adsabs/FOCAL huggingface dataset:
Table 1 depicts the heliolatitudes of the solar offset points on different experiment days and the corresponding heliocentric distances. It is to be noted that owing to the trajectory constraints of the spacecraft along the ecliptic plane, a pure latitudinal dependence (at a constant heliocentric distance) of the spectral index cannot be assessed here. However, variations in the spectral index values with respect to the complementary heliolatitudes of the proximate points of the ray path between 5° and 39° N can be observed, where the higher-heliolatitude spectra are flatter (lower αf) than the lower-heliolatitude spectra (see Table 1). Low-heliolatitude coronal regions have streamer structures where the slow solar winds, which have a highly turbulent structure, are generated (Woo & Martin 1997). The fast solar winds emanate from coronal holes at higher heliolatitudes (Zirker 1977). The coronal holes, which generally have a higher magnetic field, lead to a lower sound wave to Alfvénic wave ratio and a relatively lower turbulence cascading energy. Consequently, the turbulence requires a longer time (and hence larger heliocentric distances) to achieve a fully developed turbulence spectrum. Thus, at a given heliocentric distance (but varying heliolatitude) of a proximate point of the satellite radio ray path, the fast (high-latitude) solar wind has flatter spectra compared with slow (low-latitude) solar wind spectra, as reported in earlier studies (Pätzold et al. 1996; Efimov et al. 2008, 2017). Efimov et al. (2008) and Chashei et al. (2007) observed that this change of turbulence regime is prominent during periods of minimum solar activity, as coronal holes (the source of the high-latitude fast solar wind) exist in abundance, and there is relative lack of the active regions that generate slow solar winds. It is, however, intriguing that we notice this aspect in our study, which was conducted during a period of relatively low solar activity.
Citation: Zirker 1977
Citation function: Uses
In this work, the difference between G and NG clusters plays a major role. In a first step, we focus our attention on characterizing the structure and distribution of galaxy member properties in each class. In Fig. 1, we show the distributions of: (a) logarithmic virial mass ($\rm log(\mathit{ M}_{200}/M_{\odot })$); (b) r-band absolute magnitude; (c) a proxy for the concentration of stellar mass in each cluster, defined as $R_{80}/R_{20}$, where $R_{x}$ is the projected radius within which the stellar mass represents x per cent of the total stellar mass within $R_{200}$; (d) velocity dispersion along the LOS; and (e) the distribution of cluster mean stellar mass of bright galaxies within $R_{200}$ ($\langle M_{\mathrm{ stellar}}^{C} \rangle$).3 We compare the distributions using two different statistical tests: AD and Wilcoxon Rank Test (Wlx; see Engmann & Cousineau 2011 and Gehan 1965 for a review of both).4 The results are shown in each panel. Aside from the histograms, we add a kernel smoothed curve, shown as shaded area, which is derived directly from the data set using an Epanechnikov Kernel Density estimator (Silverman 1986) with a bandwidth equal to 1.5 times the bin size. We find that G and NG clusters have statistically different distributions. In panel (a), we note that NG clusters tend to have higher values of $M_{200}$ in comparison to G clusters. Namely, we find that 32.4 per cent (11/34) of NG clusters have $\mathrm{ log}(M_{200}/\mathrm{ M}_{\odot })\gt 14.75$, while this fraction decreases to 6.3 per cent (9/143) in G clusters. The majority of G systems ($\sim 71.3{{\ \rm per\ cent}}$) have $\mathrm{ log}(M_{200}/\mathrm{ M}_{\odot })\lt 14.5$. Panel (b) shows an excess of fainter galaxies5 in NG clusters in comparison to G systems. We find that 30.5 per cent of NG cluster members have $M_{r} \ge -19.5$, while the equivalent cut in absolute magnitude yields 14.1 per cent of galaxies in G systems. Panel (c) shows the distribution of concentration in G and NG clusters, revealing that NG cluster galaxies are less concentrated than their G cluster counterparts. Only one NG cluster reaches C > 4.7. In panel (d), we observe an excess of NG clusters with higher velocity dispersion in comparison to G clusters. NG clusters are presumed to be found in a non-virialized state, so that the expected velocity dispersions are higher, and, possibly, the estimated mass may also be an overestimate of the real cluster mass. In any case, this is one more piece of evidence regarding the different state of NG clusters with respect to G systems. Finally, we note in panel (f) an excess of NG clusters with mean stellar mass of $\rm 3 \le \langle \mathit{ M}_{stellar}^{\mathit{ C}} \rangle \lt 4 \times 10^{11}\, M_{\odot }$. In other words, we find that NG clusters have higher virial mass and radii, an excess of fainter galaxies, are less concentrated, contain more massive B galaxies and have higher velocity dispersion in comparison to G systems.
