adsabs/FOCAL huggingface dataset:
The number of BHs, their mass distribution, and the number density are poorly known in NSCs. Theoretically, a single-mass distribution of objects forms a power-law density cusp around a massive object with n(r) ∝ r−1.75 (Bahcall & Wolf 1976), where n(r) is the number density, and r is the distance from the MBH. For multi-mass distributions, lighter and heavier objects develop shallower (∝r−1.5) and steeper cusps (typically ∝r−2 to r−2.2, and r−3 in extreme cases), respectively (Bahcall & Wolf 1977; Freitag et al. 2006; Hopman & Alexander 2006b; Keshet et al. 2009; Aharon & Perets 2016). Recent observations of the stellar distribution in the Milky Way NSC identify a cusp with n(r) ∝ r−1.25 (Gallego-Cano et al. 2018; Schödel et al. 2018), which is consistent with the profile after a Hubble time (Baumgardt et al. 2018). As BHs are heavier than typical stars, they are expected to relax into the steeper cusps. The relaxation time of BH populations is much shorter: 0.1–1 Gyr (O’Leary et al. 2009), although it can become much longer than that in the case of a shallow stellar density profile (Dosopoulou & Antonini 2017). In this work, we assume that the BH number density follows a cusp with either n(r) ∝ r−2 or r−3 in our two sets of calculations.The BH mass in the two cases is set arbitrarily to 107 and 4 × 106 M⊙, respectively, and we refer to the two models as Bahcall–Wolf–like (BW) and GC examples. Note that here GC refers to the assumed MBH mass (Ghez et al. 2008) and the observed stellar distribution (see below). In reality, the cusp distribution varies with the BH mass. Thus, we have also generated initial conditions for the BW case so that β in the number density distribution, n(r) ∝ r−(3/2+β), is calculated by β(m) = m/4M0, where m is the binary mass, and M0 is the weight average mass (e.g., Alexander & Hopman 2009; Keshet et al. 2009; Aharon & Perets 2016). We found that, due to the stability conditions, the initial condition distribution does not change significantly. For the GC case, the initial conditions’ distribution will change more significantly if we allow β to vary with the mass. However, we keep β = 3 to investigate the effects of a steep number density distribution on the rates. As we will show later in this work, the choice of the number density distribution will have a very limited effect on the merger rate.
Citation: O’Leary et al. 2009
Citation function: Compare/Contrast
Since the launch of Swift (Gehrels et al. 2004) in 2004, the nature of jet breaks in gamma-ray burst (GRB) afterglows has become increasingly puzzling. There is strong evidence that the ejecta from the GRB central engine must be jet-like (Zhang & Mészáros 2004). Thus a collimation correction factor, 





, where 





 is the jet opening angle with typical values of 5°–10°, can be applied to relieve the energy budget problem, so that a typical GRB energy is 





 





 erg, where 





 is the isotropic equivalent energy release in gamma-rays. Such collimated ejecta expand outward relativistically with Lorentz factors Γ of several hundred initially. Internally, the ejecta release their energy through internal shocks (Rees & Meszaros 1994; Kobayashi et al. 1997; Daigne & Mochkovitch 1998), or magnetic dissipation processes (e.g., the ICMART model; Zhang & Yan 2011) or photospheric dissipation (e.g., Guiriec et al. 2010, 2011, 2015; Lazzati & Begelman 2010; Ryde et al. 2010, 2011; Pe’er & Ryde 2011) and produce the prompt gamma-ray emission of GRBs. Externally, the ejecta are further decelerated by an ambient medium (e.g., a constant density interstellar medium (ISM); or a stellar-wind environment with density inversely proportional to distance squared) and produce long-term broadband afterglows through external shocks (see e.g., Gao et al. 2013, for a review). Due to relativistic beaming, only a portion of the radiation from the ejecta front surface, which is within a cone of half-opening angle 





, can be observed (Rhoads 1997, for reviews see Piran 2004; Granot 2007; van Eerten 2013). An unavoidable consequence of this general picture is that when the ejecta are decelerated to 





, the light curve should steepen because (1) the maximum observable portion of the ejecta (the cone of the whole jet with opening angle 





