adsabs/FOCAL huggingface dataset:
In spite of the above considerations, we can appreciate a statistical trend of increasing spectral index (i.e. softer average spectra) while moving through the HSP, ISP, and LSP classes. This result, summarized in Table 1, is consistent with our natural expectations and it supports the reliability of spectral decomposition techniques to extract and analyse the non-thermal emission of these objects. As a consequence, we can investigate the relation existing between the non-thermal component of the optical spectra and the corresponding γ-ray spectra. It is well known that the γ-ray photon index correlates with the position of the synchrotron peak in frequency (e.g. Ajello et al. 2020). So far, however, the presence of thermal contributions in the optical domain has made it very difficult to perform direct comparisons of spectral indices between the optical and the γ-ray energy domains. From our sample, instead, we can infer a substantial degree of linear correlation between the optical spectral index αopt and its γ-ray counterpart αγ. The situation, summarized in Fig. 4, is well represented by a linear relation described by
(2)$$\begin{eqnarray}
\alpha _{\rm opt} = (0.862 \pm 0.110) \cdot \alpha _{\rm \gamma} - (0.818 \pm 0.445),
\end{eqnarray}$$with weighted correlation coefficient R = 0.743 and null-hypothesis probability p0 = 2.71 × 10−10 (i.e. statistical significance at $6.3\, \sigma$ level), for the whole sample, and by
(3)$$\begin{eqnarray}
\alpha _{\rm opt} = (1.469 \pm 0.224) \cdot \alpha _{\rm \gamma} -(1.931 \pm 0.919),
\end{eqnarray}$$with weighted correlation coefficient R = 0.751 and null-hypothesis probability p0 = 1.54 × 10−8 (i.e. $5.7\, \sigma$), for the subset of 98 objects that were fitted with Δαopt ≤ 0.6 (corresponding to 3 times the systematic uncertainty estimated for the method). The observed correlations are particularly suggestive if compared to the situation that is obtained by simply fitting a power-law model to the optical data without accounting for the host component (more details in Appendix C). Another interesting result, also presented in Fig. 4, is that the above correlation results in a situation where the power-law fits of the γ-ray spectra appear to be softer than the associated optical non-thermal emission by a value that lies mostly close to 1.0. This circumstance points towards a tight relationship between the population of particles that explain the non-thermal component of the optical spectra and the ones that are responsible for the γ-ray emission. The existence of a similar relationship between the observed spectral indices is a characteristic prediction of SSC-based models. However, the systematic difference between the optical and γ-ray energy regions requires further investigation to be properly interpreted.
Citation: Ajello et al. 2020
Citation function: Background; Compare/Contrast
Figure 1 shows that in the regions where activity was detected visually, i.e., Hapi, Seth, and Ma’at pits have lower (8–13%/100 nm) spectral slopes than the rest of the comet surface (13–22%/100 nm). In addition to those places, Seth alcoves, the wall of the large Anuket alcove, around the circular features, both clustered and isolated bright features (see Thomas et al. 2015b; Auger et al. 2015; Pommerol et al. 2015b, for definitions) show similar lower spectral slopes than the rest of the surface, even though there was no visual detection of activity features rising from them at the time of the observations used in this study4. This may be because the observing geometry was not suited for their detections during the observations. In the regions we investigated, the Hapi region displays the lowest spectral slopes 8–11%/100 nm (see also Fornasier et al. 2015) together with the isolated bright features (IBFs) and the clustered bright features in the Imhotep region. The locations of the bright features on the Imhotep image (image #4) are shown in Fig. B.4. According to the spectral slope values, the IBFs of Imhotep seem to be more similar to the Hapi region than the active pits of Seth and Ma’at regions. Active pits, alcoves, and the large alcove of Anuket have slope values of typically 10–13%/100 nm. The Ma’at region, which is located on the smaller lobe (head) of the comet, displays higher spectral slope values than the Seth region, which is located on the larger (body) lobe of the comet. In the investigated regions, the highest slope values are detected in the Imhotep region (see Fig. 1d). Here it should be mentioned that the comparison of spectral slopes is performed under the assumption of no spectral reddening between the phase angles of the images we investigated, although the spectral slopes show reddening by phase as presented in Fornasier et al. (2015). Unfortunately, the previous work does not cover all the phase angles of the images we investigated, but the spectral slope variation between 35–54° (Fig. 3 of  Fornasier et al. 2015) is small so that we can make this comparison. However, if we follow the linear trend of the phase reddening, for the image taken in 70.45° phase angle (image #4), the spectral slopes would vary from 15%/100 nm to 18%/100 nm in the observations we used.