Citation: Silverman 1986
Citation function: Uses; Uses
On the other hand, at separations ≲ 0.01 pc, smaller than those characteristics of spectroscopic binary candidates, many theoretical studies have predicted a significant variability in the observed nuclear light curve due to different physical processes. For example, in studies of the evolution of MBHBs in circumnuclear discs, a modulated gas inflow from the outer gas distribution periodically fuels the accretion discs within the Hill radii of the individual MBHs (that, being smaller than the surrounding circumbinary disc, are commonly referred to as ‘mini-discs’ in the literature, Artymowicz & Lubow 1994; Ivanov, Papaloizou & Polnarev 1999; Hayasaki, Mineshige & Ho 2008; Cuadra et al. 2009; Roedig et al. 2011, 2012; D’Orazio, Haiman & MacFadyen 2013; Farris et al. 2015; D’Orazio et al. 2016; Miranda, Muñoz & Lai 2017; Tang, MacFadyen & Haiman 2017; Bowen et al. 2018; d’Ascoli et al. 2018) as a consequence of the non-axisymmetric and time-dependent potential of the binary. Such modulated inflow could result in a similarly variable luminosity, depending on the properties of the inflowing gaseous streams and of the preexisting mini-discs (see the discussion in Sesana et al. 2012). An alternative cause of observed variability could be the plunging of a very eccentric secondary MBH on to the primary disc, as proposed by Valtonen et al. (2008) for the observed variability of OJ287. Finally, even in the absence of periodic inflows or very eccentric binaries (as expected in the case of a low-mass secondary; D’Orazio et al. 2016; Duffell et al. 2020), variability can be caused by the relativistic Doppler boost of the emitted spectrum during the orbit of the MBHB, resulting in a variable flux observed in fixed observational bands, as proposed for PG 1302-102 in D’Orazio, Haiman & Schiminovich (2015). This last model has the peculiarity of predicting different variability amplitudes at different wavelengths, as demonstrated for the UV versus optical light curves of PG 1302-102 (Xin et al. 2019).
Citation: Farris et al. 2015
Citation function: Background; Background
It may be appropriate to single out at this point the recent and interesting works by Lin & Ishak (2017a,b) in which the authors run a so-called (dis)cordance test based on using a proposed index of inconsistency (IOI) tailored at finding possible inconsistencies/tensions between two or more data sets in a systematic and efficient way. For instance, it is well known that there is a persistent discrepancy between the Planck CMB measurements of H0 and the local measurements based on distance ladder (Riess et al. 2016, 2018b). At the same time, if one compares what is inferred from Planck 2015 best-fitting values, the LSS/RSD measurements generally assign smaller power to the LSS data parametrized in terms of the weighted linear growth rate f(z)σ8(z). This feature is of course nothing but the σ8-tension we have been addressing in this paper. It is therefore natural to run the IOI test for the different kinds of H0 measurements and also to study the consistency between the H0 and the growth data. For example, upon comparing the constraints on H0 from different methods, Lin & Ishak (2017b) observe a decrease of the IOI when the local H0 measurement is removed. From this fact they conclude that the local measurement of H0 is an outlier compared to the others, what would favour a systematics-based explanation. This situation is compatible with the observed improvement in the statistical quality of the fitting analysis by Solà, Gómez-Valent & de Cruz Pérez (2017b,c) when the local H0 measurement is removed from the overall fit of the data using the RVM and the ΛCDM. In this respect, let us mention that a recent model-independent analysis of data on cosmic chronometers and an updated compilation of SNIa seem to favour the lower range of H0 (Gómez-Valent & Amendola 2018), what would be more along the line of the results found here, which favour a theoretical interpretation of the observed σ8 and H0 tensions in terms of vacuum dynamics and in general of DDE (cf. Fig. 10).
