adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019ApJ...871...89E (fulltext)
s −1 ). Figure 2 shows the integrated intensity maps of C 18 O ( J = 1–0) (cyan contours) and 13 CO ( J = 1–0) (color) observed with the NRO 45 m telescope for the entire region of HCL2 (Sunada Kitamura 1999 ). A prominent straight structure is seen in the northern part of HCL2 in the C 18 O map, which also has been pointed out by Narayanan et al. ( 2008 ) and Goldsmith et al. ( 2010 ). Interestingly, the velocity of the blueshifted component detected in the OH 18 cm transition ( km s −1 ) corresponds to that of C 18 O, exhibiting the straight structure, whereas the velocity of the redshifted component seen in OH ( 6–10 km s −1 ) corresponds to that of the southwestern extended gas in the 13 CO map. These two structures are interfacing with each other, suggesting a compressive motion as the origin of the straight structure (Figure 20 ). Such a compressive motion is consistent with the decreasing velocity from southwest to northeast across the straight structure observed in 13 CO and C 18 O. In addition, the existence of an [H i ] cloud in the northeast of TMC-1FN is suggested by the archival data of the Galactic Arecibo L-band Feed Array H i (GALFA-H i ) Survey maps (Peek et al. 2011 ). Furthermore, the 6–10 km s −1 component traces a diffuse warm gas, whereas the 5.5 km s −1 component traces a cold and dense core illuminated by the FIR radiation from dust grains, according to our statistical equilibrium calculation. It is likely that the 5.5 km s −1 component traces cold cores surrounded by the warm envelope gas in HCL2. Figure 20. Schematic illustration of the cross section of TMC-1FN along the line of sight. The cold (∼20 K) and dense core (5 km s −1 ) is formed by compressive motion of the extended warm gas (6–10 km s −1 ). We observe the source from the right-hand side. The 6.5 and 8.2 km s −1 components show the 1612 MHz line absorption, in spite of the FIR pumping effect seen in the 5.5 km s −1 component. This can be explained by the proposed picture of TMC-1FN (Figure 20 ). A warm envelope gas traced by the 6.5 and 8.2 km s −1 components is likely to be illuminated by fainter FIR radiation than the gas traced by the 5.5 km s −1 component. The OH column density for the 6.5 and 8.2 km s −1 components is also lower. According to the results in Appendix C , the FIR intensity at 160 μ m is estimated to be ∼400 and 100 MJy sr −1 at positions 0.5 and 1.0 pc distant from the center of the TMC-1FN filament, respectively. The OH column densities are determined to be 3.6 × 10 14 cm −2 and 1.4 × 10 14 cm −2 for the 6.5 and 8.2 km s −1 components, respectively (Table 3 ). As described in Section 4.1.7 , our calculation shows that the FIR pumping effect is negligible for an OH column density lower than 10 15 cm −2 and I 160 lower than 100 MJy sr −1 . On the other hand, the 1612 MHz line indeed appears in absorption for I 160 lower than 4 × 10 3 MJy sr −1 , if the OH column density is 3 × 10 14 cm −2 (Figure 15 (d)). Hence, even though there might be
Paper: 2017MNRAS.472.2028F (fulltext)
represent the 1.3-mm (G09-83808, NGP-190387) and 3-mm (SGP-261206) continuum emission, at 5, 6, 7, 8, 9 × σ. Near-IR emission is seen clearly, coincident or near-coincident with the 1.3- or 3-mm continuum emission. At the depth of the VIKING observations (limiting magnitude, K AB ≃ 21.2), we do not expect to detect dust-obscured distant galaxies. The sources coincident with G09-83808 and SGP-261206 show, therefore, that these two DSFGs are amplified gravitationally by the foreground galaxies seen in the near-IR images. The galaxy in the foreground of SGP-83808 has z spec = 0.778, obtained using X-shooter on the VLT (Fig. 5 ), and is magnified by 8.2 ± 0.3, a factor determined using high-resolution ALMA continuum imaging (see Oteo et al., in preparation-a). The three galaxies revealed by the deeper Gemini imaging of NGP-190387 are considerably fainter, with K AB = 21.0, 22.3, 21.4, yet they are not coincident with the z = 4.42 DSFG and likely constitute a foreground lensing group. Towards G09-81106 we have detected two emission lines, CO(4–3) and CO(5–4), at 83.36 and 104.19 GHz (Fig. 3 ), both at z = 4.531. There is no suggestion of gravitational lensing for G09-81106, either via the presence of unusually bright near-IR galaxies in the field, or via its submm morphology as seen in high-resolution ALMA continuum imaging (Oteo et al., in preparation-a). Towards G09-83808 we have detected two faint emission lines, at 82.02 and 98.39 GHz (Fig. 3 ), corresponding to CO(5–4) and CO(6–5) at z = 6.026 ± 0.001 and 6.028 ± 0.001, respectively, so an average of 6.027 ± 0.001. These lines were also noted by Zavala et al. ( 2017 ) in a spectrum obtained using the Large Millimeter Telescope. G09-83808 is near-coincident with