adsabs/FOCAL huggingface dataset:
The most unique and characteristic observational feature of blazars’ highly variable broadband emission is the broad bimodal SED extending from the lowest accessible EM band, i.e., the radio, to the highest accessible, i.e., GeV-TeV γ-rays. The broadband SED of all blazars can be categorized into three different spectral subclasses: low-energy-peaked (LBL/LSP), intermediate-energy-peaked (IBL/ISP), and high-energy-peaked (HBL/HSP; Fossati et al. 1998; Abdo et al. 2010), based on the location of the low-energy hump. A remarkable property of each spectral subclass is the stability of the location of the two peaks despite huge variations in flux and often spectral shape. Only in a few rare instances has an appreciable shift in the location of the peaks been observed, e.g., the 1997 outburst of Mrk 501 (Pian et al. 1998; Ahnen et al. 2018) and the activity of OJ 287 from the end of 2015 to the middle of 2017 (Kushwaha et al. 2018a, 2018b). Even these two cases are remarkably different. In the case of Mrk 501, the locations of both the peaks shifted to higher energies. On the contrary, in OJ 287, a shift in the location of only the high-energy peak was observed during the 2015–2016 activity (Kushwaha et al. 2018a, 2019), while in 2016–2017 a new broadband emission component overwhelmed the overall emission, appearing as an overall shift in both the peaks as revealed in the detailed study by Kushwaha et al. (2018b). With the SED being the prime observable for exploration of the yet-debated high-energy emission mechanisms, such changes offer invaluable insights about the emission processes. For example, in Mrk 501 the shift in both peaks strongly implies the same particle distribution for the overall emission, while for OJ 287 the shift of only the high-energy peak can be reproduced by either inverse Compton scattering of the broad-line region photon field (Kushwaha et al. 2018a) or emission of hadronic origin (Oikonomou et al. 2019; Rodríguez-Ramírez et al. 2020).
Citation: Kushwaha et al. 2018a
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Citation: Kushwaha et al. 2018a
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The shock is then revived by adding an energy injection following the parameterized method of Fryer et al. (2018). In this model, roughly 





 was deposited into the inner 





 in the first 





. Some of this energy is lost through neutrino emission and the total explosion energy at late times for this model is 





. This explosion is then mapped into our three-dimensional calculations, using one million SPH particles. The mapping took place when the supernova shock had moved out of the iron core and propagated into the Si–S rich shell at 





. We note that our 1D methods employed for modeling the collapse, core bounce, and initial explosion do not capture the full physics of the central engine (for a discussion, see Fryer et al. 2018), and this is a source of uncertainty in our yield calculations. The details of the engine change the shock trajectories, and neutrino chemistry can change Ye values (Saez et al. 2018; Fujimoto & Nagakura 2019). The nature of the shock affects mostly the yields after the shock falls below NSE (before it falls out of NSE, the yields are set by the equilibrium values, not the time-dependent evolution). Our model captures one instance of the range of asymmetric trajectories, and it should be noted that no model at this time is sufficiently accurate to dictate exactly the properties of the asymmetries (Janka et al. 2016). In addition, any model that does not include convection-driven asymmetries from the progenitor star cannot properly capture the asymmetries (Arnett et al. 2015). The 3D explosion model used here also displays stochastic asymmetries, implying that any manner of convective asymmetry could generate similar results. If this behavior is universal, it could have important implications. These points taken together indicate that nucleosynthetic patterns arising from convection-like behavior are robust, regardless of the driver. As discussed below, this increases the utility of NSE nucleosynthesis, particularly of 





 and 





, as diagnostics of the conditions in the progenitor star.
Citation: Fryer et al. 2018
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Citation: Fryer et al. (2018)
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Figure 2 shows the measured spectral indices of stars as functions of K magnitude, obtained from the 2MASS catalog. The CN, HK′, and CH indices increase with decreasing magnitude because the brighter RGB stars have lower temperatures and the strengths of these molecular bands generally increase with decreasing temperature. Therefore, the chemical abundances of stars are compared on the δ-index versus magnitude diagrams. It is important to note that the observed stars show a large spread in δ-index that is at least several times larger than the measurement error. The standard deviations for all sample stars are 0.23 for CN, 0.07 for HK′, and 0.07 for CH. In particular, the CN index distribution shows the largest spread. Note that a bimodality or a large spread in CN distribution is generally observed in most GCs (Norris et al. 1981; Norris 1987; Briley et al. 1992; Harbeck et al. 2003; Kayser et al. 2008; Martell et al. 2008).2

