adsabs/FOCAL huggingface dataset:
Gibson (2014) proposed that marginalization over many systematics is more robust than simple model selection, and that the BIC-based model selection could be the worst criterion in their experiments. To assess the impact of BIC-based model selection choices (hereafter Method 1) on our derived transmission spectrum, we also performed a separate analysis on the spectroscopic light curves employing the systematics marginalization approach (hereafter Method 2). We followed the implication of this approach described in Wakeford et al. (2016), and refer the reader to that work for more details. Instead of using the wavelet-based MCMC to account for the correlated noise (see Method 1), for simplicity, Method 2 employed the MPFIT package to fit the data and accounted for the correlated noise using the time-averaging β approach. We calculated the marginal likelihood for all the systematics models using the Akaike information criterion (AIC; Akaike 1973) as the approximation, that is, \hbox{$\ln\mathcal{P}(D|S_q)\approx-\mathrm{AIC}/2$}ln𝒫(D | Sq) ≈ −AIC/2, which provides more adequate fits and performs better than BIC as suggested by Gibson (2014). The resulting Rp/R⋆ in each spectroscopic channel was then calculated as the marginal-likelihood-weighted average of the best-fitting values from all the systematics models, whose uncertainty was propagated from both the deviation from the weighted average and the best-fitting error bar for each systematics model. The derived Rp/R⋆ values are also listed in Table 3. The middle and bottom panels of Fig. 5 show the transmission spectra derived by Method 2 and the comparison between these two methods, respectively. The great consistency confirms that the BIC-based model selection in this work does not bias the derived transmission spectrum. Since the two methods have almost the same transit-depth values in any given spectral channel, we decide to present the results from Method 2 in the following discussion, as they have smaller error bars.
Citation: Wakeford et al. (2016)
Citation function: Uses; Uses
The fireball model remains the most popular scenario for the gamma-ray burst (GRB) phenomenon (Cavallo & Rees 1978; Goodman 1986; Paczynski 1986; Shemi & Piran 1990; Rees & Mészáros 1992, 1994; Mészáros & Rees 1993). In this model, the GRB central engine is a stellar-mass black hole or a rapidly spinning and highly magnetized neutron star formed by either the collapse of a supermassive star (collapsar; Woosley 1993; MacFadyen & Woosley 1999; Woosley & Heger 2006) or the merger of two compact objects (Paczynski 1986; Fryer et al. 1999; Rosswog 2003). In both cases, the original explosion creates a bipolar collimated jet composed mainly of photons, electrons, positrons, and a small fraction of baryons. The relativistic explosion ejecta within the jet are not homogeneous—they form multiple high density layers, which propagate at various velocities. When the fastest layers catch up with the slowest, the charged particles contained in the layers are accelerated through mildly relativistic collisionless shocks (internal shocks; Rees & Mészáros 1994; Kobayashi et al. 1997; Daigne & Mochkovitch 1998). The particles subsequently cool via emission processes such as synchrotron, Synchrotron Self Compton (SSC), and Inverse Compton (IC). The internal shock phase is usually associated with the so-called GRB prompt emission,17

