. 2015; Kocharov et al. 2017). On the other hand, enhanced fluxes of energetic particles affect the radiation environment near the Earth (e.g., Webber et al. 2007; Mishev et al. 2015), making not only the peak fluxes but also the fluence (event-integrated flux) and its spectral shape of significant importance, especially for extreme events (e.g., Cliver et al. 2020). We emphasise that SEP fluences can not be used for the detailed study of SEP acceleration processes, because (i) the observations at 1 AU are also modified by transport, and (ii) different energies in the fluence spectrum can be dominated by different acceleration mechanisms or by the same mechanism operating under different conditions. It is evident from the proton time-intensity profiles alone that the fluence at MeV and 10 MeV energies is often dominated by acceleration at interplanetary shocks (e.g., Reames 1999). However, the question is more open at 100 MeV and GeV energies peaking much earlier, with possible contributions from flares and/or coronal shocks as the main candidates to account for the acceleration (see Cliver 2016, and references therein). Even if the same CME-driven shock were responsible for the acceleration of 10 MeV and 1 GeV protons, the former would typically be accelerated mainly in the solar wind and the latter in the corona, and there is no reason to suggest that the spectral form of the fluence would reveal something common about the accelerator properties.
Citation: Kouloumvakos et al. 2015
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It is still unclear whether this incompatibility is evidence against the spatially flat ΛCDM model or is caused by unidentified systematic errors in one of the established cosmological probes or by evolution of the parameters themselves with the redshift (Dainotti et al. 2021b, 2022). Newer, alternate cosmological probes could help alleviate this issue. Recent examples of such probes include reverberation-mapped quasar (QSO) measurements that reach to redshift z ∼ 1.9 (Czerny et al. 2021; Khadka et al. 2021a,b; Yu et al. 2021; Zajaček et al. 2021), H ii starburst galaxy measurements that reach to z ∼ 2.4 (Mania & Ratra 2012; Chávez et al. 2014; González-Morán et al. 2019, 2021; Cao, Ryan & Ratra 2020, 2022a; Cao et al. 2021a; Johnson, Sangwan & Shankaranarayanan 2022; Mehrabi et al. 2022), QSO angular size measurements that reach to z ∼ 2.7 (Cao et al. 2017, 2020, 2021a; Ryan, Chen & Ratra 2019; Lian et al. 2021; Zheng et al. 2021), QSO flux measurements that reach to z ∼ 7.5 (Risaliti & Lusso 2015, 2019; Khadka & Ratra 2020a,b, 2021, 2022; Lusso et al. 2020; Yang, Banerjee & Ó Colgáin 2020; Li et al. 2021; Lian et al. 2021; Luongo et al. 2021; Rezaei, Solà Peracaula & Malekjani 2021; Zhao & Xia 2021),1 and the main subject of this paper, gamma-ray burst (GRB) measurements that reach to z ∼ 8.2 (Amati et al. 2008, 2019; Cardone, Capozziello & Dainotti 2009; Cardone et al. 2010; Samushia & Ratra 2010; Dainotti et al. 2011, 2013a,b; Postnikov et al. 2014; Wang, Dai & Liang 2015; Wang et al. 2016, 2022; Fana Dirirsa et al. 2019; Khadka & Ratra 2020c; Hu, Wang & Dai 2021; Dai et al. 2021; Demianski et al. 2021; Khadka et al. 2021c; Luongo et al. 2021; Luongo & Muccino 2021; Cao et al. 2021a). Some of these probes might eventually allow for a reliable extension of the Hubble diagram to z ∼ 3–4, well beyond the reach of Type Ia supernovae. GRBs have been detected to z ∼ 9.4 (Cucchiara et al. 2011), and might be detectable to z = 20 (Lamb & Reichart 2000), so in principle GRBs could act as a cosmological probe to higher redshifts than 8.2.