2
Although the evolutionary mixing effect can also contribute to the large spread in CN index distribution among bright RGB stars (Sweigart & Mengel 1979), this effect alone cannot explain a discrete distribution and a wide spread in the unevolved stars (see, e.g., Kayser et al. 2008).
 Therefore, we have divided subpopulations of RGB stars in NGC 5286 on the histogram of the δCN index (see Figure 3). It is clear from this histogram that RGB stars are divided into three subpopulations: CN-weak (δCN  −0.2; blue circles), CN-intermediate (−0.2 ≤ δCN  0.1; green circles), and CN-strong (0.1 ≤ δCN; red circles). The distribution of CN index into three or more subpopulations is similar to that reported in NGC 1851 (Campbell et al. 2012; Lim et al. 2015; Simpson et al. 2017). This is also consistent with the recent results from population models and spectroscopic observations that show that most GCs host three or more subpopulations (see, e.g., Jang et al. 2014; Carretta 2015). The presence of multiple populations is also observed from recent photometry using UV filters, which are mainly sensitive to N abundance (Milone et al. 2015; Piotto et al. 2015). In this regard, further observations are required to check that the trimodal CN distribution, observed in NGC 1851 and NGC 5286, is a ubiquitous feature in other GCs as well.
Citation: Martell et al. 2008
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According to our interpretation, the observed high velocity dispersion region in zone ‘F’ associated with the zone totally empty of gas between the two opposite velocity clouds (that the 3Dbarolo model would instead predict to be filled) resembles an expanding superbubble. Superbubbles are known to be associated with very massive stars (OB-associations): strong stellar winds and subsequent SN explosions from those stars inject energy and mass into the ambient ISM, creating shock fronts that sweep-up the ISM (e.g. Castor, McCray & Weaver 1975). As the SN explosions start occurring within the cavity formed by the stellar wind bubbles, super-bubbles are created (e.g. McCray & Kafatos 1987), which may eventually reach kiloparsec sizes. These explosions never form a visible SNR, but instead expend their energy in the hot interior as sound waves. Both stellar winds and stellar explosions thus power the expansion of the superbubble in the ISM. The interstellar gas swept up by superbubbles generally cools, forming a dense shell around the cavity (observed in Hi and Hα). The bubble interior contains hot (>106 K), rarefied material, usually associated with extended diffuse X-ray emission (thus appearing as an empty cavity in cold and molecular gas). In this interpretation, the two blobs of gas above and below the observed empty cavity in region ‘F’ (at +1.5 arcsec offset, with velocities of +70 and −70 km s−1, corresponding to the two peaks in Fig. 6), can be easily explained as gas surrounding the ‘superbubble’ being thrown away in different directions by the expanding spherical shell (see Kamphuis, Sancisi & van der Hulst 1991; Boomsma et al. 2008, for detected velocities up to 100 km s−1 associated with ‘holes’ in Hi). In Fig. 13, we show different CO(2–1) velocity channels, where the ‘superbubble’, surrounded by material moving in opposite directions (e.g. peaks at −70 and 70 km s−1, not present at the systemic velocities), shows up as an empty area (encircled in the plot), with a clear shock front at its left-hand side, peaking around the systemic velocities (≃0 km s−1).
Citation: McCray & Kafatos 1987
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Gaia 1 is a star cluster that was recently discovered by Koposov et al. (2017) in the first Gaia data release (Gaia Collaboration 2016), alongside with another system of lower mass. Its observation and previous detections were seriously hampered by the nearby bright star Sirius, which emphasized the impressive discovery power of the Gaia mission. This object was first characterized as an intermediate-age (6.3 Gyr) and moderately metal-rich (−0.7 dex) system, based on isochrone fits to a comprehensive combination of Gaia, 2MASS (Cutri et al. 2003), WISE (Wright et al. 2010), and Pan-STARRS1 (Chambers et al. 2016) photometry. Hence, this object was characterized by Koposov et al. (2017) as a star cluster, most likely of the globular confession. Further investigation of Gaia 1 found a metallicity higher by more than 0.5 dex, which challenged the previous age measurement and rather characterized it as a young (3 Gyr), metal-rich (−0.1 dex) object, possibly of