) is now smaller than that which is expected (a cone with half-opening angle 





 and (2) the onset of lateral spreading of the ejecta, predicted to become noticeable in the observer frame around the same time (Rhoads 1999), causes the blast wave to decelerate further. Such a “jet break” in a GRB light curve is expected to behave achromatically because it only reflects the ejecta geometry, under the assumption that the afterglow emission regions and mechanisms do not change in different spectral regimes (Rhoads 1999; Sari 1999; Huang et al. 2000; Granot et al. 2002; see also reviews by Mészáros 2002; Piran 2004; Zhang & Mészáros 2004). The achromaticity was apparently confirmed in the optical and near-IR band in a few cases of pre-Swift GRBs (Kulkarni et al. 1999; Harrison et al. 2001; Klose et al. 2004).
Citation: Guiriec et al.; 2015
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Figure 9 shows the components required for such a time-domain survey, with the PC beamformer as specified above. The required components are shown in four different colors. Visibilities computed in the uGMRT backend (GWB; marked in blue) at 1 ms time resolution are transferred to the the PC beamformer nodes (marked in orange) with an aggregate data rate of ∼3 GB s−1. We aim to implement in-field phasing (Kudale & Chengalur 2017) using a sky model derived from the time-averaged visibilities in order to improve the coherence in phasing up to a baseline length of several kilometers. This optimizes the GMRT PA sensitivity beyond a central compact core (most current PA observations use only the antennas in the central square). In addition to deriving the phasing model, a baseline-based flag masking the bad baselines will also be generated in real time from these time-averaged visibilities. Coherent additions of these visibilities will result in 128 such visibility beams. The multi-DM search for single pulses (colored in yellow) on each of these visibility beams would need to be executed on a separate FRB cluster, followed by coincidence filtering to remove spurious events (Bhat et al. 2013). We also propose recording these 128 beams with a 1 ms time resolution, giving a total data rate of 200 MB s−1 into a disk for a quasi-real-time search for pulsars using the same cluster. We note that the proposed 1 ms time resolution is sufficient to detect double neutron star systems, young pulsars, and normal pulsars, as well as objects like radio magnetars. Visibility buffers corresponding to candidate single-pulse events will be recorded at a 1 ms time resolution covering the full DM sweep time-range. For an event at a DM of 2000 pc cm−3, the total DM sweep time over 200 MHz band in uGMRT 300–500 MHz band is ∼50 s, which results in a 40 GB buffer size on each of the PC beam nodes. This means one can easily hold few buffers for accommodating the pipeline delay and flush them into storage based on the real-time triggers. These visibilities will be processed through the processing blocks (marked in green) for millisecond imaging localization at quasi-real-time. This block includes removal of dispersion delay, followed by a flagging and calibration pipeline and snapshot imaging. We note that part of this imaging pipeline for localizing time-domain events has already been demonstrated for the GHRSS Phase1 survey (Bhattacharyya et al. 2016).
Citation: Bhat et al. 2013
Citation function: Future Work
It is interesting that the sources with suspected or confirmed Lyman continuum radiation at high redshifts are peculiar and rare, bright SFGs with rather hard ionizing spectra marked by high-ionization emission lines (e.g., N v, C iv, He ii, O iii, C iii). Their presence at high redshift can be hardly explained by pure SFGs, even requiring uncommon assumptions (e.g., large stellar rotation, binary stellar population, top-heavy initial mass function, extremely low metallicity) as discussed by a number of recent works (see Bowler et al. 2014; Bradley et al. 2014; Kehrig et al. 2015; Stark et al. 2015; Jaskot & Ravindranath 2016; Stark 2016; Senchyna et al. 2017, 2019, 2020; Berg et al. 2018; Nakajima et al. 2018; Chisholm et al. 2019; Jaskot et al. 2019; Le Fevre et al. 2019b; Nanayakkara et al. 2019; Schaerer et al. 2019; Stanway & Eldridge 2019). The majority of galaxies showing Lyman continuum emission (both at low z and at z ∼ 3–4) populate the upper end of the Baldwin, Phillips, and Terlevich (BPT) diagram (Baldwin et al. 1981) or occupy a region of high-ionization line ratio in between SFGs and AGNs, or mainly populated by AGNs (see, e.g., Figures 11 and 14 by Nakajima et al. 