adsabs/FOCAL huggingface dataset:
Classifying galaxies into different star formation regimes at high redshift is facilitated by the fact that star formation rate (SFR) and stellar mass (M*) of star-forming galaxies (SFGs) are strongly correlated out to at least 





 (Daddi et al. 2007; Noeske et al. 2007; Pannella et al. 2009, 2015; Elbaz et al. 2011; Whitaker et al. 2012, 2014; Lee et al. 2015; Salmon et al. 2015; Schreiber et al. 2015; Tomczak et al. 2016). This correlation is commonly called the “main sequence of star formation” (MS). A common interpretation of the MS is that the location of galaxies relative to the MS follows a different time evolution of SFR (Renzini 2009; Daddi et al. 2010; Rodighiero et al. 2011; Sargent et al. 2012; Renzini & Peng 2015). The tight MS with near unity slope reflects that the majority of SFGs follow a steadily increasing star formation history governed by a set of gradual physical processes like gas exhaustion (Noeske et al. 2007). A small fraction of galaxies exhibit quasi-exponential mass and SFR growth, either through major mergers or through strong bursts of star formation in the densest regions (Elbaz et al. 2011; Sargent et al. 2012). While typical galaxies therefore spend most of their time on the MS prior to additional quenching processes, these starburst galaxies are located above the MS and play a relatively minor role in the star formation history of the universe (Rodighiero et al. 2011). Galaxies located below the MS include quiescent galaxies (QGs), with spheroidal-like structures and little star formation activity, as well as fading SFGs with diminishing star formation activity. The transient galaxies, such as those in the green valley, can dominate the lower region of the MS. At 





, green valley galaxies are known to be off the MS (Schawinski et al. 2014), and they have intermediate morphologies combining disk-dominated and bulge-dominated systems (Salim et al. 2009; Mendez et al. 2011; Pandya et al. 2017).
Citation: Renzini 2009
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Hu, Holz & Vale (2007b) proposed the idea of using the ratio of CMB convergence to galaxy lensing convergence as a way to measure the distance ratio (distance to surface of last scattering relative to the distance to the source galaxy sample used to estimate the galaxy lensing) and hence constrain the geometry, Ωk and the equation of state of dark energy. The ratio is defined as

(40)

\begin{equation}
\mathcal {R}(z_{\rm l})=\frac{\kappa (z_{\rm l},z_{\ast })}{\kappa (z_{\rm l},z_{\rm s})}=\frac{\Sigma _{\rm c}(z_{\rm l},z_{\rm s})}{\Sigma _{\rm c}(z_{\rm l},z_{\ast })}.
\end{equation}

