adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.464.4463K (fulltext)
forest algorithm (TPC), while the probability estimates of our ConvNet model appear to be better calibrated. When the same network architecture is applied to the SDSS data set, the network fails to outperform TPC, but the probabilities are still slightly better calibrated. The major advantage of ConvNets is that useful features are learned automatically from images, while traditional machine learning algorithms require feature engineering as a separate process to produce accurate classifications. ConvNets have recently achieved record-breaking results in many image classification tasks (LeCun et al. 2015 ) and have been quickly and widely adopted by the computer vision community. One of the main reasons for the success is that ConvNets are general-purpose algorithms that are applicable to a variety of problems without the need for designing a feature extractor. The lack of requirement for feature extraction is a huge advantage, e.g. when the task is to classify 1000 classes in the ImageNet data set (Russakovsky et al. 2015 ), as a good feature extractor for identifying images of cats would be of little use for classifying sailboats, and it is impractical to design a separate feature extractor for each class. However, when there already exists a good feature extractor for the problem at hand, e.g. the concentration parameter, the weight-averaged spread_model parameter from DES (Desai et al. 2012 ; Crocce et al. 2016 ), or even the SE xtractor software, conventional machine learning algorithms that have been shown to be effective, such as TPC (Carrasco Kind Brunner 2013 ; Kim et al. 2015 ), remain a viable option. As the ‘no free lunch’ theorem (Wolpert 1996 ) states, there is no model that works for every problem. For the CFHTLenS data set, our ConvNet model outperforms TPC. Since the SDSS catalogue provides the concentration parameter that is highly optimized for star–galaxy classification, TPC works better for SDSS. Although we used various techniques to combat overfitting, it is possible that our ConvNet model has overfit the data. Overfitting could explain why our ConvNet model with maximal information fails to significantly outperform a standard machine learning algorithm that uses the reduced summary information from catalogues. The most effective way to prevent overfitting would be to simply collect more training images with spectroscopic follow-up, as the performance of ConvNets generally improves with more training data. However, spectroscopic observations are expensive and time-consuming, and it is unclear if sufficient training data will be available in future photometric surveys. If enough training data become available in DES or LSST, ConvNets become an attractive option because it can be applied immediately on reduced, calibrated images to produce well-calibrated posterior probabilities. We also note that using more training images will require the use of multi-GPU systems, which was beyond the scope of this work. Deep learning
Paper: 2018ApJ...862...56S (fulltext)
model are included here for completeness. We employ a predictor–corrector integration method based on the description in Stone Gardiner ( 2009 ), along with a second-order spatial reconstruction technique with limiting applied in the characteristic variables (LeVeque 2002 ) and the HLLC Riemann solver (Toro et al. 1994 ; Batten et al. 2006 ). Radiative cooling is included via operator splitting, with the thermal energy losses accounted for after the hydrodynamic update. The cooling rates are determined using a forward Euler integration, and subcycling of the radiative cooling time step is employed to ensure a numerically robust solution. The cooling function is a piecewise parabolic fit to a solar-metallicity, collisional ionization equilibrium curve calculated using Cloudy (Ferland et al. 2013 ). Cooling is only included for gas above 10 4 K; below this cutoff a temperature floor is assumed for all the gas in the simulation. The cutoff is primarily employed to prevent collapse of the isothermal gas disk, but we note that the rarefied gas in the outflow will be subject to photoionization heating from the cosmic UV background and therefore would not be expected to cool much below 10 4 K (see Schneider Robertson 2017 ). Photoionization heating is not explicitly included in these simulations. All of the gas in the simulations is assumed to be ionized, with an adiabatic index γ = 5/3 and a mean molecular weight μ = 0.6 used in all calculations and analysis. 