adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.505.1180B (fulltext)
function layers can be concatenated together to form a deep CNN. This is usually followed by a fully connected layer also known as a dense layer, which is then finally connected to the output layer. The output layer for a classification problem is the amount of class labels we have in our data. Additionally for deep CNNs, a dropout layer is typically added after the max-pooling operation, which randomly drops off a certain percentage of the preceding nodes. This is done so that the neural network learns to better generalize its performance across the entire data. This is used as a regularization technique to avoid overfitting. CNNs are also trained using back-propagation. However, in practice, since it is computationally difficult to calculate the gradient of the loss function for all images in the training data, we typically divide the data into mini-batches and use the stochastic gradient descent algorithm. Figure 2. Schematic diagram of a typical CNN. Here the input image is a grey scale image of size 48×48 and the output contains two nodes for the binary class labels. 8 and 16 filters of size 4×4 were used for the convolutional layers. These act as feature extractors that identify import patterns in the input image. This diagram was created using an open source tool called NN-SVG 5 (LeNail 2019 ). 2.4 Generative adversarial network (GAN) GANs are a class of machine learning algorithms (Goodfellow et al. 2014 ) in which two neural networks are trained simultaneously with opposing goals. They act against each other as adversaries in a minmax two-player game. A generative model G is tasked with generating new data that capture the distribution of the input data. A discriminator model D is tasked with classifying samples as either REAL (that belong to the original data distribution) or FAKE (samples generated by G ). Assuming is the value/loss function, we are interested in then the first term in equation ( 2 ), is the expectation of the logarithm of D ’s predictions when an input is from the real data sample. D ’s goal is to maximise this term. The second term represents one minus the expectation of the logarithm of D ’s predictions when data generated by G is passed on to to D . The goal of D is to maximize the second term but the goal of G is to minimize this term. This sets up the adversarial framework. In the ideal case, the generator perfectly samples the distribution of the input data distribution, and the discriminator output equals to 1/2. In practice however, we use a minibatch stochastic gradient descent method and train the generator and the discriminator alternatively. The algorithm and the proof for the convergence of the algorithm can be found in Goodfellow et al. ( 2014 ), however for the benefit of the reader, we briefly summarize the training of this network in algorithm 1 . Algorithm 1: Minibatch stochastic gradient descent algorithm used for training a GAN. GANs have been used successfully in a wide range of tasks ranging
Paper: 2018MNRAS.481..930Y (fulltext)
Like black holes, this distribution is relatively flat. Figure 6. Distribution of periods for NSLC binaries detectable by Gaia . Blue is the fiducial model, orange is low αλ, green is the kick model with the same momentum as neutron stars, and red is kicks with the same velocity. For completeness we have also included in Appendix C the distribution of distances from earth (Fig. C1 ), apparent magnitudes of the luminous companions (Fig. C2 ), ratios between the semimajor axes and distances (Fig. C3 ), and radial velocity amplitudes (Fig. C4 ). 5 CONCLUSIONS In this work we give some predictions for the detection of binaries with a single luminous companion with Gaia . Similar endeavours exist in the literature, but two novel ideas that we introduce in this paper are that (i) similar methods can be used to detect neutron star luminous companion systems (which are expected to be more numerous) and (ii) the data from Hipparcos can be used to constrains the theoretical models. Our fiducial model predicts a few dozen BHLC detections with Gaia , and about one detections with Hipparcos . The last result is consistent with non-detection by Hipparcos , due to uncertainties in the models and statistical fluctuations. All other variants of the fiducial model have similar properties. The corresponding predictions for NSLC binaries are hundreds to thousands with Gaia and a few with Hipparcos . We emphasize that the numbers reported here only apply to binaries where the Chandrasekhar mass is beyond the dark companion’s mass error margin. One of the challenges in this kind of work is estimating the uncertainty. This is because this work is largely based on poorly understood physics and empirical models calibrated with incomplete