adsabs/FOCAL huggingface dataset:
The leading FRB source model invokes magnetars as the power source to produce repeating bursts. There are two versions of this model. One version invokes rapidly spinning young magnetars that are produced in extreme stellar transients such as GRBs and SLSNe. The main motivation is that the host galaxy of FRB 121102 resembles those of LGRBs and SLSNe (Metzger et al. 2017; Nicholl et al. 2017; Wadiasingh & Timokhin 2019). The fact that the hosts of all other FRBs do not resemble that of FRB 121102 disfavors the simplest version of this proposal. A possible fix of this proposal is to introduce rapidly spinning magnetars born from binary neutron star (BNS) mergers (Margalit et al. 2019; Wang et al. 2020). In order to make this scenario work, one needs to require that rapidly spinning magnetars made from BNS mergers should be much more abundant than those made from LGRBs and SLSNe. Comparing the event rate densities of BNS mergers, LGRBs, and SLSNe (e.g., Sun et al. 2015; Abbott et al. 2017; Nicholl et al. 2017), this may be possible if a significant fraction of BNS mergers leave behind stable neutron stars (e.g., Gao et al. 2016). However, if this fraction is very low, as required if GW170817 leaves behind a black hole (Margalit et al. 2019), the fast magnetar model may fail to explain the small fraction of LGRB/SLSN-like hosts in FRB samples. The second version of the magnetar model invokes emission (e.g., giant flares) from slowly rotating magnetars like the ones observed in the Galaxy (e.g., Popov & Postnov 2010; Katz 2014; Kulkarni et al. 2014). The births of these magnetars do not require extreme explosions such as GRBs and SLSNe (e.g., Beniamini et al. 2019). If this is the case, the host galaxy distribution may be more analogous to that of SNe II. All FRBs but FRB 121102 are consistent with this scenario (Figure 4). In order to interpret FRB 121102, the more extreme channel of forming rapid magnetars is still needed. So we conclude that the magnetar model would work only if both fast magnetars produced in extreme explosions and slow magnetars produced in regular channels (Beniamini et al. 2019) can produce FRBs. In any case, since the birth rate of these magnetars is very high (Beniamini et al. 2019), an additional factor is needed to select a small fraction of magnetars to produce FRBs (e.g., Ioka & Zhang 2020).
Citation: Margalit et al. 2019
Citation function: Compare/Contrast; Compare/Contrast
Citation: Margalit et al. 2019
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Having found evidence that galaxies show morphological signatures of a recent galaxy merger over time-scales on the order of 300–500 Myr, we now consider the typical BH merger time-scales and the impact that may have on our results. Within the Illustris simulation (and indeed many similar simulations), a pair of BHs merge as soon as their separation is less than the particle’s smoothing length, rather than incorporating a coalescence time for the binary. Recently, several works have attempted to estimate the expected coalescence time for binary BHs by post-processing cosmological simulations, finding time-scales on the order of 100s of Myr to Gyr for the binary coalescence time (e.g. Blecha et al. 2016; Rantala et al. 2017; Kelley, Blecha & Hernquist 2017a; Mannerkoski et al. 2019; Sayeb et al. 2021). Additionally, the time for a satellite BH to reach the centre of a galaxy and form a binary with the central BH can also be on the order of 100s of Myr to Gyr, based on the dynamical friction time-scale for infall to the galactic centre (e.g. Volonteri et al. 2020). The galaxy structure, e.g. the existence or lack of a dense stellar core, can affect the time satellite BHs spend at large radii (Tremmel et al. 2018a,b; Barausse et al. 2020), and directly incorporating dynamical friction into cosmological volumes suggests that the orbital decay time-scale may be both substantial and redshift dependent (Bartlett et al. 2021). The Illustris simulation uses a re-centring scheme whereby BHs are re-positioned towards the local potential minimum; this prevents numerical wandering of BHs, but also means the the full infall time to reach the galaxy centre may be notably underestimated. Furthermore, both simulations (e.g. Bellovary et al. 2019) and observations (e.g. Reines et al. 2020) suggest that BHs in dwarf galaxies may frequently be located offset from the galaxy centre, which could further delay any mergers involving low-mass BHs seeded into Illustris (which are initially placed at the galaxy centre). Overall, this suggests that the Illustris simulation likely overestimates the speed with which BHs merge following the merging of their host galaxies, and properly accounting for this has the potential to impact the expected GW detection rate, shift the peak detection time to lower redshift, and prevent GW hosts from being visibly disturbed. Although a complete investigation into accurately estimating the time delays remains beyond the scope of this paper, we address this, by imposing a delay between when the BH particles merge in the simulation (which is closer to when the BH binary may form) and when the final coalescence and GW emission occur.
