adsabs/FOCAL huggingface dataset:
It is now well established that the weak lensing of distant galaxies by foreground mass provides a relatively clear window on to the large-scale structure of the Universe. This is true whether that foreground mass is in the form of discrete matter concentrations, as traced by galaxies (i.e. galaxy–galaxy lensing; Mandelbaum et al. 2013; Leauthaud et al. 2017; Joudaki et al. 2018; Prat et al. 2018; Blake et al. 2020), massive dark matter haloes (cluster lensing; Melchior et al. 2017; Dark Energy Survey Collaboration 2020), or the continuous large-scale matter distribution (cosmic shear; Heymans et al. 2013; Dark Energy Survey Collaboration 2016; Troxel et al. 2018; Chang et al. 2019; Hamana et al. 2020; Hildebrandt et al. 2020; Amon et al. 2021; Asgari et al. 2021; Secco, Samuroff et al. 2021). Though the measurement method and the exact form of the theory predictions differ slightly in the three cases, they are all fundamentally probes of the growth of structure at low redshift. Similarly cross-correlations between galaxy lensing and other observables can be powerful probes in their own right; recent examples include galaxy lensing × CMB lensing (Schaan et al. 2017), voids correlated with CMB lensing (Vielzeuf et al. 2021), and galaxy weak lensing crossed with gamma-ray emission (Ammazzalorso et al. 2020), each of which provide probes of dark matter with slightly different sensitivities. A measurement of cosmological weak lensing, however, is subject to a range of systematic effects; that is, observational effects that mimic a cosmological lensing signal, and so bias cosmological inference if one neglects them. Depending on the systematic in question, the most effective mitigation strategy may be quite different. In broad terms, however, the standard approach is to either (i) mitigate systematics where possible, either by applying a calibration to the data, or discarding the data points most strongly affected or (ii) marginalize over them with a parametric model. Often a combination of the two is appropriate, and the prior used in (ii) is informed by additional data or simulations, and detailed testing of the calibration step in (i).
Citation: Chang et al. 2019
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Lithium is the only metal element produced during the Big Bang nucleosynthesis (BBN) due to the lack of stable nuclei with mass number eight (Fields, Molaro & Sarkar 2014). The element abundances predicted by the standard BBN theory for the baryonic density coming from the Planck mission agree well with those observed, except for 7Li (Fields 2011; Coc, Uzan & Vangioni 2014). Indeed, the abundance of lithium measured in the low-metallicity Galactic halo stars is A(7Li)  = log[N(7Li)/N(H)] + 12 = 2.25 (Spite & Spite 1982; Sbordone et al. 2010; Bonifacio et al. 2015), which is ∼3 times below the estimate of the standard cosmological model A(7Li) = 2.72 ± 0.06 (Cyburt et al. 2016). The latter value depends on the baryon-to-photons ratio $\eta = \frac{N_b}{N_{\gamma }} \propto \Omega _B h^2$, with Ωb is the cosmological baryon density and h is the dimensionless hubble parameter (Planck Collaboration XIII 2016). This problem is also known as the Cosmological Lithium problem (Fields et al. 2014). A possible solution can be ascribed to convective diffusion in the pre-main-sequence phase as well as during the lifetime of these halo stars (Fu et al. 2015) or to new physics beyond the standard model. On the other hand, the young stellar populations in our Galaxy show Li-abundances four times greater than the SBBN estimate and more than one order of magnitude greater than the halo stars (Spite 1990; Lambert & Reddy 2004; Lodders, Palme & Gail 2009; Ramírez et al. 2012; Fu et al. 2018). The evidence of a growth requires the existence of additional lithium factories. In the last decades several astrophysical Li sources have been proposed, like Galactic cosmic-rays, AGB stars, low-mass Carbon stars, type II supernovae, and classical novae (D’Antona & Matteucci 1991; Romano et al. 1999; Prantzos 2012; Matteucci 2021). The recent detection in the outburst spectra of classical novae of 7Li and 7Be ii, an isotope whose unique decay channel is into lithium through electron capture, have confirmed these objects as Li producers. The corresponding yields inferred have placed nova explosions as the main lithium factories in the Galaxy. The time-scales involved also match, as shown by detailed Galactic chemical evolution (Izzo et al. 2015; Tajitsu et al. 2015; Molaro et al. 2016; Izzo et al. 2018; Cescutti & Molaro 2019; Grisoni et al. 2019; Molaro et al. 2020a; Matteucci 2021).