data. One way to get an estimate for the uncertainty is to explore the sensitivity of the results to changes in the different model parameters. This was done in Yamaguchi et al. ( 2018 ), and the conclusion was that while the number of BHLC binaries might change by a factor of order unity, the shapes of the distribution functions do not. The results from Gaia , as well as future observations, are necessary to refine these models and improve their precision. To date, there are no reports of quiescent neutron stars (i.e. neither pulsars nor accreting) in binaries. As for BHLC binaries, there is one reported detection of a ≥2.5 M ⊙ mass black hole candidate with a red giant companion (Thompson et al. 2018 ). We note that another isolated black hole candidate with mass of about four solar masses was detected in the globular cluster NGC 3201 (Giesers et al. 2018 ). This discovery is less relevant to this study, because stars in a cluster are thought to have formed in a relatively short period of star formation, whereas we are interested in the case where star formation proceeds continuously. Another important difference is that we neglect gravitational interaction between stars that are not mutually bound, which is justified
Paper: 2020ApJ...905...61P (fulltext)
As described in Sections 3.6 and 3.7 , the H 2 and PAH emission, indicating the location of the PDR, encircles the western border of the H ii region. A curved morphology of the radio continuum emission in the western periphery further suggests a stalling effect for the ionized gas against the PDR in that direction. As shown by Pellegrini et al. ( 2012 ), the [S ii ]/[O iii ] ratio map can be used to distinguish density-bounded, radiation-bounded, and blister-type H ii regions. Pellegrini et al. ( 2012 ) found that the optically thick nebula with highly ionized gas (low [S ii ]/[O iii ] ratio; appears dark in ratio map) is usually surrounded by an ionization transition zone with a higher [S ii ]/[O iii ] ratio (appears as lighter gray scale). We made an [S ii ]/[O iii ] ratio map of the central part (∼10′ × 10′) of the Sh2-112 H ii region, which is shown in Figure 8 (bottom left panel). The ratio map consists of bright and dark features. We notice a pronounced ionization transition zone (bright region) toward the south of the massive star, where H α emission is traced. This ionization transition zone appears to be surrounded by a relatively diffuse feature in light gray. In the southwestern direction, the ratio of [S ii ]/[O iii ] reveals the presence of ionized gas (dark feature) in the region, which is spatially correlated with the IBL. The distribution of the ionized hydrogen, H 2 , and 13 CO emission indicates that the IBL structure is seen edge-on. An example of this kind of edge-on geometry is the Orion bar, which is located in a blister-type H ii region, the Orion Nebula (Goicoechea et al. 2018 ). In our case, the bright S ii emission could actually be on the surface of the H ii cavity. Therefore, from the above analysis, it seems that Sh2-112 is an example of a blistered H ii region that is ionization-bounded on the high-density sides (western direction) and density-bounded on the low-density sides (eastern direction). The intensity gradient of the radio emission (see 610 MHz emission contours in Figure 7 (b)) supports that the ionized gas is escaping from the H ii region toward the low-density ISM in the eastern direction. Based on radial velocity measurements of the H α (i.e., −9.7 km s −1 ) and CO (i.e., −12.2 km s −1 ) emissions, Israel ( 1978 ) also suggested that the ionized gas in the Sh2-112 region is probably undergoing a champagne flow. Thus, the structure of the radio continuum, dust extinction, 13 CO, and [S ii ]/[O iii ] ratio maps; the spatial distribution of the young stars; and the NIR-to-MIR images of the H ii region indicate that Sh2-112 is a blistered H ii region in which the ionized gas is possibly undergoing a champagne flow. In Figure 9 , we show the AKARI 160 μ m image superimposed by Planck 353 GHz contours (left panel) and a simple geometry of the H ii region (right panel). Based on the distribution of the cold dust emission and young stars, we suggest that the massive star possibly formed on the eastern
Paper: 2021ApJ...909...45Y (fulltext)