Citation: Kelley, Blecha & Hernquist 2017a
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Several well-studied TeV blazars show rich spectral behavior in X-rays, which may represent the general behavior of the synchrotron peak of all AGN jets. The X-ray spectra are usually curved (Massaro et al. 2004) and can only locally be fitted by a power law. The spectral variation with flux can be complex (Zhang et al. 2002; Cui 2004). Generally, the spectrum hardens when the flux increases (e.g., Gliozzi et al. 2006; Xue et al. 2006; Tramacere et al. 2009), but photon indices can saturate at higher fluxes (Xue & Cui 2005; Giebels et al. 2007). The synchrotron peak usually moves to higher frequencies with increasing flux during outbursts (e.g., Pian et al. 1998), but no correlation between the break energy and the flux exists when a broken power law is adopted to fit the X-ray spectra (Xue & Cui 2005; Giebels et al. 2007; Garson et al. 2010). A cooling break in the spectrum of emitting particles cannot explain these features (Wierzcholska & Wagner 2016), and some special particle acceleration processes may be involved (Madejski & Sikora 2016). There are also energy-dependent lags between the variations of different energy bands. In some flares, soft bands lag behind hard bands (e.g., Zhang et al. 2002), while lags in the opposite direction can also happen (e.g., Ravasio et al. 2004; Sato et al. 2008). Hysteresis in the HR (hardness ratio)–flux diagram is often used as a diagnostic of lags. Clockwise loops (e.g., Acciari et al. 2009; Kapanadze et al. 2016) in the HR–flux plane are a sign of soft lags while counterclockwise loops (e.g., Tramacere et al. 2009) are a sign of hard lags. The same source can exhibit both clockwise and counterclockwise loops; the observed patterns are further complicated by the superposition of flares at different timescales (Cui 2004). The above knowledge of TeV blazars in the X-ray regime comes from studies focusing on timescales of hours to weeks. We will extend this kind of analysis to much smaller timescales in this paper.
Citation: Giebels et al. 2007
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Citation: Giebels et al. 2007
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Several nucleosynthesis processes have been proposed as production sites of these light neutron-capture elements, including r-process in neutron star mergers (Wanajo et al. 2014; Watson et al. 2019), in magneto-rotational supernovae (Winteler et al. 2012), or in collapsars (Siegel et al. 2019), s-process in low- to intermediate-mass stars (Karakas & Lattanzio 2014), weak s-process in rapidly rotating massive stars (Frischknecht et al. 2012; Choplin et al. 2018), and weak r-process in electron-capture supernovae (Wanajo et al. 2011). It is not yet clear which process is the dominant source of the elements in the early Universe (see discussions by, e.g., Côté et al. 2019; Prantzos et al. 2018; Kobayashi et al. 2020). Since we here discuss the low-metallicity end of the sample, the production of neutron-capture elements would not be dominated by low- to intermediate-mass stars (e.g., de los Reyes et al. 2022). One possible explanation for the low light neutron-capture element abundances of the Helmi streams is that, as a result of the low stellar mass of the galaxy, the progenitor did not experience rare r-process nucleosynthesis events, such as neutron star mergers, electron capture supernovae, and magneto-rotational supernovae. In this case, a small amount of light neutron-capture elements could be produced by rapidly rotating massive stars (Hirai et al. 2019; Tarumi et al. 2021). Another explanation is that the progenitor dwarf galaxy had a small number of rotating massive stars. The small number of rotating massive stars might be a result of the top-light initial mass function in dwarf galaxies (Weidner & Kroupa 2005), or different distribution of initial rotation velocity of stars. The observational indication by Gull et al. (2021) that metal-poor stars of the Helmi streams show r-process abundance pattern in neutron-capture elements heavier than Ba might favor the second possibility. However, it is necessary to investigate the abundance pattern of light neutron-capture elements in order to understand the cause of the low light neutron-capture element abundance of the Helmi streams. A larger sample of low-metallicity Helmi stream stars with neutron-capture element abundances would also be welcomed. They would enable us to constrain the property of the nucleosynthesis processes, such as their event rates, by studying how neutron-capture elements were enriched as a function of metallicity (e.g., Tsujimoto et al. 2017).