Citation: Fields et al. 2014
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A couple of years after the closure of the solar flare myth debate, Dere et al. (1997) and Thompson et al. (1998) reported on the presence of wavefronts observed on the solar disk that propagated away from the site of the flare. They were originally termed “EIT waves” (Thompson et al. 1999) after the instrument that first detected them (EIT on board the Solar and Heliospheric Observatory (SOHO) Delaboudiniére et al. 1995) and have now been intensely studied for over 15 years. Over that time they have been assigned different terms, such as flare waves (e.g., Thompson et al. 2000; Warmuth et al. 2003), extreme-UV (EUV) waves (e.g., Patsourakos & Vourlidas 2012; Muhr et al. 2014), coronal waves (e.g., Douglas & Ballai 2007; Webb & Howard 2012), and coronal bright fronts (e.g., Gallagher & Long 2011, and references therein). While there are differences in the precise definitions of these terms, broadly speaking they describe the same phenomenon: a bright front propagating away from a point-like origin on the solar disk. In many cases the origin appears to be from the site of a solar flare associated with a CME (e.g., Thompson 1999; Thompson & Myers 2009; Wills-Davey & Attrill 2009; Nitta et al. 2013), but some workers have reported a stronger correlation of wave occurrence with CMEs rather than flares (e.g., Wills-Davey & Attrill 2009, and references therein). For simplicity, henceforth in this paper we refer to them as EIT waves although we do not use the EIT instrument for any measurements in our study. We define EIT waves with the criteria described in Section 4. Nitta et al. (2013) provide a recent review of the various physical narratives that have been assigned to EIT waves, such as the coronal manifestations of Moreton waves (e.g., Thompson 1999; Warmuth et al. 2001, 2002), the base of expanding overhead CME magnetic structures (e.g., Delanée & Aulanier 1999), and solitons (Wills-Davey et al. 2007). Other reviews on EIT waves can be found by Wills-Davey & Attrill (2009), Gallagher & Long (2011), and Patsourakos & Vourlidas (2012).
Citation: Douglas & Ballai 2007
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The mass accretion onto the black hole is important for a better understanding of AGN evolution. The Eddington ratio, the ratio between the AGN bolometric luminosity and the Eddington luminosity (Lbol/LEdd), provides insight into the black hole growth because the bolometric luminosity reflects the mass accretion rate. We show AGN bolometric luminosity versus black hole mass for our sample of broad-line AGNs in the different redshift bins in the left panel of Figure 5. The different X-ray surveys are shown with different symbols as labeled. The dotted reference lines indicate constant Eddington ratios of 1, 0.1, 0.01, and 0.001. Our sample of broad-line AGNs covers the black hole mass range 7.0  log MBH/M⊙  9.5 and the bolometric luminosity range 43  log Lbol  47 with a wide dispersion in the Eddington ratio distribution. For comparison, we show published observations in the same redshift range from the literature in the right panel of Figure 5 (Gavignaud et al. 2008; Merloni et al. 2010; Shen et al. 2011; Nobuta et al. 2012; Matsuoka et al. 2013). The SDSS quasar sample (gray points; Shen et al. 2011) is limited to the high-mass and high-luminosity regime because the SDSS detection limit corresponds to a luminosity of log Lbol ∼ 46 at z ∼ 1. Compared to the SDSS quasar sample, our sample of broad-line AGNs shows a wider dispersion in the black hole mass, AGN bolometric luminosity, and Eddington ratio distribution, consistent with previous studies on deep AGN samples (Gavignaud et al. 2008; Merloni et al. 2010; Nobuta et al. 2012; Matsuoka et al. 2013), which fill in the low-mass and low-luminosity region. The figure shows contours at the 1σ level, together with the literature data, except the SDSS quasar sample. The figure also reveals that only a small number of AGNs exceed the Eddington limit by a small amount. AGNs with similar black hole masses show a broad range of bolometric luminosities spanning about two orders of magnitude, indicating that the accretion rate of black holes is widely distributed. This suggests that the AGN cosmic downsizing phenomenon could be explained by some more-massive black holes with low accretion rates, which are relatively fainter than less-massive black holes with efficient accretion. Lusso et al. (2012) suggest that AGNs show higher Eddington ratios at higher redshift at any given MBH, and the Eddington ratio increases with bolometric luminosity. We confirm that there is a tendency for low-luminosity AGNs (log Lbol ≲ 45.5) with less-massive black holes (log MBH/M⊙ ≲ 8) to have lower Eddington ratios than high-luminosity AGNs (log Lbol ≳ 45.5) with massive black holes (log MBH/M⊙ ≳ 8), consistent with Lusso et al. (2012). It is important to note that, when comparing with results in the literature, one should take into account the different methods of spectral line fitting and correction for bolometric luminosities. Nevertheless, they show similar distributions of the accretion rate of black holes over a wide range, consistent with previous studies.
