adsabs/FOCAL huggingface dataset:
One of the main debates in the star formation community is whether the massive young stellar objects are a scaled-up version of low-mass young stellar objects (YSOs) where disc-accretion plays the dominating role for gaining the stellar mass. For the formation of low-mass stars, bipolar outflows driven by the accretion discs are proposed to be the basic formation mechanism theoretically (Shu, Adams & Lizano 1987), and are also verified observationally (e.g. Bontemps et al. 1996; Richer et al. 2000; Arce et al. 2007, and references therein). However, on the other hand, understanding of the formation mechanism of massive stars is still elusive (Tan et al. 2014). Two major competing models for massive star formation are (i) core accretion via disc (McKee & Tan 2003) and (ii) competitive accretion (Bonnell et al. 2001). The most obvious way to distinguish between these two models might be the detection of the accretion disc around massive protostars. But a direct detection of accreting disc is difficult because the accretion disc is small and short-lived, and also because of complicated gas dynamics at that scale (Kim & Kurtz 2006). Here, the study of the properties of molecular outflows that are the manifestation of disc-accretion in young sources, could help us to improve our understanding of the underlying formation process (Shepherd & Churchwell 1996; Beuther et al. 2002; Molinari et al. 2002; Arce et al. 2007). If it is assumed that massive stars do form via an accretion disc similar to the low-mass stars, they should generate massive and powerful outflows (see de Villiers et al. 2014, and references therein). A few recent studies (e.g. de Villiers et al. 2014; Li et al. 2018) indeed found the applicability of the same scaling between outflow activity and clump masses for both low-mass and massive objects, suggesting a similar formation mechanism. However, an extensive study of molecular outflows toward massive star-forming regions is still lacking owing to their large distances and high level of clustering. Recent interferometric observations with the ALMA enable us to target such regions thanks to its high spatial resolution and sensitivity. Studies of outflows associated with Galactic massive star-forming regions may provide us clues to better understand the launching mechanism of molecular outflows, and hence, the underlying star formation mechanism.
Citation: Tan et al. 2014
Citation function: Background
For most of our models, we can already determine, or at least extrapolate, the final neutron star properties quite well, barring the possibility of late-time fallback. Except for the $12.5 \, \mathrm{M}_\odot$ model, mass outflow already dominates over mass accretion on to the PNS, and the PNS mass has practically stabilized at its final value. Correcting for the binding energy of the neutron stars, we obtain gravitational masses between $1.22 $ and $1.44 \, \mathrm{M}_\odot$, which is compatible with the distribution of observed neutron star masses (Özel & Freire 2016; Antoniadis et al. 2016; Tauris et al. 2017). While the neutron star kicks are still growing at the end of the simulations due to the long-range gravitational tug by the asymmetric ejecta, the subsequent acceleration of the neutron star can be smoothly extrapolated to obtain tentative final values in all but one case. The extrapolated kicks range from $11$ to $695 \, \mathrm{km}\, \mathrm{s}^{-1}$. Thus, the most extreme, ECSN-like models with the smallest helium cores can reproduce the very low kicks required to explain some double neutron star systems and pulsars in globular clusters (Tauris et al. 2017), while the models with higher He core masses are compatible with the typical kicks of young pulsars (Arzoumanian, Chernoff & Cordes 2002; Hobbs et al. 2005; Ng & Romani 2007). If the extrapolated kick of $1236 \, \mathrm{km}\, \mathrm{s}^{-1}$ for the $18 \, \mathrm{M}_\odot$ model of Müller et al. (2017a) is included, the 3D coconut-fmt models roughly span the full range of observed kick velocities. We see tentative evidence for a correlation of the kick velocity with the explosion energy as proposed by Janka (2017) and Vigna-Gómez et al. (2018, as a refinement of earlier ideas for progenitor-dependent kicks by Bray & Eldridge 2016). Our models suggest that this correlation may not be a tight one, however, and that the kicks may scatter between zero and an upper limit that scales with the explosion energy. Low kicks can be achieved in more energetic explosions if the explosion geometry is bipolar rather than unipolar, as has already been noted in 2D by Scheck et al. (2006). Such a bipolar explosion occurs in one of our seven simulations (the $12 \, \mathrm{M}_\odot$ model). Although there is some concern that the bipolarity may be connected to the grid geometry, we find unipolar models even in cases where we do not include strong aspherical seed perturbations in the convective O shell that break grid alignment; this suggests that the possibility of bipolar neutrino-driven explosions with low kicks is real in 3D. We also find a loose correlation between the neutron star mass and the kick velocity, which is in line with current observations, and partly theoretical expectations, of double neutron stars (Tauris et al. 2017), but cannot make as strong a case for this correlation based on our simulations. An investigation of a larger suite of supernova simulations of ultra-stripped stars is needed to confirm this hypothesis.
