adsabs/FOCAL huggingface dataset:
Semi-analytical disc models indicate that the frequency of giant planets must increase with the mass of the host star between 0.2–1.5 M⊙ (Ida & Lin 2005; Kennedy & Kenyon 2008). However, this trend is expected to decrease above 1.5 M⊙ due to a smaller growth rate, longer migration time-scale, and shorter lifetime of the protostellar disc (Reffert et al. 2015). Looking for planets around main sequence stars more massive than the sun can help shed some light on this aspect. These stars have few spectral lines for Doppler measurements and are often broadened by the rapid rotation of the star. This has been the reason for RV surveys to have traditionally targeted slow rotating FGK type stars. However, as rapidly rotating stars evolve off the main sequence they slow down considerably and become much cooler making it relatively easy to search for planets around them. This fact was exploited by dedicated planet searches around intermediate mass sub-giants leading to dozens of planet discoveries (Johnson et al. 2007, 2010a, 2011). An important result obtained from the survey of giant stars at the Lick observatory pointed out that the occurrence rate peaks at a stellar mass of $1.9^{+0.1}_{-0.5}$ M⊙. However, many of the discovered planets around evolved stars were found at large orbital separations (Hatzes et al. 2003; Fischer et al. 2007; Robinson et al. 2007; Johnson et al. 2008). This is not a surprise as star–planet interaction is largely governed by tidal forces. When the stellar rotational period is longer than the planet orbital period, the star experiences spinning up, leading to orbital decay. Synchronization and circularization of orbit occurs in systems where the total angular momentum exceeds a critical value. When this total angular momentum is small enough, the orbit of the planet can continue to shrink and be engulfed by the host star. This phenomenon entirely depends on the dissipation time-scales for the star (Mazeh 2008, and references therein). The role of tidal forces becomes increasingly important in the context of host stars being in an evolved state. There is a higher chance of the planet being destroyed by the evolved star (Kunitomo et al. 2011; Schlaufman & Winn 2013). However, there is no obvious way to estimate these tidal dissipation forces. The circularization time-scale for such planets can be used to quantify tidal dissipation inside planets (Hansen 2010; Socrates et al. 2012). Most of the discovered hot Jupiters with periods less than 3 d are found to be on circular orbits. We calculate the circularization time-scales for the orbit of TOI-1789 b, which is τcir  = 0.08 Gyr (for QP = 106, equation 3 of Adams & Laughlin 2006). This is less than the age of the star as calculated from our work (Section 3.3.1).
Citation: Johnson et al.; 2011
Citation function: Background
While gas cooling and stellar feedback can transform dark matter cusps to cores, it is energetically challenging for this process to create large dark matter cores (typically >500 pc) in the very smallest ‘ultra-faint’ dwarfs, since they form so few stars (M*  105 M⊙; Peñarrubia et al. 2012; Garrison-Kimmel et al. 2013; Di Cintio et al. 2014; Maxwell, Wadsley & Couchman 2015; Oñorbe et al. 2015; Tollet et al. 2016). However, smaller dark matter cores may still form inside the half-light radius (R1/2 ∼ 20–200 pc for UFDs2), where the gravitational potential fluctuations are strongest (e.g. Oñorbe et al. 2015; Read et al. 2016). Whether this is expected to happen in a ΛCDM cosmology remains an active area of debate. Most studies to date find that cusp-core transformations are challenging at the likely mass-scale of UFDs (M200c ∼ 109 M⊙; M* ∼ 105 M⊙; e.g. Chan et al. 2015; Wheeler et al. 2019a; Gutcke et al. 2022). However, there are some notable exceptions. In recent work, Orkney et al. (2021) studied cusp-core transformations for UFDs drawn from the ‘Engineering Dwarfs at Galaxy formation’s Edge’ (EDGE) simulation project (with a mass, baryonic mass, and spatial resolution of 120 M⊙, 20 M⊙, and 3 pc, respectively, sufficient to resolve dark matter cores in UFDs larger than ∼20 pc). They found that UFDs can lower their inner dark matter density by up to a factor ∼2 through a combination of early heating due to star formation, followed by late-time heating from minor mergers. While none of their simulated dwarfs formed a completely flat central core, their small sample size left open the possibility that this combination of mechanisms could form flatter cores for some rarer assembly histories. It is also important to note that dark matter core formation on these mass scales is very sensitive to small changes in the star formation and feedback modelling. Pontzen et al. (2021) showed that a small increase in variability of the star formation rate on time-scales shorter than the local dynamical time is sufficient to form a full dark matter core in an ultra-faint, without significantly altering its stellar mass. As such, the question of whether complete dark matter core formation is expected in some or all UFDs in ΛCDM remains open.