the divergence of velocity shown in panels (a) and (b) for a short period of time to display the continuous generation of wave trains. (An animation of this figure is available.) In order to study the dynamical behavior of the wave trains outside the CS, we plot the time–distance diagram of the normalized running difference of the density Δ ρ / ρ along the line x = −0.15 L from t = 857.9 to 2038.6 s in Figure 8 (a). There are violent variations below the edge of the CME, and they may have quasiperiodic behavior indicating the propagation of the coronal wave trains. Since the flux rope keeps rising, it is impossible to fix a point aside to check the periodicity of the wave trains. The estimation of the periodicity is also sensitive to the chosen line, along which the calculation is executed. For instance, we followed the dashed line almost parallel to the edge of the CME from panel (a). The associated distribution shows periodicity in panel (b), and the period is calculated to be ∼76.3 s. Therefore, we directly investigate the evolution of the turbulent region above the upper TS. Figure 8. (a) Normalized running difference of the density along the line x = −0.15 L extracted from t = 857.9 to 2038.6 s. The blue diagonal line corresponds to the low-density wing to the left of the TS in Figure 2 (f), and the red diagonal line is where the density jumps back to the coronal value outside the flux rope. (b) Associated distribution along the dashed line in panel (a). The symbol Δ ρ / ρ is defined as ( ρ ( t ) − ρ ( t − Δ t ))/ ρ ( t ). A typical Δ t ≈ 9.2 s. Takasao Shibata ( 2016 ) described how the CS outflow can form a blob of gas between the sides of a “tuning fork” and how the backflow of the gas drives an oscillation of the magnetic structure. The oscillation can drive waves that propagate well outside the “buffer region” where it occurs. Figures 7 (c) and (d) show the dynamics of the backflow. The relation between the coronal wave trains and the turbulence under the CME is analyzed in Figure 9 . In panel (a), we show the time evolution of the maximum of the horizontal velocity V x of the backflow and the fast magnetosonic Mach number before the upper TS. Note that the Mach number is defined as the ratio of the flow velocity to the fast-mode magnetosonic speed. The max( V x ) decreases in general, indicating that the coronal wave trains may appear at different speeds, which could be caused by variations in the local plasma density and magnetic strength. The peaks reveal the oscillation of the MTF. Interestingly, one can find that the Mach number mainly varies around 2.4, which is larger than that before the TS above the flare loops (1.5–1.8 found in observations and simulations; Chen et al. 2015 ; Ye et al. 2019 ). This reveals that it is easier to form the upper TS than the lower one, and the variation of the Mach number is related to the interplay between plasmoids and the TS (Shen et al. 2018 ). Then, we performed the Morlet transform on
Paper: 2018MNRAS.479..807L (fulltext)
morphologies (Brinchmann et al. 2004 ). The number of non-AGN decreases steadily as B/T increases, however, there is a significant population (12 per cent of the non-AGN sample) with B/T = 1, i.e. pure elliptical galaxies. Of the 181 pure ellipticals that we classify as non-AGN, only four are unclassifiable via the BPT analysis. 114 are BPT-classified as composite, AGN(non-LINER), or low S/N LINER implying that there is some AGN component in these galaxies, but the radio emission is not significantly enhanced to be selected by our method. The remaining 63 non-AGN pure ellipticals are classified by the BPT analysis as either star forming or low S/N star forming, which is consistent with these galaxies being truly non-AGN. Figure 6. The distribution of bulge-to-total light ratios, which we use as a proxy for galaxy morphology, for the non-AGN galaxies (blue), all AGN i.e. our faint AGN + the BH12 AGN (red), and the faint AGN sample only (green). Non-AGN galaxies have predominantly low B/T values, i.e. are morphological spirals, while almost all BH12 AGN and the faint identified in this work have B/T = 1, i.e. are morphological elliptical galaxies. For the AGN samples, we find that there are few sources with disc-like morphologies (89 per cent have B/T ⩾ 0.5), and the majority of the galaxies have B/T=1, 78 per cent for the total AGN sample and 73 per cent for the faint AGN sample. This also agrees with previous studies that show that radio-loud AGN are preferentially found in massive elliptical galaxies (Wilson Colbert 1995 ; Dunlop et al. 2003 ; Kauffmann et al. 2003 ; Kauffmann, Heckman Best 2008 ). We complete our analysis by briefly checking our morphological results with the local colour–magnitude relation. Using the k-corrected g-band and r-band magnitudes from the MPA–JHU catalogue, we split the sample into red and blue galaxies. We then recreate Fig. 1 showing the density of red sequence and blue cloud galaxies (Fig. 7 ). Galaxies identified as AGN (both the BH12 AGN and those in our faint AGN sample) are typically found on the red sequence that is dominated by early-type galaxies (e.g. Bower, Lucey Ellis 1992 ; Kodama Arimoto 1997 ; Blanton Moustakas 2009 ), in agreement with our results using the bulge-to-total light ratios that find that the majority of AGN have early-type morphology. In contrast, the non-AGN galaxies cover a much broader region of the colour–magnitude diagram and are predominantly in the blue cloud that is typically dominated by star-forming spiral galaxies (e.g. Tully et al. 1998 ; Baldry et al. 2004 ). This is again in agreement with our morphological results where we find that the non-AGN galaxies are disc-dominated. Figure 7. Comparison of the SFRs obtained via optical spectro-photometric data from the SDSS, with the FIRST 1.4 GHz luminosities as in Fig. 1 but coloured by position in the colour–magnitude diagram. Galaxies that are found in the blue cloud are mostly purely star-forming galaxies (shown in