17
See Pe’er (2015) for a recent review of GRB prompt emission.
 mainly observed in the keV−MeV energy range (see, e.g., the spectral catalogs by Gruber et al. 2014; von Kienlin et al. 2014) and usually lasting from a few ms up to several tens to hundreds of seconds. As the ejecta interact with the interstellar medium they slow down via relativistic collisionless shocks (external shocks; Rees & Mészáros 1992; Mészáros & Rees 1993), accelerating charged particles, which then emit non-thermal synchrotron photons. This external shock phase is usually associated with the so-called GRB afterglow emission observed at radio wavelengths up to X-rays and in some cases even up to the GeV regime hours after the prompt phase, and days and even years for the lowest frequencies. The detailed origin of the gamma-ray emission, however, is not fully understood and many theoretical difficulties remain, such as the composition of the jet, the energy dissipation mechanisms, as well as the radiation mechanisms (e.g., Zhang 2011).
Citation: Rees & Mészáros 1994
Citation function: Background
Walton et al. (2017) suggested that the broad-band spectral variability seen in Holmberg IX X-1, similar to that reported here for NGC 1313 X-1, could potentially be related to the presence of the expected funnel-like geometry for the inner accretion flow. In such a scenario, the funnel is expected to geometrically collimate the emission from the innermost regions within the funnel (discussed further in Section 4.2). Regardless of the nature of the accretor (black hole or neutron star), the highest energy emission probed by NuSTAR is usually expected to arise from these regions, either powered by a centrally located Compton-scattering corona (e.g. Reis & Miller 2013), or a centrally located accretion column. The stability of this emission would therefore imply that any geometrical collimation it experiences remains roughly constant, despite the change in observed broad-band X-ray flux (which would suggest a change in accretion rate, $\dot{M}$). In principle, an increase in accretion rate would be expected to result in an increase in the scale height of the funnel (e.g. King 2008; Middleton et al. 2015a). However, while this must happen over some range of $\dot{M}$ in order for the disc structure to transition from the thin disc expected for standard sub-Eddington accretion to the funnel-like geometry expected for super-Eddington accretion, as discussed by Lasota et al. (2016), once the disc reaches the point of being fully advection-dominated the opening angle of the disc should tend to a constant (H/R ∼ 1, where H is the scale height of the disc at radius R). Walton et al. (2017) speculated that once this occurs, rather than closing the funnel further, an increase in $\dot{M}$ instead simply increases the characteristic radius within which geometric beaming occurs, such that emission that is already within this region (the highest energies probed) experiences no further collimation with an increase in $\dot{M}$, while emission from larger radii (i.e. from more intermediate energies) does still become progressively more focused, and would exhibit stronger variability. In essence, this idea invokes a radially dependent beaming factor in which the beaming of the innermost regions has saturated to explain (in only a qualitative sense) the unusual, energy-dependent broad-band spectral evolution seen from Holmberg IX X-I (and now NGC 1313 X-1).
Citation: Middleton et al. 2015a
Citation function: Motivation; Motivation
In astrophysics, the number of studies that apply ML techniques has risen substantially in the last years. Unsupervised learning algorithms have been used to identify different kinematic components of simulated galaxies (Obreja et al. 2018, 2019), to compare stellar spectra (Traven et al. 2017), to classify pulsars (Lee et al. 2012), and to find high-redshift quasars (Polsterer, Zinn & Gieseke 2013). Supervised learning has been used to classify variable stars (Richards et al. 2011), to classify galaxies morphologically (Huertas-Company et al. 2008), and to determine the redshift of galaxies (Hoyle et al. 2015; Hoyle 2016; D’Isanto & Polsterer 2018). Recently, ML has also been used to connect the properties of galaxies and dark matter haloes using supervised learning techniques. Kamdar, Turk & Brunner (2016a), Kamdar, Turk & Brunner (2016b) use tree-based methods to predict several galaxy properties from a set of halo properties and train the models on galaxy catalogues obtained from semi-analytic models and the Illustris hydrodynamic simulation (Vogelsberger et al. 2014). Sullivan, Iliev & Dixon (2018) train a simple neural network with one hidden layer to predict the baryon fraction within a dark matter halo at high redshift, given several halo properties (features). As training data they use the results of a cosmological hydrodynamic simulation with Ramses-RT. Similarly, Agarwal, Davé & Bassett (2018) use several ML methods to link input halo properties to galaxy properties, training on the Mufasa cosmological hydrodynamical simulation. Taking a reverse approach, Calderon & Berlind (2019) train tree-based methods and a neural network to derive halo mass from galaxy properties, training on an SDSS group catalogue. The limitation of all these studies is the supervised training and the training data. As labelled galaxy-halo data is not available for observed systems, the data for supervised learning has to be taken from a model. Even if the ML algorithms learn to reproduce the training data perfectly, the connection between galaxy and halo properties is the same as in the simulations. If the simulations predict the true relations poorly, so will the ML method. Therefore ML algorithms should not be trained on simulated data, but on observed data directly.
Citation: D’Isanto & Polsterer 2018
Citation function: Background; Background
Citation: Hoyle 2016
Citation function: Background; Background
Inspired by these works, we investigated the possibility of introducing a color diagram for the characterization of irradiated planets using their effective temperature instead of their absolute magnitude. The effective temperature can be used as a proxy for the luminosity/absolute magnitude because the reference radius is assumed to be constant; see Section 3. To take an even more practical approach, we chose a normalized color parameter based on the Spitzer Infrared Array Camera (IRAC; Allen et al. 2004; Fazio et al. 2004) as a commonly used photometer for the observation of exoplanets (see, e.g., Charbonneau et al. 2005; Deming et al. 2007; Todorov et al. 2009; Sing et al. 2016). The IRAC photometric channels 1 and 2 are centered at 3.6 and 4.5 μm, respectively. Channel 1 (3.6 μm) is more suited to studying CH4/H2O spectral features, while channel 2 (4.5 μm) is more sensitive to CO/CO2 features (see, e.g., Désert et al. 2009; Swain et al. 2009a, 2009b). Depending on the type of spectroscopy, i.e., transmission or emission, the ratio of the transit depth or the ratio of the secondary eclipse depth at these channels could potentially provide information regarding the relative presence of these molecules in the atmosphere of a planet. For transmission spectroscopy, we define this ratio as
15