Citation: Côté et al. 2019
Citation function: Motivation
The discovery of ultraluminous pulsars (ULPs or PULXs; Bachetti et al. 2014; Fürst et al. 2016; Israel et al. 2017a,b; Carpano et al. 2018) has revolutionised the field of ultraluminous X-ray sources (ULXs; see the review of Kaaret, Feng & Roberts 2017). Whilst ULXs were long considered to be possible candidates for hosting intermediate-mass black holes (IMBHs), it was immediately apparent that the explanation for the extreme observed luminosities (>1039 erg s−1) in at least some ULXs was accretion in excess of the classical Eddington limit on to common-place primary objects – in this case neutron stars. However, whilst the mass regime of the compact object in ULXs has been at least partly resolved (we note that candidate IMBHs still remain, e.g. Farrell et al. 2009), the relative number of black hole to neutron star primaries in ULXs (see King, Lasota & Kluźniak 2017; Middleton & King 2017) and the nature of the accretion flow in ULPs remain outstanding puzzles. At the centre of the debate is the strength of the surface dipole field and any multipolar component. Should the dipole field strength be similar to that of Galactic HMXBs (1012 G – e.g. Bellm et al. 2014; Fürst et al. 2014; Tendulkar et al. 2014; Yamamoto et al. 2014) then it is quite plausible that the flow will be supercritical ($\dot{m}/\dot{m}_{\rm Edd} \gt $ 1 where $\dot{m}_{\rm Edd}$ is the Eddington accretion rate) at radii greater than the magnetospheric truncation radius (rM). In this case, the supercritical portion of the disc will have a large – close to unity – vertical scale height and winds will be launched from the surface (see Shakura & Sunyaev 1973; Poutanen et al. 2007 and the simulations of Ohsuga et al. 2009; Jiang, Stone & Davis 2014; Sa̧dowski et al. 2014). Within rM, the flow will take the form of an accretion curtain (Mushtukov et al. 2017, 2019) and shock-heated column as material falls on to the magnetic poles. Due to collimation by the disc and outflows beyond rM, it is expected that the intrinsic luminosity is then partially geometrically beamed (see King 2009). Conversely, should the dipole field strength be very high (typically > 1013 G) then it is quite probable that the disc will truncate before becoming locally supercritical. The geometry in this case is then expected to take the form of a geometrically thin disc down to rM, an accretion curtain and shock-heated column. Rather than a supercritical disc and geometrical beaming, super-Eddington luminosities can then be explained by a magnetic pressure supported accretion column (Basko & Sunyaev 1976) and high field strength, the latter allowing for a substantially increased luminosity from a reduction in the electron scattering cross-section (e.g. Herold 1979; Paczynski 1992; Thompson & Duncan 1995; Mushtukov et al. 2015).