Paper: 2017MNRAS.464.1990M (fulltext)
1 , and (c) 3s 2 3p 4 3 P 0 . Strong Rydberg series are present as are the much weaker series converging to the Cl + () threshold. Similar strong Rydberg series are present as are the much weaker series converging to the Cl + () threshold. Theoretical cross-section calculations were carried out with the darc codes, and convoluted with a Gaussian having a profile of 15 meV. The assigned Rydberg series are indicated as vertical lines grouped by horizontal lines. The corresponding series limits E ∞ of equation ( 2 ) for each series are indicated by vertical lines at the end of the line groups. Several of the nd interloping resonances are seen to disrupt the regular Rydberg resonance pattern of the spectra. Resonance energies and quantum defects for the various series are tabulated in Tables 1 and 2 and compared with the available experimental values. Table 1. Principal quantum numbers n , resonance energies (eV), and quantum defects μ of the Cl + ( Rydberg series seen in the Cl + (3s 2 3p 4 3 P 2,1,0 ) photoionization spectra, converging to the Cl + () threshold. The experimental resonance energies (Hernández et al. 2015 ) are calibrated to ±13 meV. The theoretical results are obtained from the 512-level darc calculations performed within the Dirac–Coulomb R -matrix approximation. The experimental entries in parenthesis are uncertain. Notes. a ALS experimental results (Hernández et al. 2015 ). b Present R -matrix darc calculations. c Rydberg series limits E ∞ for Cl + are from the NIST tabulations (Ralchenko et al. 2014 ). Table 2. Principal quantum numbers n , resonance energies (eV), and quantum defects μ of the Cl + ( Rydberg series seen in the Cl + (3s 2 3p 4 3 P 2,1,0 ) photoionization spectra, converging to the Cl + () threshold. The experimental resonance energies (Hernández et al. 2015 ) are calibrated to ±13 meV. The theoretical results are obtained from the 512-level darc calculations performed within the Dirac–Coulomb R -matrix approximation. The experimental entries in parenthesis are uncertain. Notes. a ALS experimental results (Hernández et al. 2015 ). b Present R -matrix darc calculations. c Rydberg series limits E ∞ for Cl + are from the NIST tabulations (Ralchenko et al. 2014 ). 3.3 Photoionization of the metastable Cl<sup>+</sup>(3s<sup>2</sup>3p<sup>4</sup> <sup>1</sup>D<sub>2</sub> and <sup>1</sup>S<sub>0</sub>) states Photoionization cross-section calculations of the singly ionized atomic chlorine ion from the initial 3s 2 3p 4 1 D 2 and 1 S 0 metastable states were also investigated using the same large-scale R -matrix model within the Dirac–Coulomb approximation ( darc ), as these states were present in the parent ion beam of the recent ALS measurements (Hernández et al. 2015 ). Fig. 3 shows the results for the 3s 2 3p 4 1 D 2 initial state and Fig. 4 the corresponding 3s 2 3p 4 1 S 0 case. Cross-section calculations are shown for photon energies from their respective thresholds to a photon energy of 25 eV and convoluted
Paper: 2018MNRAS.475.2553P (fulltext)
took advantage of recent studies that thoroughly examined the LMC cluster population in some selected fields located within the B17 's surveyed area. Fig. 2 shows the distribution of the B17 's objects and the seven well-studied LMC regions considered here overplotted. The large hexagons correspond to DECam fields (∼2° × 2° FOV, pixel size = 0.263 arcsec; Flaugher et al. 2015 ) analysed by Piatti ( 2017c ), while the small rectangles correspond to MOSAIC II fields (36 arcmin×36 arcmin FOV, pixel size = 0.269 arcsec) studied by Piatti ( 2017b ) and Piatti et al. ( 2018 ). The 4-m Blanco telescope at the Cerro Tololo Interamerican Observatory (CTIO) was employed to observe those fields, among others distributed outwards the B17 's surveyed area as part of different photometric surveys aimed at studying the most metal-poor stars outside the Milky Way (rectangles: CTIO 2008B-0296 programme; PI: Cole) and the Magellanic Clouds stellar history (hexagons; Nidever et al. 2017 ). Figure 2. Spatial distribution of B17 's objects with selected LMC fields studied by Piatti ( 2017c ) (hexagons) and Piatti ( 2017b ) and Piatti et al. ( 2018 ) (rectangles) overplotted. These