where IRACλ is the transition depth observed at the wavelength λ (μm) channel. In transmission spectroscopy, absorption features appear as positive signals in the unit of transit depth. This is not the case for emission spectroscopy, where absorption features are negative signals with respect to a blackbody curve. As a result, we rearrange the terms and define this ratio of channels for emission spectroscopy as
16





where IRACλ is the secondary eclipse depth observed at the wavelength λ (μm) channel. By applying IRAC’s spectral response curves to our 56,448 synthetic spectra, we estimated these ratios for the two spectroscopy methods. Figure 10 shows the IRAC synthetic color–temperature diagrams for cloud-free atmospheres under equilibrium chemistry conditions.
Citation: Deming et al. 2007
Citation function: Uses; Uses
According to the Dst index reconstructed by Love, Hayakawa & Cliver (2019), the interval of the telegraphic glitches taking place between ∼13:35 ut and ∼17:20 ut corresponds to the storm main phase (Dst ∼ −320 nT to ∼ −570 nT). This interval also corresponds to that of the low-latitude aurorae witnessed at many points at ±40° MLAT. A large-amplitude of the GIC is induced by magnetospheric and/or ionospheric current system. One possible cause of the telegraphic disturbance is the storm-time ring current that developed between ∼11:40 ut and ∼17:40 ut. Kappenman (2004) has shown that the magnitude of GICs flowing in the Japanese power grid increases with the magnitude of the Dst index. Another possible cause is the substorm–current wedge system that consists of field-aligned current and the tail current (McPherron, Russell & Aubry 1973). Downward field-aligned current is connected to the dawnside ionosphere, and upward field-aligned current is connected to the duskside ionosphere. The pair of field-aligned currents causes magnetic disturbances at mid- and low- latitudes (Pytte, Mcpherron & Kokubun 1976). The magnetic disturbance recorded at Tokyo (Fig. 5) shows a decrease in the H-component and a positive excursion of the D-component during the interval of ∼22–23 LT (∼13–14 ut). It is probable that Tokyo would be located at southwest of the upward field-aligned current during this interval. The D-component variation shows a few negative excursions during ∼25:40–30:00 LT (∼16:40–21:00 ut). Although the disturbance of the H-component is unavailable during this interval, it can be speculated that Tokyo was located at southeast of the downward field-aligned current. In addition to the development of the ring current, the formation of the substorm current wedge may also cause the telegraphic disruption. By considering the above discussion, at mid-latitude, we suggest that there are two regions where GICs can be caused by field-aligned currents. One is southwest of the upward field-aligned current, and the other is southeast of the downward field-aligned current. In these regions, the H-component of the magnetic field is expected to decrease significantly because of the combination of the effects of the ring current and the current wedge. Tokyo was probably situated in such hazardous regions during the 25 September 1909 storm.
Citation: Pytte, Mcpherron & Kokubun 1976
Citation function: Uses
