adsabs/FOCAL huggingface dataset:
Following the short gamma-ray burst (sGRB) associated with this event, GRB 170817A (Abbott et al. 2017a,b; Goldstein et al. 2017), radio emission was anticipated as the associated merger outflow interacted with the circum-merger medium. Monitoring the radio emission could therefore provide crucial information on the energetics and geometry of the outflow, as well as the ambient environment. At radio frequencies, telescopes were observing the Advanced LIGO–Virgo probability region for GW170817 within 29 min post-merger (Callister et al. 2017a), and subsequent monitoring of AT 2017gfo resulted in an initial radio detection 16 d after the event (Abbott et al. 2017a; Hallinan et al. 2017). Further monitoring, predominantly at frequencies between 0.6 and 15 GHz, has since taken place (e.g. Alexander et al. 2017, 2018; Corsi et al. 2018; Dobie et al. 2018; Margutti et al. 2018; Mooley et al. 2018a,b,c;Resmi et al. 2018; Troja et al. 2018, 2019). At these frequencies, a general picture emerged in which the radio light curve was first observed to steadily rise, before it turned over and began a more rapid decay. Using a compilation of 0.6–10 GHz radio data from 17 to 298 d post-merger, Mooley et al. (2018c) derived both a fitted time for the radio peak of 174$^{+9}_{-6}$ d and a fitted 3-GHz peak flux density of 98$^{+8}_{-9}\, \mu$Jy (also see similar analyses in Dobie et al. 2018 and Alexander et al. 2018). The fitted radio spectral index α1 from this study is −0.53 ± 0.04, consistent with broad-band spectral indices determined using radio, optical, and X-ray data at various epochs, where the typical value is approximately −0.58 (e.g. Alexander et al. 2018; Margutti et al. 2018; Troja et al. 2018, 2019; Hajela et al. 2019). Mooley et al. (2018c) also found power-law dependencies for the rise and decay phases of approximately t0.8 and t−2.4, respectively, where t is the time since the merger. Within the associated uncertainties, these results are consistent with the broad-band evolution of AT 2017gfo (e.g. Alexander et al. 2018; Hajela et al. 2019; Lamb et al. 2019; Troja et al. 2019).
Citation: Troja et al. 2018
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Citation: Troja et al. 2018
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The albedo profile is then computed by cloudy after having specified the values of temperature T(r) and density ρ(r) for each radial patch (with r the radial distance). Results for the energy-dependent albedo profile A(Eloc) obtained for different radial patches in the cases of a = 0 (left), 0.9 (centre), and 0.998 (right) are shown in Fig. 8. As it can be clearly seen, in all the cases explored the simplifying assumption of 100 per cent albedo turns out to be a good approximation only at very high energies, i.e. Eloc ≈ 10–100 keV. Elsewhere, the albedo significantly deviates from unity (except for some values of r at lower energies), especially in the 0.1–10 keV band, which is indeed the working energy range of the forthcoming X-ray polarimeters like IXPE. Here different line features appear, more or less visible depending on the BH spin and radial distance, such as the clear iron absorption edge that occurs at around ∼6–7 keV (see e.g. Young, Ross & Fabian 1998). Plots in Fig. 9 give a more exhaustive view on how the albedo profile depends on the density of the slab in which calculations are performed. In this case the outputs are obtained for a = 0.998, three different values of the temperature, i.e. those corresponding to r = 2 (left), 5 (middle), and $10\, r_{\rm g}$ (right) according to the Novikov & Thorne (1973) temperature profile, and different values of the total hydrogen density n(H) between 1015 and 1023 cm−3. The plots show that the dependence of A(Eloc) on the density is stronger at low energies, where it exhibits an increasing behaviour by decreasing n(H). In particular, it attains values close to 0 at around 0.1 keV for particle densities in excess of 1022–1023 cm−3, especially for lower temperatures (i.e. for larger radial distances, see the right-hand panel). On the other hand, at higher energies ($\gtrsim 10$–20 keV), the albedo tends to reach the same value (≈1) in the entire range of densities explored.