Citation: Riess et al; 2018b
Citation function: Motivation
To distinguish the emission from AGNs and star formation we use the fact that star-forming galaxies (SFGs) are known to exhibit radio–IR correlation across a wide range of luminosities and redshifts (see Condon 1992; Appleton et al. 2004; Basu et al. 2015). The correlation between 1.4 GHz luminosity and IR luminosity (monochromatic IR luminosity as well as bolometric IR luminosity between 8.0 and 1000 $\mu$m) in SFGs is attributed to the fact that both radio and IR emission are closely related to star formation (Ivison et al. 2010). AGNs with predominant radio emission from jet deviate from radio–IR correlation by showing radio-excess (Morić et al. 2010; Del Moro et al. 2013). Therefore, we examine if our NLS1s show radio-excess in the radio–IR correlation. We note that radio–IR correlation can be represented as the ratio of IR flux to 1.4 GHz radio flux density i.e. q=log(SIR/$S_{\rm 1.4 \, GHz}$) (see Appleton et al. 2004). For our radio-detected NLS1s we estimate q$_{\rm 22\, {\mu }m}$=log($S_{\rm 22 {\mu }m}$/$S_{\rm 1.4 \, GHz}$), where IR flux at 22 $\mu$m is taken from Wide-field Infrared Survey Explorer (WISE),2 and 1.4 GHz flux density is taken from FIRST whenever available, otherwise from NVSS. WISE is an all sky survey carried out at four MIR photometric bands namely W1 [3.6 $\mu$m], W2 [4.6 $\mu$m], W3 [12 $\mu$m], and W4 [22 $\mu$m], with 5σ sensitivity of 0.08, 0.11, 1.0, and 6.0 mJy, and angular resolution of 6.1, 6.4, 6.5, and 12.0, respectively (Wright et al. 2010). Using the most recent version of WISE source catalogue (AllWISE3 data release) we obtain MIR counterparts for 481, 480, 440, and 354 of our 498 radio-detected NLS1s with SNR ≥ 5.0 in W1, W2, W3, and W4 bands, respectively. The WISE counterparts of our radio-detected NLS1s are searched within a circle of 2.0 arcsec radius centred at SDSS optical positions. q$_{\rm 22 {\mu }m}$ is estimated using k-corrected fluxes, where IR spectral index is derived using W3 and W4 band fluxes, and radio spectral index is derived using 1.4 GHz and 150 MHz flux densities, whenever available, otherwise an average radio spectral index of −0.7 is considered.
Citation: Morić et al. 2010
Citation function: Uses; Uses
The equilibrium between the MFR and the two magnetic charges can lead to an infinite twist on the surface of the MFR because no toroidal field exists outside the MFR. The magnetic field of the line current I0 is included to decrease this to reasonable values, typically less than 4π, suggested by many observations (Liu & Alexander 2009; Wang et al. 2015; Guo et al. 2012). Combining contributions from line current I0 and the poloidal current inside the MFR, the expression for the toroidal field is written as (7)\begin{eqnarray} \label{eq:bt} B_{t}&amp;\approx&amp; \frac{\mu_0 I_0}{2\pi}\sqrt{\frac{2 \alpha I^2}{a^2 I_0^2}\left(1-\frac{r_{\rm a}^2}{a^2}\right)+\frac{1}{R^2}},~r_{\rm a}\leq a,\nonumber \\ &amp;=&amp; \frac{\mu_0 I_0}{2\pi r_{\perp}},~r_{\rm a}>a. \end{eqnarray}Bt≈μ0I02π2αI2a2I021−ra2a2+1R2,ra≤a,=μ0I02πr⊥,ra>a.For an analytical expression of the poloidal magnetic field, we follow the practice of Titov & Démoulin (1999), in which poloidal field is given in terms of the vector potential as in (8)\begin{equation} B_{p}=-\frac{\partial A_{t}}{\partial x}\frac{\vec{r}_{\perp}}{r_{\perp}}+\left(\frac{\partial A_t}{\partial r_{\perp}}+\frac{A_t}{r_{\perp}}\right)\vec{x}. \end{equation}Bp=−∂At∂xr⊥r⊥+∂At∂r⊥+Atr⊥x.The toroidal At outside the MFR (ρ ≥ a) is (9)\begin{equation} A_{t}(x,r_{\perp}) \approx \frac{\mu_{0}I}{2\pi}\sqrt{\frac{R}{r_{\perp}}}\mathcal{A}(k), \end{equation}At(x,r⊥)≈μ0I2πRr⊥𝒜(k),and inside (ρa) is (10)\begin{equation} A_{t}(x,r_{\perp}) \approx \frac{\mu_{0}I}{2\pi}\sqrt{\frac{R}{r_{\perp}}} (\mathcal{A}(k_{\rm a})+\mathcal{A}'(k_{\rm a})(k-k_{\rm a})). \end{equation}At(x,r⊥)≈μ0I2πRr⊥(𝒜(ka)+𝒜′(ka)(k−ka)).Here, \begin{eqnarray} &amp;&amp;\mathcal{A}(k)=k^{-1}[(2-k^2)K(k)-2E(k)], \\ &amp;&amp;k=2\sqrt{\frac{r_{\perp}R}{(r_{\perp}+R)^2+x^2}}, \\ &amp;&amp;k_{\rm a}=2\sqrt{\frac{r_{\perp}R}{4r_{\perp}R+a^2}}, \end{eqnarray}𝒜(k)=k-1[(2−k2)K(k)−2E(k)],k=2r⊥R(r⊥+R)2+x2,ka=2r⊥R4r⊥R+a2,and its derivative (14)\begin{equation} \mathcal{A}'(k)=\frac{(2-k^2)E(k)-2(1-k^2)K(k)}{k^2(1-k^2)}\cdot \end{equation}𝒜′(k)=(2−k2)E(k)−2(1−k2)K(k)k2(1−k2)·These expressions contain the complete elliptic integrals of the first and the second kinds, K(k) and E(k).
