adsabs/FOCAL huggingface dataset:
For LLAGNs, there are two competing components for the emission at optical/UV wavebands. One component is the thermal emission from the outer truncated SSD, which peaks in optical or UV bands, depending on the location of the truncated radius. The other component is the Compton scattering of synchrotron emission from the inner hot accretion flow (Manmoto et al. 1997; Yuan & Narayan 2014). Since our sample is limited to sources whose λ  10−3, we argue that the emission at 2500 Å mainly comes from the inner hot accretion flow.3 Interestingly, for hot accretion flows around supermassive BHs (the LLAGN case), the optical/UV emission usually is the first Compton up-scattered bump (Manmoto et al. 1997; Yuan & Narayan 2014). The emission from such a process has a positive correlation with $\dot{m}$, as $\dot{m}$ will mainly determine the optical depth (surface density), a quantity that controls the probability of Compton scattering (Sunyaev & Titarchuk 1980; Dermer, Liang & Canfield 1991). On the other hand, the first Compton bump is even more sensitive to the energy of electrons (a.k.a. the electron temperature), as it determines the location (in wavebands) of the peak. For a given $\dot{m}$, larger α means lower density, or equivalently lower radiative cooling to the electrons. Consequently, the electrons will be more energetic at higher temperatures (Xie & Yuan 2012). The dependence of emission at optical/UV band on the parameter α relies on detailed numerical calculations, where Manmoto et al. (1997) reported that the emission at 2500 Å has a positive correlation with α. For the above reasons, we simply write LUV as
(6)
\begin{equation}
L_{\rm UV}\sim (\dot{m} \alpha )^b,
\end{equation}
where b ∼ 2. The estimation of b comes from the fact that the first-order Compton scattering (responsible for the optical/UV emission) depends on the product of the seed photon flux (synchrotron emission, $\propto \dot{m}^{0.5-1}$; cf. Mahadevan 1997; Yuan et al. 2015; note that electron temperature also depends on $\dot{m}$ for the expression in Mahadevan 1997) and optical depth in a vertical direction ($\propto \dot{m}^{1-2}$).
Citation: Yuan et al. 2015
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LISA Pathfinder (LPF; Antonucci et al. 2011), a European Space Agency (ESA) mission that operated near the first Sun–Earth Lagrange point (L1) from 2016 January through 2017 July, is in an ideal orbit to make such measurements. However, LPF flew no instrumentation dedicated to micrometeoroid or dust detection. LPF’s primary objective was to demonstrate technologies for a future space-based observatory of millihertz-band gravitational waves. The key achievement of LPF was placing two gold-platinum cubes known as “test masses” into a freefall so pure that it was characterized by accelerations at the femto-g level (e.g., Armano et al. 2016, 2018b), the level required to detect the minute disturbances caused by passing gravitational waves. In order to reach this level of performance, the test masses were released into cavities inside the spacecraft and a control system was employed to keep the spacecraft centered on the test masses. This control system was designed to counteract disturbances on the spacecraft, including those caused by impacts from micrometeoroids. Shortly before LPF’s launch, it was realized that data from the control system, if properly calibrated, could be used to detect and characterize these impacts and infer information about the impacting particles (e.g., Thorpe et al. 2016). While such impact events have been reported by other spacecraft, LPF’s unique instrumentation makes it sensitive to much smaller and much more numerous impacts and allows the impact geometry to be more fully constrained. Early results from the first few months of LPF operations suggested that such events could indeed be identified and were roughly consistent with the pre-launch predictions of their effect on the control system (e.g., Thorpe et al. 2017). In this paper we present results from the first systematic search for micrometeoroid impacts in the LPF data set. Our data set consists of 4348 hr of data in both the nominal LPF configuration and the “Disturbance Reduction System” (DRS) configuration, in which a NASA-supplied controller and thruster system took over control of the spacecraft (Anderson et al. 2018). Our data set corresponds to the times when LPF was operating in a “quiet” mode, without any intentional signal injections or other disturbances. During this period, we have identified 54 impact candidates using our detection pipeline and manual vetoing. We have characterized the properties of this data set and compared it to several theoretical models for the underlying dust population.
Citation: Anderson et al. 2018
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Citation: Thorpe et al. 2016
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Citation: Thorpe et al. 2017
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Since Colgate & White (1966) proposed that neutrinos play a crucial role in reviving the stalled shock in core-collapse supernovae (CCSNe), numerical models have incorporated neutrino transport in some form, with varying degrees of sophistication. The neutrino transport problem in CCSNe poses one of the most computationally challenging problems to date. The extreme range in densities within the supernova results in optically thick regions in which neutrinos are diffusive, and optically thin regions in which neutrinos are decoupled from the fluid and can stream freely. The so-called gain region behind the stalled shock, which the success of the explosion hinges upon, lies within the semitransparent region between the two regimes, where an accurate treatment of the neutrino distribution function is important (for modern reviews of the topic, see Mezzacappa 2005; Janka 2012; Burrows 2013). The most accurate treatment of neutrino transport calls for the solution of the full Boltzmann equation (Mihalas & Mihalas 1984),
(1)$$\begin{eqnarray*}
\frac{\partial f}{\partial t} + \boldsymbol {u} \cdot \nabla f = \mathcal {C}(f),
\end{eqnarray*}$$(shown here the Newtonian form for clarity) which describes the neutrino momentum distribution f at every point in space (Fig. 1). Evolving through time, this results in a 7D problem. The left-hand side of the equation is the advection equation, which couples together the spatial dimensions. The right-hand side contains the collision integral, which accounts for interactions between neutrinos and matter, and couple together the momentum space dimensions. Interaction rates are sensitive to the neutrino energy, necessitating an energy-dependent treatment. Additionally, there are three flavors of neutrinos (electron, muon, and tauon) and respective antineutrinos for a total of six species. Due to the different interaction rates of each species, their transport behaviour is considerably different, thus the core-collapse problem also calls for a flavour-dependent treatment. In general, even flavour can evolve via neutrino oscillations, a purely quantum mechanical effect (Wolfenstein 1978; Mikheyev & Smirnov 1985).
Citation: Mihalas & Mihalas 1984
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Citation: Colgate & White (1966)
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We have carried out analyses of X-ray and optical photometric data of an MCV Paloma. Using the X-ray data from XMM-Newton, we derive 10 significant periods in Paloma, including two fundamental periods of 156 ± 1 minutes and 130 ± 1 minutes. However, using our optical data, we derive seven significant periods in which 





