adsabs/FOCAL huggingface dataset:
Here, we report Li–Be–B and Al–Mg isotopic compositions of seven CAIs from Sayh al Uhaymir (SaU) 290 (CH) and one CAI from Isheyevo (CH/CB) chondrites, which are one of the most pristine (unmetamorphosed) meteorites in our collections (e.g., Bischoff et al. 1993; Weisberg et al. 2001; Krot et al. 2002). The CH and CH/CB (hereafter: CH–CB) chondrites and their components are characterized by enrichments in 15N (up to 1,100‰; Murty et al. 2007; Ivanova et al. 2008; Briani et al. 2009; Bonal et al. 2010), which is similar to the characteristics of comets (Füri & Marty 2015). In addition, Van Kooten et al. (2016) and Olsen et al. (2016) proposed that Mg and Cr isotopic compositions of bulk CH–CB chondrites require significant amounts (20%–50%) of primordial molecular cloud matter in their precursor material. Hence, CH–CB chondrites may have accreted a significant amount of outer solar system materials. Most CAIs in CH–CB chondrites also have a unique absence of excess 26Mg derived from the decay of the short-lived radionuclide 26Al (Kimura et al. 1993; Weber et al. 1995; Krot et al. 2008a), which is unlikely attributed to thermal metamorphism and/or aqueous alteration in the parent bodies (Kimura et al. 1993; Krot et al. 2002; Krot et al. 2008b; Zhang & Hsu 2009). Thus, CH–CB CAIs may also have distinctive information about the origin of 10Be in the solar protoplanetary disk. However, because of their small sizes, it is difficult to measure B isotopic compositions of CAIs in CM, CO, and CH–CB chondrites using a conventional mass spectrometer. To overcome this problem, we have developed a protocol for high accuracy and high spatial resolution measurement techniques using a NanoSIMS 50 (Fukuda et al. 2018). Based on a newly obtained data set in this study, together with the data of previous studies, we will discuss the origin of 10Be in the early solar system and its implications for the astronomical setting of CAI formation in the solar protoplanetary disk.
Citation: Bonal et al. 2010
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However, most Herschel sources are not SMGs; they are, instead, less luminous dusty star-forming galaxies at lower redshifts (z  2; Casey et al. 2012, 2014). To select Herschel sources that are likely to be SMGs, we chose only the subsample that satisfies the following criteria: (1) flux density peak at 350 μm (





 and S500 S350; i.e., “350 μm peakers”), (2) S500 > 20 mJy, and (3) >3σ detections in all three SPIRE bands. Criterion 1 is essentially a photometric redshift selection because emission from dusts at T = 35 K would peak at 350 μm if redshifted to z ∼ 2.5. This is confirmed by the blind carbon monoxide (CO J = 1–0) survey of a subsample of the brightest 350 μm peakers (S350 ≥ 115 mJy), which has shown a strikingly similar redshift distribution as 850 μm selected SMGs (zCO = 2.5 ± 0.8; Harris et al. 2012). But note that most of these bright sources are strongly lensed and they do not overlap with our sample. Criterion 2 is introduced to ensure that the Rayleigh–Jeans extrapolation would give S850 > 3 mJy, the classic definition of an SMG, given a typical power-law slope of 3.5 for a modified blackbody with a frequency-dependent absorption cross section (





). Criterion 3 ensures that all of the sources we considered are statistically significant. This is necessary because the image depth varies substantially from field to field, ranging from σ500 = 15 mJy beam−1 for the large HeLMS and HerS fields (confusion noise included; Oliver et al. 2012; Viero et al. 2014) to confusion limited with σ500 = 6.8 mJy beam−1 for the deeper HerMES fields (Nguyen et al. 2010). Given the range of observed far-IR SEDs at z = 2 from Casey et al. (2012), our color selection and the high threshold on the 500 μm flux density ensure that ∼95% of our sample would be classified as SMGs if they were observed at 870 μm. Nevertheless, we should keep in mind that the Herschel-selected SMGs are a subsample of SMGs and they likely cover a smaller range of dust temperatures than 870 μm selected SMGs (e.g., Hwang et al. 2010; Magnelli et al. 2012). Only 70,823 Herschel sources remained after this selection. The average surface density of 92 deg−2 is five times lower than the observed 870 μm source count above S870 ≳ 3 mJy (∼500 deg−2; Coppin et al. 2006; Weiß et al. 2009). This is not surprising given that almost half of the total Herschel area is only 10%–20% complete at S500 = 20 mJy. Note that this incompleteness in the Herschel catalogs is not a concern for compiling a sample of SMG−QSO pairs.