Citation: Fornasier et al. (2015)
Citation function: Compare/Contrast
In past decades, single-dish submillimeter surveys have identified populations of massive, dusty star-forming galaxies at z > 1 (e.g., Casey et al. 2014). While these galaxies are rare even at Cosmic Noon (1  z  3) when the star formation activity in the universe peaks, their contribution to the cosmic star formation rate density (CSFRD) equals that of all optical and near-infrared selected galaxies combined (Madau & Dickinson 2014). However, at z > 3 the situation is much less certain. A tail of submillimeter-selected galaxies have been confirmed beyond z > 4, but they trace only the very tip of the star formation rate (SFR) distribution at early times (e.g., Cooray et al. 2014; Strandet et al. 2017; Marrone et al. 2018). The total contribution of dust-obscured star formation, and therefore the census of star formation in the early universe, is unknown. Despite the strongly negative k-correction allowing sources to be found to z = 10, the overwhelming majority of (sub)millimeter-selected galaxies continue to be confirmed at z  3 with Atacama Large Millimeter/submillimeter Array (ALMA) spectroscopy (Brisbin et al. 2017; Danielson et al. 2017). While some dusty galaxies have been discovered beyond z > 5 through gravitational lensing (Spilker et al. 2016; Zavala et al. 2018b), the lensing correction and selection effects make it challenging to establish their contribution to the CSFRD. Progress is hampered by the limited sensitivity and low spatial resolution of single-dish submillimeter observations and the difficulty of associating detections with counterparts in the optical–near-infrared (NIR). Ultradeep SCUBA surveys over moderate ∼100 arcmin2 are now pushing into the range of “normal” SFRs (several 100 M⊙ yr−1, main-sequence galaxies; e.g., Koprowski et al. 2016; Cowie et al. 2017, 2018) and extending to z > 4, but the analysis is often limited by the ability to identify counterparts at other wavelengths and derive accurate redshifts.
Citation: Marrone et al. 2018
Citation function: Background
As a class, the fastest stars in our Galaxy are expected to be hypervelocity stars (HVSs). These were first theoretically predicted by Hills (1988) as the result of a three-body interaction between a binary star and the massive black hole in the Galactic Centre (GC), Sagittarius A*. Following this close encounter, a star can be ejected with a velocity ∼1000 km s−1, sufficiently high to escape from the gravitational field of the MW (Kenyon et al. 2008; Brown 2015). The first HVS candidate was discovered by Brown et al. (2005); a B-type star with a velocity more than twice the Galactic escape speed at its position. Currently about ∼20 unbound HVSs with velocities ∼300–700 km s−1 have been discovered by targeting young stars in the outer halo of the MW (Brown, Geller & Kenyon 2014). In addition, tens of mostly bound candidates have been found at smaller distances but uncertainties prevent the precise identification of the GC as their ejection location (e.g. Hawkins et al. 2015; Vickers, Smith & Grebel 2015; Zhang, Smith & Carlin 2016; Marchetti et al. 2017; Ziegerer et al. 2017). HVSs are predicted to be ejected from the GC with an uncertain rate around 10−4 yr−1 (Yu & Tremaine 2003; Zhang, Lu & Yu 2013), two orders of magnitude larger than the rate of ejection of RSs with comparable velocities from the stellar disc (Brown 2015). Because of their extremely high velocities, HVS trajectories span a large range of distances, from the GC to the outer halo. Thus, HVSs have been proposed as tools to study the matter distribution in our Galaxy (e.g. Gnedin et al. 2005; Sesana, Haardt & Madau 2007; Kenyon et al. 2014; Fragione & Loeb 2017; Rossi et al. 2017; Contigiani, Rossi & Marchetti 2018) and the GC environment (e.g. Zhang et al. 2013; Madigan et al. 2014), but a larger and less observationally biased sample is needed in order to break degeneracies between the GC binary content and the Galactic potential parameters (Rossi et al. 2017). Using the fact that their angular momentum should be very close to zero, HVSs have also been proposed as tools to constrain the solar position and velocity (Hattori, Valluri & Castro 2018a). Other possible alternative mechanisms leading to the acceleration of HVSs are the encounter between a single star and a massive black hole binary in the GC (e.g. Yu & Tremaine 2003; Sesana, Haardt & Madau 2006, 2008), the interaction between a globular cluster with a single or a binary massive black hole in the GC (Capuzzo-Dolcetta & Fragione 2015; Fragione & Capuzzo-Dolcetta 2016), and the tidal interaction of a dwarf galaxy near the centre of the Galaxy (Abadi et al. 2009). Another possible ejection origin for HVSs and high-velocity stars in our Galaxy is the Large Magellanic Cloud (LMC; Boubert & Evans 2016; Boubert et al. 2017; Erkal et al. 2018), orbiting the MW with a velocity ∼380 km s−1 (van der Marel & Kallivayalil 2014).