Similar distance ratio tests have also been proposed using galaxy or galaxy cluster lensing alone, in both strong lensing (e.g. Link & Pierce 1998; Golse, Kneib & Soucail 2002) and weak lensing regimes (e.g. Jain & Taylor 2003; Bernstein & Jain 2004). Several studies have already measured the distance ratios (e.g. Taylor et al. 2012; Diego et al. 2015; Kitching et al. 2015; Caminha et al. 2016, and references therein), though they are afflicted by several systematics such as, uncertainties in modelling cluster profiles and cosmic variance in the case of multiple strong lens systems, and photometric redshift uncertainties as well as imaging systematics that cause a redshift-dependent shear calibration in the case of weak lensing. The small redshift baseline also limits the cosmological applications of these measurements using optical weak lensing alone (see discussion in Hu et al. 2007b; Weinberg et al. 2013). Using CMB lensing in cosmographic measurements is advantageous in several ways. First, the source redshift for the CMB (redshift of surface of last scattering) is well known, so one of the two redshift slices being compared has no redshift uncertainty. The long redshift baseline between CMB and galaxy lensing sources also improves the sensitivity of $\mathcal {R}$ to cosmological parameters (Hu et al. 2007b). However, using CMB lensing with galaxy lensing makes $\mathcal {R}$ become more sensitive to some of the systematics in galaxy lensing (for example, multiplicative bias), and $\mathcal {R}$ can also be used as test for the presence of such systematics.
Citation: Diego et al. 2015
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The ESPRESSO DFS concept (Di Marcantonio et al. 2018) was conceived during its preliminary design phases with the goal of maximizing operational efficiency, flexibility, and scientific output while complying with the standard Paranal Observatory operational scheme. The main challenge derives from the requirement to operate ESPRESSO in a seamless way with any of the UT’s or with all four UT’s simultaneously. This must be possible not only with a predetermined schedule, but also “on the fly”. The flexibility in ESPRESSO’s operations has been tackled by adopting a new DFS deployment plan described in Di Marcantonio et al. (2018) that is exceptional under various aspects because it has to cope with various telescope and instrument configurations while remaining operationally simple. Figure 8 shows the main ESPRESSO DFS elements and their final deployment. Besides the software packages already described, part of the software for the control of the CT devices has been incorporated into the VLT telescope CS to allow CT operations even when ESPRESSO is offline (thus avoiding conflicts, e.g., with instruments of the VLT Interferometer operations). In addition to the standard DFS software packages, ESPRESSO is the first instrument to also provide a data analysis package that is able to extract relevant astronomical observables from the reduced data. The following DFS subsystems are specific to ESPRESSO: (1) the ETC hosted on the ESO web page9; (2) the CS with the full suite of acquisition, observation, and calibration templates that are able to control all vital parts of the instrument and the CT (Calderone et al. 2018); (3) the data reduction software (DRS) package (or “pipeline”) capable of providing “science-ready” reduced data only minutes after the end of the individual observation; (4) the data analysis software (DAS) package that produces higher-level astronomical observables with no or limited supervision; (5) the DRS and DAS are distributed to the community10.
Citation: Di Marcantonio et al. (2018)
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Citation: Di Marcantonio et al. 2018
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Using Eq. (16), we derive the probability density in the parameter space (σDRW, RBLR) for a given set of parameters ⟨M⟩, fBLR, ve, and R0. By marginalising on ve and R0 we obtain the measurement of RBLR shown in Fig. 10. One could argue that we obtain a bi-modal distribution in the posterior probability for RBLR. This observation can be explained by the fact that, as shown in Sect. 4, the R-band encapsulates the Mg II and Fe II emission lines which can arise from two distinct regions of the BLR. Indeed, the Hβ (used in Mosquera & Kochanek 2011) and Mg II lines seem to arise from the same part of the BLR in various quasars (e.g., Karouzos et al. 2015; Khadka et al. 2021) and should both yield similar sizes; whereas the Fe II line is thought to arise from a larger part of the BLR (e.g., Sluse et al. 2007; Hu et al. 2015; Zhang et al. 2019; Li et al. 2021). Therefore, the combination of the two signals modelled as a single BLR emission could broaden our measurement and induce its slight bi-modality. Still, the core of the probability lies in the [0.1–1.5] RBLRMK11 range and the second mode observed for higher values of RBLR rises only for the highest values of σDRW. The marginalisation of this posterior over σDRW yields a probability distribution for RBLR and by taking its 16th, 50th, and 84th percentiles we measure 




R
BLR

=
1
.