Citation: Harris et al. 2012
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We measured the apparent axial ratio on the IF814W-band data, which correspond to the rest-frame V band for galaxies at z ∼ 0.4 and the rest-frame B band for those at z ∼ 0.8. Such differences in the rest-frame wavelength could cause some biases in the morphological analysis due to the color differences between bulge and disk, the blue star-forming regions/clumps, the dust extinction effect, and so on (e.g., Windhorst et al. 2002; Huertas-Company et al. 2009; Wuyts et al. 2012; Vika et al. 2013; Murata et al. 2014; Mager et al. 2018). In order to check the effects of the morphological K-correction, we used publicly available HST/ACS VF606W-band data over a 0.05 deg2 region in the COSMOS field from the CANDELS survey (Grogin et al. 2011; Koekemoer et al. 2011). With the VF606W-band data, we can measure the apparent axial ratio of galaxies at 0.2  z  0.6 in the rest-frame B band and investigate to what extent the difference in the rest-frame wavelength affects the measurements. There are 92 main-sequence and 51 passively evolving galaxies with VF606W  25 at 0.2  z  0.6 in the region, and we measured the apparent axial ratio of these galaxies on the VF606W-band data in the same way. In Figure 17, we compare the apparent axial ratios b/a measured on the VF606W-band data with those measured on the IF814W-band data. The differences between the VF606W and IF814W bands are also summarized in Table 4. The apparent axial ratios measured on the VF606W- and IF814W-band data agree well with each other for both main-sequence and passively evolving populations. The average values of (b/a)F606W − (b/a)F814W are −0.006 and −0.007 for main-sequence and passively evolving galaxies, respectively. These systematic offsets from zero are slightly larger than the averages of the measurement errors but much smaller than the dispersion around the mean value. When we use only bright subsamples with VF606W  22, the results do not significantly change, although the average offsets and measurement errors become slightly smaller. Since these systematic offsets are much smaller than the bin width of 0.1 in the distribution of b/a we used, the morphological K-correction does not significantly affect the distribution of the apparent axial ratio.
Citation: Grogin et al. 2011
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From the result of the χ2 minimization, we found that the minimized χ2 values agree with the expected values, i.e. the computed χ2 are typically in the range of ($\mathrm{d.o.f.}-\sqrt{2 \mathrm{d.o.f.}}$, $\mathrm{d.o.f.}+\sqrt{2 \mathrm{d.o.f.}}$), where d.o.f. is the number of degrees of freedom. This means that the fits describe the observed data rather well. The only exception is with χ2 ≈ 20, which occurs for nearby AGN, z  0.2, and for the highest energy band, E > 10 GeV. Note that there is a strong contribution of the source, Mrk 421, in the first redshift interval at high energies, E > 10 GeV for quiescent states. Mrk 421 is a very hard spectrum γ-ray source with a photon index of ≈1.77 and its semiminor and semimajor axes at 68 per cent confidence are of 0$_{.}^{\circ}$0067 as derived in Nolan et al. (2012). Semiminor and semimajor axes of many 2FGL sources are derived with one order of magnitude higher uncertainties than those for Mrk 421 in the 2FGL catalogue (Nolan et al. 2012). We noted that the discrepancy between the observation and model is particularly strong in the annular bin, r  0$_{.}^{\circ}$05, for the redshift interval z  0.2 and for the highest energy band, E > 10 GeV. If we exclude photons from Mrk 421, then the minimized χ2 value is 7.5 and is consistent with the expected one. In the limit of a large number of counts in each bin, the likelihood is given by $\mathcal {L}=\text{exp}(-\chi ^{2}/2)$, so that minimizing χ2 is equivalent to maximizing the likelihood, $\mathcal {L}$. We found that the inclusion of a pair halo component in the model does not improve the likelihood value sufficiently to establish the presence of this pair halo component in the data. Therefore, we derived the one-sided 95 per cent upper limit on the fraction of photons attributable to a pair halo component by fitting the normalization of this component, for which we increase its normalization until the maximum likelihood decreases by 2.71/2 in logarithm. The computed upper limits are between 2 and 6 per cent depending on energy band and redshift interval. These upper limits are stronger than those obtained before. Note that the model for a point-like source used in the likelihood analysis is considered to be precisely established, however, the number of photons recorded during flaring states is close to those numbers of photons recorded during quiescent states for each of these redshift intervals. The expression, such as equation (1), leads to more conservative upper limits on the fraction of photons attributable to a pair halo component, since it takes the error bars assigned to the model into account. If the point-like source model is considered well established, then the error bars shown in Table 3 would decrease by a factor of ≈1.5.