a foreground galaxy, seen clearly in near-IR imaging from the VIKING survey (see Fig. 4 – Edge et al. 2013 ), indicative of gravitationally lensing. This foreground galaxy has a spectroscopic redshift of 0.778, obtained using X-shooter on the 8-m Very Large Telescope (VLT; see Fig. 5 ). A lens model based on the morphology determined by high-resolution ALMA continuum imaging predicts a gravitational amplification of 8.2 ± 0.3 (Oteo et al., in preparation-a). Figure 5. Top: Spectrum of the lensing galaxy in the foreground of SGP-83808, as seen in the optical arm of VLT/X-shooter after integrating for 4 × 20 min on 2013 March 17. Below: The 1D spectrum, in grey, extracted using a simple box-car summation [090.A-0891(A), PI: Christensen]. To identify the lens redshift we binned these data heavily (see Modigliani et al. 2010 ; Christensen et al. 2012 ; Kausch et al. 2014 ), as illustrated in black, with the error spectrum shown in green, then fitted the redshift by χ 2 minimization using a 5-Gyr stellar population model (shown in red) from Bruzual Charlot ( 2003 ). We find z spec = 0.778 ± 0.006. Strong absorption features are labelled. The flux units are 10 −17 erg s −1 cm −2 Å −1 . For NGP-284357, we find at least two emission lines, at
Paper: 2019MNRAS.484.1031P (fulltext)
to test this connection more rigorously, but the match is poor, being both too blue and too broad (Fig. 11 ) compared to even the earliest spectra of SN1998bw or SN2002ap (Patat et al. 2001 ; Kinugasa et al. 2002 ), or of the spectrum of SN2008D (Modjaz et al. 2009 ) during its shock-cooling phase. 9 As of yet we have no convincing explanation for the origin of this feature, other than that it implies very fast (nearly relativistic) ejecta. Figure 11. Comparison of an early spectrum of AT 2018cow to the early spectrum of SN 2008D (Ib) and two SNe Ic-BL (top row), and of a later-time spectrum to SN 1998S (IIn) and two tidal disruption events (bottom row). The extremely blue, smooth continuum bears little resemblance to SN Ib/c, even after attempting to subtract the blue continuum. (A power-law plus a constant have been subtracted from AT 2018cow; the reference SN spectra have been subtracted by a constant only. The strength of the features in the SNe after subtraction has been suppressed by a factor of 2.) The later-time spectra are dominated by weak emission features of hydrogen and helium; these features are also present in IIn SNe but are much narrower compared to what is seen in AT 2018cow. These features are seen in known TDEs with similarly broad widths, although typically much greater strengths. The identities of the features seen in later spectra (H and He in broad emission) are secure. In spite of this, these later spectra bear no obvious resemblance to any class of known supernova. The strongest similarities are to Type IIn supernovae (which can also remain hot for several weeks after explosion, and are emission-dominated by definition): in the bottom panel of Fig. 11 , we plot AT 2018cow versus an early spectrum of SN 1998S from Fassia et al. ( 2001 ), which shows a similar blue continuum and most of the same H and He transitions. However, the lines in AT 2018cow are not narrow for most of their evolution ( ∼ 6000 km s −1 , versus a few hundred km s −1 for SN 1998S). Thomson scattering within ionized matter could broaden a line enough to wash out the narrow component, but this would not produce the net redshift in the emission component that we observe. The H and He thus must be in the ejecta itself (and seemingly preferentially in receding ejecta given the net redshift). In fact, the best spectroscopic analogues to AT 2018cow are not supernovae at all. Our spectra bear a striking resemblance to tidal disruption events: the high temperatures, presence of helium and hydrogen features in emission, and moderate velocities all match what is observed for TDEs. The spectral features in AT 2018cow are substantially weaker than in the examples of TDEs that we are aware of (bottom panel of Fig. 11 ; comparison spectra are from Holoien et al. 2014 , 2016 ), but the resemblance to a TDE is much stronger than to any supernova. We summarize the key observational features of AT 2018cow in Table 3 . 5 INTERPRETATION 5.1 Supernova models: A
Paper: 2020A&A...639A.107S (fulltext)