2018). Indeed, Le Fevre et al. (2019b) find a marginal 2σ detection in the X-ray stacking of strong C iii emitters at 2  z  4, consistent with the presence of low-luminosity AGNs. Interestingly, evidence recently emerged that local confirmed Lyman continuum emitters or low-z Green Peas, blue compact galaxies, Lyman break analogs, which are usually associated with reliable Lyman continuum candidates, are characterized by significant X-ray emission, not compatible with star formation activity but more plausibly powered by low-luminosity AGN activity or by a large population of high-mass X-ray binaries (Kaaret et al. 2017; Bao et al. 2019; Bluem et al. 2019; Latimer et al. 2019; Plat et al. 2019; Prescott & Sanderson 2019; Senchyna et al. 2019, 2020; Svoboda et al. 2019; Wu et al. 2019; Baldassare et al. 2020; Birchall et al. 2020; Dittenber et al. 2020). It is thus possible that pure stellar radiation from SFGs is a negligible source of H i ionizing radiation, and the bulk of Lyman continuum photons escaping at low and high z are produced instead by accretion onto supermassive black holes (SMBHs).
Citation: Stanway & Eldridge 2019
Citation function: Compare/Contrast
Observationally, it is a big challenge to conclude whether AGN’s feedback could regulate star formation or not, and how it regulates star formation. On one hand, if strong outflows emerge, they could clear out the star-forming gas to suppress star formation (Alexander & Hickox 2012; García-Burillo et al. 2014; Alatalo et al. 2015; Hopkins et al. 2016; Wylezalek & Zakamska 2016). The heating by jets propagating through the galaxies could prevent gas from cooling and cut-off the gas supply for further star formation (Karouzos et al. 2014; Choi et al. 2015). On the other hand, the outflows and jets interact with the gas in host galaxies and compress it to trigger new star formation (Silk 2013; Zubovas et al. 2013; Zubovas & Bourne 2017). In fact observations show either no or positive relationships between star formation rates and SMBH accretion rates but no negative trends are seen (Shi et al. 2007, 2009; Baum et al. 2010; Xu et al. 2015; Zhang et al. 2016; Mallmann et al. 2018). Whether AGN’s feedback plays the role may also depends on the spatial scale that observations could resolve and time-scale that the observed tracers could probe (Harrison et al. 2012; Cresci et al. 2015; Feruglio et al. 2015). For example, radiation from AGNs nearly instantaneously impact the surrounding ISM while the attenuation from ISM probably limits their impact to the nuclear regions (Roos et al. 2015). Outflows or jets travel slowly and may be decelerated after interactions with ambient gas, which delays their effects on star formation at large distances from the nuclei (Harrison 2017; Harrison et al. 2018). Feedback by jets or outflows on ISM also depends on their orientation relative to the dusty torus, making their effects on star formation to be anisotropic. The short duty cycle of AGNs could also make feedback by radiation from AGNs temporally variable in strength. Case studies of individual AGNs find evidence of coexistence of positive and negative feedback on star formation (Zinn et al. 2013), suggesting the complicated nature of AGN feedback.
Citation: Mallmann et al. 2018
Citation function: Motivation
Citation: Choi et al. 2015
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In principle, we could measure the multipole moments in a given simulation by directly populating dark matter haloes in the simulation with galaxies. However, this is computationally very expensive and comes with realization noise due to the random number and phase-space positions of galaxies. Instead, we use a tabulation method (Neistein & Khochfar 2012; Reid et al. 2014; Zheng & Guo 2016) to speed up the computation dramatically and eliminate any realization noise. We first take all haloes in a given simulation to serve as tracers of central galaxies. We furthermore assign to each halo of mass M a Poisson number of satellite tracers with expectation value $3 \times (M/10^{13} \, h^{-1}\rm M_\odot)$. This expectation value is chosen to be significantly larger than the number of satellites we typically expect in haloes of that mass (see e.g. Guo et al. 2015). We then bin all haloes and their central and satellite tracers by halo mass and whether the concentration is above or below the median. Next, we measure all cross- and autocorrelation multipole moments between all tracers in each bin. One can then show that an estimate for the galaxy number density and the multipole moments of any arbitrary galaxy–halo model are given by