Citation: Gutcke et al. 2022
Citation function: Compare/Contrast
Ulysses was launched on 1990 October 6 and orbited the Sun with a latitude varying from −80° to 80° and a solar distance ranging from ∼1 au to ∼5 au (Heber et al. 2009). The Kiel Electron Telescope (KET) on board Ulysses measured electrons in the energy range from ∼3 MeV to above 300 MeV, and protons and helium nuclei in the energy range from ∼5 MeV/nuc to above 2 GeV/nuc (Simpson et al. 1992). The data of the KET coincidence channel K12, which measures protons with energy in 0.25–2.0 GeV, has been used to study the GCR modulation outside the solar ecliptic plane in many works (see, e.g., Ndiitwani et al. 2005; Vos & Potgieter 2016; Boschini et al. 2017a). However, different works used different mono-energetic bins to represent this channel, e.g., 1.08 GeV (Rastoin et al. 1996), 2.5 GV (i.e., 1.73 GeV, Ndiitwani et al. 2005; Heber et al. 2009; Manuel et al. 2014), and 2.2 GeV (Boschini et al. 2017a). As the KET observations are integrated over a large energy interval (de Simone et al. 2011), it is perhaps better to weight the model results of several energy bins with the Ulysses response function and then combine them (Boschini et al. 2017a). Heber et al. (2009) obtained the 1 au equivalent count rates for this channel by correcting the proton intensity for the global spatial gradients of GCR protons, and the 1 au equivalent GCR proton flux can be obtained with the corresponding response factor. In addition, the precise cosmic-ray spectra measured by the PAMELA instrument can help us to roughly estimate the effective energy of the KET coincidence channel K12. In Figure 8, the black solid line shows the monthly averaged 0.25–2.0 GeV proton flux observed by Ulysses. Note that the original daily count rates from the Ulysses Final Archive (ufa.esac.esa.int/ufa) are divided by the corresponding response factor to obtain the proton flux. The dash-dotted line represents the 1 au equivalent 0.25–2.0 GeV proton flux, and the relevant count rates are digitized from Figure 5 in Heber et al. (2009). The 1.2 GeV proton flux observed by SOHO/EPHIN (Kühl et al. 2016) and PAMELA (Adriani et al. 2013) is shown as magenta triangles and green circles, respectively. The 1 au equivalent GCR proton flux roughly matches the 1.2 GeV GCR proton observations, and 1.2 GeV can be used to represent the Ulysses/KET channel K12. Therefore, we compute the 1.2 GeV proton flux along the trajectory of Ulysses to compare it with the Ulysses K12 measurements.