Citation: Young, Ross & Fabian 1998
Citation function: Uses
In addition, if 26Al in the solar system was inherited from a molecular cloud, one would expect the solar system to have a (60Fe/26Al)0 ratio identical to that of the ISM. If the collapse timescales – depending on the amount of turbulence, the intensity of magnetic fields, and other complex parameters (McKee & Ostriker 2007) – exceed a few 26Al half lives, the solar system’s initial (60Fe/26Al)0 ratio is even expected to be higher than that of ISM because 26Al decays much faster than 60Fe. Both radionuclides have now been identified in the ISM via γ-ray astronomy (Diehl 2014). The measured ISM flux ratio 60Fe/26Al ratio is 0.15 (Wang et al. 2007), which translates into a mass ratio of 0.35. The ISM 60Fe/26Al mass ratio is therefore two orders of magnitude higher than the solar value of 3.9 × 10-3 (see Sect. 1), ruling out that both radionuclides were inherited from the ISM. Despite the observational evidence, and to keep alive the inherited model, Young (2014) proposed to decrease the theoretical 60Fe abundance in molecular clouds by assuming that stars more massive than 30 M⊙ do not explode as SNe. These stars would not contribute to the 60Fe inventory, bringing the theoretical ISM 60Fe/26Al ratio closer to that of the solar system. Because stars more massive than 30 M⊙ contribute to less than 50% of the 60Fe production2, this proposition cannot resolve the two orders of magnitude discrepancy between the model and observations. Furthermore, the recent discovery of a supernova whose progenitor mass was far above the threshold of 30 M⊙ is at odds with that solution (Gal-Yam et al. 2014). Finally, if 26Al was inherited from the natal molecular cloud, it would be homogeneously distributed in the SPD, which is contrary to observations (Krot et al. 2012). As discussed by Gounelle & Meynet (2012), some heterogeneity in the 26Al distribution is expected in the dense shell model because the shell collapse timescale (a few 105 yr) is comparable to the 26Al half life.
Citation: Young (2014)
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We constructed the gamma-ray folded light curve of 3FGL J2039.6–5618 using the orbital period from J. Strader et al. (2018, in preparation) and the two best periods in our searches using the full data and the data before MJD 57,040. The phase zero is defined to be the epoch of the optical maximum (T0 = 57,603.95787 in MJD). In order to maximize the signal-to-noise ratio, we use the method of photon weighting (Kerr 2011) instead of an aperture selection to produce the folded light curve. The probability that the photon is emitted by 3FGL J2039.6–5618 is assigned to each of the photons using gtsrcprob in the Science Tools. The orbital phase is calculated using the TEMPO2 package (Hobbs et al. 2006) with the Fermi pulg-in (Ray et al. 2011). In the timing model, we adopted the main-sequence/pulsar binary model (MSS; Wex 1998). Using the photon probabilities as the weights, the resulting weighted light curves are shown in Figure 4. From top to bottom, the orbital periods used in folding the light curves are PStrader = 0.22798177 days, Pfull = 0.2279757 days, and Pbefore = 0.2279808 days, respectively. Two orbital periods are shown for clarity. The light curve indicates that the modulation is a single peak. The FWHM of the peak spans from about ϕ = 0.25 to ϕ = 0.7, which occupies almost half of the orbital period. Although the best period found from the Rayleigh test has different values with different time spans, the folded light curves in Figure 4 generally show similar signal structures. As we speculate that the gamma-ray modulation disappears after MJD 57,040, it is not reliable to use the best period Pfull obtained from the full data set, which includes the no-signal duration. On the other hand, the best period Pbefore is obtained from the data only containing the portion before MJD 57,040; therefore, it may be biased to be applied to the study of the full data set. Therefore, with a negligible difference in the light curves and modulation significances (