Citation: Yu & Tremaine 2003
Citation function: Compare/Contrast; Compare/Contrast
Our group is addressing this problem by combining a variety of complementary perspectives: (i) by constructing a new generation of high-quality star density profiles derived from star counts instead of surface brightness (see Lanzoni et al. 2007a, 2010, 2019; Miocchi et al. 2013; Pallanca et al. 2021); (ii) by investigating the population of stellar exotica (Ferraro et al. 2001, 2003, 2015, 2016; Pallanca et al. 2010, 2013,2014, 2017; Cadelano et al. 2017, 2018, 2020) and their connection with the dynamical evolution of the parent cluster (see Ferraro et al. 2009, 2012, 2018a, 2019; Lanzoni et al. 2016); (iii) by characterizing the three-dimensional (3D) global velocity space through the analysis of the velocity dispersion profile and rotation curve from resolved star spectroscopy (Lanzoni et al. 2013, 2018a, 2018b; Ferraro et al. 2018b) and proper motions (PMs; see Raso et al. 2020). The determination of GGC internal kinematics from resolved star velocities is particularly relevant and challenging. In this context we promoted the ESO-VLT Multi-Instrument Kinematic Survey (hereafter the MIKiS survey; Ferraro et al. 2018b, 2018c), a project specifically designed to characterize the kinematical properties of a sample of GGCs in different dynamical evolutionary stages from the radial velocities (RVs) of hundreds of individual stars distributed over the entire radial range of each stellar system. To this end, the survey fully exploits the spectroscopic capabilities of different instruments currently available at the ESO Very Large Telescope (VLT): originally designed to use the adaptive optics (AO) assisted integral-field spectrograph SINFONI, the multiobject integral-field spectrograph KMOS, and the multiobject fiber-fed spectrograph FLAMES/GIRAFFE, it has been recently complemented with individual projects and an ongoing large program (PI: Ferraro) fully exploiting the remarkable performances of the AO-assisted integral-field spectrograph MUSE.
