adsabs/FOCAL huggingface dataset:
The astrophysical environments of r-process events in the cosmos usually involve explosive physical conditions, because very high neutron densities are required. The neutron-rich isotopes produced along the r-process path have a very short half-life, causing the neutron-rich nuclei to β-decay over time-scales of the order of milliseconds, if they did not undergo further rapid neutron capture. Examples of r-process sites that have been proposed and investigated by theoretical studies include (i) neutrino-driven winds from proto-neutron stars (NSs; Takahashi, Witti & Janka 1994; Woosley et al. 1994; Qian & Woosley 1996; Hoffman, Woosley & Qian 1997; Otsuki et al. 2000; Thompson, Burrows & Meyer 2001; Wanajo et al. 2009; Hansen et al. 2013; Wanajo 2013); (ii) neutrino-driven winds from highly magnetic and potentially rapidly rotating proto-magnetars (Thompson 2003; Thompson, Chang & Quataert 2004; Metzger, Thompson & Quataert 2007, 2008; Vlasov, Metzger & Thompson 2014; Vlasov et al. 2017; Thompson & ud-Doula 2018); (iii) neutrino-driven winds around the accretion disc of a black hole (Pruet, Thompson & Hoffman 2004; Metzger et al. 2008; Wanajo & Janka 2012; Siegel, Barnes & Metzger 2019); (iv) electron-capture SNe (see e.g. Wanajo et al. 2009; Cescutti et al. 2013; Kobayashi, Karakas & Lugaro 2020); (v) magneto-rotationally driven SNe (Burrows et al. 2007; Winteler et al. 2012; Cescutti & Chiappini 2014; Mösta et al. 2014, 2015, 2018; Nishimura, Takiwaki & Thielemann 2015; Nishimura et al. 2017; Halevi & Mösta 2018; Reichert et al. 2021); and (vi) neutron-star mergers (Lattimer et al. 1977; Freiburghaus, Rosswog & Thielemann 1999; Argast et al. 2004; Goriely, Bauswein & Janka 2011; Rosswog 2013; Matteucci et al. 2014; Cescutti et al. 2015; Vincenzo et al. 2015; Kobayashi et al. 2020). Since it is likely that all these mechanisms have contributed to r-process nucleosynthesis at some level, the theoretical studies have focused on exploring the frequency of each event and the predicted template of the corresponding r-process ejecta.
Citation: Takahashi, Witti & Janka 1994
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Previous Herschel studies confirmed numerical calculations (e.g., Nagai et al. 1998) showing that a parallel orientation between filaments and the local magnetic field is to be expected for filaments having low column densities (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016). In fact, a variety of orientations have previously been observed, and the alignment of filaments and apparent field direction appear to vary between regions and for different gas densities (cf., Pillai 2017, for a review). For example, there is observational evidence that low-column density filaments, or “striations,” are aligned parallel to the magnetic field lines in Taurus, as they are more susceptible to the magnetic influence than higher column density structures (Palmeirim et al. 2013). These striations may therefore promote mass accretion onto larger filaments, in which stars are finally formed. Such studies, however, were mostly performed using lower-resolution dust column density maps obtained with Herschel (e.g., Li et al. 2013; Palmeirim et al. 2013; Soler et al. 2017) or polarization data tracing relatively low AV environments (e.g., Busquet et al. 2013; Fissel et al. 2016; Planck Collaboration Int. XXXV 2016; Soler et al. 2016; Jow et al. 2018). Nevertheless, as discussed in Section 4.3, the filament F2 studied in this work has molecular hydrogen column densities roughly 2 to 3 orders of magnitude larger than the Herschel filaments studied in Cygnus or Taurus, and may therefore possibly be self-gravitating. At face value, these results are at odds with the widely accepted paradigm of magnetically regulated star formation in filaments, in which a perpendicular orientation of dense filaments with respect to the magnetic field is expected (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016).
Citation: Pillai 2017
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Statistical investigations of a large sample of photometric data are useful in exploring the bulk properties of various types of events. Nevertheless, such studies are generally limited to LCs in selected passbands, and also the follow-up covers a short duration of the SN evolution. Further, the spectroscopic follow-up of most objects is restricted to early phases. Nonuniformity in the data sample may also be present, as these are collected at different observing facilities and detectors. Studying individual events with proper monitoring at different phases (both photospheric and nebular) is hence extremely important. The very early phase observations (hours to days after explosion) of these events are useful to constrain the progenitor radius at its end stage. This needs a very early detection and quick follow-up, which is not always possible considering their random occurrence in the sky. The large-area surveys with high cadence have contributed significantly in this regard. SN 1993J (Type IIb) is the first event among SE-SNe that shows evidence of a prominent cooling tail just after explosion (Richmond et al. 1994; Barbon et al. 1995). During the past two decades, several Type IIb SNe with such interesting features have been monitored and studied well, e.g., SN 2008ax (Pastorello et al. 2008; Roming et al. 2009), SN 2011dh (Arcavi et al. 2011), SN 2011fu (Kumar et al. 2013; Morales-Garoffolo et al. 2015), SN 2013df (Fremling et al. 2014), and SN 2016gkg (Arcavi et al. 2017; Tartaglia et al. 2017). A handful of Type Ib events have also been discovered at very early phases, such as SN 1999ex (Hamuy et al. 2002; Stritzinger et al. 2002), SN 2008D (Mazzali et al. 2008; Soderberg et al. 2008; Malesani et al. 2009; Modjaz et al. 2009; Bersten et al. 2013), iPTF13bvn (Bersten et al. 2014; Fremling et al. 2016), and LSQ13abf (Stritzinger et al. 2020), and their observational properties are studied in detail. Recently, Prentice et al. (2019) analyzed the properties of 18 SE-SNe and discussed the implications for their progenitors. Direct detection of SE-SNe progenitor candidates has been possible only for a few objects, e.g., SN 1993J (Maund et al. 2004), SN 2011dh (Maund et al. 2011; Van Dyk et al. 2013), SN 2001ig (Ryder et al. 2018), iPTF13bvn (Cao et al. 2013; Eldridge et al. 2015; Kuncarayakti et al. 2015; Folatelli et al. 2016), SN 2016gkg (Kilpatrick et al. 2017; Tartaglia et al. 2017), SN 2017ein (Kilpatrick et al. 2018; Van Dyk et al. 2018; Xiang et al. 2019), and SN 2019yvr (Kilpatrick et al. 2021). Along with very early phases, the temporal observations during maximum to nebular phases are equally important to estimate various explosion parameters and progenitor properties. Detailed investigation of more events can provide an alternative way to understand various progenitor channels.
Citation: Kumar et al. 2013
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As a baseline for understanding how the scaling relations evolve as a function of mass and redshift, we adopt the following self-similar scalings:

(2)

\begin{equation}
M_{\rm {gas},\Delta }\propto M_{\Delta },
\end{equation}


(3)

\begin{equation}
T_{\Delta }\propto M^{2/3}_{\Delta }E^{2/3}(z),
\end{equation}


(4)

\begin{equation}
Y_{\rm {X},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z),
\end{equation}


(5)

\begin{equation}
Y_{\rm {SZ},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z),
\end{equation}


(6)

\begin{equation}
L_{\Delta }^{\rm {X,bol}}\propto M^{4/3}_{\Delta }E^{7/3}(z),
\end{equation}


(7)

\begin{equation}
L_{\Delta }^{\rm {X,bol}}\propto T^{2}E(z),
\end{equation}

where $E(z)\equiv H(z)/H_0=\sqrt{\Omega _{\text{m}}(1+z)^3+\Omega _{\Lambda }}$, Δ is the chosen overdensity relative to the critical density and YX is the X-ray analogue of the integrated SZ effect. These are derived in Appendix B. Although shown to be too simplistic by the first X-ray studies of clusters (Mushotzky 1984; Edge & Stewart 1991; David et al. 1993), the self-similar relations allow us to investigate if astrophysical processes are less significant in more massive clusters or at higher redshift. To enable a comparison with the self-similar predictions, and previous work, we fit the scaling relations of our samples at each redshift. We derive a median relation by first binning the clusters into bins of log mass (width: 0.1 dex) or log temperature (width: 0.07 dex) and then computing the median in each bin with more than 10 clusters. We also remove the evolution in normalization predicted by self-similar relations. The medians of the bins are then fitted with a power law of the form