distribution is the same as for the monodispere case. For the broad distribution between 10 −8 and 10 −4 bar, the single-scattering albedo of the Gaussian distribution () is always higher than that of the monodisperse case (), see the right two plots of Fig. 5 . The increase for the initial broadening around 10 −7 bar is wavelength independentand peaks at ( Fig. 5 , bottom right). The second peak in the increase of the albedo from the monodispserse case occurs at about 10 −5 bar, and this time is wavelength dependent with a significantly lower increase of only a factor of 2 for λ = 0.1 μ m, as opposed to increases by a factor greater than 4 for all longer wavelengths. This is because in this region and the fraction of cloud particles in the Rayleigh scattering regime ( Q sca ∝ λ −4 ) varies for each wavelength for a broad size distribution. Although an increase of half an order of magnitude in albedo seems substantial when the total extinction of the atmosphere is calculated, the peak extinction is dominated by regions with high mass fractions of cloud particles ( ρ d ∕ ρ ). The mass fraction of cloud particles peaks around the millibar level in the atmosphere, which does not significantly overlap with regions of high nucleation rates ( Fig. 5 , bottom left) where the local size distribution is broad. The size distribution therefore has little effect on properties such as optical depth ( Fig. 9 ). Wakeford Sing (2015) also found that for a log-normal distribution, the cumulative effect of transmission spectra of clouds is dominated by larger particles in the distribution. An asymmetric distribution, such as the log-normal distribution, has a greater number of large particles than the Gaussian distribution considered here, and may produce greater deviations from the monodisperse case. It has been found recently to have an effect on retrieved cloud particle size ( Benneke et al. 2019a ). We note, however, that none of the size distributions used in retrieval approaches are based on a consistent cloud model, and therefore no conclusion can be drawn about the shape of the particle size distribution from such retrieval approaches. 5 Optical effects of non-spherical particles To determine the effect of non-spherical cloud particles, we return to the T eff = 1800 K, log ( g ) = 3.0 atmosphere with compact monodisperse cloud particles. When a distribution of hollow spheres is used, as described in Sect. 2.5 , the albedo generally decreases compared to the compact monodisperse case ( Fig. 6 , top right). This is due to the high f hol end of the hollow sphere distribution, where the cloud particles have a large surface area (which is not conserved across the distribution of hollow spheres) and thus increase absorption ( Min et al. 2003 ). Furthermore, Fig. 9 shows that for the compact case (red line), the inclusion of non-spherical particles leads to an increase in optical depth in the silicate features between 10− 20 μ m. This result is in good
Paper: 2018MNRAS.479.4560K (fulltext)
where m p = (4/3)ρ s 3 is the particles mass orbiting with velocity A is the particle’s cross section, and is the unit vector pointing to the direction of the radiation. The particles are assumed to be spherical with uniform density equal to 1 g cm − 3 . For transparency we give the explicit form of ω H is Haumea’s rotational frequency, n H is the mean motion about the Sun, and i is the obliquity of the dwarf planet to its orbital plane. The gravitational effect of the Sun is not included in this model because it is fifth order of magnitude weaker than that of Haumea at the distance of the ring. However, Haumea’s orbit is assumed to be circular with a period of 284 yr which is included in the time dependence of the RP. Since the obliquity of the dwarf planet is about 14 deg this implies a long-term modulation in z -component of the RP. In other words, the initial position of the Sun in our calculations is the negative x direction and Haumea’s obliquity stays in this direction during the whole integration. The effect of planetary shadowing (which blocks the stellar radiation impinging on the ring) is neglected since the ring is currently not shadowed because it is in the equatorial plane of the planet which is tilted with 13.8° ± 0.5° to the orbital plane (Ortiz et al. 2017 ). Furthermore, even for longer integration, only a small fraction of the ring will be hided during Haumea’s journey around the sun. We use a 4th order Runge–Kutta numerical scheme with a fixed step size that keeps the relative energy error below 10 −8 . Equation ( 5 ) is integrated for different times in order to cover different dynamical regimes. That is, the simulations are performed for T = 10 3 , 2.5 × 10 4 , and 3.5 × 10 5 rotation periods, yielding ca. 1, 10, 1000 yr, respectively. The calculations also have been done for grain sizes, 1, 1.75, 2.5, and 5 m. The dynamical analysis involves two different setup of initial conditions. In the one hand, when studying the fine resonant structure, particles are placed equidistantly along a line ( x -axis) in radial distance 1 r 11. On the other hand, an initial ring is defined, θ ∈ [0; 2] and r ∈ [0; 5], in order to follow the particle dynamics in general. In both cases grains are initially on circular Keplerian orbits in the equatorial plane of Haumea. 3 RING CHARACTERISTICS Our first analysis explores the lifetime of the particles around the dwarf planet. The grain dynamics yields three different end-states for the particles: (i) Escape : When the semimajor axis of a particle becomes larger than 25 r s (≈ 25575 km) it is taken to be ejected. Ejections occur due the close encounters with Haumea when the particles are perturbed to hyperbolic orbits. (ii) Collision : In our model a collision occurs when the particle approaches the dwarf planet closer than its volume-equivalent radius, 816 km (0.74 r s ) (Ortiz et al. 2017 ). (iii) Bounded motion : When a particle, until the end of the integration, neither collides nor escapes