 and 





 are consistent with ∼1.5σ level to that derived from X-ray data. The peaks corresponding to the frequencies Ω, ω, 





, and 





 are well matched with those derived by Schwarz et al. (2007). The orbital modulation in the X-ray light curves is generally explained by the absorption of X-rays emitted near the WD surface by material fixed in the orbital frame, but a strong signal at the orbital period in the X-ray power spectra of Paloma indicates that an explicit orbital modulation is most likely due to the attenuation introduced by the impact site of the accretion stream with the accretion disk or magnetosphere. On the other hand, X-ray modulation at the spin period can arise due to the self-occultation of emission regions by the WD or due to photoelectric absorption and electron scattering in the accretion curtain (Norton et al. 1996). The occurrence of the spin peak indicates that the symmetry of the two pole caps is broken and the amount of asymmetry depends on whether the signal corresponding to the rotation of the asymmetrically placed pole is strong or weak, i.e., the degree of asymmetry is proportional to the power of the spin peak (Wynn & King 1992). Along with the spin frequency, the presence of significant peaks in the X-ray power spectrum at ω − Ω, Ω, 





, 





, 





, 





, 





, 





, and 





 also predict that Paloma is an asymmetric system in which both the emission poles are not diametrically opposite and have slightly different emission properties (see Norton et al. 1996). Furthermore, the presence of significant power at spin, beat and 





 frequencies suggest that Paloma is a disk-less accreting system (see Wynn & King 1992; Norton et al. 1996). Additionally, an increase in power of the beat or 





 components accompanied by the decrease in power at the spin component as the X-ray energy increases is a characteristic of the disk-less accretion. This is clearly visible in the X-ray power density spectra of Paloma, which is further attributed to the absorption of low-energy photons by the accretion column itself. Other than the orbital, spin, and beat periods, photometric observations of Paloma show peaks at frequencies 