Citation: Hobbs et al. 2005
Citation function: Compare/Contrast
While treated as fully relativistic under strong gravity, we note that the current model is time-independent based on axisymmetric plasma. This assumption makes it impossible for us to predict any temporal nature of the soft excess considered in this work, e.g., spectral time variabilities associated with shock compression and cooling effects. The downstream plasma properties are numerically solved by considering adiabatic (nonradiative) Rankine–Hugoniot jump conditions as a pure mathematical discontinuity with no energy/mass loss. Hence, most of the heat generated at the shock front is advected with the downstream plasma. A more realistic shock process, on the other hand, is most likely accompanied by radiative cooling to some degree in which the post-shock plasma temperature may stay comparatively as cool as that of the upstream one, as in the isothermal shocks (e.g., Lu & Yuan 1998; Das et al. 2003; Fukumura & Tsuruta 2004; Fukumura & Kazanas 2007b). Radiative dissipation at the shock front could therefore drastically change the subsequent downstream plasma condition, which in turn alters the Comptonization process. In reality, furthermore, accreting plasma may be characterized by a two-temperature gas between electrons and ions (e.g., Shapiro et al. 1976; Mahadevan 1998; Manmoto 2000) unless the Coulomb coupling between the two is very efficient, whereas in this work we prescribed a single-fluid approximation for simplicity. Becker et al. (2011) have considered a particle transport process (e.g., bulk advection, spatial diffusion, and particle escape) via the effects of the first-order Fermi acceleration across a standing shock. In a more self-consistent scenario such a calculation of diffusive shock acceleration should be incorporated to reflect the energetic outflows/jets from the shock front. Although all these micro-physics should be addressed and incorporated into more sophisticated calculations of GRMHD simulations for completeness, this is beyond the scope of this paper.
Citation: Lu & Yuan 1998
Citation function: Differences
Plots of various characteristics of sub-DLAs and DLAs in the literature. Blue star points mark the detections presented in this work. The Xs mark detections from Péroux et al. (2011a, 2012). The points mark detections from previous studies in the literature. References: Pettini et al. 2000, Lacey et al. 2003, Junkkarinen et al. 2004, Chen et al. 2005, Burbidge et al. 1996, Chun et al. 2006, Rao et al. 2005, Boisse et al. 1998, Le Brun et al. 1997, Gharanfoli et al. 2007, Rao et al. 2006, Steidel et al. 2002, Lanzetta et al. 1997, Lanzetta et al. 1995, Bergeron & Boisse 1991, Deharveng et al. 1995, Meiring et al. 2007, Cristiani et al. 1987, Bowen et al. 2005, Schulte-Ladbeck et al. 2005, Bowen et al. 2001, Rosenberg et al. 2006, Steidel et al. 1997, Bergeron 1986, Prochaska & Wolfe 1997, Weatherly et al. 2005, Lu et al. 1993, Lu et al. 1997, Djorgovski et al. 1996, Christensen et al. 2004, Rao & Turnshek 2000, Ellison et al. 2005, Pettini et al. 1994, Francis et al. 1996, D'Odorico et al. 2002, Francis & Williger 2004, Francis et al. 2001, Kulkarni et al. 2005, Cherinka et al. 2009, Noterdaeme et al. 2009 and Fynbo et al. 2010. See table 4 of Péroux et al. (2011a) for the full list of measurements. For the SFR plots, colour indicates the emission from which the value was determined: blue for [O ii], red for Hα, green for Hβ, purple for [O iii], and black for Lyα. SFRs have not been dust corrected. The solid red line in the SFR versus zabs plot is an arbitrarily normalized Madau relationship from Madau & Dickinson (2014). Qualitatively, the trend in host galaxy SFR with absorber redshift is consistent with this trend. The blue outliers are the interacting pair in the field of Q1436-0051. The dashed vertical lines indicate the column density boundary between sub-DLAs and DLAs. The outlier in b at ∼180 kpc is a host galaxy that is part of a cluster environment reported in Francis et al. (2004) and references therein. Grey arrows indicate limits for non-detections. The error bars on Q1436-0051 zabs = 0.9281 correspond to the range of possible H i column densities and Zn metallicities. See the text for details.