extragalactic origin given its orbit that leads it up to ~1.7 kpc above the disk (Simpson et al. 2017). Subsequently, Mucciarelli et al. (2017) measured chemical abundances of six stars in Gaia 1, suggesting an equally high metallicity, but based on their abundance study, the suggestion of an extragalactic origin was revoked. While a more metal-rich nature found by the latter authors conformed with the results by Simpson et al. (2017), the evolutionary diagrams of both studies are very dissimilar and could not be explained by one simple isochrone fit. In particular, it was noted that “the Simpson et al. (2017) stars do not define a red giant branch in the theoretical plane, suggesting that their parameters are not correct” (Fig. 1 of Mucciarelli et al. 2017). Such an inconsistency clearly emphasizes that a clear-cut chemical abundance scale is inevitable for fully characterising Gaia 1, and to further allow for tailored age determinations, even more so in the light of the seemingly well-determined orbital characteristics, Thus, this work focuses on a detailed chemical abundance analysis of four red giant members of Gaia 1, based on high-resolution spectroscopy, which we complement with an investigation of the orbital properties of this transition object. Combined with the red clump sample of Mucciarelli et al. (2017) and reaching down to the subgiant level (Simpson et al. 2017), stars in different evolutionary states in Gaia 1 are progressively being sampled.
Citation: Mucciarelli et al. (2017)
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Observations show that the core temperatures of powerful AGN jets are estimated to be quite high (Moellenbrock et al. 1996). So the jets are hot to start with in this paper too. The advective disc model, as in most disc models, comes with a variety of inner disc temperatures. Simulations of advective discs for high viscosity parameter produced T ≳ 1012K in the PSD (Lee et al. 2016). Moreover, in the presence of viscous dissipation in curved space–time, the Bernoulli parameter (−hut) may increase by more than 20 per cent of its value at large distance and produce very high temperatures in the PSD (Chattopadhyay & Kumar 2016). For highly rotating BHs too, the temperatures of the inner disc easily approach 1012 K. It must also be remembered that inner regions of the accretion disc can be heated by the Ohmic dissipation, reconnection, turbulence heating or MHD wave dissipation may heat up the inner disc or the base of the jet (Pudritz 2003). High temperatures in the accretion disc can induce exothermic nucleosynthesis too (Chakrabarti, Jin & Arnett 1987; Hu & Peng 2008). All these processes taken together in an advective disc will produce very hot jet base. We do not specify the exact processes that will produce very hot jet base, but would like to emphasize that it is quite possible to achieve so. One may also wonder that if the jets are indeed launched from the disc, and how justified is it to consider non-rotating jets. Phenomenologically speaking, if jets have a lot of rotation then it would not flow around the axis of symmetry and, therefore, either it has to be launched with less angular momentum or it has to lose most of the angular momentum with which it is launched. It has been shown that viscous transport removes significant angular momentum of the collimated outflow close to the axis (Lee et al. 2016). Since the jet is launched with low angular momentum and it is further removed by viscosity or by the presence of magnetic field; therefore, the assumption of non-rotating hot jet is quite feasible. Incidentally, similar to this study, there are many theoretical studies of jets that have been undertaken under similar assumptions of non-rotating, hot jets at the base (Fukue 1987b; Falcke 1996; Memola et al. 2002).
Citation: Chakrabarti, Jin & Arnett 1987
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Another important consistency check regards the angular size distribution of the sources. Figure 6 shows the cumulative size distributions of the final catalogs combined together, in four flux density bins (yellow solid lines). Such distributions can be considered reliable only down to a flux-dependent minimum intrinsic size (see vertical gray lines), below which most of the sources cannot be reliably deconvolved and they are conventionally assigned Θ = 0. The observed distributions are compared with various realizations of the cumulative distribution function described by Eq. (6), obtained by varying either the function exponent q (left and right columns