Citation: Nolan et al. (2012)
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Citation: Nolan et al. 2012
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On the CND scales displayed in Fig. 12, the CO(3–2) emission in this highly inclined (i = 59°; Appendix D) barred Seyfert 2 galaxy is detected at every single position inside r  200 pc. However, a sizeable fraction of the molecular gas appears concentrated in a nuclear ring of ∼200 pc (deprojected) radius. Molecular gas in this nuclear ring is feeding an active star formation episode detected at optical as well as near and mid IR wavelengths. The molecular gas ring is the likely signature of the gas response to the ∼140″ (16 kpc) long stellar bar at its ILR region. The bar, detected in the NIR by Quillen et al. (1997) and Buta et al. (2009), shows a prominent boxy-shape morphology. At the larger radii imaged inside the ALMA FOV (Fig. 10), molecular gas shows a two-arm spiral structure that is connected to the nuclear ring. Closer to the Seyfert 2 nucleus (r  50 pc), molecular gas probed by CO is concentrated in an asymmetric ringed disk of ∼30 − 40 pc radius located around the AGN and oriented along ∼160°. The molecular disk shows a similar orientation to the extended component of the 351 GHz continuum emission detected by ALMA (PAGauss ∼ 160° −162°; Table 4). The CO disk appears to be partly incomplete: its southwest hemisphere is on average a factor of 3 weaker than its northeast counterpart. The V − H map shows a dusty ring feature in excellent correspondence with the morphology of the CO ringed disk. There is nevertheless significant molecular gas inside the ring: CO emission is detected toward the position of the AGN defined by the position of the ALMA 351 GHz continuum point source. This implies values for the molecular gas mass and H2 column densities of Mgas[r ≤ 7 − 9 pc]  ∼ (1.5 − 2.9)×105 M⊙ and N(H2)∼(3.5 − 3.6)×1022 cm−2, respectively. The galaxy shows a bright ionization cone southwest of the nucleus oriented along PAout ∼ 235° −245° and characterized by blueshifted velocities, which betray an outflow (Morris et al. 1985; Storchi-Bergmann & Bonatto 1991; Davies et al. 2016; Ricci et al. 2018a). The northeast ionization cone appears to be obscured by the host galaxy. The orientation of the 70 − 90 pc-size disk detected by ALMA in continuum and CO is therefore equatorial (torus-like).
Citation: Buta et al. (2009)
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The marginalized probability for Mgal and Mhalo is shown in Fig. 6. The contours correspond to the 68% and 95% intervals and the black dot marks the position of the best model. We note that the best model is outside the 68% region of the marginalized probability. The N-dimensional likelihood near the best model forms a shallow valley that extends toward the 68% confidence region. The best models clearly prefer a relatively narrow region in the Mgal − MDM space. An interesting result from this figure, is that a baryon-only model (i.e., MDM = 0) does not perform much worse than a model with a DM halo, although the data prefers a model with a DM halo. The relatively weaker dependence with the DM component may be a consequence of the small size of the Einstein ring, since at this radius the baryon component dominates the projected mass (see Table 1 below). The inferred mass of the galaxy (baryons) in the best model (5.4 × 1010 M⊙) is marginally consistent with the estimated stellar mass derived from the velocity dispersion – stellar mass correlation (Hyde & Bernardi 2009; Zahid et al. 2016; Cannarozzo et al. 2020). Assuming the velocity dispersion in Mörtsell et al. (2020), and the models in Zahid et al. (2016), Cannarozzo et al. (2020), the predicted stellar mass is ≈2.5 × 1010 M⊙. This is about a factor two less than the stellar mass inferred from the lens model. However, a factor two uncertainty is typical in mass estimations from the velocity dispersion (see the references above). Since the galaxy and halo masses are partially degenerate (see Fig. 6), it is possible that the galaxy mass is lower than the one at the maximum of the likelihood. Another possibility is that the gas mass is similar to that of the stars, increasing the baryonic mass by a factor two compared with the stellar mass. This is in principle feasible based on the gas to stellar mass ratios, which is close to 1 for early type galaxies (similar to the lens considered in this work) and stellar masses ∼1010 M⊙ (Calette et al. 2018). However, in the central part of the galaxy the stellar component is still expected to dominate, especially if a bulge is present, as suggested by the data. An alternative way of estimating the contribution from the gas is by comparing with galaxies of similar morphological type. According to Casasola et al. (2020), and considering a morphological type for the lens galaxy between Sa and Sb, the gas fraction for this type of galaxy is ≈20%. In Schruba et al. (2011), gas and molecular surface densities as high as O(100) M⊙ pc−2 can be found in areas with large star formation rates. In the most extreme cases of star formation rates, gas surface densities of ≈1000 M⊙ pc−2 can be found. However, even at these extreme star formation rates, the contribution from the gas to the baryonic mass is still subdominant with respect to the stellar contribution from our fiducial model.