LMC fields were carefully surveyed looking for star clusters by employing the method mentioned above (Piatti et al. 2016 ), i.e. by constraining the search using the known cluster dimension and mean stellar density ranges. The final cluster lists resulted to be statistically complete, because the derived photometry detected any star cluster based on counts of its brightest stars all the way down to the main-sequence turnoff (MSTO) of the oldest LMC clusters. Every catalogued B08 's cluster was recovered, in addition to very few new identified clusters in some of the surveyed fields. The CMDs of the recognized extended objects were then cleaned from field star contamination by using the procedure developed by Piatti Bica ( 2012 ), i.e. by building statistically meaningful star field CMDs that tightly reproduced the luminosity function, colour distribution, and stellar density of the respective star fields, to be subtracted to the cluster CMDs. The resulting cleaned CMDs for some of the objects led to conclude that they are not genuine physical systems, but random fluctuations of the field star density. Table 1 lists the number of entries in the B08 's catalogue for each selected LMC field (field ID is as in Fig. 2 ), and the number of true star clusters confirmed according to the photometric analyses performed by Piatti ( 2017c , 2017b ) and Piatti et al. ( 2018 ). Table 1. Statistics of star clusters in LMC fields. Ref.: 1) Piatti ( 2017c ); 2) Piatti ( 2017b ); 3) Piatti et al. ( 2018 ). For comparison purposes, we extracted the results of Section 2 for the selected LMC fields, namely, the number of B08 's objects matched by the B17 's ones as well as the number of B17 's new detections. They are also listed in Table 1 . For completeness, we included in its last column the deprojected
Paper: 2016A&A...587A...4T (fulltext)
orbit) and electron (black orbit) are accelerated towards opposing foot-points (as one might expect from the charge difference between the species). Furthermore, while the electron is rapidly accelerated almost all the way to the photospheric boundary, the proton never leaves the (upper) corona, but is ultimately reflected and returns along the initial trajectory. In Fig. 8 c, we see that the electron (diamonds) reaches the photosphere after approximately s. In this time, the proton (plus signs) has barely moved. The proton achieves maximum parallel speed after approximately s and mirrors approximately seven times in s, with the first mirroring taking place after approximately s. To assess the reason for the proton becoming trapped in this way, we once again calculate the loss cone (see discussion in Sect. 4.1 for more details). Along the field line in question, we estimate that initial pitch angles less than and would able particles to escape the trap caused by increasing magnetic field strength. As with the case shown in Sect. 4.1 , repeating this experiment with an initial pitch angle of shows negligible differences in proton/electron behaviour; both the acceleration and trapping behaviour displayed in Fig. 8 are caused by the electric field. In extending the simulation time from s to s, we demonstrate that the electric field continues to trap the proton in the upper atmosphere for all time, while accelerating the electron until it reaches the loop foot-point at the photospheric boundary. All particle orbit calculations are automatically terminated upon reaching a Mm layer above the lower simulation boundary (which results from the limited applicability of our test-particle approach in this region). At termination, the electron has travelled approximately Mm in less than s. By comparison, the distance between mirror points for the proton is approximately Mm. The reason for the differences between the proton and electron results are twofold; from Fig. 8 c, we can see that the proton achieves its peak velocity after approximately s, taking much longer to accelerate. Upon encountering a region of oppositely oriented , the proton is unable to overcome the resulting deceleration, and mirrors. By comparison, the electron is continually accelerated as it plummets towards the photosphere. The sign of the electric field strength is positive throughout the lifetime of the electron orbit (see Fig. 8 b), hence the electric field trapping mechanism plays no role in the electron orbit. Furthermore, the magnetic mirror effect also plays no role in this orbit, as the strong, uni-directional electric field encountered by this electron results in a high parallel velocity ( or ). 5. Discussion We have conducted a survey of test-particle behaviour in an environment which aims to describe the behaviour of a solar AR. From this survey (detailed in Sect. 3 ), it is clear that this environment (at the electric field strengths derived by the MHD model) would