adsabs/FOCAL huggingface dataset:
A number of ideas have been put forward to explain the formation and early evolution of the compact Kepler and radial velocity systems, which in cases such as Gliese 581 and HD 69830 appear to contain in excess of ∼30 M⊕ of solid material within a few tenths of an au (Lovis et al. 2006; Udry et al. 2007). This concentration of solids close to the star led to classical core accretion models combined with disc-driven migration being developed using population synthesis codes (Alibert et al. 2006). More recent population synthesis calculations that also include prescriptions for planet–planet interactions have also been presented (Ida & Lin 2010). N-body simulations, combined with either hydrodynamic simulations or analytic prescriptions for migration and eccentricity/inclination damping of planetary growth, have also been used to examine the origins of such systems (Cresswell & Nelson 2006, 2008; Terquem & Papaloizou 2007; McNeil & Nelson 2009, 2010; Hellary & Nelson 2012; Cossou, Raymond & Pierens 2013; Coleman & Nelson 2014; Hands, Alexander & Dehnen 2014). A common outcome of these N-body simulations is the formation of resonant convoys of planets in the presence of convergent migration, an outcome that is not reflected in the Kepler systems. Various ideas have been put forward to explain why the resonances may be unstable, including tidal eccentricity damping followed by separation of the resonance for short-period systems (Terquem & Papaloizou 2007), stochastic migration due to local turbulence (Adams, Laughlin & Bloch 2008; Rein & Papaloizou 2009; Rein 2012) – a process that is likely to only operate close to the star where the disc can be thermally ionized (Umebayashi & Nakano 1988; Desch & Turner 2015), resonance breaking due to overstable librations (Goldreich & Schlichting 2014), orbital repulsion due to non-linear spiral wave damping in planet coorbital regions (Podlewska-Gaca, Papaloizou & Szuszkiewicz 2012; Baruteau & Papaloizou 2013).
Citation: Cresswell & Nelson; 2008
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Cosmic H i gas density ($\Omega _{\rm {H}\,\small {I}}$) as a function of redshift (bottom axis) and lookback time (top axis). All measurements are corrected to the same cosmological parameters. Some DLA measurements adopted a different definition of the cosmic H i density taking into account neutral gas abundance (Ωgas) including helium or contributions from Lyman-α absorbers with lower column density ($\log N({\rm H\,{\small {I}}}) < 20.3$). We have corrected all measurements to a consistent definition of $\Omega _{\rm {H}\,\small {I}}$. The large red star shows the $\Omega _{\rm {H}\,\small {I}}$ measurement from this work. The small black square and triangle at z ∼ 0 are the HIPASS and ALFALFA 21-cm emission measurements by Zwaan et al. (2005) and Martin et al. (2010), respectively. The red diamonds are from the Parkes telescope with an H i stacking technique (Delhaize et al. 2013). The open circle and square are the results from the Arecibo Ultra Deep Survey (AUDS) (Freudling et al. 2011; Hoppmann et al. 2015). Two right-pointing triangles are measured using the WSRT and H i stacking technique by Rhee et al. (2013). The pink triangle is measured by Lah et al. (2007) applying the GMRT 21-cm emission stacking. The green hexagon denotes the H i stacking measurement for the COSMOS field using the GMRT (Rhee et al. 2016). The black arrow line is the upper limit constrained using the GMRT H i stacking (Kanekar et al. 2016). The closed and open diamonds, left-pointing triangle, big circle, open triangles, open diamonds and small circles are damped Lyman-α measurements from the HST and the SDSS by Rao et al. (2006, 2017), Neeleman et al. (2016), Prochaska et al. (2005), Noterdaeme et al. (2009), Noterdaeme et al. (2012) and Bird et al. (2017), respectively. The downward triangles and big square at high redshift of z > 2 are ESO UVES and Gemini GMOS measurements of DLAs by Zafar et al. (2013), Crighton et al. (2015), respectively. The black line with a shaded area shows a weighted fit of all $\Omega _{\rm {H}\,\small {I}}$ measurements and its 95 per cent confidence interval. The solid blue line shows the semi-analytic model prediction of Kim et al. (2015), and the dashed red line shows the mufasa (Davé et al. 2017) simulation.
Citation: Freudling et al. 2011
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Five XRT observations got only low exposure (i.e., much less than 1 ks) and the X-ray source was therefore undetected in these data sets. Surprisingly, we also found that the source was undetected in a “deep” observation taken on 2015 August 19 with an exposure time of about 1.6 ks (Table 2). Within a 47″ radius circular region centered at the source position (corresponding to 90% of the encircled energy fraction of XRT at 1.5 keV; Moretti et al. 2005), only one photon (which is located near the edge of the region) was detected in this 1.6 ks observation. Even assuming that only this event is from the source, the inferred count rate is much lower than the measurements in 2010 and 2015–2017; for example, seven source counts would have been detected in a 1.6 ks observation with the count rate of 4.4 × 10−3 cts s−1 measured five days later (Table 2). Using a Bayesian approach (Kraft et al. 1991), we computed 95% upper limits for all the nondetections. As expected, the upper limits for data with 1 ks are not very much constraining (i.e., a few ×10−2 cts s−1, while the average count rate of the four individual detections is about 4 × 10−3 cts s−1). The upper limit for the 1.6 ks data is deeper (i.e., 8.7 × 10−3 cts s−1), but still insufficient to clarify whether the low-count-rate measurement is physically or statistically based. For a deeper constraint, we combined all the six XRT observations, and the X-ray source can be marginally detected in the stacked image with 