6

−
0.8


+
1.5


×

10
17



$ R_{\mathrm{BLR}} = 1.6^{+1.5}_{-0.8}\times 10^{17} $


 cm. With a relative precision of ≈80% our method is less precise than recent spectroscopical reverberation mapping measurements (e.g., Grier et al. 2019; Penton et al. 2022 have around 30% relative precision for quasars with z > 1.3) but is more precise than photometric reverberation mapping (e.g., Kaspi et al. 2021 have above 100% relative precision when using a cross-correlation function with R and B filter light curves). The value of RBLRMK11 predicted by the luminosity–size relation is in agreement with our measurement at the 1−σ level.
Citation: Khadka et al. 2021
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During our monitoring period V2492 Cyg remained always undetected at our sensitivity, therefore the light curve depicted in Fig. 1 displays only upper limit values at different levels, as explained in Sect. 3. As a consequence, no fading or outbursting event can be detected, nevertheless some useful information can be derived. As mentioned above, after its discovery in 2010, V2492 underwent a long-lasting period of strong activity with intermittent burst and fading events (see Hillenbrand et al. 2013 and AAVSO2 data) and reached its maximum recorded brightness in 2017 Giannini et al. (2018). During most of this period, the source was sampled with an almost daily cadence and, for long time intervals, remained brighter than the following values: B  18, V  16, R  15, and I  14 mag. In comparison, our plate measurements are largely undersampled, presenting long periods (up to a decade) without any data. In any case (not considering the I band, which is practically uncovered, and the B band, which presents no significant upper limits), our V and R-band upper limitstentatively suggest that duringa period of about 30 yr from 1958 to 1987 an activity similar (both in duration and in brightness) to that more recently (2010–2017) monitored, did not occur. Indeed, we note that for a significant amount of time the source is brighter than the level indicated by our upper limits, thus suggesting that, in the past, the activity of V2492 Cyg was not as strong as it is now. The above scenario, if correctly described, means that an enhanced brightness variability could be an infrequent feature of V2492 Cyg. Such circumstances favour an accretion- more than an extinction-driven origin for the bursts. Indeed, the former is expected to occur with a long and irregular cadence related to the viscous motion of the matter toward the inner edge of the disk, while the latter should occur more frequently and regularly, according to the orbital motion of the obscuring matter along the line of sight.
Citation: Giannini et al. (2018)
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However, one can reduce the order of the LD equation using the method proposed in Landau & Lifshitz (1975), i.e., by rewriting the self-force in terms of the external force and the four-velocity of a particle. Substituting the higher-order terms in Equation (3) with the derivatives of the Lorentz force, we get the equation in the following form:
5





This equation, usually referred to as the Landau–Lifshitz (LL) equation, has important consequences: it is of the second order, does not violate the principle of inertia, and the self-force vanishes in the absence of the external (Lorentz) force (Rohrlich 2001; Poisson 1999). The self-contained derivation of Equation (5) in terms of retarded potentials is given in Poisson et al. (2011). Equation (5) can be applied to cases with any external forces acting on a charged particle instead of the Lorentz force. In the case where 





, the radiation-reaction force can be rewritten in the form
6





where 





 is the specific charge of the particle, 





, and the comma in the first term denotes the partial derivative with respect to the coordinate xα. Spohn (2000) concluded that using the LL equation is identical to imposing Dirac’s asymptotic condition 