(13)$$\begin{eqnarray*}
\hat{n}_{\rm gal} = \sum \limits _{i = {\rm c}, {\rm s}} \sum \limits _{k = 1}^{n_{\rm bins}} N_{{\rm h}, k} \langle N_i | M_k, c_k \rangle
\end{eqnarray*}$$and
(14)$$\begin{eqnarray*}
\hat{\xi }_\ell &amp;=&amp; \hat{n}_{\rm gal}^{-2} \sum \limits _{i = {\rm c}, {\rm s}} \sum \limits _{j = {\rm c}, {\rm s}} \sum \limits _{k = 1}^{n_{\rm bins}} \sum \limits _{l = 1}^{n_{\rm bins}} \Big [ N_{{\rm h}, k} N_{{\rm h}, l} \langle N_i | M_k, c_k \rangle \nonumber \\
&amp;&amp;\times \,\langle N_j | M_l, c_l \rangle \xi _{\ell , kl}^{ij} \Big ] \, ,
\end{eqnarray*}$$respectively. In the above expression, Nh, k denotes the number of haloes in bin k and, for example, $\xi _{\ell , kl}^{{\rm c}{\rm s}}$ denotes the multipole moments between centrals in bin k and satellites in bin l. The above estimate $\hat{\xi }_\ell$ approaches the expectation value of ξℓ for sufficiently small halo mass bins. We find that 100 logarithmic bins in halo mass is sufficient to adequately sample all haloes with mass $M \gt 3.52 \times 10^{13} (\Omega _{\rm m}/0.3) \, h^{-1}\, \rm M_\odot$ (corresponding to 100 particles). With such a bin width of ∼0.03 dex, any biases in ξ are less than $5{{\ \rm per\ cent}}$ of the observational uncertainty for a BOSS CMASS-like sample (see Guo et al. 2015). The above method only works for a fixed value of the satellite radial profile parameter η. In practice, it suffices to tabulate correlation function for bins in η of Δlog η = 0.1 and linearly interpolate between them.
Citation: Guo et al. 2015
Citation function: Uses
Multiple solar missions, such as the Solar Dynamics Observatory (SDO) and the Interface Region Imaging Spectrograph (IRIS), have shown that a diversity of waves occur in the solar atmosphere (Jess et al. 2009; McIntosh et al. 2011; Okamoto & De Pontieu 2011). The various wave types that occur include Alfvén waves (Alfvén 1942). These waves are transverse magnetohydrodynamic (MHD) waves that travel along the magnetic field lines. Alfvén waves were reported to be present in both the photosphere and chromosphere (Srivastava et al. 2017; Baker et al. 2021). As they pass by, they modify the transverse magnetic field and velocity components but, do not alter the gas pressure or the mass density (Nakariakov & Verwichte 2005), at least in the linear limit and in a homogeneous background plasma. A thorough understanding of Alfvén waves is essential because they could be a part of the solution to the major problems of heliophysics, such as the solar coronal heating and wind acceleration (Uchida & Kaburaki 1974; Ofman 2010). Recent theoretical research revealed that Alfvén waves can carry enough energy to heat the solar corona (Yang & Xiang 2016). However, the details of the mechanism(s) of the thermal energy release related to their dissipation remain unknown. One potential candidate for that may be associated with ion–neutral collisions (Soler et al. 2017). Piddington (1956), Osterbrock (1961), and Haerendel (1992) were the first to study ion–neutral collisions, but they did not find that this interaction affects the chromospheric temperature. Ballester et al. (2018) showed that ambipolar diffusion leads to substantial chromospheric heating, and Zaqarashvili et al. (2013) derived a dispersion relation for two-fluid Alfvén waves and confirmed that the damping of Alfvén waves resulting from the ion–neutral collisions is quite significant. Khomenko (2017), based on a two-fluid model, stated that the presence of neutrals affects the solar atmosphere. The effect of ion–neutral interactions is expected to influence the energy balance of the chromosphere. Zaqarashvili et al. (2013) also confirmed that low- and high-frequency photospheric Alfvén waves might not reach the solar corona because ion–neutral collisions damp them very efficiently in the upper chromosphere. According to Song & Vasyliūnas (2011), the rate of Alfvén wave damping varies with magnetic field strength and wave frequency. For a strong magnetic field, wave damping is low. Low-frequency waves are also weakly damped, and so there is a chance to detect low-frequency Alfvén waves in the solar corona under the condition of a strong magnetic field.
Citation: Nakariakov & Verwichte 2005