respectively) or the assumed median size – flux relations (see various black lines).The original function proposed by Windhorst et al. (1990) (Eq. (6) with q = 0.62, see left column) does provide a good approximation of the observed distributions, when assuming the original Θmed − S relation described by Eq. (7), only at flux densities S150 MHz≳10 mJy (see long-dashed lines). This is perhaps not surprising considering that this relation was calibrated at 1.4 GHz down to a few mJy fluxes. At the lowest flux densities (S150 MHz≲1 mJy) we need to assume a steepening of the parameter m (see Eq. (8)), to get a good match with observations (dotted line in the top left panel). This is consistent with what proposed for higher frequency deep surveys (as discussed earlier in this section). At intermediate fluxes (S150 MHz ~ 1−10) mJy, on the other hand, none of the discussed median size – flux relations can reproduce the observed size distribution (see second-row panel on the left). It is interesting to note, however, that if we assume a steeper exponent for the distribution function described by Eq. (7) (i.e., q = 0.80), we get a very good match with observations at all fluxes, when assuming a flux-dependent scaling factor (k = k(S); see Eq. (9)) for the Windhorst et al. (1990) median size – flux relation (black solid lines on the right). The median sizes derived from the T-RECS simulated catalogs (Bonaldi et al. 2019) also provide good results for q = 0.80 (dot-dashed lines on the right), except again at intermediate fluxes (S150 MHz ~ 1−10), where they show strong discrepancies with observations also in Fig. 5. This seems to indicate that the number density of extended radio galaxies in this flux density range is over-estimated in the T-RECS simulated catalogs.
Citation: Windhorst et al. (1990)
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Citation: Windhorst et al. (1990)
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In this work, we present well-calibrated optical photometric (−0.2 to +413 d), polarimetric (−2 to +31 d) and optical (−5 to +391 d), NIR (−5 to +22 d) spectroscopic studies of SN 2012au, based on data obtained using many observational facilities around the globe. Analysis based on our photometric observations suggests that SN 2012au appears to be one of the most luminous SNe Ib (MB, peak = −18.06 ± 0.12 mag), though fainter than the threshold limit of SLSNe I (M$_\mathrm{ g}\, \lt -$19.8 mag; Quimby et al. 2018). The MR, peak (∼ –18.67 ± 0.11 mag) of SN 2012au is brighter than the average values of SNe Ib and Ic, but closer to those reported for SNe Ic-BL (Drout et al. 2011). Similarly, the peak bolometric luminosity of SN 2012au (∼ [6.56 ± 0.70] × 1042 erg s−1) is higher than the mean peak luminosities of SNe Ib and Ic, but still lower than those of SNe Ic-BL (Lyman et al. 2016). Using the early bolometric light curve of SN 2012au, the estimated values of Mej, Ek, MNi, and T0 are ∼5.1 ± 0.7 M⊙, ∼ (4.8 ± 0.6) × 1051 erg, ∼0.27−0.30 M⊙, and ∼66.0 ± 9.4 d, respectively. These physical parameters of SN 2012au are close to those inferred for SN 2009jf (a bright SN Ib: Sahu et al. 2011) and – on average – larger than for classical SNe Ib/c but smaller than for some SNe Ic-BL. SN 2012au manifests larger Mej and MNi in comparison with most of the SNe IIb, Ib, and Ic, which may be the prime reason behind the luminous peak of SN 2012au, as seen in the case of SLSNe I (Nicholl et al. 2015). On the other hand, light-curve decline rates of SN 2012au (at phases ≥+40 d) in all the optical bands are shallower than typically observed in the case of SNe Ib and slow-decaying SLSNe I, and theoretically predicated for 56Co $\rightarrow \, ^{56}$fe decay. As SN 2012au exhibits comparatively larger Mej, a larger optical depth resulting in a larger diffusion time-scale (for the trapped energy to cross the outer envelope) could broaden the light curve. Therefore, high trapping of gamma-rays at late phases or higher opacity of massive ejecta are among the plausible interpretations for the modest luminosity decline rate of SN 2012au in comparison with other SNe Ib (Clocchiatti & Wheeler 1997). However, smoothly distributed circumstellar media up to a larger radius could be another possibility behind the late-time shallower decay rate for SN 2012au, but an absence of the CSMI in the late-time spectra ruled out this scenario (Milisavljevic et al. 2018). The late-time bolometric light curve of SN 2012au is better constrained by $L~\varpropto\, t^{-2}$, a conventional magnetic dipole equation. Hence for SN 2012au the shallower decay of the late-time light curve might be a potential indicator of a central engine powering source that is accelerating the inner ejecta.