Citation: Yamamoto et al. 2014
Citation function: Motivation; Motivation
Although it is a simple concept, obtaining galaxy sizes is not an easy task and is subject to a number of assumptions. The most common way to derive galaxy sizes is by performing light-profile fitting assuming a given shape of the surface brightness profile using a χ2 minimization (e.g. Simard et al. 1999; Peng et al. 2002; Ravindranath et al. 2004; Daddi et al. 2005; Ravindranath et al. 2006; Trujillo et al. 2006; Akiyama et al. 2008; Franx et al. 2008; Tasca et al. 2009; Cassata et al. 2010, 2013; Williams et al. 2010; Mosleh et al. 2011; Huang et al. 2013; Ono et al. 2013; Stott et al. 2013; Morishita et al. 2014; van der Wel et al. 2014; Straatman et al. 2015; Shibuya et al. 2015). Another method assumes circular or elliptical apertures around a predefined galactic center and computes the size enclosing a given percentage of the total galaxy flux (e.g. Ferguson et al. 2004; Bouwens et al. 2004; Hathi et al. 2008; Oesch et al. 2010; Ichikawa et al. 2012; Curtis-Lake et al. 2016). A third approach, involving counting the number of pixels belonging to the galaxy to derive its size, was also explored in Law et al. (2007). Studies of galaxy sizes at z> 2 became possible with the deep imaging obtained with HST. The first reports on size evolution found that galaxy sizes as observed in the UV rest-frame were becoming smaller at the highest redshifts (Bouwens et al. 2003, 2004; Ferguson et al. 2004). We have now access to the size evolution up to z ~ 10 from the deepest HST imaging data (e.g., Hathi et al. 2008; Jiang et al. 2013; Ono et al. 2013; Kawamata et al. 2015; Holwerda et al. 2015; Shibuya et al. 2015). With the multiwavelength and near-infrared coverage of CANDELS (Grogin et al. 2011; Koekemoer et al. 2011) optical rest-frame measurements are reported up to z ~ 3 for a large collection of galaxies in diverse populations (e.g. Bruce et al. 2012; van der Wel et al. 2014; Morishita et al. 2014). At z ~ 2 the size of star-forming galaxies (SFGs) is, to first order, independent of the observed rest-frame bands (Shibuya et al. 2015). It is generally accepted that galaxy sizes tend to decrease with increasing redshift (e.g. Bouwens et al. 2003, 2004; Ferguson et al. 2004; Mosleh et al. 2012) and that galaxy sizes depend on stellar mass (e.g. Franx et al. 2008; van der Wel et al. 2014; Morishita et al. 2014) and luminosity (e.g. Grazian et al. 2012; Huang et al. 2013). However, some results point to a scenario consistent with no size evolution as seen in UV rest-frame from HST data (Law et al. 2007; Curtis-Lake et al. 2016) and, at a fixed stellar mass, from optical rest-frame ground-based data (Ichikawa et al. 2012; Stott et al. 2013).
Citation: Shibuya et al. 2015
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Citation: Shibuya et al. 2015
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DIG contributes with a fraction of 20% to 90% of the total Hα flux in galaxy disks, with a mean fraction around 50%–60% (Hoopes & Walterbos 2003; Oey et al. 2007; Sanders et al. 2017; Tomičić et al. 2017; Poetrodjojo et al. 2019; Della Bruna et al. 2020; Tomičić et al. 2021). This large contribution may cause star formation rates (SFRs) to be overestimated as Hα flux from DIG may be wrongly associated with star formation. There is a debate about the extent to which DIG affects measurements of gas-phase metallicity and its radial slope (Searle 1971; Vila-Costas & Edmunds 1992; Sánchez et al. 2014; Belfiore et al. 2017b; Sanders et al. 2017; Sánchez-Menguiano et al. 2018; Zhang et al. 2017; Vale Asari et al.2019; Kumari et al. 2019; Poetrodjojo et al. 2019) as some observations indicate lower metallicity (up to 1 dex) in DIG compared to nearby H ii regions. DIG may also exhibit different values of line ratios and ionizing parameter log(q), further affecting observations and analysis of ISM characteristics as well as adding scatter in the distribution of galaxy properties measured from unresolved observations (Martin 1997; Flores-Fajardo et al. 2011; Dopita et al. 2014; Zhang et al. 2017; Poetrodjojo et al. 2018; Mingozzi et al. 2020). Furthermore, the detection of gas that shows different line ratios and ionization parameters located at large distances from H ii regions—larger than the thickness of a typical galactic disk ( ≈ 1 kpc)—would indicate that sources other than star-forming (SF) regions are ionizing such gas (for example HOLMES, shocks, or mixing of different gas layers; Flores-Fajardo et al. 2011; Zhang et al. 2017; Poetrodjojo et al. 2018; Poggianti et al. 2019a). Different galactic characteristics (like mass, SFR, age, etc.) and external physical processes, such as galaxy interactions and gas stripping caused by ram pressure (Gunn et al. 1972; Toomre & Toomre 1972), may affect the ionization parameter and various line ratios (Maier et al. 2006; Nagao et al. 2006; Flores-Fajardo et al. 2011; Zhang et al. 2017; Sánchez 2020).