On the other hand, the Australia Telescope Compact Array (ATCA) 21 cm line survey of GRB host galaxies revealed high levels of atomic hydrogen (H i), suggesting that the connection between atomic gas and star formation is stronger than previously thought (Michałowski et al. 2015). Star formation may be directly fuelled by atomic gas, as has been theoretically shown to be possible (Glover & Clark 2012; Krumholz 2012; Hu et al. 2016), and this is supported by the existence of H i-dominated, star-forming regions in other galaxies (Bigiel et al. 2008, 2010; Fumagalli & Gavazzi 2008; Elmegreen et al. 2016). This can happen in a low metallicity gas that is recently acquired, even if the metallicity in other parts of a galaxy is higher, near the onset of star formation because cooling of gas (necessary for star formation) is faster than the H i-to-H2 conversion (Krumholz 2012). Indeed, large atomic gas reservoirs, together with low molecular gas masses (Hatsukade et al. 2014; Stanway et al. 2015b) and stellar masses (Perley et al. 2013, 2015; Vergani et al. 2015), indicate that GRB hosts are preferentially galaxies that have very recently started a star formation episode. This provides a natural route for forming GRBs in low-metallicity environments, as found for most GRB hosts (Fruchter et al. 2006; Modjaz et al. 2008; Levesque et al. 2010a; Han et al. 2010; Boissier et al. 2013; Schulze et al. 2015; Vergani et al. 2015; Japelj et al. 2016; Perley et al. 2016), except of a few examples of hosts with solar or super-solar metallicities (Prochaska et al. 2009; Levesque et al. 2010b; Krühler et al. 2012; Savaglio et al. 2012; Elliott et al. 2013; Schulze et al. 2014; Hashimoto et al. 2015; Schady et al. 2015; Stanway et al. 2015a). Indeed, the GRB collapsar model requires that most of the GRB progenitors have low metallicity (below solar) in order to reduce the loss of mass and angular momentum that is required for launching the jet (Yoon & Langer 2005; Yoon et al. 2006; Woosley & Heger 2006). We note however that other models, while still predicting the metallicity preference (e.g. Izzard et al. 2004; Podsiadlowski et al. 2004; Detmers et al. 2008), allow higher metallicities owing to differential rotation (Georgy et al. 2012), binary evolution (Podsiadlowski et al. 2010; van den Heuvel & Portegies Zwart 2013), or weaker magnetic fields (Petrovic et al. 2005).
Citation: Vergani et al. 2015
Citation function: Compare/Contrast; Compare/Contrast; Compare/Contrast
Citation: Vergani et al. 2015
Citation function: Compare/Contrast
The heating due to the reprocessing of non-thermal photons produced in the nebula can be efficient even at late times. Here, we treat these processes in a simple way as follows. At early times, electromagnetic cascades proceed in the saturation regime, leading to a flat energy spectrum up to ∼1 MeV (Metzger et al. 2014). At later times, the spectrum depends on the seed photon spectra, but it can roughly be estimated to be a flat spectrum from ∼1 eV to ∼0.1 TeV, while the supernova emission continues, which is expected based on more detailed calculations (e.g., Murase et al. 2015; K. Murase et al. 2017, in preparation). High-energy γ-rays (≳1 MeV) heat up the ejecta through the Compton scattering and the pair production process. X-ray and UV photons are absorbed and heat up the ejecta through the photoelectric (bound-free) absorption unless the ejecta are fully ionized. Here, we describe the heating rate as
12








Q


˙




rad


(
t
)
≈


(



f


γ


+


f





X
−
UV


,
bf



)