 cts s−1. Although this marginal detection shows a ∼50% decrease on flux in the period from 2015 February 04 through August 19, the variability is not statistically significant (i.e., less than 2σ). To check whether this variability was seen at other frequencies, we performed a Fermi-LAT analysis with the data collected between 2015 February 04 and August 19, and the γ-ray flux (100 MeV–100 GeV) did not vary significantly. In UV, there are some Ultraviolet/Optical Telescope (UVOT) images taken simultaneously with the XRT observations. Although the UVOT magnitudes (obtained by aperture photometry using the uvotsource task in HEAsoft v6.22) significantly changed over time (Table 2), this was due to the orbital modulation (Figure 3; will be discussed in the coming sections).
Citation: Kraft et al. 1991
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There are only three exceptions from the picture discussed above. In Fig. 2 we see that the model with the smallest number of objects, $N=40\, 000$, does not show abrupt dissolution. This model is characterized with the shortest initial relaxation time (among models considered in this paper), about 1.6 Gyr, and the smallest number of retained BHs, about 30. In such a situation, according to Breen & Heggie (2013), BHs are quickly kicked out from the system and BHS cannot survive for a long time. So, the cluster evolution is not governed by strong energy generation by BHs in BHS. In Fig. 3 we can see that the model with W0 = 3 dissolves extremely fast and the model with W0 = 9 does not show the abrupt dissolution feature. The very fast dissolution of tidally filling models with low King model concentration was already extensively discussed in the literature (e.g. Weinberg 1993; Fukushige & Heggie 1995; Whitehead et al. 2013; Contenta et al. 2015). The dissolution of such clusters is controlled by very strong initial mass-loss powered by stellar/binary evolution. Relaxation process is not important at all. The situation is much different for the model with W0 = 9. As it can be seen from Fig. 5, the cluster enters the core-collapse phase, so it has to be controlled by the relaxation process. The difference between the model with W0 = 6 and the model with W0 = 9 is connected with the rate of BHS evolution. Model W0 = 9 is initially much denser, so its half-mass radius and half-mass relaxation time are shorter than for the W0 = 6 model. Nevertheless, for both models the BH mass segregation ends nearly at the same time, about 4 Gyr. At that time, the models contain about 50 BHs and 160 BHs for W0 = 9 and W0 = 6, respectively. According to Breen & Heggie (2013) the evolution of BHs is controlled by the energy flow through the cluster half-mass radius, which is proportional to Eb/Trh ≈ GM/Rh/Trh, where Eb is the cluster binding energy. Models with W0 = 9 have smaller half-mass radius and half-mass relaxation time than models with W0 = 6, so the energy demand is larger for this model and leads to a much faster burning out of BHs – larger number of dynamical interactions leading to BH removal from the system. After the time of BH mass segregation (about 4 Gyr) the model W0 = 6 contains enough BHs to form a BHS and enters the phase of balanced evolution (Breen & Heggie 2013). Contrarily, the model with W0 = 9 has too small a number of BHs and continues to remove BHs quickly to support the needed energy flow. Finally, it enters the phase when other energy sources connected with ordinary binaries take over and the cluster enters the core-collapse and then core-bounce phases. It is important to note that the tidal field plays an important role in the above picture. The less concentrated model has larger Rh and loses more mass, so it is easier for the BHS to provide the needed energy to support the cluster structure. This phase of evolution ends when the cluster suddenly loses its virial equilibrium.
Citation: Weinberg 1993
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Since its launch in March of 2009, the NASA Kepler mission has been monitoring ∼160,000 stars in order to detect transiting extrasolar planets with high relative photometric precision (∼20 ppm in 6.5 hr, Jenkins et al. 2010). In 2013 May, the Kepler main mission ended with the failure of a second reaction wheel; however, the first four years of Kepler data have led to a wealth of planetary discoveries with a total of 4706 announced planet candidates13