3. Results We begin our results by presenting a phenomenological description of the three high-resolution simulations: the central feedback model without cooling (Model A), the central feedback model with cooling (Model B), and the clustered feedback model with cooling (Model C). As explained in Section 2 , the only difference between Models A and B is the inclusion of radiative cooling in the simulation. Model A therefore represents a 3D numerical test of the original CC85 analytic model with the inclusion of a galactic disk. Similarly, Model B can be directly related to the Thompson et al. ( 2016 ) outflow model that accounts for radiative energy losses. A comparison between Figures 1 and 2 shows the dramatic effect that radiative cooling can have on the overall character of a mass-loaded outflow. In these figures, we plot density, radial velocity, and temperature slices in the centered x − z plane. Some features are common to both simulations—namely, the 10 4 K disk and the spherical gain region at the center where mass and energy are injected. In Model A, a biconical free-wind region is clearly visible in both the density and temperature slices, surrounded by a region of more turbulent, higher-temperature gas where the outflow is interacting with the disk gas. In Model B, turbulence at the disk–outflow interface is highly suppressed, leading to a free-wind region with opening angle nearly ΔΩ = 4 π . Although energy is injected into the hot wind at the same rate in Models A and B, the
Paper: 2017ApJ...847L..18S (fulltext)
the TRES spectra as described by Buchhave et al. ( 2010 ). We derived the RVs by cross correlating each spectrum order by order against the observed spectrum with the highest S/N in the wavelength range of 4520–6280 Å. EPIC 202900527 11 TRES RVs are listed in Table 1 , and the phase-folded RV curve is shown in Figure 2 . The reference (or template) spectrum is at BJD = 2457854.95084 and its RV is listed as 0.0 km s −1 in Table 1 . To allow putting the TRES RVs on an absolute scale, we determined the template spectrum absolute RV by cross correlating it with a synthetic spectrum to be −57.87 ± 0.10 km s −1 . This RV offset is not added to the RVs in Table 1 to avoid inflating their uncertainties. 3. Data Analysis and Results 3.1. Stellar Spectroscopic Parameters We derived the spectroscopic stellar parameters using the SpecMatch package (Petigura 2015 ; Petigura et al. 2017 ) and the iodine-free HIRES spectra of each star. Those include the effective temperature , surface gravity , metallicity [Fe/H], and stellar rotation projected on the line of sight , where V is the equatorial rotation and I is the stellar rotation inclination angle. We averaged the parameters extracted from all of the individual observations. The observation-to-observation parameter variance was smaller than the quoted uncertainties in all cases. The SpecMatch results are listed in Table 2 . Table 2 Fitted and Derived Parameters Notes. a Derived using SpecMatch analysis of the spectra. b Model fit parameters, fitted to the K2 light curve, RVs, and stellar isochrones. Gaussian priors are applied to [Fe/H] using the values derived from the SpecMatch spectroscopic analysis. See Section 3 for more information. c For EPIC 206155547 and EPIC 206432863 γ is the binary system’s center of mass RV since the HIRES RVs are on an absolute scale. For EPIC 202900527 the RV of the template spectrum (−57.87 ± 0.10 km s −1 ; see Section 2.3 ) needs to be added to γ to get the center of mass RV. d Parameter fixed during the model fitting process. e upper limit given when no eclipse was detected. f The transit, or primary eclipse, is at phase zero. 3.2. Global Model Fitting We performed a global modeling of the available photometric and RV measurements, along with the spectroscopic atmospheric properties of the primary star, to derive the parameters for each system. For stellar binaries, the transit-derived orbital semimajor axis normalized by the primary stellar radius is dependent on the sum of the two components’ masses and the volume of the primary star, as per Sozzetti et al. ( 2007 ):where P is the orbital period and G the gravitational constant. To take advantage of this relation, we fit directly for the masses of the two stars, the primary radius, and the secondary-to-primary radii ratio , as well as the standard transit and RV orbital parameters including the orbital period P , mid-transit time T 0 , line-of-sight orbital inclination i , orbital eccentricity parameters and
Paper: 2020MNRAS.497.4196S (fulltext)