Citation: Pettini et al. 1994
Citation function: Uses
At a distance of 4.8 Mpc (Karachentsev et al. 2002), NGC 5408 X-1 is one of the best studied ULXs. It has been observed by several of the current generation of X-ray satellites, on a multitude of occasions. Observations include: an XMM-Newton large programme (e.g., Pasham & Strohmayer 2012); Swift XRT monitoring (Kaaret & Feng 2009; Grisé et al. 2013); and eight Chandra exposures, which we re-analyze here. The flux variability of the source rules out an X-ray supernova remnant and confirms that it is powered by accretion onto a compact object (Kaaret et al. 2003; Soria et al. 2004). It persistently displays a distinct soft ultraluminous two component X-ray spectrum in XMM-Newton data (Sutton et al. 2013) at an average 0.3–10 keV unabsorbed luminosity of 





 (Strohmayer 2009; although we note that they fit the high energy spectrum with a soft power-law, so may over-estimate the intrinsic luminosity, cf. Middleton et al. 2014). Additional soft residuals have been detected in the XMM-Newton spectra which can be well modeled as thermal plasma emission (Strohmayer & Mushotzky 2009; Miller et al. 2013; Middleton et al. 2014). It has previously been assumed that these were the result of diffuse star formation related emission in the host galaxy (Strohmayer et al. 2007; Strohmayer & Mushotzky 2009; Miller et al. 2013). However, we know from observational studies of galaxies that the X-ray luminosity of such emission is correlated with star formation rate (





/





; Mineo et al. 2012), and Middleton et al. (2014) contend that the luminosity of the putative thermal plasma emission (





 calculated from Miller et al. 2013) greatly exceeds that inferred from star formation, even over the entirety of NGC 5408 (





 calculated from a 24 μm flux density of 





, Dale et al. 2005). Instead, Middleton et al. (2014) show that the putative plasma emission features could actually be commensurate with broadened, blueshifted absorption in a partially ionized, optically thin medium. Such a medium would be expected to occur in a super-Eddington wind, as it becomes optically thin at large distances (





) from the central black hole.
Citation: Soria et al. 2004
Citation function: Compare/Contrast
The Hamiltonian used in the present work is the so-called RAM (rho axis method) internal-rotation Hamiltonian based on the work of Kirtman (Kirtman 1962), Lees and Baker (Lees & Baker 1968), and Herbst et al. (Herbst et al. 1984). Since rather complete descriptions of this method, which takes its name from the choice of axis system, have been presented several times (Hougen et al. 1994; Kleiner 2010) we do not repeat this general description here. The main advantage of the RAM Hamiltonian is its general approach that simultaneously takes into account the A- and E-symmetry species and all the torsional levels, intrinsically taking the intertorsional interactions into account within the rotation-torsion manifold of energy levels. This method was successfully applied to a number of molecules containing a C3v rotor and Cs frame, including the main isotopolog of acetaldehyde (Smirnov et al. 2014). As for the main isotopolog (Smirnov et al. 2014) we employed the RAM36 (rho-axis-method for 3- and 6-fold barriers) code that uses the RAM approach for the molecules with the C3v top attached to a molecular frame of Cs or C2v symmetry and having 3- or 6-fold barriers to internal rotation, respectively (Ilyushin et al. 2010, 2013). The Hamiltonian in the RAM36 program is presented by the following expression: (1)\begin{eqnarray} \nonumber  H &amp;=&amp; (1/2) \sum_{knpqrs} B_{knpqrs0} \left[P^{2k}P_z^nP_x^pP_y^qp_{\alpha}^r \cos(3s{\alpha}) \right.