Paper: 2017ApJ...835..149Z (fulltext)
XTE J1752-223 discovered by the Rossi X-ray Timing Explorer ( RXTE ) and Swift satellites on 2009 October 23 (Markwardt et al. 2009b ) suggested that it is a black hole candidate (Markwardt et al. 2009a ; Nakahira et al. 2009 ; Remillard et al. 2009 ; Shaposhnikov et al. 2009 , 2010 ). Three days after the X-ray discovery, a bright optical counterpart was found by the Magellan Baade telescope (Torres et al. 2009b ). Also, a bright near-infrared (NIR) counterpart was identified immediately (Torres et al. 2009a ). Afterwards, a bright radio counterpart was discovered by the Australia Telescope Compact Array (ATCA) with a flux density of at both 5.5 and (Brocksopp et al. 2009 , 2010 ). The radio light curves of XTE J1752-223 at 5.5 and were measured by ATCA, the European VLBI (Very Long Baseline Interferometry) Network (EVN), the Very Long Baseline Array (VLBA), and the Expanded Very Large Array (Brocksopp et al. 2009 ; Yang et al. 2010 ; Miller-Jones et al. 2011 ). Furthermore, the X-ray outbursts during 2009 October and 2010 July were obtained by the Monitor of All-sky Image (MAXI), RXTE , and Swift (Nakahira et al. 2010 ; Shaposhnikov et al. 2010 ; Curran et al. 2011 ). The long baseline observations of XTE J1752-223 are shown in Figure 1 , in which four jet features marked as A, B, C, and D have been reported by Yang et al. ( 2011 ). Component A appeared first, and then components B1 and B2 emerged, which suggests that components farther from the core can be seen earlier than components nearer the core. Moreover, components A and B were observed after the peaks of the radio light curve. It is of interest to understand such a connection between jet features and light curve. Figure 1. The positions of the components A, B, and D of XTE J1752-223 from the VLBA images (Yang et al. 2011 ) for each observing epoch. Note that components B and C in Yang et al. ( 2011 ) are written as B1 and B2 here. NRAO 150, discovered in the radio band by Pauliny-Toth et al. ( 1966 ), is a prominent quasar from the millimeter to radio band at redshift z = 1.52 (Agudo et al. 2006 ). Observations of this source by VLBI from 2001 October to 2004 April and by VLBA from 1997 June to 2002 June have exhibited a moving core–jet structure as shown in the left panel of Figure 2 . The core–jet structure consists of three components, Q1, Q2, and Q3. The evolution of the relative position of these three components is plotted in the right panel of Figure 2 , where Q1, Q2, and Q3 moved in a counterclockwise direction with respect to the core. The projected angular speed of these components can be up to , which is the first superluminal motion found within the innermost region () of the jet in NRAO 150 (Agudo et al. 2007 ). Figure 2. The ridge line structure of the jet of quasar NRAO 150 and the evolution of the position of components Q1, Q2, and Q3 from left to right (Agudo et al. 2007 ). In this paper, the observations of the kinematics of XTE J1752-223 and NRAO 150 are
Paper: 2019A&A...632A..56K (fulltext)
descent, that is good for learning acceleration, but can lead to missing the loss minimum), and max_depth (greater values correspond to a large complexity of the trees, and can lead to overfitting). Moreover, GB algorithms usually allow the use of a stop criterion, responsible for the termination of the learning when an overfitting occurs (the so-called, early_stopping parameter). It is expressed via the number of constructed trees, after which the quality of the metric no longer increases. Usually this parameter ranges between 10 and 1000 trees, depending on learning_rate . If the early stopping criterion is met, the GB algorithm accepts the number of trees, satisfied to the best score. Paying particular attention to the early_stopping parameter is the best way to avoid as much overfitting as possible. To quantify, how the quality of the classification changes over the iterations, for XGBoost and CatBoost, it is necessary to define a quality metric. Defining this metric allows one to express the changing of the classification quality against the complexity of the GBDT algorithm and can be easily used to control the overfitting. Widely used quality