Citation: Hyde & Bernardi 2009
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These new limits, in conjunction with the inconsistency of isotropic flux interpretations, leave no room for an astrophysical interpretation of AAEs in the context of the standard model for time windows as short as 103 s. However, it has been shown that these events can be explained using physics beyond the standard model, as many models suggest that AAEs lend support for axionic dark matter, sterile neutrinos, supersymmetry, or heavy dark matter (Anchordoqui et al. 2018; Connolly et al. 2018; Dudas et al. 2018; Fox et al. 2018; Huang 2018; Abdullah et al. 2019; Anchordoqui & Antoniadis 2019; Borah et al. 2019; Chauhan & Mohanty 2019; Cherry & Shoemaker 2019; Chipman et al. 2019; Cline et al. 2019; Collins et al. 2019; Esmaili & Farzan 2019; Esteban et al. 2019; Heurtier et al. 2019a, 2019b; Hooper et al. 2019). Many of these models, excluding the axionic dark matter explanation (Esteban et al. 2019) or those heavy dark matter scenarios that are tuned to prevent signatures in IceCube (Hooper et al. 2019), can be constrained by this nonobservation at IceCube. Dedicated tests to quantify these constraints are beyond the scope of this work and may be the focus of a future study. In addition to explanations that incite new physics, it has recently been suggested that AAEs could be explained by downward-going CR-induced EASs that reflected off of subsurface features in the Antarctic ice (Shoemaker et al. 2019). Another possible explanation could be coherent transition radiation from the geomagnetically induced air shower current, which could mimic an upgoing air shower (Motloch et al. 2017; de Vries & Prohira 2019). Explaining these anomalous events with systematic effects or confirming the need for new physics requires a deeper understanding of ANITA’s detection volume. Efforts such as the HiCal radio frequency pulser, which has flown alongside ANITA on the last two flights (Prohira et al. 2018), are already underway to try to characterize the various properties of the Antarctic ice surface.
Citation: Dudas et al. 2018
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Citation: Connolly et al. 2018
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The existence of winds is shown by blueshifted absorption lines from highly ionized ions. These are only seen in soft state but not in hard state (Ponti et al. 2012), anticorrelated with the radio jet which is seen in the hard state but not in the soft. This was thought to be evidence that the wind was magnetically driven by the same field as was responsible for the jet, but in a different geometric configuration (Miller et al. 2012). However, in Tomaru et al. (2019, hereafter Paper I) we show instead that thermally driven winds can explain this switch (see also Done, Tomaru & Takahashi 2018; Shidatsu & Done 2019). Thermal driving produces a wind by irradiation from the central source heating the surface of accretion disc up to the Compton temperature ($T_\text{IC} \sim 10^7 \!-\!10^8\, \text{K}$), which is hot enough for its thermal energy to overcome the gravity at large radii. The characteristic radius at which the wind can be launched is called the Compton radius, defined by RIC = μmpGM/kTIC ∼ 105 − 106Rg (Begelman, McKee & Shields 1983). Paper I show the first modern radiation hydrodynamic simulations of thermal (and thermal-radiative) winds designed to investigate the switch in wind properties between the hard and soft states changing illumination spectra. These simulations were tailored to the BHB system H1743−332, where there is Chandra high-resolution data in both states giving detailed spectral information on the wind or its absence (Miller et al. 2012; Shidatsu & Done 2019). They incorporate radiation force on the electrons, both bound and free, as they show that this is important factor driving the escape of the thermal wind in the fairly high Eddington fraction (L/LEdd ∼ 0.2–0.3), fairly low Compton temperature (TIC ∼ 0.1 × 108 K) characteristics of the soft state. The only other modern hydrodynamic simulation of thermal winds (e.g. Luketic et al. 2010; Higginbottom & Proga 2015; Higginbot et al. 2016) has not included radiation pressure, which is important in setting the velocity structure for L ≥ 0.3LEdd as required here (Paper I).