observed Galactic clouds the full spectrum of PRS shapes found in our simulations is recovered: PRS curves which show (i) local minima, (ii) an increase towards the highest N , (iii) and random or perpendicular orientation over the entire column density range. In the context of the results presented here, the clouds in these observations might still have a preferentially perpendicular orientation of the magnetic field and density structures in 3D, though not observed in 2D due to projection effects. This could in particular explain observations of Soler et al. ( 2017 ) and Soler ( 2019 ), which report significant variations in the PRS when considering different subregions of the same molecular cloud complex. Finally, we note that radiative transfer effects and imperfect dust alignment are unlikely to contribute significantly to these projection effects: As we have shown in Seifried et al. ( 2019 ), the observed magnetic field traces well the mass-weighted, LOS-integrated magnetic field. To further support this, we also calculated the PRS using this mass-weighted, LOS-integrated magnetic field instead of that inferred from the polarization maps. We find that these PRS do not differ significantly from those shown in Fig. 7 . 5.2 A critical magnetic field strength Overall, our findings of a change from a parallel to a perpendicular orientation of magnetic fields with respect to dense structures with increasing field strength is in good agreement with previous theoretical works (e.g. Heitsch et al. 2001a ; Ostriker et al. 2001 ; Li Nakamura 2004 ; Nakamura Li 2008 ; Collins et al. 2011 ; Hennebelle 2013 ; Soler et al. 2013 ; Chen Ostriker 2015 ; Li et al. 2015 ; Chen et al. 2016 ; Zamora-Avilés et al. 2017 ; Mocz Burkhart 2018 ; Chen et al. 2020 ). As pointed out by Soler Hennebelle ( 2017 ), this change of relative orientation is an indicator of compressive motions, i.e. ∇ v 0, coupled with a dynamically important magnetic field. The compressive motions could be created either by converging flows or gravitational collapse. As indicated in the Figs 1 and 3 , a stronger magnetic field results in (i) more guided motions towards the central collision interface, (ii) suppressing turbulent motions perpendicular to it, (iii) a stronger gravitation collapse (see also Heitsch et al. 2009 ; Zamora-Avilés et al. 2018 ; Iwasaki et al. 2019 ), and (iv) consequently a more pronounced perpendicular relative orientation between magnetic fields and the density structures, in good agreement with other theoretical works (Soler et al. 2013 ; Chen et al. 2016 ; Soler Hennebelle 2017 ; and Girichidis et al. (submitted)). For the CF runs, we find a critical field strength of ∼ 5 μ G above which we observe a flip in field orientation. The SILCC-Zoom simulations have an initial magnetic field strength of 3 μ G, which is close to this critical value. As demonstrated in Section 4.2 , the observed trend of ζ in the SILCC-Zoom runs supports the idea of a critical field
Paper: 2020MNRAS.491.2447R (fulltext)
only increases the number count by ∼0.01. There is a dependence of number count on the cadence, and it can be used to set the actual cadence. Although the cadence of the main survey is already fixed at 27d on average, number counts in the ADS increase until the cadence reaches a few times 10 d and are more or less flat thereafter. Consequently, number counts in the ADS are not sensitive to cadence above 1–2 months. Also, we note that with just two pointings one year apart in DD fields, our number counts could be contaminated by false positives due to AGNs. However, these objects can be disentangled from high- z SNe to some degree because their luminosities are typically higher than those of SNe and fluctuate stochastically by about an order of magnitude over the time-scales that SN LCs evolve. In contrast, SNe brighten and then fade by a larger factor. 3.4.3 Scattering/absorption We treat attenuation of flux due to Ly α scattering with the model of Madau ( 1995 ) at z 6 and with Gunn Peterson ( 1965 ) for z > 6. The errors in Madau ( 1995 ) can be large but λ Ly α is only redshifted through the i band and of the z band at z 6. The large uncertainties in Madau ( 1995 ) therefore have little impact on our results because of the low numbers of counts in the i band and because they only affect part of the z band. High-redshift SNe might be obscured by dust at lower galactic altitudes. However, this will likely have little effect on the number of detections since this region of the sky is not covered by the WFD. There may also be extinction in the host galaxies. This extinction depends on the metallicity. Pop III galaxies are believed to be free of dust, so they would not obscure explosions (Rydberg et al. 2017 ). Dust in the host galaxies of Pop II SNe may dim them but the extinction is expected to be weak because they are being observed in rest-frame UV. At z ∼ 5–7 the host galaxies also likely have low metallicities and less dust. 3.4.4 Lensing model For the systemic errors due to the lensing models, we use the factor two, as mentioned in Section 2.4 , as an to denote the possible errors in the predicted numbers of SNe. A source of potentially large uncertainty in our lensing models is the fraction of the sky with 1 μ 2 (Section 2.4 ). The interpolation we use yields a rather severe lower limit because at μ = 1 it returns a lensed area that is just a few per cent of the sky. As a simple test of how robust our counts are with respect to this interpolation we have done it with a second-degree polynomial in the log 10 –log 10 plane as well. As constraints we use f (μ = 1) = 1 and f (μ = 2) = f 2 , where f(μ) is the fraction of the sky magnified by at least μ. The slope of the interpolation between μ = 2 and μ = 3 is set equal to the derivative of the polynomial. None of the counts in the WFD change because detections there always require magnifications greater than two, as seen in Table 1 . In the ADS the use of this interpolation adds on average