(8)

\begin{equation}
E^{\beta }(z)Y=10^A\left(\frac{X}{X_0}\right)^{\alpha }\!\!,
\end{equation}

where A and α describe the normalization and slope of the best fit, respectively, β removes the expected self-similar evolution with redshift, X is either the total mass or temperature and Y is the observable quantity (Mgas, LX, bol, etc.). X0 is the pivot point, which we set to 4 × 1014 M⊙ for observable–mass relations and to 6 keV for observable–temperature relations. We note that we fix the pivot for all samples and all redshifts. Fitting to the medians of bins, rather than individual clusters, prevents the fit from being dominated by low-mass objects, which are significantly more abundant due to the shape of the mass function. For the hot sample and its relaxed subset, there are too few bins with 10 or more clusters to reliably derive a best-fitting relation at z ≥ 1. By limiting our sample to systems with M500 ≥ 1014 M⊙, we avoid any breaks in the power-law relations that have been seen both observationally and in previous simulation work (Le Brun et al. 2016).
Citation: David et al. 1993
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There are multiple sources of spurious detections in strong lensing analysis, and some of them are just image artefacts. None the less, others are images that might look like a lens, as in the case of edge-on galaxies and some spiral galaxies. One popular approach to reduce this issue is to use colours and look for red galaxies with a blue object close. The colour information is considered an important feature to find lenses (see e.g. Ostrovski et al. 2018; Spiniello et al. 2018). The use of colour queries is considered at least an interesting way to make a pre-selection and improve the final deep learning result purity. In fact, even in the I SGLC results the single-band scenario found lower performance in terms of the considered metric, the AUC of ROC. The II SGLC in a Euclid-like scenario allowed to make deep learning classifications using images with colour information in multiple bands but also higher resolution images. Interestingly the networks using only HJY bands did not find a competitive fit. On the other hand, using the VIS band only with a higher resolution found better results than HJY, which is close to a random guess. Still, the VIS band only has inferior results if compared to the runs using all information. This result suggests that the use of high-resolution images might play an important role and was in fact, more relevant than the multiple bands in the cases tested in this contribution. It is worth mentioning that the threshold defined by the maximum Fβ, with β = 0.001, privileged a pure sample instead of a complete one, obtaining in our best network, HJY + VIS with alternative pre-processing, around ${\sim} 99{{\ \rm per\ cent}}$ purity with completeness of around $45{{\ \rm per\ cent}}$. This choice is justified for the same reason a pre-selection of targets is implemented by colour queries and other methods to reduce the number of non-lenses in a survey where we have billions of non-lenses for thousands of lenses.
Citation: Spiniello et al. 2018
Citation function: Uses; Uses
On balance, a large abundance of baryon-dominated, dark matter cored galaxies at z ∼ 2, most strongly correlated with baryonic surface density, angular momentum, and central bulge mass, may be most naturally accounted for by the interaction of baryons and dark matter during the formation epoch of massive halos. Massive halos (log(Mhalo/M⊙) > 12) formed for the first time in large abundances in the redshift range z ∼ 1–3 (Press & Schechter 1974; Sheth & Tormen 1999; Mo & White 2002; Springel et al. 2005). At the same time, gas accretion rates were maximal (Tacconi et al. 2020). This resulted in high merger rates (Genel et al. 2008, 2009; Fakhouri & Ma 2009), very efficient baryonic angular momentum transport (Dekel et al. 2009; Zolotov et al. 2015), formation of globally unstable disks, and radial gas transport by dynamical friction (Noguchi 1999; Immeli et al. 2004; Genzel et al. 2008; Bournaud & Elmegreen 2009; Bournaud et al. 2014; Dekel & Burkert 2014). These processes enabled galaxy mass doubling on a timescale  0.4 Gyr at z ∼ 2–3, and massive bulge formation by disk instabilities and compaction events on 1 Gyr timescales. However, central baryonic concentrations would naturally also increase central dark matter densities through adiabatic contraction (Barnes & White 1984; Blumenthal et al. 1986; Jesseit et al. 2002). For adiabatic contraction to be ineffective requires the combination of kinetic heating of the central dark matter cusp by dynamical friction from in-streaming baryonic clumps (El-Zant et al. 2001; Goerdt et al. 2010; Cole et al. 2011), with feedback from winds, supernovae, and AGNs driving baryons and dark matter out again (Dekel & Silk 1986; Pontzen & Governato 2012, 2014; Martizzi et al. 2013; Freundlich et al. 2020; K. Dolag et al. 2020, in preparation). Using idealized Monte Carlo simulations, El-Zant et al. (2001) demonstrated that dynamical friction acting on in-spiraling gas clumps can provide enough energy to heat up the central dark matter component and create a finite dark matter core (see also A. Burkert et al. 2020, in preparation). They argue that dark matter core formation in massive galaxies would require that clumps be compact, such that they avoid tidal and ram-pressure disruption, and have masses of >108 M⊙. Other idealized simulations (e.g., Tonini et al. 2006) confirm these results.