Citation: Manuel et al. 2014
Citation function: Differences; Differences
Theoretically, coronal loops are made up of magnetically confined denser and hotter plasma, and can thus support modes of oscillations (Aschwanden 1987; Roberts 2008). These oscillations are closely related to the physical properties of their host coronal loops and may open a new window for detecting the inhomogeneous corona (Nakariakov & Verwichte 2005; Yuan & Van Doorsselaere 2016a; Li & Liu 2018). Theories of magneto-acoustic oscillations in coronal loops have indicated that the fast kink mode possesses both a unique dispersion relation and zero asymptotic dispersion for a long-wave approximation, and could provide a potential diagnostic tool for determining physical properties of the inhomogeneous corona (Edwin & Roberts 1983; Roberts et al. 1984). Due to the specific role of coronal loops’ fast kink oscillations in coronal seismology, the proposal of using the fast kink oscillations to diagnose the physical properties of their host coronal loops has attracted a great deal of attention since it was first introduced (Roberts et al. 1984). Along with an increasing number of observational cases, the study of fast kink oscillations in coronal loops has been significantly advanced (Aschwanden et al. 1999; Nakariakov et al. 1999; Verwichte et al. 2004; Aschwanden & Schrijver 2011; Wang et al. 2012; Jain et al. 2015; Yuan & Van Doorsselaere 2016b; Shen et al. 2017; Pascoe et al. 2018; Shen et al. 2018; Nechaeva et al. 2019; Anfinogentov & Nakariakov 2019). Numerous detailed observations, on the other hand, have also brought new challenges to the current theory (De Moortel & Brady 2007; Guo et al. 2015; Li et al. 2017; Su et al. 2018; Li et al. 2019). Among these challenges, a prominent one is the increasingly presented oscillating coronal loops whose frequencies show significant changes, which is also known as the frequency drift (Su et al. 2018). It indicates that the frequency drift for the oscillation in fast kink mode can occur in a quiet loop (Su et al. 2018) or a quiet fiber (Li et al. 2018). It also indicates that the brightness of the oscillating structures undergoes a significant change during the occurrence of the frequency drift, which may imply changes in the loop’s thermal properties. A detailed investigation present in Table 1 and Figure 5 of Su et al. (2018) indicates that the period of fast kink oscillation has an ≈25% increase corresponding to an ≈40% increase in the loop’s density. A decrease in the period of fast kink oscillation is also observed by Li et al. (2018). It is also worth noting that the oscillation reported by Li et al. (2018) shows a significant increase of the amplitude as the period decreases, which is very unusual.
Citation: Jain et al. 2015
Citation function: Background
The newly discovered arcs and new spectroscopic redshifts have been incorporated into updated HFF+ versions of the Abell 2744 and MACSJ0416.1-2403 lensing models (Table 5); many of these have 





 (see Figure 8 for a comparison of the arc redshift distributions adopted by the pre-HFF and new HFF+ lensing models; Cypriano et al. 2004; Okabe & Umetsu 2008; Zitrin et al. 2009; 2013; Okabe et al. 2010a, 2010b; Merten et al. 2011; Christensen et al. 2012; Mann & Ebeling 2012; Jauzac et al. 2014; Lam et al. 2014; Richard et al. 2014; Balestra et al. 2015; Diego et al. 2015; Grillo et al. 2015; Jauzac et al. 2015; Rodney et al. 2015; Wang et al. 2015; Kawamata et al. 2016). The incorporation of these new multiple image systems often results in a reduction in the statistical uncertainty in the galaxy magnifications for a given model. All of the public HFF lensing models provide a range of possible realizations from which the statistical uncertainty of a given model set may be calculated (typically 100 but no fewer than 30). We plot the cumulative distribution of the galaxy magnification uncertainties σ(model)/





, for the galaxies and photometric redshifts provided by the ASTRODEEP catalogs (Merlin et al. 2016; Castellano et al. 2016a) for Abell 2744 (Figure 9) and MACSJ0416.1-2403 (Figure 10). Generally, the statistical uncertainties are reduced for the models computed with the new HFF data sets, with more dramatic reductions for the methods that rely strongly upon the strong-lensing constraints. The parametric methods (CATS, Sharon, Zitrin, GLAFIC) report median statistical magnification errors of 0.2%–5%, while the non-parametric methods (Bradac Williams, Diego) report median statistical magnification errors of 2%–11% for the post-HFF calculations (green curves), versus 2%–22% and 2%–17% respectively for pre-HFF models (blue curves). (We note that the statistical errors for the MACSJ0416.1-2403 Bradac post-HFF models (Hoag et al. 2016) included additional uncertainties due to the photometric redshift uncertainties of the multiple images. These were not included in the pre-HFF Bradac model, and thus may explain why the post-HFF statistical errors are larger for this model.)