\\[2mm] \nonumber  &amp;&amp;\left. + \cos(3s\alpha) p_{\alpha}^rP_y^qP_x^pP_z^nP^{2k}\right] \\[2mm] \nonumber &amp;&amp; + (1/2) \sum_{knpqrt} B_{knpqr0t} \left[P^{2k}P_z^nP_x^pP_y^qp_{\alpha}^r \sin(3t\alpha) \right.\\ [2mm]  &amp;&amp;\left. + \sin(3t\alpha) p_{\alpha}^rP_y^qP_x^pP_z^nP^{2k}\right] \end{eqnarray}H=(1/2)∑knpqrsBknpqrs0[P2kPznPxpPyqpαrcos(3sα)+cos(3sα)pαrPyqPxpPznP2k]+(1/2)∑knpqrtBknpqr0t[P2kPznPxpPyqpαrsin(3tα)+sin(3tα)pαrPyqPxpPznP2k]where the Bknpqrst are fitting parameters; pα is the angular momentum conjugate to the internal rotation angle α; and Px,Py,Pz are projections on the x,y,z axes of the total angular momentum P. In the case of a C3v top and Cs frame (as is appropriate for acetaldehyde), the allowed terms in the torsion-rotation Hamiltonian must be totally symmetric in the group G6 (and also must be Hermitian and invariant to the time reversal operation). Since all individual operators pα,Px,Py,Pz,P2,cos(3sα) and sin(3tα) used in Eq. (1) are Hermitian, all possible terms provided by Eq. (1) will automatically be Hermitian. The particular term to be fit is represented in the input file with a set of k,n,p,q,r,s,t integer indices that are checked by the program for conformity with time reversal and symmetry requirements, to prevent accidental introduction of symmetry-forbidden terms into the Hamiltonian.
Citation: Ilyushin et al. 2010
Citation function: Uses; Uses
In the last few years, similar (though not identical) experimental developments to our method have been reported. In 2017, Wehres et al. (2017) described the design, construction, and operation of two laboratory broad-band emission spectrometers for gas-phase characterization of large molecules. The first is based on a Schottky-barrier diode heterodyne receiver spectrometer operating between 80 and 110 GHz, whilst the second uses cryo-cooled SIS technology and operates at higher frequency between 270 and 290 GHz (Wehres et al. 2018). Gas phase pyridine and methyl cyanide features were successfully detected and matching analytic simulations were presented for the observed spectral transitions. In 2018, a new technique combining a Terahertz radiometer (41–49) GHz and a vacuum chamber was developed to observe the generation of cold plasma and UV photochemistry in the gas phase under low pressure as described by Tanarro et al. (2018). However, in the context of the experiment described here, it is important to note that the spectrometer developments were not coupled to a system where the gas-phase molecules originated from the solid state. More recently, Yocum et al. (2019) reported on an experiment using a THz source and hot-electron bolometer to detect the gas-phase absorption spectra of simple molecular species such as H2O, D2O, and CH3OH desorbing into the gas-phase from ices grown in an ultra-high vacuum (UHV) chamber. This experimental set-up (SubLIME), combined with a Fourier-transform Infrared spectrometer, gave the spectroscopic insight on the ultraviolet photolysis and warm up of methanol ice sample, for the first time observed at those frequencies in a laboratory environment (Yocum et al. 2021). This showed the potential of the use of submillimetre/far-infrared technique to identify molecules in complex gas mixtures. One challenge of this technique was the sensitivity of absorption spectroscopy to the desorbing molecules. The combination of temperature programmed desorption and microwave spectroscopy techniques was discussed by Theulé et al. (2020). In these experiments, desorption of water, deuterium, methanol, and ammonia was measured using a hot electron bolometer and Si-diode technology combined with Fourier transform spectrometers. While Theule et al. observed the desorption through a waveguide cavity, Yocum et al. (2019) detected the desorption above a metal substrate as seen in standard TPD studies.