 on the LD equation. It was later confirmed by Rohrlich (2001) that the reduced form of the equation of motion is exact, rather than approximative, though the LL equation was proposed in Landau & Lifshitz (1975) as an approximative solution to the third-order LD equation. More details on the treatment of the radiation reaction of charged particles in flat spacetime can be found in the book by Spohn (2004). In our numerical study, we found that the LL approximation is perfectly applicable if the Schott term is small with respect to the radiation recoil term, which is the case we consider here. Below we show a representative example of charged particle motion in an external uniform magnetic field, integrating both LD and LL equations. The results of the numerical studies of LD and LL equations for the motion of a charged particle in a uniform magnetic field in flat spacetime are in accord with the analytical treatment of the radiation-reaction force performed in Spohn (2000).
Citation: Poisson 1999
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BE values can also be obtained by means of computational approaches that, in some situations, can overcome the experimental limitations. Many computational works have so far focused on a few important astrochemical species like H, H2, N, O, CO, and CO2, in which BEs are calculated on periodic/cluster models of crystalline/amorphous structural states using different computational techniques (e.g., Al-Halabi & Van Dishoeck 2007; Karssemeijer et al. 2014; Karssemeijer & Cuppen 2014; Ásgeirsson et al. 2017; Senevirathne et al. 2017; Shimonishi et al. 2018; Zamirri et al. 2019a). In addition, other works have computed BEs in a larger number of species but with a very approximate model of the substrate. For example, in a recent work by Wakelam et al. (2017) BE values of more than 100 species are calculated by approximating the ASW surface with a single water molecule. The authors then fitted the most reliable BE measurements (16 cases) against the corresponding computed ones, obtaining a good correlation between the two data sets. In this way, all the errors in the computational methods and limitations due to the adoption of a single water molecule are compensated by the fitting with the experimental values, in the view of the authors. The resulting parameters are then used to scale all the remaining computed BEs to improve their accuracy. This clever procedure does, however, consider the proposed scaling universal, leaving aside the complexity of the real ice surface and the specific features of the various adsorbates. In a similar work, Das et al. (2018) have calculated the BEs of 100 species by increasing the size of a water cluster from one to six molecules, noticing that the calculated BE approaches the experimental value when the cluster size is increased. As we will show in the present work, these approaches, relying on an arbitrary and very limited number of water molecules, cannot, however, mimic a surface of icy grain. Furthermore, the strength of interaction between icy water molecules, as well as with respect to the adsorbates, depends on the hydrogen bond cooperativity, which is underestimated in small water clusters.
Citation: Zamirri et al. 2019a
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Next, we applied a correction for the expected difference between the magnitude of each Cepheid at the observed phase and the magnitude at the epoch of mean intensity of its light curve. These phase corrections are derived from ground-based light curves of each Cepheid in filters with wavelengths best corresponding to the WFC3 filters. Because the phase corrections are relative quantities, they do not change the zero-point of the light curves, which remain on the HST WFC3 natural system.6