Citation: Lian et al. 2021
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Citation: Johnson, Sangwan & Shankaranarayanan 2022
Citation function: Motivation; Background; Background
Citation: Lian et al. 2021
Citation function: Background
Recent studies have shown that one can use energy balance arguments that include the large-scale magnetic field, ${\mathrm{{\boldsymbol {\mathit {B}}}}_0}$, to derive scaling laws between the Alfvénic and kinetic fluid quantities (Federrath 2016; Beattie et al. 2020; Skalidis & Tassis 2021; S+2021). The dimensionless magnetic energy density, by which we mean the magnetic energy density normalized to the mean thermal pressure3$\rho _0 c_{\rm s}^2$, is
(5)$$\begin{eqnarray}
{e_{\mathrm{ mag}}}= \frac{B^2}{8\pi c_{\rm s}^2 \rho _0} = \frac{1}{8\pi c_{\rm s}^2 \rho _0}\Bigg (B_0^2+ \underbrace{2\delta \mathrm{{{\mathit {B}}}}\cdot \mathrm{{{\mathit {B}}}}_0}_{{\substack{\text{coupling} \\\text{term}}}} {} + \delta B^2 \Bigg),
\end{eqnarray}$$where $B_0^2$ is the large-scale field contribution to the total energy, δB2 is the turbulent field contribution, and $2\delta \mathrm{{\boldsymbol {\mathit {B}}}}\cdot \mathrm{{\boldsymbol {\mathit {B}}}}_0$ is the coupling term between the two field components. In the linear perturbation theory limit of the MHD equations, δB2 includes contributions from shear Alfvén, fast and slow magnetosonic compressive eigenmodes (e.g. Landau & Lifshitz 1959). Because $\delta \mathrm{{\boldsymbol {\mathit {B}}}}\cdot \mathrm{{\boldsymbol {\mathit {B}}}}_0 = \delta B_{\parallel }B_0$, the coupling term only contains the component of magnetic field fluctuations that are parallel to the large-scale field. In linear theory, both fast and slow magnetosonic compressible modes are able to perturb the field variables parallel to ${\mathrm{{\boldsymbol {\mathit {B}}}}_0}$, so under the lens of linear theory, the coupling term is the fluctuation contribution from the compressible modes in the turbulence scaled by B0 (Bhattacharjee, Ng & Spangler 1998). Furthermore, for sub-Alfvénic turbulence Beattie et al. (2021b) showed that converging, shocked flows along magnetic field lines excite strong δB∥ fluctuations, which travel roughly at the theoretical fast Alfvén mode speed. Therefore, it is likely, assuming that δB∥/B0 ≪ 1 (this is indeed the case for ${\mathcal {M}_{\text{A0}}}\lt 1$ plasmas; see left panel of fig. 5 in Beattie et al. 2022) where a linear theory may be valid for the magnetic field, the coupling term contains significant energy contributions from fast magnetosonic modes excited by shocked gas that converges and forms dense filaments perpendicular to magnetic field lines.
Citation: Landau & Lifshitz 1959
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To tackle this problem, one must study the recent star formation history (SFH) of galaxies. This information is embeddedin their spectral energy distribution (SED). However, recovering it through SED modeling is complex and subject to many uncertainties and degeneracies. Although an average SFH of galaxies can be derived assuming that they follow the MS (Heinis et al. 2014; Ciesla et al. 2017), galaxies are expected to exhibit complex SFHs, with short-term fluctuations, requiring sophisticated SFH parametrizations to model them (e.g., Lee et al. 2010; Pacifici et al. 2013; Behroozi et al. 2013; Pacifici et al. 2016). However, implementation of these models is complex and a large library is needed to model all galaxies properties. Instead, numerous studies have used simple analytical forms to model galaxies SFH (e.g., Papovich et al. 2001; Maraston et al. 2010; Pforr et al. 2012; Gladders et al. 2013; Simha et al. 2014; Buat et al. 2014; Boquien et al. 2014; Ciesla et al. 2015; Abramson et al. 2016; Ciesla et al. 2016, 2017). Furthermore, SFH parameters are known to be difficult to constrain from broadband SED modeling with a general agreement on the difficulty to constrain the age of the galaxy, here defined as the age of the oldest star, from broad-band SED fitting (e.g., Maraston et al. 2010; Pforr et al. 2012; Buat et al. 2014; Ciesla et al. 2015, 2017). To understand the origin of the scatter of the MS, we need to use an analytical SFH that is able to recover recent variations of the SFR with a precision better than the scatter of the MS itself, that is, 0.3 dex. Recently, Ciesla et al. (2017) showed that a delayed SFH to which we add a flexibility on the recent SFH provides SFRs that are more accurate than those estimated by other typical analytical SFHs (τ models, delayed, etc.). This SFH was tested in Ciesla et al. (2016). Studying a sample of local galaxies from the Virgo cluster, that is, galaxies known to have undergone a fast drop of star formation activity due to ram pressure stripping, we showed that the amplitude of the flexibility can be constrained by broadband SED modeling as long as ultraviolet (UV) rest frame and near-IR data are available.
Citation: Gladders et al. 2013
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