Citation: Lyman et al. 2016
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Despite significant advances, the solar wind remains challenging to model (and importantly forecast) due to the large range of scales that need to be incorporated. It is well known that energy is injected into the corona at all scales, from magnetohydrodynamic (MHD) waves (Nutto et al. 2012; Van Doorsselaere et al. 2020) to flares and coronal mass ejections with global extent (Aschwanden et al. 2017; Green et al. 2018; Wyper et al. 2018). In addition to the range of spatial scales, heating events take place on a variety of timescales (Hollweg 1973; Viall & Klimchuk 2017). Putting aside large-scale eruptions so as to focus on the quasi-steady input of energy into the corona, it is possible to distinguish three broad magnetic configurations of the solar atmosphere for modelling purposes. These are the quiet Sun (Danilovic et al. 2010; Rempel 2014), active or enhanced regions (Carlsson et al. 2016; Chen et al. 2021), and coronal holes (Wójcik et al. 2019). In each of these configurations, energy is channelled from the convection into the low corona. Coronal holes are the dominant source of the solar wind in the heliosphere (Cranmer et al. 2017; Stansby et al. 2021), typically producing the fast solar wind (McComas et al. 2008; Ebert et al. 2009; Macneil et al. 2020a; Wang 2020). The magnetic field configuration of a coronal hole is relatively simple, compared with the quiet Sun and active regions, given that the field is principally open to the solar wind (Lowder et al. 2017; Hofmeister et al. 2019). However, there are still a range of dynamic processes taking place, such as the braiding of magnetic field lines (Wedemeyer-Böhm et al. 2012; Wedemeyer et al. 2013; Huang et al. 2018) and the emergence of new magnetic flux (Murray et al. 2009). These can trigger the formation of jets (Shen et al. 2017; Yang et al. 2017) and other phenomena, which are then observed as spicules (Martínez-Sykora et al. 2017; Bose et al. 2021) or fibrils (Hansteen et al. 2006; Leenaarts et al. 2015).
Citation: Carlsson et al. 2016
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While the Loison et al. (2015) CH3C3N model corroborates the upper atmospheric abundance of C4H3N inferred by Vuitton et al. (2007) from the T5 INMS measurements (a factor of 2 higher than those derived from T40 in Vuitton et al. 2019), a large disparity between the photochemical models (and within the ensemble of models produced by Loison et al. 2015) arises in the lower atmosphere due to the poorly constrained C4H3N branching ratios and reaction rate coefficients at temperatures appropriate for Titan. Aside from electron dissociative recombination of C4H3NH+ (Vuitton et al. 2007), neutral production of CH3C3N can occur in a few ways, as found through crossed beam experiments and theoretical and photochemical modeling studies (Huang et al. 1999; Balucani et al. 2000; Zhu et al. 2003; Wang et al. 2006; Loison et al. 2015). First, through the reactions of larger hydrocarbons with CN radicals,
1







2





Similarly, with CCN radicals following their formation through H + HCCN (Takayanagi et al. 1998; Osamura & Petrie 2004) and subsequent reactions with ethylene,
3





or through the chain beginning with acetylene,
4





While both reactions (3) and (4) are found to be equally likely by Loison et al. (2015), the production of CCN via H + HCCN is not well constrained, and the synthesis of CH3C3N through CN radicals (Equations (1) and (2)) are not included in their photochemical model. Additionally, cyanoallene may be produced through reactions (1)–(4) instead of (or in addition to) methylcyanoacetylene. CH3C3N itself may form the protonated species, C4H3NH+, through reactions with the HCNH+ and C2H5+ ions producing HCN and C2H4, respectively (Vuitton et al. 2007). The other mechanism for forming C4H3NH+ is through the combination of HCN and l-C3H3+, though the reaction rate coefficient for this reaction and the abundance of l-C3H3+ are unknown (Vuitton et al. 2007). As such, the production and loss pathways for both C4H3NH+ and CH3C3N require further investigation.
Citation: Vuitton et al. 2007
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Citation: Vuitton et al. (2007)
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Citation: Vuitton et al. 2007
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