Citation: Ferraro et al. 2018b
Citation function: Motivation; Motivation
Consequently, the neutral gas measured in situ in the coma of comet 67P by the ROSINA experiment (and also MIRO, VIRITS, and Alice) on board Rosetta likely originated several tens of meters beneath the primordial surface of the comet. ROSINA observations provided evidence that this comet is formed from pristine material that has not been significantly altered after its formation in the first Myr of the solar nebula stage. The high abundance of super-volatiles like CO and CO2 (Le Roy et al. 2015), the detection of argon (Balsiger et al. 2015), of molecular nitrogen (Rubin et al. 2015), of molecular oxygen (Bieler et al. 2015), of a high D/H in HDO/H2O and D2O/HDO and HDS/H2S (Altwegg et al. 2015, 2017), and of hydrogen halides (De Keyser et al. 2017; Dhooghe et al. 2017), coupled with the low density, high porosity, and homogeneity of the nucleus (Pätzold et al. 2016) and the absence of signatures of aqueous alteration (see Capaccioni et al. 2015; Davidsson et al. 2016; Quirico et al. 2016; Bardyn et al. 2017) all indicate that comet 67P formed at low temperature and did not experience any substantial global-scale heating after its formation. This suggests that 67P is representative of the solar nebula material from which the solar system had formed. This has strong implications not only for how the measurements made in cometary environments can be used to constrain the protosolar environment but also for the contribution of comets to Earth’s composition. For instance, the measurement of the D/H isotopic ratio in 67P (Altwegg et al. 2015) suggests that comets cannot be considered as the main source of water on Earth. The discovery of significant amounts of O2 in comets (Bieler et al. 2015) was not predicted by astrochemical models and challenges our understanding of the chemistry of molecular clouds and of the protosolar nebula. However, the Jupiter-family comets (JFCs; which include 67P) are a diverse groups. Indeed, even if Giotto measurements indicate that comet 1P/Halley contains similar amounts of O2 (Rubin et al. 2015), different D/H ratios (lower than observed for 67P and compatible with the D/H ratio in the Earth’s oceans) have been measured for other JFC comets like Hartley 2 (Balsiger et al. 2015) and 46P/Wirtanen (Lis et al. 2019). The causes for this diversity may be already present at the formation of these comets or may result from a different evolution after their formation.
Citation: Bieler et al. 2015
Citation function: Compare/Contrast; Compare/Contrast
For the magnetic field configurations considered in this work, the polarization angle can only change exactly by Δϕ = 90○ and a gradual change of the PA is not possible. There are tantalizing hints of a 90○ change in the PA in some of the GRBs, as discussed above, but the results are not yet conclusive. The result presented by Sharma et al. (2019) where the PA changes by 90○ twice over the emission is again very exciting as such a change over a single pulse can only occur for the Btor field configuration. The only difficulty, according to the modelling done here, is that both 90○ changes occur in the decaying tail of the pulse when high latitude emission dominates the flux. In the measurement presented by Sharma et al. (2019), the PA shows a change close to the peak of the emission. Another scenario in which a 90○ PA change can be obtained includes contribution from multiple pulses and when the LOS is close to the edge of the jet, such that θobs ≈ θj, along with a change in bulk Γ between the pulses which would change ξj = (Γθj)2. Alternatively, such a change in the PA can be obtained due to magnetic reconnection, e.g. in the ICMART model (Zhang & Yan 2011), where the local magnetic field orientation, which is orthogonal to the wave vector of the emitted photon, itself changes by 90○ as the field lines are destroyed and reconnected in the emission region (Deng et al. 2016). To obtain a change in the PA other than Δϕ = 90○ or to get a gradually changing PA the condition for axisymmetry must be relaxed and the magnetic field configuration or orientation in the emission region must change. One possibility is that if the different pulses that contribute to the emission arise in a ‘mini-jet’ within the outflow (e.g. Shaviv & Dar 1995; Lyutikov & Blandford 2003; Kumar & Narayan 2009; Lazar, Nakar & Piran 2009; Narayan & Kumar 2009; Zhang & Yan 2011). In this case, the different directions of the mini-jets or bright patches w.r.t. the LOS (e.g. Granot & Königl 2003; Nakar & Oren 2004) would cause the PA to also be different between the pulses even for a field that is locally symmetric w.r.t the local radial direction (e.g. B⊥ or B∥) as well as for fields that are axisymmetric w.r.t to the centre of each mini-jet (e.g. a local Btor for each mini-jet). Finally, broadly similar result would follow from an ordered field within each mini-jet (Bord) which are incoherent between different mini-jets. Time-resolved measurement in such a case would naturally yield a time-varying PA. Alternatively, as shown by Granot & Königl (2003) for GRB afterglow polarization, a combination of an ordered field component (e.g. Bord) and a random field, like B⊥, can give rise to a time-varying PA between different pulses that, e.g. arise from internal shocks. The ordered field component here would be that advected from the central engine and the random field component can be argued to be shock-generated. Notice that the ordered field component should not be axisymmetric in order for the PA to smoothly vary.