Paper: 2016ApJ...821..116U (fulltext)
2012 ). Numerical simulations suggest that this sample is prevalently composed of relaxed clusters (∼70%) and largely free of orientation bias (Meneghetti et al. 2014 ). An important effect of the CLASH selection function is to significantly reduce the scatter in concentration because of their X-ray regularity (Section 6.2 ). For a lensing-unbiased subsample of 16 CLASH X-ray-selected clusters, we have examined the mean c – M relation and its intrinsic scatter using Bayesian regression methods (Section 6 ; Figures 6 and 7 ). Our analysis takes into account the correlation between the errors on mass and concentration and the effects of nonuniformity of the intrinsic mass probability distribution. Our model yields a mean concentration of at and an intrinsic scatter of , or . The normalization, slope, and scatter of the observed c – M relation are all consistent with ΛCDM predictions (Meneghetti et al. 2014 ) when the projection effects and the CLASH selection function based on X-ray morphological regularity are taken into account. Our regression results are in agreement with the SaWLens analysis of Merten et al. ( 2015 ) and the stacked shear-only analysis of Umetsu et al. ( 2014 ). This multi-probe approach for cluster lensing is one of the key advantages of the CLASH survey, providing consistency checks between different lensing methods and different data sets. We have derived an ensemble-averaged surface mass density profile at an average redshift of for the X-ray-selected subsample of 16 clusters by stacking their individual profiles (Section 5 ; Figure 3 ). The stacked lensing signal is detected at 33 σ significance over the entire radial range, R ≤ 4000 kpc h −1 , accounting for the effects of intrinsic profile variations and uncorrelated LSS along the line of sight (Figure 1 ). Our CLASH lensing determination of the cluster mass distribution provides a firm basis for a detailed comparison with theoretical models. We show that the profile is well described by a family of density profiles predicted for DM-dominated halos in gravitational equilibrium (Table 4 ; Figure 4 ), namely, the NFW, Einasto, and DARKexp models. Of these, the first two are phenomenological models and the last is a theoretically derived model (Hjorth Williams 2010 ; Williams Hjorth 2010 ). The single power-law, cored isothermal and Burkert density profiles are disfavored by the observed mass profile having a pronounced radial curvature. We find that cuspy halo models that include the large-scale two-halo contribution using the b h – M relation of Tinker et al. ( 2010 ) provide improved agreement with the data. Independent of the chosen halo density profile, we find b h ( M 200c ) ∼ 9.3 (). For the NFW halo model (NFW+LSS (i)), we measure a mean concentration of at , demonstrating consistency between complementary analysis methods (Figure 7 ). This model yields an Einstein radius of arcsec at z s = 2, which agrees within 2 σ with the observed median Einstein radius of
Paper: 2018ApJ...858...95G (fulltext)
(electron + proton) fluid, we find that the post-shock proton temperature in the direction perpendicular to the field, which we take as the initial condition in our periodic boxes, will beThe parallel proton and electron temperatures are the same as in the pre-shock region; in particular, With regard to the initialization of electrons, we assume that the electron anisotropy is rapidly erased due to the rapid development of electron-scale instabilities, as it indeed occurs in the shock ramp. 8 8 Despite the fact that we now assume isotropic electrons, the ansatz of a Γ = 2 gas that we employed to calculate the jump conditions will still hold because the post-shock pressure is mostly contributed by protons (see Section 5 ). If this happens without any energy exchange with the protons, the isotropic electron temperature isFinally, the initial electron plasma beta can be computed from flux freezing as The physical and numerical parameters used for the periodic box experiments of this section are summarized in Table 2 (the run names have the suffix “nc” to indicate that the boxes are not compressing). We indicate the Mach number M s and plasma beta β