Citation: Shen et al. 2011
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Level crossing statistics is a pioneering approach for characterizing stochastic processes introduced by S. O. Rice (Rice 1944, 1945). Up-, down-, and conditional crossing statistics are modifications to the primary definition of level crossing (Bardeen et al. 1986; Bond & Efstathiou 1987; Ryden 1988; Ryden et al. 1989; Matsubara 1996, 2003; Brill 2000; Shahbazi et al. 2003; Movahed & Khosravi 2011; Ghasemi Nezhadhaghighi et al. 2017). Minkowski functionals, which are also closely related to the crossing statistics, provide 1 + D functionals to quantify the morphology in D dimensions (Hadwiger 2013) and have been utilized for cosmological random fields (Mecke, Bucheri & Wagner 1994; Schmalzing, Buchert & Kerscher 1995; Schmalzing & Górski 1998; Matsubara 2003, 2010; Hikage, Komatsu & Matsubara 2006; Codis et al. 2013; Ling et al. 2015; Fang, Li & Zhao 2017). A number of critical sets including peaks (hills), troughs (lakes), saddles, voids, skeleton, genus, and Euler characteristics, are more popular in cosmology for different purposes and they have been fully explored for Gaussian stochastic fields. Some extensions for non-Gaussian and anisotropic conditions have been done in some research (Matsubara 2003; Pogosyan et al. 2009; Pogosyan, Pichon & Gay 2011; Gay et al. 2012; Codis et al. 2013). More recently, Betti numbers, Euler characteristics, and Minkowski functionals for a set of cosmological 3D fields have been examined extensively (Pranav et al. 2019). The scaling approach for investigating cosmological stochastic fields has been discussed by Borgani (1995), Movahed et al. (2011). Standard estimators like three- and four-point functions in real space, bispectra and trispectra in harmonic space, multiscaling methods such as wavelets (Planck Collaboration 2014b, 2016d; and references therein), and regeneration of stochastic processes based on the Fokker–Planck equation (Ghasemi et al. 2006) have also been considered.
Citation: Schmalzing, Buchert & Kerscher 1995
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CME-driven shocks accelerate not just protons, but also the electrons in the solar corona (Holman & Pesses 1983; Schlickeiser 1984; Kirk 1994; Mann et al. 1995, 2001; Mann & Klassen 2005). These accelerated electron beams can be observed as type II bursts in the solar radio radiation in the metric wave range (Wild & McCready 1950; Uchida 1960). Type II bursts require electrons escaping from the shock front, and the lack of these bursts implicates the absence of accelerated electrons as type II bursts occur when 0.2–10 KeV electrons are accelerated in the shock front (see, e.g., Bale et al. 1999; Knock et al. 2001; Mann & Klassen 2005). Energetic electrons are unstable to Langmuir waves, thus they are converted into radio emission at the local plasma frequency and its harmonic (see Nelson & Melrose 1985). Therefore, type II radio bursts hold crucial information of both the shock and the surrounding ambient medium in which the CME-driven shock propagates (Gopalswamy et al. 2008a). Although almost every large SEP event is accompanied by a type II radio burst (Gopalswamy 2003; Cliver et al. 2004) that indicates CME-driven particle acceleration (Gosling 1993; Reames 1999), we have ten events in our sample that lack a type II burst: 14 August 2010, 03 August 2011, 04 March 2012, 26 May 2012, 27 May 2012, 14 June 2012, 08 September 2012, 14 December 2012, 21 April 2013, and 06 November 2013. Of the ten events, two originate in the west, three in the east, and five at the disk center. On average, these events have a maximum velocity of about 1000 km s−1 and maximum Mach number of about 0.94. The protons accelerated by these events have peak fluxes of about 23.2 cm−2 s−1 sr−1 in the > 10 MeV band, making these events the slowest and weakest SEPs in the sample. As the propagation of radio bursts does not depend on the magnetic connectivity, a possible explanation for their absence could be that the path of the radio burst did not coincide with the instrument on board the satellite or that the detection of the waves was below the range of the radio instrument, hence missing the signature. Detailed investigation is required to understand the absence of DH type II radio bursts in these events.