Citation: Titov & Démoulin (1999)
Citation function: Uses; Uses
Another possible indication of the different nature of the smallest clouds with respect to the largest ones is shown by their behaviour in the velocity dispersion vs. radius relation. This relation, known as the first Larson relation (Larson 1981), is expected to be of the form σv ∝ Rβ. The observed non-thermal motions in MCs are always supersonic (thermal motions for gas at T = 10 K is ≃0.1 km s−1), and the expected value of β, if the origin of these motions is purely supersonic turbulence, the so-called Burger turbulence, is β = 0.5 (McKee & Ostricker 2007). Various values of β in MCs have been derived by several authors, ranging from β ∼ 0.38 (Larson 1981) to β = 0.6 ± 0.3 (Miville-Deschênes et al. 2017). Restricting the evaluation of the relation to the interstellar clouds of the outer Galaxy, previous estimates give β = 0.47 ± 0.08 (Sodroski 1991), β = 0.53 ± 0.03 (Brand & Wouterloot 1995), β = 0.45 ± 0.04 (May et al. 1997), and β = 0.53 ± 0.06 for MCs in the third quadrant of the Galaxy (Rice et al. 2016), all in agreement with the a Burger-like turbulence. However, the nature of the non-thermal motions in MCs is still debated (Krumholz et al. 2019), and it is not yet clear whether they originate solely from turbulence or from large-scale gravitational collapse (Ballesteros-Paredes et al. 2011; Traficante et al. 2018a,b; Merello et al. 2019). We plot the σv–R relation for clouds of the FQS catalogue in Fig. 15. Two different regimes appear for clouds with R ≥ 2 pc and clouds with R   2 pc. For MCs with R ≥ 2 pc the velocity dispersion increases with radius with an exponent β = 0.59, similar to what is expected from pure supersonic turbulence, and to that previously measured. On the other hand, for MCs with R   2 pc the relation is almost flat, with β = 0.08. The non-thermal motions in the smallest clouds seem to be independent of cloud size. These condensations, likely to be only transient structures, are probably not formed by the turbulent ISM, which would lead to a Burger-like spectrum. It may be possible that these structures are the result of a large-scale effect, such as spiral-arm density-wave shock that injects the same amount of kinetic energy to all these objects. This large-scale effect would dominate over the local turbulence or the self-gravity within the smallest clouds, becoming increasingly irrelevant in the larger clouds.
Citation: Miville-Deschênes et al. 2017
Citation function: Compare/Contrast; Compare/Contrast
More interestingly, there are hints suggesting that the impacts of projection effects extend beyond richness misidentification. First, when Miyatake et al. (2016) reported a possible detection of assembly bias for subsamples of clusters divided based on the concentration of member galaxies, the signal appeared too large compared to theoretical predictions (Wechsler et al. 2006; Dalal et al. 2008) for the ΛCDM model. Subsequent works found that this large apparent signal might be due to projection effects (Busch & White 2017; Zu et al. 2017; Sunayama & More 2019). Secondly, when More et al. (2016) reported a detection of the so-called ‘splashback’ radius, a physically motivated boundary of cluster haloes, for redMaPPer clusters, the location of the splashback radius was found to be smaller than theoretical expectations (also see Chang et al. 2018, for a similar detection). Follow-up studies (Shin et al. 2019; Zürcher & More 2019) used samples of clusters selected based on the Sunyaev-Zel’dovich effect and found a different location of the splashback radius consistent with theoretical expectations, albeit with larger errors, suggesting that the original location may have been impacted by projection effects. The recent analysis in Murata et al. (2020) further indicated that previous analyses of the splashback radius for optically selected clusters may suffer from projection effects. Third, Murata et al. (2018) developed a forward modelling approach to calibrate the richness–mass relation from a joint measurement of cluster abundances and cluster lensing. However, they found that a population of less massive haloes, down to 1012h−1M⊙, had to be introduced in order for the ΛCDM model prediction to match the observations. Overall, these studies indicate that projection effects may impact not only cluster richnesses but also other cluster observables such as cluster lensing, which, if true, can render problematic the standard approach for cluster cosmology employed by photometric surveys.