p 0 of the corresponding shock simulations, the initialization values of , , , and employed in the periodic boxes, the number of computational particles per cell (), and the box length L y along the y -direction of the background large-scale field, in units of the proton skin depth . We employ a reduced mass ratio . From Table 2 , it is apparent that the Mach number M s has a pronounced effect on the initial proton anisotropy. In fact, in the limit of , we have and , so that from Equations ( 12 ) and ( 13 ). Table 2 Parameters for the Anisotropic Protons Box Experiments Described in Section 4 Note. We employ a fixed mass ratio and resolve the electron skin depth with seven cells, which is sufficient to capture the electron debye length. 4.2. Dependence on M<sub>s</sub> In this subsection, we compare the periodic box simulations , , , , that correspond to shocks with fixed but different M s , ranging from 2 to 5. Our results are shown in Figure 5 . The case corresponding to M s = 2 is plotted with a dashed line to emphasize that its time axis has been reduced by a factor of two, to facilitate comparison with the other cases. Figure 5. Dependence on M s of various quantities in periodic box experiments with anisotropic protons (runs , , , ), at fixed . As a function of time in units of , we plot: (a) energy in magnetic field fluctuations, normalized to the energy of the initial field in the box (b) proton temperature anisotropy; (c) magnetic pressure in electron-scale fluctuations, normalized to the electron parallel pressure; (d) electron temperature anisotropy (solid lines for the box-averaged values, shaded regions for the 50–90 percentiles) and threshold of the electron whistler instability (dotted lines with the same color coding as the solid lines); (e) rate of violation of the electron
Paper: 2019MNRAS.484.5236C (fulltext)
the observed CMD of core He-burning stars. Figure 7. CMDs of Hodge 6 and NGC 1978, the two youngest clusters in our sample. Observations are denoted with grey open triangles, synthetic HB models are overplotted in red. From left to right: Δ Y = 0 models, models with Δ Y > 0 (η R fixed to the best-fitting value used in the left-hand panel), and with Δη R > 0 ( Y fixed to the He-normal value of the left-hand panel). The arrows describe the direction along which variations in Y and mass-loss work, the amplitude being arbitrary here. If we make the assumption that η R must be constant, in agreement with the results for the other clusters in our sample, we would obtain Δ Y ∼0.06 for Hodge 6, and Δ Y ∼0.04 for NGC 1978. But without this assumption, the CMD analysis does not discriminate between a spread in η R or in Y for these two clusters. However, we also note that Hodge 6 has the largest photometric errors of any of the clusters in our sample, adding further uncertainty for this cluster. 5 DISCUSSION We have determined the total initial He abundance spread Δ Y in a sample of intermediate-age, massive LMC and SMC clusters – and the old cluster NGC 121 – by reproducing the shape and colour extension of their HB/RC stars with synthetic HB models. Our derived Δ Y values are shown in Table 2 . The typical error on these estimates of Δ Y is below 0.01. Table 2. Initial helium abundance spread derived from the HB fitting. The values in parenthesis are determined in the assumption that the RGB mass-loss does not vary amongst clusters’ stars, because of a degeneracy between the effects of mass-loss spread and He spread in these clusters (see the text for details). We can compare our results with the estimates by Lagioia et al. ( 2019 ). These authors found spreads of initial He abundances equal to 0.009 ± 0.006, 0.007 ± 0.004, 0.010 ± 0.003, and 0.000 ± 0.004 for NGC 121, NGC 339, NGC 416, and Lindsay 1, respectively. These values are clearly smaller than our results in Table 2 . But as mentioned already in the Introduction section, the method employed by Lagioia et al. ( 2019 ) most likely determines mean abundance spreads amongst the cluster subpopulations, whereas our modelling tends to determine the maximum abundance spread, irrespective of the exact distribution of initial He abundances. This is quite clear by looking at the HB of NGC 121 in Fig. 4 . The bulk of the HB population has ( m F555 W − m F814 W ) > 0.8, consistent with a negligible Δ Y with just a plume of stars extending towards bluer colours and brighter magnitudes, that is, with significantly different initial Y . The values in Table 2 are also plotted in Fig. 8 as a function of the mass of the host cluster. In the same figure, we display also the maximum initial Y spread determined for a sample of Galactic GCs by Milone et al. ( 2018 ). Milone et al. ( 2018 ) found a trend between Δ Y and the mass of the host cluster, that is clearly visible in Fig. 8 and the results for our clusters