 vs. 





 for data before MJD 57,040), we adhere to the orbital period PB = PStrader = 0.2279817(7) days, which is reported from the independent optical observation by J. Strader et al. (2018, in preparation), for the rest of this study. Figure 5 shows the energy-dependent orbital light curves of 3FGL J2039.6–5618 in the energy ranges of 0.1–500 GeV (top), 0.1–3 GeV (middle), and 3–500 GeV (bottom). It is clear that the orbital modulation is mostly contributed by the lower part of the energy.
Citation: Hobbs et al. 2006
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The formation and evolution of black holes (BHs) in the universe is one of the major issues to be addressed by the modern research in astrophysics and cosmology. In the mass range m
• ∼ 5–150 M
⊙, BHs are originated from the final, often dramatic stages in the evolution of massive stars (possibly hosted in binary systems). These compact remnants can produce luminous X-ray binaries (e.g., Mapelli et al. 2010; Farr et al. 2011; Inoue et al. 2016), can constitute powerful sources of gravitational waves for ground-based detectors like the current LIGO/Virgo facility (e.g., Belczynski et al. 2010; Dominik et al. 2015; Spera & Mapelli 2017; Boco et al. 2019; Spera et al. 2019; Abbott et al. 2021a, 2021b), can possibly energize short gamma-ray bursts and associated kilonovas (e.g., Abbott et al. 2020, 2021c; Ackley et al. 2020; Gompertz et al. 2020), can inject strong energy inputs in the primeval universe (e.g., Mirabel et al. 2011; Justham & Schawinski 2012; Artale et al. 2015; Madau & Fragos 2017; Lehmer et al. 2021), and can provide light seeds for the subsequent growth of more massive BHs (e.g., Madau et al. 2014; Volonteri et al. 2015; Lupi et al. 2016; Pacucci et al. 2017; Boco et al. 2020; Das et al. 2021). At the other end, in the range M
• ∼ 106–1010
M
⊙, supermassive BHs grow mainly by gaseous accretion that energizes the spectacular broadband emission of active galactic nuclei (AGNs). Such an activity can have a profound impact on galaxy evolution (e.g., Alexander & Hickox 2012; Lapi et al. 2014, 2018), as testified by the strict relationships between the relic BH masses and the physical properties of the hosts (e.g., Kormendy & Ho 2013; Shankar et al. 2016, 2020; Zhu et al. 2021). The intermediate-mass range m
• ∼ 103–106
M
⊙ is the most uncertain. So far, only tentative evidence of these systems has been identified (see Paynter et al. 2021). However, the chase is open in view of their astrophysical relevance. Most noticeably, they can provide heavy seeds for quick (super)massive BHs growth (e.g., Mayer & Bonoli 2019; Boco et al. 2020), as it seems required by the puzzling observations of an increasing numbers of giant monsters M
• ≳ 109
M
⊙ when the age of the universe was shorter than ≲0.8 Gyr (e.g., Mortlock et al. 2011; Venemans et al. 2017; Banados et al. 2018). Moreover, such intermediate-mass BHs will constitute important targets for space-based gravitational wave detectors like LISA and DECIGO (see eLisa Consortium 2013; Kawamura et al. 2021; also Barausse & Lapi 2021; Boco et al. 2021b).
Citation: Madau & Fragos 2017
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There are also simplified problems, less complex than fully developed turbulence, in which one finds the formation of small scales in the direction perpendicular to an applied magnetic field 