Citation: Nelson & Melrose 1985
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To provide enough galaxies to adequately train a neural network, EAGLE galaxies from the simulation snapshots with a redshift of less than 1.0 were used. Objects with M⋆ greater than 1010 M⊙ were selected while the merging partner of the merging systems must be larger than 109 M⊙. The merging partner must also be more than 10% of the M⋆ of the primary galaxy. Galaxies were deemed to have merged when they are tracked as two galaxies in one simulation snapshot and then tracked as one galaxy in the following snapshot in the EAGLE merger trees catalogue (Qu et al. 2017). This prevents the inclusion of chance flybys that may be selected as mergers if the EAGLE galaxies were selected based on proximity. Systems that are projected to merge, using a closing velocity extrapolation, within the next 0.3 Gyr (pre-merger) or are projected to have merged, again using a closing velocity extrapolation of the progenitors, within the last 0.25 Gyr (post-merger) were selected, along with a number of non-merging systems, and gri band images were created of these systems. Springel et al. (2005) have shown that the effects of a merger are visible for approximately 0.25 Gyr after the merger event while the pre-merger stage is much longer. However, we chose to have the pre and post merger period approximately equal as tests conducted with longer pre-merger times showed no improvement, see discussion in Sect. 4.2. We note, however, that the merger timing may suffer from imprecision as a result of the coarse time resolution of the EAGLE simulation, that is the time between snapshots, which becomes coarser at lower simulation redshift. Each galaxy was imaged at an assumed distance of 10 Mpc and each image contains all material within 100 kpc of the centre of the target galaxy and is 256 × 256 pixels, where 256 pixels corresponds to a physical size of 60 kpc. There are 537 pre-merger, 339 post-merger and 335 non-merging systems, each with six random projections to increase the size of the training set. Each of the six projections are treated as individual galaxies resulting in 3222 pre-merger, 2034 post-merger and 2010 non-merging galaxy images for training. The pre-mergers and post-mergers were combined to form the merger class, keeping the pre-merger image if the same galaxy appears in both sets.
Citation: Springel et al. (2005)
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In Table 3, we present a detail comparison of our theoretical energy values from the layer n = 7. For this purpose, we have taken four data sets available in the literature. First, we compare with the ab initio values of Fritzsche (1995) because they have used a larger configuration space than previous ab initio computations (Vajed-Samii & MacDonald 1981; Huang et al. 1983). Secondly, theoretical values from the CHIANTI V.8 (Landi et al. 1999; Del Zanna et al. 2015) because these are considered the most appropriate data for astrophysical applications (Träbert 2014). Thirdly, with the most recent semi-empirically predicted and tentative experimental data values of Del Zanna & Badnell (2016). Fourthly, with the NIST data base (NIST Atomic Spectra Database (ver. 5.3) 2017), which is considered as the most critically analysed data base. Comparison with observed energies (Del Zanna & Badnell 2016) and with the NIST values shows that the present ab initio values are not fully converged to experimental values. In order to achieve better accuracy relative to experimental values, we suggest more balanced and extended reference configuration sets as a starting point of the orbital expansion. This, however, results in very large expansions of wave functions, which is computationally very expensive. Nevertheless, with the present set of calculations for CV correlations, results for the lowest 31 levels are better than obtained before in any ab initio investigation. A very good agreement with experiment is achieved to an accuracy better than 0.5 per cent for low lying levels and 0.8 per cent for higher lying levels, whereas this accuracy is 1–2 per cent for the mcdf calculations of Fritzsche (1995) and for superstructure calculations available at CHIANTI V.8 (Landi et al. 1999; Del Zanna et al. 2015). In some more detail, there is no experimental connection in NIST data base to the 5 levels with J = 7/2 and two levels with J = 9/2 for the 3s23p43d configuration with other levels. The present results of energies for these seven levels with less than 0.45 per cent error with new tentative experimental assignments (Del Zanna & Badnell 2016) testify them. However, we suggest different level ordering than provided by semi-empirical formalism of Del Zanna & Badnell (2016). We briefly discuss later the shift in level ordering and reasons for it. We also note that there is 2100 cm−1 difference between the latest experimental energy (Del Zanna & Badnell 2016) and NIST data base for the level no. 31 (2D3/2). The present value of 683 070 cm−1 confirms the experimental energy (Del Zanna & Badnell 2016), and we suggest a replacement in the NIST data base with new experimental value.