13

http://exoplanetarchive.ipac.caltech.edu/ as of 2015 November 11.
 (Borucki et al. 2010, 2011; Batalha et al. 2013; Burke et al. 2014). The confirmed and candidate exoplanets typically have orbital periods shorter than 1000 days because at least three detected transits are needed for identification by the automated Transit Planet Search algorithm. Therefore, transiting exoplanets with periods longer than ∼1000 days are easily missed. The detection of short-period planets is further favored because the transit probability decreases linearly with increasing orbital distance. For these reasons, estimates of the statistical occurrence rate of exoplanets tend to focus on orbital periods shorter than a few hundred days (e.g., Dong & Zhu 2013; Fressin et al. 2013; Petigura et al. 2013). Radial velocity (RV) techniques also favor the detection of shorter period orbits. While gas giant planets have been discovered with orbital periods longer than a decade, their smaller reflex velocity restricts detection of sub-Neptune mass planets to orbital radii less than ∼1 AU (Lovis et al. 2011). In principle, astrometric observations favor longer period orbits; however, high precision needs to be maintained over the correspondingly longer time baselines. For shorter periods, the planets need to be massive enough to introduce a detectable astrometric wobble in the star and Gaia should begin to contribute here (Perryman et al. 2001). Microlensing offers sensitivity to planets in wider orbits and has contributed to our statistical knowledge about occurrence rates of longer period planets (e.g., Gaudi 2010; Cassan et al. 2012) and direct imaging of planets in wide orbits is also beginning to contribute important information (Oppenheimer & Hinkley 2009).
Citation: Borucki et al. 2010
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Even though most of the energy released by a gamma-ray burst (GRB) is emitted during the prompt emission phase, the emission mechanism is still not understood. In order to determine the emission process typically, the low-energy spectral index, α, of the GRB spectrum is analysed. This analysis has not given a conclusive answer since the α-distribution is broad and has not been uniquely explained by a single emission process. For instance, the peak of the distribution has been used as an argument for synchrotron emission since it is close to the expected value. However, a large fraction (${\sim } 28{{\ \rm per\ cent}}$) was found to be inconsistent with the theoretical limit of −2/3 (‘line of death’; Preece et al. 1998; Ghirlanda, Celotti & Ghisellini 2002; Guiriec et al. 2015; Goldstein et al. 2016; Yu et al. 2016); and only specific physical scenarios remain plausible (large emitting radii and Lorentz factors of the flow, Beniamini & Piran 2013; Iyyani et al. 2016; Beniamini, Barniol Duran & Giannios 2018; Burgess et al. 2018). Alternatively, the spectral width or sharpness angle has been used as a tool to characterize the spectrum, which also take into account the high-energy spectral slope (Axelsson & Borgonovo 2015; Yu et al. 2015b). Around 80 per cent of the bursts were found to have fitted Band spectra that are narrower than what was expected for synchrotron emission. However, direct fitting with a synchrotron model decreases this fraction (Burgess 2017). Therefore, a firm conclusion based on the spectral width or sharpness angle alone cannot either be reached. Yet another approach has been to study the correlation between spectral parameters. A well-studied and strong correlation is the Golenetskii correlation (Golenetskii et al. 1983) which relates the instantaneous flux, F, and peak of the spectrum Epk (Kargatis et al. 1994; Borgonovo & Ryde 2001; Lu et al. 2012). Another prominent correlation is between Epk and α which is valid in a fraction of bursts (Crider et al. 1997; Kaneko et al. 2006). Both of these correlations show a variety of behaviours. In some individual pulses, the spectral parameters track each other, while in others the correlation is different during the rise and decay phases of the pulses. The variety of behaviours have complicated the search for physical explanations.
Citation: Iyyani et al. 2016
Citation function: Differences; Differences
Six out of the eight sources (75 per cent of the sample) show evidence for high obscuration by cold gas (column density NH≳ 1023 cm−2). The number of AGN with NH≳ 1024 cm−2 is in the range 2–4 when 90 per cent confidence errors on the column density are considered (see Table 4). Although our sample is definitely too small to make any strong conclusion in terms of statistical incidence of obscured AGN in dual systems, we note that the fraction of CT AGN in our sample of dual AGN is 25–50 per cent, i.e. higher than the fraction of hard X-ray selected CT AGN in isolated systems in a similar luminosity interval (BAT: 27 ± 4 per cent, Ricci et al. 2015, NuSTAR: ∼4 per cent, Marchesi et al. 2018). Recently, it has been suggested (Ricci et al. 2017) that AGN in multiple systems in a late stage of merging (with a projected distance d ≤ 10 kpc) are often type 2 AGN due to the huge amount of gas and dust obscuration channelled towards the nuclear region by the close galaxy encounter. Furthermore, in a sample of 44 UltraLuminous IR Galaxies (ULIRG) from the GOALS survey (Sanders et al. 2003) in the local universe, it has been demonstrated that the fraction of CT AGN in late mergers is higher (65$^{+12}_{-13}$ per cent) than in local isolated hard X-ray selected AGN (27$^{+4}_{-4}$ per cent, Ricci et al. 2017), while it is marginally higher than isolated AGN in early mergers (35$^{+13}_{-12}$ per cent). The increasing of the obscuration with the disturbed or interacting morphology of the galaxy host has been also observed up to higher redshift (up to z = 1.5) using X-ray Chandra data from deep surveys (Kocevski et al. 2015). These authors have analysed 154 heavily obscured AGN and compared their morphologies with control samples composed of moderately obscured and unobscured AGN. They found that the fraction of galaxies undergoing mergers is higher in AGN with NH above 3 × 1023 cm−2 with respect to unobscured AGN with NH  1022 cm−2 (7.8$^{+1.9}_{-1.3}$ versus 21.5$^{+4.2}_{-3.3}$ per cent). X-rays (Kocevski et al. 2015; Ricci et al. 2017) as well as more recent mid-IR colour selection (Satyapal et al. 2017) strongly suggest that AGN in late state of merging (10 kpc) are highly absorbed (but see also Ellison et al. 2017), with NH above 1024 cm−2. All these observational results suggest that CT AGN in dual systems correspond to a phase where the supermassive black hole is still accreting gas during an early stage of an interaction or merging.
Citation: Ricci et al. 2015
Citation function: Compare/Contrast
Table 2