 It is well known that this effect appears in the context of MHD when imposed parallel-propagating waves interact with an inhomogeneous background consisting either of pressure-balanced structures or velocity shears (Ghosh et al. 1998). Phase mixing of torsional Alfvén waves in axisymmetric-equilibrium magnetic configurations have been studied in Ruderman et al. (1999), where possible applications to the solar corona and solar wind have been proposed. In two-dimensional (2D) equilibria, where the Alfvén velocity varies in directions perpendicular to the magnetic field, two mechanisms have been investigated in detail: (1) phase mixing (Heyvaerts & Priest 1983), in which differences in group velocity at different locations progressively bend wave fronts, and (2) resonant absorption, which concentrates the wave energy in a narrow layer where the wave frequency locally matches a characteristic frequency (Alfvén or cusp). These processes have been studied both by investigating normal modes of the inhomogeneous structure (Kappraff & Tataronis 1977; Mok & Einaudi 1985; Steinolfson 1985; Davila 1987; Hollweg 1987; Califano et al. 1990, 1992) and by considering the evolution of an initial disturbance (Lee & Roberts 1986; Malara et al. 1992, 1996). Effects of density stratification and magnetic line divergence (Ruderman et al. 1998), nonlinear coupling with compressive modes (Nakariakov et al. 1997, 1998), and evolution of localized pulses (Tsiklauri & Nakariakov 2002; Tsiklauri et al. 2003) have been considered. The propagation of MHD waves in inhomogeneous magnetic fields containing null points has also been studied in detail (Landi et al. 2005; see also McLaughlin et al. 2010 for a review). Phase mixing in 3D inhomogeneous equilibria has also been considered in the small-wavelength limit (Similon & Sudan 1989) using a WKB approximation (Petkaki et al. 1998; Malara et al. 2000), also within the problem of coronal heating (Malara et al. 2003, 2005, 2007). Particle acceleration in phase mixing of Alfvén waves in a dispersive regime has been studied by Tsiklauri et al. (2005) using particle-in-cell simulations, both in 2D (Tsiklauri 2011) and in 3D (Tsiklauri 2012) configurations. Finally, instabilities generating KAWs in a plasma with transverse density modulations have been considered by Wu & Chen (2013). Similar ideas involving dissipative mechanisms related to the interaction of Alfvén waves or KAWs and phase mixing have been examined in the context of the magnetospheric plasma sheet (Lysak & Song 2011) and in coronal loops (Ofman & Aschwanden 2002).
Citation: Davila 1987
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The origin of nitrogen in the Solar system is still an open question. More specifically, the main repository of nitrogen in the protosolar nebula (PSN) is still unclear, although there is some consensus that it may be atomic, N, or molecular, ${\rm N_2}$ (Schwarz & Bergin 2014). Furthermore, the large variations of the isotopic ratio of nitrogen (${\rm ^{14}N}/{\rm ^{15}N}$), as measured in various carriers within different types of Solar system objects, remain unexplained (Aléon 2010; Hily-Blant et al. 2013a, 2017; Füri & Marty 2015). One striking problem is the ${\rm ^{14}N}/{\rm ^{15}N}$ isotopic ratio of nitrogen in comets. Its average value, 144 ± 3 (Jehin et al. 2009; Bockelée-Morvan et al. 2015; Shinnaka et al. 2016; Hily-Blant et al. 2017), is three times lower than the bulk ratio of 441 ± 6 in the protosun as inferred from solar wind measurements (Marty et al. 2011). The reasons for these different ratios remain elusive, casting doubts on our understanding of the origin of the composition of comets and more generally of the origin of nitrogen in the Solar system. Several possibilities (not mutually exclusive) could explain the discrepancy: (i) the tracers of nitrogen observed so far in comets – HCN, CN, and ${\rm NH_2}$ – are minor reservoirs of cometary nitrogen and thus naturally do not reflect the bulk ratio in the PSN, (ii) efficient fractionation processes in the PSN at the time of comet formation, (iii) efficient fractionation processes in the parent interstellar cloud, and (iv) exchange processes within cometary ices since their formation. Recently, it was shown that protoplanetary discs – or equivalently PSN analogues – carry at least two isotopic reservoirs of nitrogen, traced, respectively, by CN and HCN, with HCN probing a secondary, fractionated, reservoir (Hily-Blant et al. 2017). Furthermore, the isotopic reservoirs traced by HCN and CN are found to be in a 1:3 ratio, respectively, reminiscent of the factor of 3 between the cometary and bulk isotopic ratios (144:441) in the PSN. It follows that exchange processes in parent bodies [possibily (iv) above] are not necessary. The PSN hypothesis is supported by models of selective photodissociation of ${\rm N_2}$ in protoplanetary discs (Heays et al. 2014) that predict a strong enrichment of HCN in ${\rm ^{15}N}$, but also of CN, in contrast with observations (Hily-Blant et al. 2017). At present, clear-cut observational evidences supporting the PSN or interstellar hypothesis are still lacking.
Citation: Jehin et al. 2009
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Solar wind velocity distribution moments are described in Behar et al. (2017). The ion density nsw is the moment of order 0, and the ion bulk velocity usw (a vector) appears in the moment of order 1, the flux density 

$n_{\mathrm{sw}} \ \underline{\mathbf{u}_{\mathrm{sw}}}$



n

sw

 