Citation: NIST Atomic Spectra Database (ver. 5.3) 2017
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WASP-43b is the most heavily scrutinized phase curve, with four analyses of this data set already published (Stevenson et al. 2017; Mendonça et al. 2018; Morello et al. 2019; May & Stevenson 2020). Our phase curve semi-amplitude, eclipse depth, and radius are consistent with all of these works. The more contentious issue is that of the phase curve’s phase offset and nightside temperature. Stevenson et al. (2017) initially reported only a 2σ upper limit on the nightside temperature of 650 K, while all subsequent reanalyses (including ours) favour a significantly detectable nightside temperature of ∼800 K. As for the planet’s phase offset, Stevenson et al. (2017) and May & Stevenson (2020) favour a larger phase offset (21 ± 2 °E) than Mendonça et al. (2018) and Morello et al. (2019) (12 ± 3 °E and 11 ± 2 °E). May & Stevenson (2020) claimed that the differences between the retrieved phase offsets is the result of temporal binning which was not used by Stevenson et al. (2017) and May & Stevenson (2020) but was used by Mendonça et al. (2018), Morello et al. (2019), and this work. Fitting the temporally binned photometry for all 17 phase curves with each of our detector models already required more than 2000 CPU hours, and expanding this to unbinned photometry for all phase curve fits would require more than 125 000 CPU hours (or 434 d using our 12× multithreading computer) optimistically assuming all of detector models scaled linearly with the number of input data. However, we did try fitting just the WASP-43b unbinned phase curve with our preferred detector model (BLISS) and found that our phase offset and nightside temperature was unchanged. Including a linear slope in time also did not affect our phase offset or nightside temperature. Instead, we find that the phase offset inferred by our models depends on the choice of phase curve model, as our 4-parameter (v2) phase curve models are consistent with those of Stevenson et al. (2017) and May & Stevenson (2020), while our 2-parameter phase curve models (v1) are consistent with Mendonça et al. (2018) and Morello et al. (2019). Ultimately, we cannot decide between these two discrepant offsets as the ΔBIC between the two phase curve models for our preferred BLISS detector model is only 3.7 (insignificantly favouring the 20.4 ± 3.6 offset from the v2 model). For reference, Stevenson et al. (2014b) found phase offsets ranging from roughly −6 to 17 deg east in the Hubble/WFC3 bandpass.
Citation: Stevenson et al. (2017)
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Citation: Stevenson et al. (2017)
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Citation: Stevenson et al. (2017)
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Recently, forms of feedback that are fundamentally different from SNe have been shown to be essential to galaxy formation. Murray, Quataert & Thompson (2010) analysed the dynamical effects of several forms of stellar feedback on parent molecular clouds. In their models they include momentum input from ionized gas in H ii regions, shocked stellar winds, hot gas pressure, protostellar jets and cosmic rays. Murray et al. (2010) conclude that radiation pressure (RP) on dust grains is likely to be the dominant form of feedback in star-forming galaxies. A variety of other studies have reached the same conclusions, placing the combination of RP and photoionization of gas by massive stars as the dominant mechanism for disruption of molecular clouds and internal regulation of the SF process (Indebetouw et al. 2009; Krumholz & Matzner 2009; Murray et al. 2010; Andrews & Thompson 2011; Hopkins, Quataert & Murray 2011; Lopez et al. 2011; Pellegrini, Baldwin & Ferland 2011). RP alone might also be the only mechanism that explains galactic fountains and the warm gas outflows observed in absorption in high-redshift galaxies (Murray, Ménard & Thompson 2011). In addition, recent numerical work by Krumholz & Thompson (2012) shows that radiation feedback fully accounts for the large gas velocity dispersions measured in young star clusters in the MW. There are at least three reasons why radiative feedback is an essential ingredient of the galaxy formation process. First, observations show that molecular clouds begin to disperse shortly after the O stars form and before the first SNe explode and deposit their energy into the gas (Kawamura et al. 2009). Secondly, the total energy output of a stellar cluster is dominated by radiation. The rate of radiative energy output by O and B stars is ∼200 times larger than the average power injected by SNe and stellar winds during the lifetime of the most massive stars. Thirdly, it is difficult to explain the large gas turbulence values observed in star-forming regions without including the momentum input by radiation (Murray et al. 2010).
Citation: Murray et al. 2010
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Citation: Murray et al. 2010
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