Catalogue description.



Column
 Description







1
Source number


2−3
X-ray coordinates, right ascension and declination (epoch 2000.0)


4
Uncertainty of X-ray position [′′]. For XMM-Newton positions taken from Sturm et al. (2013c) the 1σ  error includes a systematic uncertainty of 0.5′′.


5
Origin of the X-ray coordinate (A: ASCA, C: Chandra, E: Einstein, I: Integral, N: XMM-Newton, R: ROSAT, S: Swift, X: RXTE). When no reliable position could be determined from the non-imaging RXTE collimator-instruments, a radius of 30′ for the position error indicates the size of the field of view.


6
Reference for source discovery.


7
Identification of optical counterpart with emission-line star from Meyssonnier & Azzopardi (1993). The negative number indicates a star found in the catalogue of Murphy & Bessell (2000).


8−15
Flags indicating different source properties. For their description see Table 3.


16
Confidence class (values 1−6, see Table 4).


17−18
Optical coordinates, right ascension and declination (epoch 2000.0) for the identified counterpart from Zaritsky et al. (2002), or – when not available there – from Massey (2002).


19−26
The Magellanic Clouds Photometric Survey (MCPS): U, error(U), B, error(B), V, error(V), I, error(I) [mag] from Zaritsky et al. (2002).


27−32
Colour indices U−B, error(U−B), B−V, error(B−V), V−I, error(V−I) [mag] derived from MCPS photometry.