Citation: Shidatsu & Done 2019
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Citation: Shidatsu & Done 2019
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Capturing the full information content of the cosmic large-scale structure requires a field-based approach to infer the entire three-dimensional cosmic large-scale structure from observations. This poses a particular challenge for the analyses of Ly-α forest observations, which provide sparse inherently one-dimensional information along the lines of sight. Various approaches to perform three-dimensional density reconstructions from one-dimensional Ly-α forests have been proposed in the literature (e.g. Kitaura et al. 2012; Cisewski et al. 2014; Stark et al. 2015a; Ozbek et al. 2016; Horowitz et al. 2019). Gallerani et al. (2011) and Kitaura et al. (2012) proposed a Gibbs sampling scheme to jointly infer density and velocity fields and corresponding power-spectra. However, these approaches assume matter density amplitudes to be log-normally distributed. The log-normal distribution reproduces one- and two-point statistics but fails to reproduce higher-order statistics associated with the filamentary dark matter distribution. In an attempt to extrapolate information from one-dimensional quasar spectra into the three-dimensional volume, Cisewski et al. (2014) applied a local polynomial smoothing method. Ozbek et al. (2016) and Stark et al. (2015a) employed a Wiener filtering approach to reconstruct the three-dimensional density field between lines of sight of Ly-α forest data. In order to reproduce higher-order statistics, Horowitz et al. (2019) recently used a large-scale optimization approach to fit a gravitational structure growth model to Ly-α data, showing that this approach allows recovering the more filamentary structure of the cosmic web. Although the approach improves over linear and isotropic Wiener filtering approaches, it shows systematic deviations of reconstructed matter power-spectra and underestimates density amplitudes at scales corresponding to the mean separation between lines of sight (Horowitz et al. 2019).
Citation: Gallerani et al. (2011)
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The problem we are facing consists in the reconstruction of two maps of a given region of an astrophysical object: the former consists in point sources, such as stars, of very high intensity, the latter of smooth structures surrounding these sources. The standard approach, i.e. simply solving Eq. (2), fails since the presence of the point sources destroys all the information about the smooth structures. Hence, the main idea is to consider the object x as the sum of two components, namely x = xP + xE, where xP represents the point sources and xE the extended source. This approach was proposed for the first time in De Mol & Defrise (2004), assuming that the positions of the point sources are known. Denoting by pj the position of the jth source, we can write (4)\begin{equation} \label{2:point} x_{\rm P}=\sum_{j=1}^qc_j\delta(p_j) , \end{equation}xP=∑j=1qcjδ(pj),where q is the total number of sources, cj is the jth unknown intensity, and δ(pj) is the delta function centred in pj. Thus, xP is a vector with zero entries except in the q positions corresponding to the known locations of the sources. Then, instead of computing f1 on the whole object x in Eq. (2), we only regularize the extended source, since the structure induced on xP already works as a regularization. This requires a slightly modification in the computation of f0. We introduce the vector c = (c1,c2,...,cq)t containing the intensities of the sources, and we define \hbox{$\overline{x} = (c^t,x_{\rm E}^t )^t$}x=(ct,xEt)t and the matrix \hbox{$\mathcal{H}=[\overline{H}, H]$}ℋ=[H,H], where \hbox{$\overline{H}=[h_{p_1},h_{p_2},\dots,h_{p_q}]$}H=[hp1,hp2,...,hpq], with hj denoting the jth column of H. We are hence led to solve(5)\begin{equation} \label{2:solve} \tilde{x} =\arg\min_{x\in\mathcal{C}} KL(\mathcal{H} \overline x+b;g)+\beta f_1\left(x_{\rm E}\right) . \end{equation}˜x=argminx∈𝒞KL(ℋx+b;g)+βf1(xE).It may happen that \hbox{$\tilde x$}˜x has a loss in contrast, even using the optimal value for β. To overcome this difficulty, we then propose the use of the inexact Bregman procedure, which permits the use of an overestimation of the regularization parameter, and at the same time allows us to obtain a contrast enhancement.
Citation: De Mol & Defrise (2004)
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