adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018A&A...614A.102O (fulltext)
for further studies of substructures and galaxy populations. The paper opens with a short description of the 15 constrained simulations used for the study proposed in this paper and of the unique Virgo candidate identified in each one. In a third section, the merger trees and in particular the main and second (second after the main) branches are studied in detail. Finally, we conclude that the constrained scheme proves to be efficient to some extent also at the merger tree level and provides some further indications regarding the mergers that formed the Virgo cluster within the last few gigayears. 2 Virgo halos 2.1 Constrained simulations Sorce et al. (2016b ) described in detail the scheme used to build the constrained initial conditions and to run the simulations. Furthermore the introduction of this paper summarises the various existing techniques. Thus, we summarise in this section only the main steps required to produce the simulations constrained with observational radial peculiar velocity catalogs we use here. We also give a brief description of their purpose with the latest references in the literature of the algorithms used: grouping (e.g., Tully 2015a , b ) of the radial peculiar velocity catalog to remove nonlinear virial motions that would affect the linear reconstruction obtained with the linear method (e.g., Sorce et al. 2017 ; Sorce Tempel 2017 ); minimizing the biases ( Sorce 2015 ) inherent to any observational radial peculiar velocity catalog; reconstructing the cosmic displacement field with the Wiener-Filter technique (linear minimum variance estimator, WF, Zaroubi et al. 1995 , 1999 ) applied to the peculiar velocity constraints; relocating constraints to the positions of their progenitors using the reverse Zel’dovich approximation and the reconstructed cosmic displacement field ( Doumler et al. 2013 ) and replacing noisy radial peculiar velocities by their WF 3D reconstructions ( Sorce et al. 2014 ). This ensures that, after evolving the structures with an N-body code from an early redshift until today, structures are at the same position (within the 2 h −1 Mpc limit of the linear-threshold) to that observed. Keeping constraints at their current position to build initial conditions would indeed result in a shift of ≥10 h −1 Mpc between observed and simulated structures after a complete evolution of the initial conditions until today; producing density fields constrained by the modified observational peculiar velocities combined with a random realization to restore statistically the missing structures using the constrained realization technique (CR, Hoffman Ribak 1991 , 1992 ); rescaling the density fields to build constrained initial conditions and increasing the resolution by adding small-scale features (e.g., GINNUNGAGAP code 1 ). Tully et al. (2013) supplied the observational catalog used as constraints. Since the Virgo cluster is the object of study here, Fig. 1 permits us to grasp the distribution of the constraints
Paper: 2018ApJ...855...37D (fulltext)
The overall diffuse intensity is comparable within both models. Figure 6. Fraction of successful jets in the (, ζ SN ) plane for the simple GRB model. Only a small fraction of all CCSNe can harbor a successful jet. 4. Neutrino Diffuse Emission in the Advanced GRB Model In this section, we take a somewhat more realistic model of the physics within a jet and allow Γ to vary across the jet angle from some maximum value Γ max at the center of the jet ( θ = 0) out to Γ = 1: the advanced GRB model. In this model, we do not rely on θ j = 1/Γ anymore. Because we anticipate that multiple shocks will be accelerating the protons, the resulting Γ distribution is a von-Mises–Fisher distribution,where the concentration κ ≈ 1/ σ 2 for σ small, with σ the usual standard deviation. We take motivated by random walks of the accelerated particles within the jet. In this model, we define the volume of the jet (Equation ( 3 )) with where is defined by Γ( θ max ) = 1, orWe note that for κ = Γ max , θ max has a maximum at Γ max = e . For a representative jet with Γ max = 300 this corresponds to θ max = 11°. The component of the GRB rate R ( z , Γ) introduced in Section 3 depending on Γ is assumed to follow a distribution similarly defined as in the previous section but this time this is a function of Γ max : . The constraint in Equation ( 30 ) to reproduce the observed HL-GRB rate becomes,The maximum value of is 1 and the minimum value of (the maximum value of θ ) is when Γ = 200, which is .Similarly to Equation ( 29 ), for the advanced GRB model we havewhere the beaming angle factor on each side has canceled. The diffuse neutrino intensity isThe integral goes over θ ∈ [0, θ max ], and we again require that