 and 





. Such similar positive and negative sideband frequencies corresponding to the spin and orbital periods are also observed from photometric observations in nearly synchronous IP V697 Sco (Warner & Woudt 2002), suggesting that Paloma may have a close resemblance to V697 Sco.
Citation: Norton et al. 1996
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Citation: Norton et al. 1996
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Citation: Norton et al. 1996
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Citation: Warner & Woudt 2002
Citation function: Similarities; Similarities
Top left: the revised, CO(J = 1 → 0)-based Mgas from VLASPECS confirm that z = 2–3 galaxies detected in the ASPECS survey (green circles; tentative detections are marked with a plus sign) closely follow the “star formation law” (i.e., Mgas–SFR relation) at high redshift. CO-detected main-sequence galaxies at similar redshifts from the PHIBBS1/2 surveys (typically based on CO J = 3 → 2, but using a metallicity-dependent conversion factor; Tacconi et al. 2018) and local galaxies from the xCOLD GASS CO(J = 1 → 0) survey (Saintonge et al. 2017) are shown for comparison. Bottom left: same as the top left panel, but plotting the depletion time tdep against Mgas. All samples cover a similar range in tdep, but the average tdep for the (higher Mgas) high-z samples appear lower. Top right: the r31 brightness temperature ratio of VLASPECS galaxies (green circles) is similar to that of strongly lensed z ∼ 3 Lyman-break galaxies (red triangles; Riechers et al. 2010), z > 2 main-sequence galaxies from the PHIBSS survey (gray crosses; Bolatto et al. 2015), and z > 2 dusty star-forming galaxies (DSFGs; blue squares; compilation from Sharon et al. 2016; including data from Danielson et al. 2011; Ivison et al. 2011; Riechers et al. 2011a, 2011b, 2013; Thomson et al. 2012; Fu et al. 2013; Sharon et al. 2013, 2015; other DSFGs shown as light gray squares are from Nayyeri et al. 2017; Dannerbauer et al. 2019; Harrington et al. 2019; Leung et al. 2019; Sharon et al. 2019) and clustered DSFGs (dark gray squares; Bussmann et al. 2015; Gómez-Guijarro et al. 2019), but ∼2 times higher on average than BzK-selected main-sequence galaxies at z ∼ 1.5 (magenta crosses; Daddi et al. 2015). Nearby galaxy samples from the xCOLD GASS survey (Lamperti et al. 2020) and two studies of infrared-luminous galaxies (Yao et al. 2003; Papadopoulos et al. 2012) are shown for comparison. Dashed lines and shaded regions indicate mean/median values and spread for high-z samples with >2 galaxies or clusters, with the same color coding as the symbols. Dashed–dotted lines indicate mean values for the low-z samples. Bottom right: same as the top right panel, but shown as binned histograms in r31 (excluding upper limits) and across the full redshift range, and only including samples for which mean/median values are indicated in the top right panels.
Citation: Lamperti et al. 2020
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Two additional interesting sources for a comparison are the radio galaxies M 87 (z = 0.0044) and 3C 84 (also called NGC 1275, z = 0.0176). Both sources have a viewing angle similar to NGC 3894: 15°  θ   25° (Mertens et al. 2016) and θ   18° (Giovannini et al. 2018), respectively. The velocities of the jet components are about 0.2c in 3C 84 (Suzuki et al. 2012) and in the inner part of M 87 (Mertens et al. 2016), similar to what we find for NGC 3894. The three sources have a comparable photon index (Γ ∼ 2.1). Although it is less distant and more extended (∼100 kpc), M 87 shows a γ-ray luminosity of 6−7 × 1041 erg s−1, similar to NGC 3894. Different is the case of 3C 84, which has two-sided compact (parsec scale) jets and a distance similar to NGC 3894, but it presents a γ-ray luminosity more than 200 times higher (2 × 1044 erg s−1). Unlike NGC 3894, for which no observations with Cherenkov telescopes have been performed, M 87 and 3C 84 are also detected at very high energy (VHE, E > 100 GeV; Aharonian et al. 2006; Aleksić et al. 2012). In particular, at VHE, M 87 displayed strong variability on timescales as short as one day, but no unique signature of the region responsible for