metrics are accuracy, precision, recall, and F1-score; however, these metrics are sensitive to the imbalance in number of training sources among different classes. Therefore, we decided to use Matthews correlation coefficient (MCC, Matthews 1975 ) which is instead insensitive to this imbalance. Finally, a good way to decrease the number of stars and galaxies classified by the algorithm as quasars is to apply a weight to the loss function of these two classes. In fact, this trick, applied to CatBoost, helped us in the paper to improve the final purity of the quasar selection with a minimal decrease in the completeness of the training set. In particular, we weighted the loss function for the STAR and GALAXY samples in the following way: where w i = 1 if source is a QSO and w i = 4 if source is STAR or GALAXY . A.2. Random Forest Another method of ensemble learning with decision trees is based on the use of an RF ( Breiman 2001 ) algorithm. This was the choice adopted in KiDS DR3 by N19. The basic idea of RF is that a set of decision trees can fit a robust classifier by averaging their decisions. From the performance side, the RF constructs a large number of decision trees and then uses the majority vote among them. The main pipeline of the single decision tree includes the following steps: (1) generation of different samples from the training set with the same size, but using random subsets of all the objects. The repetition of some objects among different subsamples is required to make the sample complete (so-called, random subsample with replacement); (2) let the decision tree learn on the “random subsample with replacement”, generated at step (1), but using randomly selected features. The RF therefore consists of many learning processes, each performed on a single decision tree using different
Paper: 2018MNRAS.476.4543B (fulltext)
that the near-isothermal density profiles were found to be broadly consistent with MOdified Newtonian Dynamics (MOND). With this greater sample of total- mass profiles that cover a greater stellar mass range, new tests of MOND and the general mass-discrepancy–acceleration relations can be made. In order to make useful comparisons between simulations and observations, it is necessary that exactly the same measurements are made for each. Currently, there is great difficulty in achieving this, since observational measurements are best constrained in the inner regions of galaxies, while the resolution limitations of cosmological simulations mean that measurements for simulated galaxies are best constrained at radii greater than ∼0.5 R e . This means that while care can be taken to make measurements in the same way, the underlying data are fundamentally different. 8 SUMMARY AND CONCLUSIONS We measure total-mass density slopes for 21 fast-rotator ETGs of the SLUGGS survey (Brodie et al. 2014 ), including a greater number of galaxies in the low-mass range than the previous study by Cappellari et al. ( 2015 ). These measurements are made by conducting dynamical mass modelling on 2D kinematics that extend to large radii. We apply the jam mass-modelling technique (Cappellari 2008 ), using two different parametrizations of mass distribution to recover the total-mass density profiles for individual galaxies. We determine that the best fits of the model kinematics to the input kinematics of each galaxy are produced by the model in which the total mass is separated into its stellar and DM components, where the DM distribution is parametrized as a double power law (as given by equation 2 ). We find that the measured total-mass density slope values are robust to slight variations within the applied modelling, whereas the separation of stellar and DM is much more degenerate, and susceptible to such variations in the modelling. In order to be consistent with Cappellari et al. ( 2015 ), we measure γ tot values for each galaxy by fitting a power-law slope to the stellar plus DM profile in the radial range 0.1–4 R e , and find an average total-mass density slope of γ tot = −2.24 ± 0.05 for our sample of 21 galaxies, identifying no discernable trend with stellar mass. We collate measurements of total-mass density slopes from multiple observational studies, for a comprehensive overview of the γ tot behaviour across a broad range of stellar masses, utilizing an array of data-types and measurement techniques. Overwhelmingly, the studies all tend to produce consistent results when including the central regions, as shown in Fig. 6 . This is despite the fact that the data utilized have varying radial extents for individual galaxies – an indication that these slopes do not display much variation with radius. We find that there tends to be very little trend with stellar mass, and that the slight shallowing of total-mass density slopes we see at lower stellar masses may not