Citation: Chang et al. 2018
Citation function: Motivation; Motivation; Motivation
IRIS always takes short exposure (with the shortest possible exposure time of 2.2 s) and long exposure with specified exposure time (14.5 s) one by one. In order to estimate and subtract the sky background, each field was observed in 10 dithered positions. Raw exposures were sky-subtracted and flat-fielded using standard IRAF (Tody 1986) routines. An astrometric solution was performed with Sextractor (Bertin & Arnouts 1996) and SCAMP (Bertin 2006), and then 10 dithered frames were resampled and combined with SWARP (Bertin 2010) into a single, final image (details of the pipeline used for calibrations can be found in Watermann 2012). Aperture photometry was performed with the DAOPHOT (Stetson 1987) package, and instrumental photometry was tied to the 2MASS system using the constant stars present in a given field as standards (usually more than 3 stars of brightness similar to our target with quality flag AAA in the 2MASS catalog). If there were no comparison stars of similar magnitude in a given field (cases of VY Pyx, SW Tau, and AL Vir), we used long exposures (14.5 s) to measure the brightness of comparison stars while the target was measured in the short exposure (2.2 s). We found a non-negligible color term in J band, and it amounts to −0.07 (J − K

s
). Internal precision of our photometry is at a level of 0.02 mag. In order to check correctness of our transformation to the 2MASS system, we compared the magnitudes of the constant stars present in the observed fields (transformed to the 2MASS system using an approach identical to our scientific objects) with the corresponding magnitudes from the 2MASS catalog. The observed fields offered very limited numbers of bright constant stars; thus for this test, we mostly used sources that were adopted as comparison stars in the transformation of the photometry of science targets. Each considered star was excluded from the set of comparison stars while transforming its own photometry. This test is presented in Figure 2. The mean difference between IRIS and 2MASS photometry is zero with the error on the mean of 0.002 mag in each band. We adopt this value as our zero-point uncertainty, which contributes to the systematic error of our calibration of PLRs.
Citation: Stetson 1987
Citation function: Uses; Uses
The problem of imaging global magnetic fields through helioseismology has been relatively underexplored, as studies have mainly focused on the solar rotational profile and other global and local flow fields (Lavely & Ritzwoller 1992; Basu et al. 1999; Giles 2000; Zhao & Kosovichev 2004; Hanasoge et al. 2012b, 2017). Some studies have analyzed the effects of local fields, such as those contained in a sunspot, on the reduction of wave power (Cally 2000; Schunker & Cally 2006), and changes in wave speed and flow patterns (Gizon et al. 2009; Švanda et al. 2014; Khomenko & Collados 2015; Rabello-Soares et al. 2018; Braun 2019). Nevertheless, early attempts (e.g., Gough 1990) that were made to study the impact of global magnetic fields (Lorentz-stress perturbations) on the eigenfrequencies and eigenfunctions of the standard solar model (model S in Christensen-Dalsgaard et al. 1996) focused on the details of the forward problem of frequency shifts induced by an axisymmetric magnetic field not aligned with the rotation axis. Dziembowski & Goode (2004) considered near-surface, small-scale as well as a large-scale deep toroidal fields at the tachocline. However, lacking a formal relation between Lorentz stresses and the consequent seismic signatures (sensitivity kernels), these efforts suffered from the drawback of restricting the field geometry to make the problem tractable. Several numerical studies have also considered small perturbations around a magnetized background medium (Cameron et al. 2008, 2010; Hanasoge et al. 2012a; Schunker et al. 2013). Attempts at deducing temporal changes in magnetic field configurations from variations in angular velocity have been met with limited success (Antia et al. 2013). In a recent analysis, Cutler (2017) explored the potential of learning about magnetic fields from mode-coupling theory. Hanasoge (2017) derived analytical forms of the Lorentz-stress sensitivity kernels in the context of normal-mode coupling. This has made it possible to consider the treatment of a completely general magnetic field configuration as a perturbation around a hydrostatic background state. This forms the basis of the current study.
Citation: Gough 1990
Citation function: Background; Background