L


sd


,


where fγ and 






f





X
−
UV


,
bf




 are the heating efficiencies of γ-rays and X-ray and UV photons to the spin-down luminosity, respectively. We calculate the frequency dependent heating efficiency of γ-rays at each time:
13






f


γ


(
t
)
=





∫




ν


min






ν


max







d
ν


ν




min
(


K


γ
,
ν




τ


γ
,
ν


,
1
)




∫


1

eV


1

TeV





d
ν


ν






,


where the frequency range of γ-rays is 




(
h


ν


min


,
h


ν


max


)

=
(
10

keV
,
1

TeV
)


, and h is the Planck constant. Here, τγ, ν is the optical depth of the ejecta to γ-rays and Kγ, ν is the photon inelasticity at a given frequency, where the Klein–Nishina cross section and the cross section for the Bethe–Heitler pair production in the field of a carbon nucleus are taken into account (Chodorowski et al. 1992; Murase et al. 2015). Note that the coefficient of the γ-ray optical depth depends on the density profile of the ejecta. Here, we simply assume a density profile to be constant with the radius. Adopting a realistic density profile may result in different ejecta mass and velocity estimates by a factor of a few.
Citation: Murase et al. 2015
Citation function: Uses
Citation: Murase et al. 2015
Citation function: Uses
We recorded the IRPD spectra of mass-selected H+MF and H+MF-Ln≤2 clusters (L = Ar/N2) in the 2950–3650 cm−1 spectral range in a tandem quadrupole mass spectrometer interfaced with an electron ionization source (Dopfer 2003, 2005), a setup used previously to record IR spectra of a variety of hydrocarbon cations and their clusters (Dopfer et al. 2002; Solcà & Dopfer 2002; Andrei et al. 2008; Patzer et al. 2010; Chatterjee & Dopfer 2017, 2018a, 2018b, 2020; Chatterjee et al. 2019). Briefly, the parent ions are produced in an ion source, which combines a pulsed supersonic expansion with electron (chemical) ionization close to the nozzle orifice. The expanding gas mixture is generated by passing a carrier gas mixture of Ar (N2) and 5% H2 in He in a ratio 5:1 at 10 bar through a reservoir containing liquid MF (Sigma-Aldrich, 99%, heated to 323 K). One possible reaction pathway begins with electron ionization of H2, followed by exothermic proton transfer reactions to form H+MF and subsequent three-body association to generate H+MF-Ln clusters (Harrison & Tsang 1976; Hopkinson et al. 1979; Dopfer et al. 1999; Lawson et al. 2012; Chatterjee & Dopfer 2018a), according to:
5







6







7







8





This route is reminiscent of the synthesis of interstellar protonated molecules (Oka 2006; Etim et al. 2017). An alternative pathway involves charge transfer from Ar+ (or 





 or 





) and subsequent self-protonation (Lawson et al. 2012; Chatterjee & Dopfer 2018a):
5a







6a







7a





The desired ions are mass-selected in the first quadrupole and irradiated in an adjacent octupole with a tunable IR laser pulse (νIR, 10 Hz, 2–5 mJ pulse−1, 1 cm−1 bandwidth) of a Nd:YAG-pumped optical parametric oscillator. Calibration of νIR to better than 1 cm−1 is achieved by a wavemeter. Resonant vibrational excitation upon single-photon absorption leads to the fragmentation of neutral ligands or molecules:
9a







9b







9c





Resulting fragment ions are mass-selected by the second quadrupole and monitored with a Daly detector as a function of νIR to derive the IRPD spectrum of the parent ions, which is linearly normalized for the energy of the laser pulse. The contribution from metastable decay is subtracted from the laser-induced dissociation signal by triggering the ion source at twice the laser repetition rate. The observed widths of the vibrational bands mainly originate from unresolved rotational structure, sequence hot bands involving inter- and intramolecular modes, lifetime broadening, and congestion produced from overlap of various structural isomers. Low-energy collision-induced dissociation (CID) mass spectra at ∼10 eV collision energy in the laboratory frame are recorded by introducing 10−5 mbar N2 into the octople to confirm the composition of the ions and their clusters and establish their fragmentation channels.
Citation: Harrison & Tsang 1976
Citation function: Compare/Contrast