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Recent observations of transit spectra of hot Jupiter atmospheres show limited spectral modulation due to H2O that has been largely interpreted as the indicator of the presence of aerosols (Barstow et al. 2016; Iyer et al. 2016; Sing et al. 2016; Pinhas et al. 2019). Whether these aerosols are condensate clouds of photochemical organic aerosols or other refractory materials remains unknown. Although thermochemical equilibrium models predict the formation of condensate clouds with various composition in these hot atmospheres (Lecavelier Des Etangs et al. 2008; Lee et al. 2015; Parmentier et al. 2016), recent laboratory works highlighted that photochemistry could strongly affect the composition of exoplanet atmospheres and lead to the formation of aerosols in a variety of conditions, including the ones encountered in hot Jupiters (Hörst et al. 2018; Fleury et al. 2019; He et al. 2019, 2018a, 2018b). These photochemical aerosols could represent another source of opacity to explain some of the observed transit spectra of hot Jupiter atmospheres, e.g., of HD 189733 b (Lavvas & Koskinen 2017). On the other hand, the bulk elemental ratio can also drastically affect the molecular composition of these atmospheres. In the external layers (region with pressure 1 bar) of atmospheres with temperatures higher than 1000 K, carbon preferentially bonds with oxygen to form CO, and the excess of oxygen bonds with hydrogen to form H2O when the C/O ratio is 1. At a higher CO ratio ≥ 1, CO remains an abundant species but the water mixing ratio decreases (Lodders & Fegley 2002; Moses et al. 2013; Venot et al. 2015; Heng & Lyons 2016; Tsai et al. 2017; Goyal et al. 2018; Drummond et al. 2019). For this reason, another explanation for the low spectral modulation due to water observed in some hot Jupiter atmospheres is that these atmospheres have low H2O abundances presumably reflecting high C/O ratios (Madhusudhan et al. 2011; Madhusudhan 2012). However, the existence of such “carbon-rich” exoplanets continues to be debated. The first analysis of the hot Jupiter WASP-12b observations suggested a C/O ratio > 1 (Madhusudhan et al. 2011), but another study found a C/O ratio  1 using another approach (Kreidberg et al. 2015), leaving the question of the C/O ratio in WASP-12b’s atmosphere open. In addition, a recent survey suggests that the carbon enrichment of hot Jupiter atmospheres compared to their host stars may be common, but uncertainties on C/O measurements in exoplanet atmospheres are large and prevent a firm conclusion from being reached (Brewer et al. 2017).
Citation: Hörst et al. 2018
Citation function: Compare/Contrast; Compare/Contrast
On the basis of the 1.3 mm continuum image made from the SMA archival data, Nakamura et al. (2012) found conspicuous substructures inside a prestellar core in the Oph B2 region, which had been previously identified as a single core (B2-N5) in single-dish molecular line (N2H+ (J = 1−0)) observations (Friesen et al. 2010). The substructures consist of several small condensations, and their typical mass and size are around 0.05 M⊙ and 500 au, respectively. The mass is comparable to or larger than the local Jeans mass of 0.04 M⊙, and thus the self-gravity of the condensations appears to play an important role in their dynamics. Similarly, Kamazaki et al. (2001) found small condensations of 1000 au scale inside the SM1 core in the Oph A region based on 3 mm dust continuum observations with the Nobeyama Millimeter Array (see also Nakamura et al. 2012; Friesen et al. 2014). The masses of the condensations are around 0.01−0.1 M⊙, comparable to or larger than the local Jeans mass. Recently, Kirk et al. (2017) carried out comprehensive survey observations with ALMA Band 3 toward 60 dense cores that were identified in Oph with SCUBA at the James Clerk Maxwell telescope (JCMT). They found 38 compact emission structures of ∼100 au size within the dense cores. On the other hand, on the basis of 3 mm continuum observations with CARMA, Schnee et al. (2010) found no significant substructures inside prestellar cores in Perseus (see also Dunham et al. 2016), although their target cores are in relatively isolated environments. These observations suggest that internal structures of prestellar cores and their physical properties may strongly depend on cloud environment. It still remains unclear, however, whether or not they are really different between clustered environments and isolated environments. As a step toward a more comprehensive understanding of the star formation process, it is important to characterize better the substructures inside dense cores in cluster-forming regions. In the present paper, we further investigate the internal structures of the Oph B2 region using Cycle 2 ALMA data at a spatial resolution of ∼3″, corresponding to ∼410 au at Oph distance.
Citation: Friesen et al. 2014)
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