Citation: Grillo et al. 2015
Citation function: Uses
Efforts have been made to model the signals caused by different sources of stellar variability within the RV time series (e.g., Tuomi et al. 2013; Rajpaul et al. 2015; Davis et al. 2017; Simola et al. 2019). Several solutions have been successfully proposed in order to deal with stellar oscillations and granulation phenomena, such as: calculating stellar evolution sequences (e.g., Christensen-Dalsgaard et al. 1995); fitting a two-level structure tracking (TST) algorithm based on a two-level representation of granulation (Del Moro 2004); using daytime spectra of the Sun in order to measure the solar oscillations (e.g., Kjeldsen et al. 2008; Lefebvre et al. 2008); and characterizing the statistical properties of magnetic activity cycles focusing on HARPS observations (e.g., Pepe et al. 2011; Dumusque et al. 2011b). However, properly modeling the other sources of stellar activity remains extremely challenging (e.g., Nava et al. 2019). In the present work, we deal with the cross correlation function (CCF) that is derived from the stellar spectrum (e.g., Hatzes 1996; Hatzes & Cochran 2000; Fiorenzano et al. 2005). As it is well known, the CCF barycenter estimates the RV of the star. The asymmetry and the full width at half maximum (FWHM) of the CCF give a strong indication for stellar activity, meaning that variations in RV are caused by active regions rather than by an exoplanet (e.g., Hatzes 1996; Queloz et al. 2001; Boisse et al. 2011; Figueira et al. 2013; Simola et al. 2019). Several solutions have been successfully proposed for mitigating stellar activity perturbations when working with RV measurements, including: decorrelating RV data against activity indicators such as log ${{R'}_{{\rm{HK}}}}$R′HK (e.g., Wilson 1968; Noyes et al. 1984) or Hα (e.g., Robertson et al. 2014); modeling stellar activity by fitting Gaussian processes (GPs, Rasmussen & Williams 2005; Haywood et al. 2014; Rajpaul et al. 2015); or moving averages (e.g., Tuomi et al. 2013) to the RV data. A common statistic employed for identifying changes in the shape of the CCF is the bisector span (e.g., Hatzes 1996; Queloz et al. 2001).
Citation: Lefebvre et al. 2008
Citation function: Background; Background
The source shows some preferred tracks in its movement along the HID. Dips occur as the source is in the SUL regime, either from SUL2 or from SUL3 regions; ingress and egress times populate the D3 and D2 spectra, whereas the D1 spectrum comes from the time segments characterized by the lowest count rate (deep dip). We noted that during the longest dips, the source occasionally switched to harder flaring episodes in D2 state. Most dips show the following pattern: SUL2/SUL3 → D3 → D2 → D1 → D2 → D3 → SUL2/SUL3. The passage from the SSUL to SUL state is only occasionally observed. We selected seven regions on the HID and extracted the corresponding spectra. For the normal branch spectra, the continuum emission is well fitted with two thermal components: a soft blackbody and a disc multicolour blackbody. The blackbody emission represents the bulk of the emitted power in each spectrum. It is likely due to strong reprocessing in an optically thick environment formed at Rsph, where disc inflow is mainly inflated by internal radiation pressure. This picture is consistent with the observed low temperature, large radius, and super-Eddington luminosity (see Shen et al. 2015; Soria & Kong 2016; Urquhart & Soria 2016; Guo et al. 2019). The hotter component, which we fitted using a diskbb, dominates the emission above 2 keV. Although its origin is still debated, it might come from internal hard X-ray emission, which has been inefficiently reprocessed, or simply scattered along our line of sight. Alternatively, it can be continuum emission (bremsshtrahlung and/or Comptonization) from an extended optically thin plasma where the emission lines are produced, or a tail of the blackbody emission which has been Compton upscattered in a coronal environment around the photosphere. In addition to the continuum, we added multiple emission and absorption lines derived by the combined averaged PN / RGS analysis to mimic the emitting and absorbing plasmas found in Paper I. Their shifts suggest different Doppler motions in several states as a consequence of a velocity field which changes depending on the launching site and on the geometry of the system. This seems supported by correlations among the parameters of the emission lines and the underneath hard X-ray flux (see Table 3). However, given the limited energy resolution of the EPIC we are not able to distinguish between a varying ionization state of the plasma, the effect of a different line broadening for the different ionized species, a complex absorption/emission pattern. A thorough study of the lines is left to a dedicated forthcoming study. For consistency the same spectral model has been applied also to the dipping branch spectra, although the physical conditions in and out of the dips might be different.