Citation: Zhang & Yan 2011
Citation function: Compare/Contrast
Set II: we now focus on the observational constraints on the model parameters after the inclusion of the local measurement of H0 by Riess et al. (2018) with the previous data sets (CMB, Pantheon, and CC) in order to see how the parameters could be improved with the inclusion of this data point. Since for this present UM, the estimation of H0 from CMB alone is compatible with the local estimation of H0 by Riess et al. (2018), thus, we can safely add both the data sets to see whether we could have something interesting. Following this, we perform another couple of tests after the inclusion of R18. The observational results on the model parameters are summarized in Table 4. However, comparing the observational constraints reported in Table 3 (without R18 data) and Table 4 (with R18), one can see that the inclusion of R18 data (Riess et al. 2018) does not seem to improve the constraints on the model parameters. In fact, the estimation of the Hubble constant H0 remains almost similar to what we found in Table 3. In order to be more elaborate in this issue, we have compared the observational constraints on the model parameters before and after the inclusion of R18 to other data sets. In Figs 7 (CMB versus CMB+R18), 8 (CMB+CC versus CMB+CC+R18), 9 (CMB+Pantheon versus CMB+Pantheon+R18), and 10 (CMB+Pantheon+CC versus CMB+Pantheon+CC+R18), we have shown the comparisons which prove our claim. One can further point out that the strong correlation between the parameters μ and H0 as observed in Fig. 5 still remains after the inclusion of R18 [see specifically the (μ, H0) planes in Figs 7–10]. The physical nature of μ does not alter at all. That means the correlation between H0 and μ is still existing after the inclusion of R18 to the previous data sets, such as CMB, Pantheon, and CC. In addition to that since μ ≲ 0.9 according to all the observational data sets, thus, the transition from past decelerating era to current accelerating era occurs to be around z ≲ 0.6, similar to what we have found with previous data sets (Table 3).
Citation: Riess et al. 2018
Citation function: Compare/Contrast; Compare/Contrast
Citation: Riess et al. (2018)
Citation function: Similarities; Uses
Citation: Riess et al. (2018)
Citation function: Uses; Uses
The measurement of galaxy clustering statistics is one of the most powerful probes in observational cosmology (Tegmark et al. 2004; Cole et al. 2005; Percival et al. 2010; Saito et al. 2011; Zehavi et al. 2011; Reid et al. 2012, 2014; Samushia et al. 2014). However, our lack of a detailed understanding of the relationship between the distribution of galaxies and that of dark matter limits the full use of the measured amplitude of the clustering signal for constraining cosmological parameters. Seljak et al. (2005) first proposed and demonstrated that it is possible to break this degeneracy between the unknown bias between galaxies and matter and the cosmological parameters by utilizing the theoretical dependence of galaxy bias on halo mass (Mo & White 1996; Sheth & Tormen 1999). By probing the halo masses of galaxies via the weak gravitational lensing around galaxies, commonly referred to as galaxy–galaxy lensing, on small scales (see also Yoo et al. 2006; Cacciato et al. 2009; Li et al. 2009; Leauthaud et al. 2012; Tinker et al. 2012; Cacciato et al. 2013, 2014; Gillis et al. 2013; More et al. 2013; Simpson et al. 2013; see also for the galaxy–galaxy lensing measurements Fischer et al. 2000; Mandelbaum et al. 2006; van Uitert et al. 2011; Velander et al. 2014). An alternative method, which uses the ratio of the clustering and lensing signals, has also been proposed in order to avoid the complex astrophysics that complicates the interpretation of these observables on scales smaller than the typical halo radii (Baldauf et al. 2010). As a proof of this concept, Mandelbaum et al. (2013, hereafter RM13) used the state-of-the-art measurement of galaxy–galaxy lensing signal and galaxy clustering up to large-scales (





) by combining the spectroscopic and multi-color imaging galaxy catalogs from the Sloan Digital Sky Survey I/II (SDSS I/II; York et al. 2000; Eisenstein et al. 2001; Strauss et al. 2002). They showed that the joint analysis provides a significant improvement in the cosmological parameters 





 and 





 when combined with results from the WMAP7 experiment (Komatsu et al. 2011).
Citation: Sheth & Tormen 1999
Citation function: Background