6
In practice, the ground-based light curves are transformed to the HST system using color terms. While an uncertainty in color term could produce systematic errors, these are negligible. We determined empirically that a 10% error in the color terms changes the mean phase correction by ≤0.1 mmag.
 We derived and applied these phase corrections following the same methodology described in R18b. The periods and phases for F555W and F814W were determined using the V- and I-band light curves from OGLE surveys (Szymanski 2005; Udalski et al. 2008, 2015). For some Cepheids (OGL0434, OGL0501, OGL0510, OGL0512, OGL0528, OGL0545, OGL0590, OGL0712, OGL0757, OGL0966, and OGL0992), we also included V-band light curves from the ASAS survey (Pojmanski 1997) and/or ASAS-SN survey (Shappee et al. 2014; Kochanek et al. 2017) to increase the baseline coverage. We made use of the J- and H-band light curves from M15 and Persson et al. (2004) to correct the F160W random phased measurements to mean intensity. The standard deviations of these corrections are 0.29, 0.17, and 0.11 mag in F555W, F814W, and F160W, respectively, decreasing with the smaller light-curve amplitudes at redder wavelengths. Phase corrections also account for the difference between the Cepheid light-curve magnitude mean (the average of many measured magnitudes) and the magnitude at the epoch of mean intensity (the standard convention for distance measurements). This expected difference is consistent with our sample average correction of −0.048, −0.013, and −0.001 mag, in F555W, F814W, and F160W, respectively. The uncertainties in these phase corrections depend on the quality of the ground-based light curves; the average uncertainty is 0.013, 0.008, and 0.029 mag per epoch in F555W, F814W, and F160W, respectively, which dominates over the statistical photometry errors (i.e., photon statistics) in a single epoch. The differences between repeat measurements for the same target, available for a subset of 19 epochs and filters, is consistent with these uncertainties. The final mean individual uncertainty for these 70 Cepheids is 0.016, 0.012, and 0.029 mag in F555W, F814W, and F160W, respectively. The final mean photometry for each Cepheid in three colors is given in Table 2.
Citation: Shappee et al. 2014
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The situation seems to be more complicated in luminous AGNs, which can be divided into radio-quiet (RQ) and radio-loud (RL) AGNs according to their radio loudness R (R = f5 GHz/f4400 Å, where f5 GHz and f4400 Å are the radio flux at 5 GHz and optical flux at 4400 Å, respectively, Kellermann et al. 1989). The accretion process can be well described by a disk-corona model in RQAGNs (Jin et al. 2012; Lusso & Risaliti 2017; Qiao & Liu 2018), where the optical-UV flux is emitted from an optically thick, geometrically thin accretion disk and the hard X-ray flux comes from the inverse Compton scattering of optical-UV photons by the hot corona. This model can naturally fit the observational positive relationships between αOX and λO as well as Γ and λO in RQAGNs (Shemmer et al. 2006; Risaliti et al. 2009; Lusso et al. 2010; Brandt & Alexander 2015; Li 2019), where the optical to X-ray spectral index αOX is defined as αOX = 0.384 log[Lν(2500 Å)/Lν(2 keV) (e.g., Lusso et al. 2010), and λO = 8.1νLν(5100 Å)/LEdd is the Eddington ratio based on the optical luminosity at 5100 Å (Runnoe et al. 2012a), with LEdd being the Eddington luminosity. In contrast, the origin of X-ray emission is still unclear in RLAGNs. In observations, RLAGNs appear to have different properties than RQAGNs, for instance the positive relationships listed above in RQAGNs have not been found in RLAGNs (Li 2019; Zhou & Gu 2020). Furthermore, the average X-ray luminosity in RLAGNs is found to be 2–3 times higher than that in RQAGNs (Zamorani et al. 1981; Wilkes & Elvis 1987; Wu et al. 2013; Gupta et al. 2018). This seems to indicate that the X-ray emission from jets is important in RLAGNs. However, Gupta et al. (2018) recently compiled an excellent sample to investigate the differences of X-ray properties between luminous radio galaxies and their radio-quiet counterparts, where the black hole mass and Eddington ratio were well selected and the bolometric luminosity were calculated from mid-infrared emission observed by the Wide-field Infrared Survey Explorer (WISE) mission. They argue that the X-ray emission in radio-loud radio galaxies should also come from a disk-corona system because their distribution of the X-ray slope is very similar with those of radio-quiet counterparts (see Gupta et al. 2018 for details, see also Gupta et al. 2020).
Citation: Li 2019; Li 2019
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Finally, in order to measure the ICL fraction once we have a background-free ICL map, we created an image of the cluster removing the CHEF models of the foreground and background galaxies. As described in Jiménez-Teja & Benítez (2012), the cluster membership is determined in a two-step process, the PEAK+GAP algorithm (Owers et al. 2011), using the spectroscopic data available for each system. This composite method first identifies the peak of the cluster in the redshift space and selects a redshift window wide enough to contain the whole distribution of velocities assigned to that peak. Implicitly, the size of this window is proportional to the velocity dispersion of the clusters: merging clusters, with a more scattered velocity distribution, will need a wider window, compared to relaxed systems. This crude selection of cluster member candidates is obviously prone to contamination by interlopers. So, we further refine it using the shifting gapper method (Fadda et al. 1996; Girardi et al. 1996; Boschin et al. 2006; Owers et al. 2011), which uses velocity and spatial information on the candidates simultaneously. The shifting gapper method spatially distributes the candidates according to their clustercentric distance in radial bins. The mean velocity of the candidates within each bin is calculated, and those candidates with velocities that are too far from the others are rejected. As unrelaxed clusters are more likely to have a broader spatial distribution, this procedure naturally allows candidates at larger distances to be identified as cluster members for these systems. These two steps are, thus, essential to guarantee that our cluster membership algorithm implicitly takes into account the dynamical state of the systems and does not bias the measurement of their total luminosity, while minimizing contamination by interlopers at the same time. We refer the reader to Jiménez-Teja & Benítez (2012) for further information on the cluster membership selection algorithm.
Citation: Fadda et al. 1996
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