Citation: Tanarro et al. (2018)
Citation function: Background; Differences
Various theoretical studies employing numerical simulations have been performed to investigate planet formation through core accretion or gravitational instability in the low-stellar-mass regime. Payne & Lodato (2007) assessed planet formation around brown dwarfs adapting models for higher stellar masses based on core accretion. Through Monte Carlo simulations, they found that Earth-like planets can form in this condition and the planet mass depends strongly on the disk mass. However, none of their simulations showed a planetary rocky core accreting a gaseous envelope to form a giant planet. A way to overcome the radial drift barrier is a rapid rocky core growth. Pebble accretion is a mechanism able to speed up significantly the giant planet formation process (i.e., Lambrechts & Johansen 2012; Bitsch et al. 2015). Liu et al. (2020) carried out a theoretical study on planet formation driven by pebble accretion in the (sub)stellar mass range between 0.01 and 0.1 M⊙. First, they calculated the initial masses of protoplanets by extrapolating previous numerical simulations conducted in previous literature. Next, they performed a population synthesis study to track the growth and migration of a large sample of protoplanets under the influence of pebble accretion. Their results show that, around a 0.01 M⊙ brown dwarf, planets can grow up to 0.1−0.2 M⊕, while, around 0.1 M⊙ stars, planets can reach a maximum mass of 2−3 M⊕. Findings from this study show that even pebble accretion does not seem to be sufficient to form gas giants around VLM stars and brown dwarfs. Miguel et al. (2020) used a population synthesis approach based on planetesimal accretion to explore planet formation in the stellar mass range between 0.05 and 0.25 M⊙. They let the synthetic population of planetary systems evolve for 108 yr. The authors find that to form planets with masses higher than 0.1 M⊕ they need stars of at least 0.07 M⊙, implying that planet formation around brown dwarf may not be a usual outcome. Then, stars with masses higher than 0.15 M⊙ are necessary to form planets more massive than the Earth. Therefore, from all of these studies, we conclude that core accretion model currently cannot explain the presence of gas giants around VLM stars or brown dwarfs. Either the core accretion theory is incomplete, or another mechanism for planet formation is needed. This is confirmed by Lodato et al. (2005), who discussed the origin of the 5 MJup planet detected around the 25 MJup brown dwarf 2MASSW J1207334-393254 (Chauvin et al. 2005). They found that the core accretion mechanism is far too slow to generate such a planet in less than 107 yr, the estimated age of the system. Therefore, the authors proposed gravitational instabilities arising during the early phases of the disk lifetime as a viable possibility for the formation of the planet.
Citation: Lambrechts & Johansen 2012
Citation function: Background
This last, more technical aspect gave birth to a branch of research devoted to the problem of fitting a galaxy SED (Arnouts et al. 1999; Bolzonella et al. 2000; Cid Fernandes et al. 2005; Ilbert et al. 2006; Ocvirk et al. 2006; Tojeiro et al. 2007; Fritz et al. 2007, 2017; Franzetti et al. 2008; Pappalardo et al. 2010; Han & Han 2014; Leja et al. 2017; Weaver et al. 2021). Historically, models reproducing the evolution of stellar populations and dust emission have been developed separately, with reliable prescriptions to build synthetic spectra of different stellar populations on one side (Bruzual & Charlot 1993; Bressan et al. 1994; Worthey 1994; Fioc & Rocca-Volmerange 1997; Leitherer et al. 1999; Vazdekis 1999; Charlot & Fall 2000; Maraston 2005; Fioc & Rocca-Volmerange 2019), and a thorough comprehension of dust grains physics on the other (Draine & Lee 1984; Dale 2001; Takeuchi et al. 2003, 2005; Zubko et al. 2004; Draine & Li 2007; da Cunha et al. 2010; Silva et al. 2011; Asano et al. 2013; Calura et al. 2014; Zhukovska 2014; Mancini et al. 2015; Schneider et al. 2016; Popping et al. 2017; Aoyama et al. 2017; De Vis et al. 2017, 2019; Ginolfi et al. 2018; Graziani et al. 2019; Burgarella et al. 2019; Nanni et al. 2019; De Looze et al. 2020; Galliano et al. 2021). The two branches finally converged, performing simultaneous fitting of both components (Devriendt et al. 1999; Groves et al. 2008; da Cunha et al. 2008; Noll et al. 2009; Silva 2009; Graziani et al. 2019). Two of these methods, CIGALE (Noll et al. 2009; Boquien et al. 2019) and MAGPHYS (da Cunha et al. 2008), are based on the assumption that the radiation produced during the star formation process by the stellar and nebular components is partially absorbed by the dust and then re-emitted in the infrared part of the spectrum, satisfying the energy conservation. New families of codes have also been produced in an attempt to combine spectral and photometric analyses, recovering, through the implementation of photoionisation codes, emission lines feature; see for example Prospect (Robotham et al. 2020) and Prospector (Leja et al. 2017). Recently, even machine learning methods have been involved in this challenge, with neural network algorithms showing promising results (Simet et al. 2021).
Citation: Draine & Li 2007
Citation function: Background; Background