u

sw


_



. The bulk speed can be defined as the norm of the bulk velocity, that is, 

$u_{\mathrm{sw}} = |\underline{\vec{u}_{\mathrm{sw}}}$



u

sw

=|


u

sw


_



|. However, this bulk speed is representative of single-particle speeds as long as the velocity distribution function is compact (e.g., a Maxwellian distribution). Complex velocity distribution functions were observed by RPC-ICA within the atmosphere of 67P. For instance, partial ring distributions were frequently observed for solar wind protons at intermediate heliocentric distances, when the spacecraft approached the SWIC (Behar et al. 2017). To illustrate the effect of such distorted distributions, a perfect ring (or shell) distribution centered on the origin of the plasma reference frame can be imagined, in which all particles have the same speed of 400 km s−1. The norm of the bulk velocity in this case would be 0 km s−1, whereas the mean speed of the particles is 400 km s−1, which is the relevant speed for SWCX processes. This mean speed, noted Usw, of the particles is calculated by first summing the differential number flux over all angles, and then taking the statistical average (Behar 2018). Over the entire mission, the deceleration of the solar wind using the mean speed of the particles is much more limited than the deceleration shown by the norm of the bulk velocity (Behar et al. 2017): there is more kinetic energy in the solar wind than the bulk velocity vector would let us think. This is the main difference with the paradigm used at previously studied (and more active) comets (Behar et al. 2018b). These complex, nonthermal velocity distribution functions also prevent us from reducing the second-order moment (the stress tensor) to a single scalar value, which, for a Maxwellian distribution, could be identified with a plasma temperature. In the context of 67P and for an important part of the cometary orbit around the Sun, the temperature of the solar wind proton has no formal definition.
Citation: Behar et al. (2017)
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Citation: Behar et al. 2017
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Citation: Behar et al. 2017
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We first need to discuss that the environmental dependence of the metallicity of star-forming galaxies may depend on the scale at which the environment is defined. At small scales (a few tens of kpc), there is substantial evidence from observations and simulations for a decrement of metallicity and an enhancement of star-formation activity in galaxy close pairs, merging, and interacting systems compared to isolated, field galaxies, mostly attributed to the interaction-induced inflow of metal-poor gas from the periphery of interacting galaxies to the center, diluting their metal content, and increasing their gas fuel for star-formation (Mihos & Hernquist 1996; Kewley et al. 2006; Ellison et al. 2008, 2013; Michel-Dansac et al. 2008; Rupke et al. 2010; Sol Alonso et al. 2010; Perez et al. 2011; Scudder et al. 2012b; Ly et al. 2014). At intermediate group scales where galaxy interactions are more common compared to cluster and field environments (Perez et al. 2009; Tonnesen & Cen 2012)—owing to a combination of (1) a lower velocity dispersion of group galaxies relative to their cluster counterparts and (2) a higher number density of group galaxies compared to the field systems, which provide an ideal condition for interactions)—there is also evidence for a deficit of metals in group galaxies compared to control samples in the field (Lara-López et al. 2013b), possibly owing to a higher fraction of interacting galaxies. At larger filamentary and cluster scales, the slight metal enhancement of galaxies relative to the field might be due to (1) the inflow of already-enriched interafilamentary or interacluster gas into galaxies, as observations and simulations have shown a more metal-enriched intergalactic medium in cluster and filament environments compared to the field (Arnaud et al. 1994; Aracil et al. 2006; Stocke et al. 2006, 2007; Sato et al. 2007; Cen & Chisari 2011; Davé et al. 2011; Oppenheimer et al. 2012); (2) the environmental strangulation (Larson et al. 1980; Peng et al. 2015) of low-metallicity diluting gas falling from the surrounding LSS cosmic web into galaxies; (3) the environmental ram pressure stripping (Gunn & Gott 1972; Abadi et al. 1999) of the metal-poor diluting gas in the periphery of galaxies; and (4) trapping and recycling of metal-enriched outflows due to the hotter environment of filaments and clusters (Aracil et al. 2006; Cen & Ostriker 2006; Werner et al. 2008) compared to the field.
Citation: Ellison et al. 2008
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The main purpose of this paper is to calibrate the mass–observable relation from a joint measurement of the abundance (number counts) and the stacked cluster weak lensing profiles. We develop and apply our method to the Sloan Digital Sky Survey (SDSS) red-sequence Matched-filter Probabilistic Percolation (redMaPPer) cluster catalog that is constructed by identifying overdensities of red sequence galaxies with similar colors to galaxy clusters from the SDSS ugriz photometries (Rozo & Rykoff 2014; Rykoff et al. 2014; Rozo et al. 2015a, 2015b; see most recently Rykoff et al. 2016, for the details of the method). Since the cluster finder gives an estimation of the optical richness, λ, for each cluster, we will constrain the scaling relation between the optical richness and mass for the clusters. In this paper we develop a forward modeling approach, where we constrain the probability distribution of richness for a given halo mass, 





. This is in contrast with previous studies (Baxter et al. 2016; Jimeno et al. 2017; Melchior et al. 2017; Simet et al. 2017), where the backward modeling approach is employed to constrain the probability of mass for a given richness, 





. The forward modeling approach has several advantages. First, we can use the abundance measurements more easily to constrain the mass–observable relation, as Saro et al. (2015) constrained 





 from the abundance measurements of SZ-selected clusters after matching to redMaPPer clusters. Second, 





 can be inferred from 





 once the halo mass function 





 is given, based on the Bayes theorem, while the opposite transformation, i.e., inferences of 





 from 





, is not straightforward, because this requires knowledge of the richness function 





 over the whole range of λ, which is not generally available, or is at least very noisy (and possibly affected by contamination), for richness below a threshold richness in cluster catalogs. Third, the forward modeling is convenient to generate mock catalogs of clusters by populating halos in N-body simulations with galaxies, e.g., to test systematics in a cluster-finding algorithm.
Citation: Baxter et al. 2016
Citation function: Differences; Differences