Citation: Bournaud et al. 2014
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Citation: Tacconi et al. 2020
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The gas particles are distributed such that the initial surface density profile has the power law:
(7)$$\begin{eqnarray*}
\Sigma _\mathrm{g}=\Sigma _{\mathrm{g},0}\left(\frac{R}{R_{\rm in}}\right)^{-p},
\end{eqnarray*}$$where Σg, 0 is a normalization constant. We choose a locally isothermal equation of state with a sound speed cs such that
(8)$$\begin{eqnarray*}
c_\mathrm{s} = c_\mathrm{s,in} \left(\frac{R}{R_{\rm in}} \right)^{-q}.
\end{eqnarray*}$$where cs, in is the sound speed at the inner radius. We choose power law indices p = 1 and q = 0.5. We use a constant SPH artificial viscosity coefficient αSPH ≈ 0.55 such that the corresponding disc viscosity coefficient (Shakura & Sunyaev 1973) is αd = 0.01 at the inner radius. We note, however, that our choice of the indices p and q impact the radial dependence of the viscosity coefficient αd. The mapping between the SPH and disc viscosity coefficient is (e.g. Artymowicz & Lubow 1994, Murray 1996, Lodato & Pringle 2007)
(9)$$\begin{eqnarray*}
\alpha _\mathrm{d} \propto \alpha _{\rm SPH} \frac{\langle h \rangle }{H}
,
\end{eqnarray*}$$where 〈h〉 is the vertically averaged smoothing length and H is the disc thickness, which depends on disc radius:
(10)$$\begin{eqnarray*}
H = \frac{c_\mathrm{s}}{\Omega _\mathrm{k}} \propto R^{3/2-q}
.
\end{eqnarray*}$$The average smoothing length in 3D has the radial dependence:
(11)$$\begin{eqnarray*}
\langle h \rangle \propto \rho ^{-1/3} \propto \left(\frac{\Sigma }{H}\right)^{-1/3} \propto R^{1/2+(p-q)/3}
.
\end{eqnarray*}$$Hence, the choice of p and q determines the radial dependence of the disc viscosity coefficient. A common choice is to use p = 3/2 and q = 3/4, which results in a radially constant αd (e.g. Lodato & Price 2010). In this paper, we decide to use a physically motivated choice of indices (e.g. Aly et al. 2018), even though it results in a non-constant disc viscosity coefficient. For our choice of p = 1 and q = 0.5, the resulting radial dependence of the disc viscosity coefficient is αd ∝ R−1/3. To prevent particle interpenetration, we employ the recommended value βSPH = 2 for the quadratic viscosity coefficient (Price & Federrath 2010; Meru & Bate 2012).
Citation: Shakura & Sunyaev 1973
Citation function: Uses; Uses
A prime example of the unique benefits of the intersection of the 3D nature of (ultra)hot exoplanet atmospheres and high-resolution spectroscopy lies in the ultrahot Jupiter WASP-76b, a gas giant orbiting an F7 star (West et al. 2016) that is well studied at lower resolution (Fu et al. 2017, 2021; Fisher & Heng 2018; Tsiaras et al. 2018; Edwards et al. 2020; von Essen et al. 2020). Using the high-resolution (R ≈ 138,000) ESPRESSO spectrograph on the Very Large Telescope (Pepe et al. 2010, 2013), the Ehrenreich et al. (2020) team was able to produce novel, high signal-to-noise ratio (S/N), phase-resolved transmission spectra of this target across two separate transits. Curiously, an anomalous Doppler signature in the planet’s transmission spectrum was detected: between 0 and −5 km s−1 at ingress, but roughly −11 km s−1 by egress. These speeds far exceed the few km s−1 planet-frame velocities detected on other planets (Snellen et al. 2010; Brogi et al. 2016). The detection team attributes this variable and strong blueshift to an asymmetric distribution of atomic iron in the planet’s atmosphere, with a considerable amount of iron existing in the gas phase east of the substellar point, but cooling and condensing out as it makes its way to the much colder nightside. This asymmetry would cause a progressively blueshifted signal as the gas-phase iron region rotates into view over the course of the planet’s transit. Ehrenreich et al. (2020) posit that their signal is composed of two independent Doppler components: solid-body rotation of ±5.3 km s−1  (the tidally locked equatorial velocity of the planet), and a uniform day–night wind contributing an additional −5.3 km s−1 across both limbs, with gas-phase iron only present on the evening limb of the planet (Figure 1). Hence, the approaching limb would exhibit a blueshift from both rotation and winds totaling –10.6 km s−1, and the receding limb would produce no Doppler signal, as it would contain no gas-phase iron to absorb starlight.