33−34
Reddening-free Q-value (Q = U−B−0.72 × (B−V)) and error(Q) [mag].


35
Near-IR counterpart to the optical star from the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006).


36−41
Near-IR magnitudes with corresponding errors: J, error(J), H, error(H), K, error(K) [mag].


42−45
Near-IR colour indices, J−H, error(J−H), H−K, error(H−K) [mag].


46−53
Spitzer IRAC fluxes at 3.6, 4.5, 5.8 and 8.0 μm [mag] with respective errors (from the SAGE project, for a description see Meixner et al. 2006).


54
Angular distance between X-ray and optical position [′′].


55
Angular distance between optical and near-IR position [′′].


56
Neutron star spin period [s] inferred from X-rays.


57
Orbital period [days] (see flags for origin).


58−59
Maximum and minimum X-ray flux [erg cm-2 s-1] when available in the 0.2−10 keV band. Fluxes in the SMC XMM-Newton catalogue of Sturm et al. (2013c) are given for the 0.2 to 4.5 keV band. To convert them into the 0.2−10 keV band, we multiplied them by a factor of 2.6 assuming a standard power law with photon index 0.9 (Haberl et al. 2008) and a column density of 1021 cm-2 (solar abundance). For Swift XRT count rates we used a flux conversion factor of 1.1 ×  10-10 erg cm-2 cts-1


60
Flag for minimum flux: 1 for a non-detection with an upper limit; −1 when unknown; 0 for detection.


61−62
References for maximum and minimum X-ray flux.


63
X-ray variability factor (ratio of maximum to minimum flux).


64
Equivalent width of the Hα line [Å] (minimum value if more than one measurement is available).


65
Maximum equivalent width of the Hα line [Å].


66
References for the Hα measurements.


67
Comments with key references.
Citation: Massey (2002)
Citation function: Uses
Our basic model for acceleration is shown in Figure 1. The left panel shows a cartoon of the model for acceleration, transport, and radiation at the X-reconnection site in the corona. Acceleration can be by the second order Fermi (or stochastic) acceleration by turbulence (see, e.g., Petrosian & Liu 2004), by a standing shock produced by the downflow from the X-reconnection site (see, e.g., Guo & Giacalone 2012), or in the merging of islands shown to arise in PIC simulations during reconnection (Drake et al. 2006, 2013; Le et al. 2012; Oka et al. 2010). The latter model has been invoked as a possible mechanism in the downstream of the CME shock (Le Roux et al. 2015; Zank et al. 2015). The right panel shows a similar cartoon for acceleration in the environment of the CME. The (red) rectangles show the cross section of the box within which particles are accelerated. For our purposes here the relevant parameters of the acceleration and transport of particles are the momentum (p) and pitch angle cosine (μ) diffusion rates 





 and 





, and direct energy gain 





 and loss 





 rates, where E is the particle energy.1

1
Here and in what follows we neglect the effects of the third diffusion coefficient 





, which are generally small (see Schlickeiser 1989; Petrosian & Liu 2004).
 For an isotropic pitch angle distribution the evolution of the volume-integrated energy spectrum, 





, is governed by the so-called leaky-box model kinetic equation (see Petrosian 2012)
1





where 





 is a source term, 





, and the direct acceleration coefficients for stochastic and shock accelerations are given as
2





Here 





 is the shock velocity, 





 is the coefficient of spatial diffusion (along the field lines), 





, 





 is the Lorentz factor, and ζ depends on the shock compression ratio and other factors (see, e.g., Steinacker et al. 1988). The energy-loss rate at low energies (for both electrons and ions) is dominated by Coulomb collisions with background particles (mainly electrons).2

2
Coulomb collisions also cause pitch angle scattering, and therefore spatial diffusion along the field lines, with a rate that is comparable to the energy-loss rate at nonrelativistic energies but decreases as 





. They also cause energy diffusion, which is negligible in the cold target case (





), but can be comparable to the energy-loss rate for a hot target as E approaches kT (see Petrosian & Kang 2015), where T is the background plasma temperature.
 At higher energies (not relevant for the discussion here) inelastic interactions (synchrotron, inverse Compton, and bremsstrahlung) for electrons and (nuclear line excitation, neutron and pion productions) for ions become important.
Citation: Zank et al. 2015
Citation function: Uses