the VHE flares has been identified (Abramowski et al. 2012). Despite the long-term monitoring of M 87 during 2012−2015 in a low-activity state, the production site of γ-rays remains unclear (MAGIC Collaboration 2020). However, the correlation observed between the radio and X-ray activities is a strong indication that most often the emitting region is close to the core in this source. In the case of 3C 84, several works on radio, X-ray, and γ-ray variability suggest that short-term and long-term variability may be produced in different regions of the source. In particular, short-term variability seems related to the injection of fresh particles that are accelerated in a shock in the core region, whereas long-term variability is more likely connected with the jet structure (e.g., Fukazawa et al. 2016; Hodgson et al. 2018). Similarly to M 87 and 3C 84, long-term radio and X-ray monitoring of NGC 3894 may provide important information about the origin of the γ-ray emission.
Citation: Fukazawa et al. 2016
Citation function: Compare/Contrast; Motivation
There are some existing studies applying machine learning to transient studies. For example, the spectral types of the SNe can be classified based on their light-curve data (Möller et al. 2016; Muthukrishna et al. 2019a; Takahashi et al. 2020; Villar et al. 2020), and transients can be identified from the astronomical survey images (Goldstein et al. 2015; Mahabal et al. 2019; Gómez et al. 2020). The light curves of SNe Ia can be well modeled by functional principal component analysis (FPCA; He et al. 2018), where it was shown remarkably that a set of FPCA eigenvectors that are independent of the photometric filters can be derived from the observed light curves of SNe Ia. There are a few studies of the application of deep learning neural networks to the spectral data of SNe. For example, Muthukrishna et al. (2019b) used a convolutional neural network (CNN) for automated SN type classification based on SN spectra. Several other works (Chen et al. 2020; Vogl et al. 2020; Kerzendorf et al. 2021) applied a Gaussian process, principal component analysis (PCA), and deep learning neural networks to radiative transfer models of SNe. Sasdelli et al. (2016) used unsupervised learning algorithms to investigate the subtypes of SNe Ia. Stahl et al. (2020) developed neural networks to predict the photometric properties of SNe Ia (phase and Δm
15) based on spectroscopic data. Saunders et al. (2018) used PCA to find low dimensional representations of the spectral sequences of 140 well-observed SNe Ia. Chen et al. (2020), in particular, built an artificial intelligence assisted inversion (AIAI) of radiative transfer models and used that to link the observed SN spectra with theoretical models. The AIAI is able to retrieve the elemental abundances and density and temperature profiles from observed SN spectra. The AIAI approach has the potential for quantitatively coupling complex theoretical models with the ever-increasing amount of high-quality observational data.
Citation: Chen et al. (2020)
Citation function: Background; Motivation
The strength of emission features scales not only with the mass-loss rate but also with the volume-filling factor (fv), terminal velocity, and radius of the star which are compressed into one parameter to reduce the effort when computing model grids. There are two ways to spectroscopically quantify mass-loss rates of hot massive stars using scaling relations. Stars with optically thin winds (OB stars), the wind strength parameter Q is usually applied (Puls et al. 1996; Sabín-Sanjulián et al. 2014, 2017; Holgado et al. 2018), where Q is proportional to the integrated optical depth over the resonance zone:
(1)$$\begin{eqnarray*}
Q=\frac{\dot{M}\, [\mathrm{ M}_{\odot }\mathrm{yr}^{-1}]/\sqrt{f_{\rm v}}}{(R\, [R_{\odot }] \upsilon _{\infty }\, [\mathrm{km\,s^{-1}}])^{3/2}},
\end{eqnarray*}$$and fV has been set to unity (see Section 4.3). For optically thick conditions, there is an additional dependence on $\upsilon _{\infty }$ (e.g. Puls et al. 1996). The transformed radius (Schmutz, Hamann & Wessolowski 1989; Gräfener, Koesterke & Hamann 2002; Hamann & Gräfener 2004) or the equivalent approach of the transformed mass-loss rate ($\dot{M}_{\rm t}$, Bestenlehner et al. 2014) is usually used for optically thick winds (WR stars), where the line equivalent width is preserved:
(2)$$\begin{eqnarray*}
\log (\dot{M}) &amp;=&amp; \log (\dot{M}_{\rm t}) + 0.5\log (f_{\rm v}) + \log \left(\frac{\upsilon _{\infty }}{1000\, \mathrm{km\, s}^{-1}} \right) \nonumber \\
&amp;&amp;+ \,0.75\log \left(\frac{L}{10^6L_{\odot }} \right).
\end{eqnarray*}$$Both scaling relations are equivalent except for the exponent of the $\upsilon _{\infty }$ dependence, $\dot{M} \varpropto\upsilon _{\infty }^{3/2}$ (wind strength Q), and $\dot{M} \varpropto\upsilon _{\infty }$ (transformed mass-loss rate $\dot{M}_{\mathrm{t}}$). In our study, we compared both scaling relations and find that optically thin winds are preferably scaled with the wind strength parameter while optically thick winds are better scaled with the transformed mass-loss rate. If $\upsilon _{\infty }$ in the model has a reasonable value, the differences between both scaling relations are small. However, if the line is in emission and the terminal velocity of the synthetic spectrum is too high, the line centre is fitted well with the Q scaling relation, but the synthetic spectrum shows extended wings. This overestimates the actual mass-loss rate. By fitting Balmer lines in absorption, a degeneracy between log g and $\dot{M}$ can occur using the $\dot{M}_{\mathrm{t}}$ scaling relation. We used the $\dot{M}_{\mathrm{t}}$ scaling relation for R136a1, a2, a3, a5, b, and H36 and Q for the remaining O stars in our sample.
Citation: Gräfener, Koesterke & Hamann 2002
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Citation: Puls et al. 1996
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Steady and smooth decline: Since falling from the peak of the 2012b event, SN 2009ip has only continued to fade and is now the faintest it has ever been in the optical. Immediately after the 2012 event (around day 200), it was declining somewhat slower than the rate of 56Co decay for a 56Ni mass of 0.04 $\, {\rm M}_{\odot }$ (shown by the dashed grey line in Fig. 2). During that time, its light curve and spectral properties were consistent with those of late-time SNe IIn, and it had an underlying broad-line spectrum similar to that of SN 1987A (Graham et al. 2014; Smith et al. 2014). Up to about day 1000 it continued to fade smoothly at a slower rate around 0.003 mag d−1, and spectroscopically it continued to resemble late-time interaction in SNe IIn (Fox et al. 2015; Smith et al. 2016b; Graham et al. 2017). Our new HST photometry shows that from about day 1000 to day 3000, SN 2009ip has continued to fade smoothly and steadily in the optical, at an even slower rate. While the HST cadence is obviously sparse at these late times, the filters with more than two observations (F555W and F814W) show no significant deviation from a steady decline; there is no evidence of any rebrightening or irregularity in the fading rate. From 2015 to 2021, the decline rates in the various optical filters are 0.00051 ± 0.00001 mag d−1 in F555W, 0.00068 ± 0.00002 mag d−1 in F606W, 0.00092 ± 0.00001 mag d−1 in F657N, and 0.00050 ± 0.00001 mag d−1 in F814W. (The UV is an exception, as discussed below.) While this decline is much slower than radioactive decay, such slow decline rates are not at all unusual for SNe IIn with late-time CSM interaction. SNe IIn span a wide diversity of late-time decay rates, ranging from some SNe IIn that have essentially flat light curves for many years, like SN 2005ip (Smith et al. 2009, 2017; Stritzinger et al. 2012; Fox et al. 2020), down to those that have only weak CSM interaction and faster decline rates that are difficult to distinguish from radioactive decay or light echoes. Some SNe IIn even rebrighten at late times, like SN 2006jd (Stritzinger et al. 2012). In any case, it seems as if SN 2009ip is levelling off and approaching a constant V absolute magnitude of −7.5 or so. In all three broad continuum filters, the object is now fainter than the progenitor, and the light curve has not exhibited any additional eruptive variability since the 2012b event.
Citation: Graham et al. 2014
Citation function: Compare/Contrast