Citation: Brogi et al. 2016
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We study here the Sco OB1 association (Figs. 1 and 2), using this and other techniques. The general properties of this large OB association, which spans almost 5° on the sky, and is surrounded by a ring-shaped HII region called Gum 55, are reviewed by Reipurth (2008). Its central cluster NGC 6231 contains several tens of OB stars, which have been extensively studied. On the other hand, many fewer studies, all recent, were devoted to the full mass spectrum, using optical photometry (Sung et al. 1998, 2013) and X-rays (Sana et al. 2006, 2007; Damiani et al. 2016; Kuhn et al. 2017a,b). The currently accepted distance of NGC 6231 is approximately 1580 pc, and its age is between 2and 8 Myr, with a significant intrinsic spread (Sung et al. 2013; Damiani et al. 2016). No ongoing star formation is known to occur therein, however. Approximately one degree North of the cluster, the loose cluster Trumpler 24 (Tr 24) also belongs to the association. There is little literature on this cluster (Seggewiss 1968; Heske & Wendker 1984, 1985; Fu et al. 2003, 2005) which unlike NGC 6231 lacks a well-defined center and covers about one square degree on the sky. Its age is  10 Myr according toHeske & Wendker (1984, 1985), who find several PMS stars, and its distance is 1570–1630 pc according to Seggewiss (1968). Other studies of the entire Sco OB1 association include MacConnell & Perry (1969 – Hα-emission stars), Schild et al. (1969 – spectroscopy), Crawford et al. (1971 – photometry), Laval (Laval 1972a,b – gas and star kinematics, respectively), van Genderen et al. (1984 – Walraven photometry), and Perry et al.(1991 – photometry). At the northern extreme of Sco OB1, the partially obscured HII region G345.45+1.50 and its less obscured neighbor IC4628 were studied by Laval (1972a), Caswell & Haynes (1987), López et al. (2011), and López-Calderón et al. (2016). They contain massive young stellar objects (YSOs; Mottram et al. 2007), maser sources (Avison et al. 2016), and the IRAS source 16562-3959 with its radio jet (Guzmán et al. 2010), outflow (Guzmán et al. 2011), and ionized wind (Guzmán et al. 2014), and are therefore extremely young (1 Myr or less). The distance of G345.45+1.50 was estimated as 1.9 kpc by Caswell & Haynes (1987), and 1.7 kpc by López et al. (2011), in fair agreement with distances of Sco OB1 stars. In Fig. 1 of Reipurth (2008) a strip of blue stars is visible, connecting NGC 6231 to the region of IC4628.
Citation: Sung et al.; 2013
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Citation: Sung et al. 2013
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In an attempt to understand the radio properties of GRBs, Chandra & Frail (2012) conducted a complete investigation of all historical events observed in the radio domain. These included both of the main GRB populations (Kouveliotou et al. 1993): long-duration GRBs (likely produced by massive stellar collapse where the gamma-ray emission lasts for more than 2 s; Woosley 1993; Kulkarni et al. 1998; Woosley & Bloom 2006) and short-duration GRBs (likely caused by the coalescence of two neutron stars or a neutron star and black hole, which lasts for less than 2 s; Lattimer & Schramm 1976; Eichler et al. 1989; Narayan, Paczynski & Piran 1992). Only 30 per cent of their sample had a detectable radio afterglow, with the radio emission peaking within a very narrow flux range. This led them to conclude that the low percentage of detections was likely due to the sensitivity of radio telescopes rather than there being two distinct GRB populations: radio-bright and radio-faint. Ghirlanda et al. (2013) and Burlon et al. (2015) then conducted simulations to demonstrate that potentially all Swift GRBs will be detectable at radio frequencies with phase 1 of the Square Kilometre Array (SKA), specifically SKA1-MID in Band 5 (∼9 GHz)1 between 2 and 10  d post-burst, as well as with the recently upgraded Karl G. Jansky Very Large Array (VLA)2 and MeerKAT (the South African SKA precursor telescope; Jonas 2009). In fact, SKA1-MID will be so sensitive it could detect the radio counterparts from GRBs with gamma-ray emission up to five times fainter than those currently detected with Swift-BAT (note that these simulations do not account for radio emission produced by the reverse-shock, only considering contributions from the forward-shock; Burlon et al. 2015). However, a study conducted by Hancock, Gaensler & Murphy (2013), which involved visibility stacking of VLA GRB radio observations, suggested the low radio detection rate may be due to there being separate radio-bright and radio-faint GRB populations, and that ≤70 per cent are likely to be truly radio bright.
Citation: Eichler et al. 1989
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