Citation: Guo et al. 2019
Citation function: Similarities
xstar predicts O to be progressively ionized through all its possible ionization stages as the central UV-X-ray source is approached providing a natural explanation for the absence of broad forbidden ${[}{\rm O\, \small{III}}{]}$ emission lines. No $[{\rm O\, \small{II}}]\, {\lambda }\, {\lambda }$3727, 3729 emission lines appear in the HST spectra, consistent with the xstar model. Furthermore, both the observed reddening-insensitive ratio $[{\rm O\, \small{III}}]\, {\lambda }\, 5008/[{\rm O\, \small{III}}]\, {\lambda }$4960 and the value predicted by xstar are consistent with the theoretical value (Dimitrijevic et al. 2007). However, there are two notable discrepancies between the HST observations and the xstar model predictions. The first is that the $[{\rm O\, \small{III}}]\, {\lambda }$5008 emission line is observed to be 230 per cent brighter than predicted by xstar. This excess emission could be attributed to the extended source of $[{\rm O\, \small{III}}]\, {\lambda}$5008 seen in the nucleus of NGC 3327 (Arribas & Mediavilla 1994; Mundell et al. 1995; Delgado & Perez 1997; García-Lorenzo, Mediavilla & Arribas 2001; Walsh et al. 2008). However, the second discrepancy that xstar predicts the $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line to be ∼25 times brighter than the upper limit estimated in D13 is not as easily addressed as the first. On the one hand, the $[{\rm O\, \small{III}}]\, {\lambda }$5008/λ4364 ratio produced by xstar is entirely consistent with that expected (Osterbrock 1989), given the temperature ∼104 K and electron density ∼108 cm−3 in the O++ region. On the other hand, the observed $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line would have to be as bright as H γ in order to match the xstar prediction. Observationally, this seems unlikely, but the resolution of the ${\it {\it HST}}$ observations is insufficient to adequately resolve the two lines in question (D13). Equally disconcerting, however, is that contrary to commonly accepted wisdom, xstar predicts the $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line to be bright even though the electron density in the O++ region exceeds the critical density of the 1S0 level responsible for the transition by about one order of magnitude.
Citation: Delgado & Perez 1997
Citation function: Compare/Contrast
It is difficult to isolate the impact of mass and environment on the rate and timing of quenching. Mass quenching is more important at earlier times in the evolution of galaxies and may be more important in denser regions (e.g., Peng et al. 2010; Muzzin et al. 2012; Lee et al. 2015; Darvish et al. 2016, 2018; Kawinwanichakij et al. 2017). And in the case of powerful radio galaxies, which lie in over-dense environments, both gas-rich and gas-poor mergers likely play an important role in both the growth of the stellar mass and the black holes. Volonteri et al. (2015b) suggest that in the merger phase, the AGN dominates the bolometric luminosity but the accretion can be very stochastic (see also Gabor & Bournaud 2013; DeGraf et al. 2017). It appears that the galaxies in our sample with the highest star-formation rates all host very powerful AGN, and are potentially all advanced mergers, consistent with this picture. In fact, PKS 0529−549, which has one of the highest SFRs of all the galaxies in our sample, has a modest gas fraction of about 15%, a high star-formation efficiency (SFR/molecular gas mass), and has been transforming its gas into stars rapidly (Man et al., in prep.). The star formation efficiencies in the other radio galaxies with high SFRs also appear extreme (10–100 Gyr−1; Man et al., in prep.). But of course, that does not explain our results in themselves. Dubois et al. (2015), in a study using numerical simulations of the relative growth of SMBHs and their host galaxies, found that star formation may regulate the black hole accretion rate. During the most rapid, gas-rich phase of the growth of massive galaxies, it may be that a larger fraction of the gas in the ISM is not available to fuel the SMBHs, but is consumed via star formation (see DeGraf et al. 2017). As the gas fractions decline, the relative power of the AGN compared to that of the star formation increases, resulting in an increased star formation efficiency. Concomitantly, the increased star formation rate can then disperse the dense gas making it easier for the jets to drive vigorous and efficient outflows (Nesvadba et al. 2006, 2017).
Citation: DeGraf et al. 2017
Citation function: Background