Citation: Kumari et al. 2019
Citation function: Motivation; Motivation
Recently, machine learning (ML) has been applied to derive various physical parameters of galaxies (e.g., Masters et al. 2015; Krakowski et al. 2016; D’Isanto & Polsterer 2018; Bonjean et al. 2019; Davidzon et al. 2019; Hemmati et al. 2019; Chang et al. 2021) and improves on linear combinations through nonlinear activations (e.g., Ackermann et al. 2018; Walmsley et al. 2019; Ferreira et al. 2020; Bickley et al. 2021; Bickley et al. 2022; Ferreira et al. 2022). In particular, classification by ML (e.g., Banerji et al. 2010; Huertas-Company et al. 2015; Domínguez Sánchez et al. 2018; Bottrell et al. 2019; Pearson et al. 2019; Barchi et al. 2020; Chang et al. 2021) can avoid time-consuming visual inspections and will be helpful for the visual classification of galaxy−galaxy interactions from the forthcoming large surveys. For instance, Pearson et al. (2019) developed a convolutional neural network (CNN) architecture with observational SDSS and simulated EAGLE images to identify galaxy mergers. They showed that the networks achieve better performance in observational data than in simulations. Ferreira et al. (2020) achieve 0.90 accuracy to classify major mergers and measure galaxy mergers in all five CANDELS fields using CNN trained with simulated galaxies from the IllustrisTNG simulation and separate star-forming galaxies from post-mergers in a following work (Ferreira et al. 2022). Bickley et al. (2022) deployed a CNN and evaluated mock observations of simulated galaxies from the IllustrisTNG simulations to identify post-mergers. Bottrell et al. (2022) examine both the morphological and kinematic features of merger remnants from the TNG100 and show that the stellar kinematic data have few contributions. Moreover, it has been discussed whether ancillary information such as kinematics and spectroscopic information in addition to the images may provide an additional basis for classification (e.g., Nevin et al. 2019; Pan et al. 2019; Bottrell et al. 2022; McElroy et al. 2022). Therefore, it is important to identify specific features for classification with ML from both photometric and spectroscopic observables.
Citation: Bottrell et al. 2019
Citation function: Future Work; Future Work
The FORCAST images of HD 168625 are shown in Figure 4. The nebula is clearly resolved, with a partially complete ring structure that has two peaks almost symmetric around the star. We concur with Meixner et al. (1999) and O’Hara et al. (2003) in their interpretation of these two peaks as limb-brightened peaks of a torus of dust with a radius of ∼10″. The appearance is consistent with previously obtained images at 8.8, 12.5, and 20.6 μm from Meixner et al. (1999) and PACS 70 μm images (Groenewegen et al. 2011). We stress that our SOFIA/FORCAST images do not detect the outer polar rings seen in Spitzer IRAC images (Smith 2007), suggesting that the rings must be cold and below the sensitivity limits of SOFIA/FORCAST. The emission detected with the ring morphology in the IRAC band 4 image was probably polycyclic aromatic hydrocarbon (PAH) emission or atomic line emission, not thermal emission from warm dust. Figure 5 shows the temperature map that was derived from stacking the λFλ SOFIA/FORCAST 7.7–37.1 μm images and performing a least-squares fit of the dust temperature, Td, using the best-fit modified blackbody of the SED (i.e., Bλ(Td) · λ0.33) at each pixel location. The images were centered relative to one another by comparing the locations of the limb brightness peaks, and the 7.7–33.6 μm images were convolved with a 2D Gaussian kernel with an FWHM of 35 to match the resolution of the 34.8 and 37.1 μm images. Our temperature map shows a large gradient in dust temperatures with inner torus temperatures of ∼180 K and outer temperatures of ∼80 K, which is in agreement with the estimate of 170 ± 40 K obtained from our least-squares fit to the IR excess but is slightly higher than the equilibrium temperature estimates made by Pasquali et al. (2002; 113 K), Robberto & Herbst (1998; 135 K), and O’Hara et al. (2003; 130 K). The inaccuracies of this temperature map are due in large part to the fact that the method used to create it assumes that emission is purely thermal and that the dust shell is in thermal equilibrium. As noted previously, much of the 8.8–12.5 μm flux arises from transient, nonequilibrium emission from PAH grains. Therefore, using images at these wavelengths to derive quantitative conclusions from the temperature map has some limitations; however, we can interpret the maps qualitatively as discussed in Section 4.
Citation: Smith 2007
Citation function: Differences