the jet can successfully accelerate protons. The fraction of successful jets for this model is shown in the top panel of Figure 7 . One can see that the fraction of CCSNe harboring successful jets is larger in this model with respect to the simple GRB model, given the different scaling laws intrinsic to the models. Figure 7. Top panel: fraction of successful jets in the (, ζ SN ) plane for the advanced GRB model. Bottom panel: fraction of jets that are choked for the advanced GRB model. Below erg, 50% of the jets are choked; below erg, 90% of the jets are choked. The total diffuse neutrino intensity for our canonical GRB and ζ SN = 0.1 for all successful jets is plotted in the bottom panel of Figure 5 for the advanced GRB model to be compared with the one from the simple GRB model. Both models produce comparable intensities. We now turn to the issue of electromagnetically choked jets. Depending on the jet properties, we expect that there will be some successful jets that are choked or invisible. In the simple model, all of the jets are bright if they successfully form. On the other hand, among all successful jets, the fraction of choked jets for the advanced model is shown in the bottom panel of Figure 7 . Below erg, we find that 50% of the jets are choked;
Paper: 2019A&A...625L...2K (fulltext)
The other possibility to explain the location and the age of the massive O3.5If* star RFS8 being nearly 69 pc from the nearest massive cluster is that the star formed in a small cluster, which has not yet been detected. To investigate if there is a cluster around RFS8, we utilize Two Micron All Sky Survey (2MASS) near-infrared JHK s imaging ( Cutri et al. 2003 ). Currently, this is the deepest photometry (allowing for extinction) available in the vicinity of RFS8. We obtain photometry of a region of 5′ surrounding RFS8. The depth of the photometry reaches roughly 16.5 mag in K s. Allowing for an extinction similar to RFS8 (of A V = 6.7), this reaches down to a spectral type of approximately mid-late A. Following the sequential sampling prescription of the stellar initial mass function (IMF) from Kroupa (2001) , we should have been able to detect a few tens of A-type stars, which might have allowed for the formation of such a massive star. This should be sufficient to identify any young cluster (3 Myr following the upper age limit of RFS8). We note that in the case of stochastic sampling of the IMF, there are no restrictions on the mass of the second most massive star in the cluster, that is, there is still a possibility of finding a cluster, but with few BA spectral type stars. From the colour–colour and colour–magnitude diagrams of stars within 5′ of RFS8, with high-quality infrared photometry, we find no evidence for a cluster sequence, and the upper locus of stars in the colour–magnitude diagram have no clear spatial correlation. In addition, we plot the stellar density (Fig. 4 ) of all point sources. From this, we find no evidence for any significant clustering around RFS8. Fig. 4. Stellar density as a function of distance from RFS8 of all infrared 2MASS sources within 5′. The lower axis shows the radius in projected distance. 4. Discussion We have shown that RFS7, a young (∼1 Myr), massive (∼100 M ⊙ ) O2.5If*/WN6 star is located ∼53 pc from the centre of the nearest massive cluster, NGC 3603 YC. Its mass is comparable to the most massive stars found within the cluster ( Roman-Lopes et al. 2016 ). From Gaia astrometry, we find that RFS7 displays a proper motion outside the locus of cluster stars of NGC 3603 YC, with a 2D projected velocity from NGC 3603 YC of 27.1 ± 1.5 km s −1 . It has a high tangential velocity (∼33 km s −1 ), and a possible bow shock located 1 pc from the star. The motion suggests the star was ejected from the cluster 1–2 Myr ago, consistent with the age of the cluster. All this indicates that RFS7 could likely be a runaway from NGC 3603 YC, making it the most massive known runaway in our Galaxy (and comparable to the behemoths found around the R 136 cluster in the Large Magellanic Cloud). A key future study in this respect would be to decipher whether the runaway was dynamically ejected, which might also help us to better understand the total mass and stellar content of the central cluster (to eject a ∼100 M ⊙
Paper: 2021A&A...646A..84A (fulltext)
GHz observations, respectively. 5.3. Computational aspects Each resolve run needs ≈500 000 evaluations of the response and ≈400 000 evaluations of its adjoint. That makes the response part of the imaging algorithm a factor of ≈50 000 more expensive compared to CLEAN approaches. The good news is that the implementation of the radio response Eq. ( 7 ) in the package ducc scales well with the number of data points and that the response calls can be parallelized over the sum in Eq. ( 26 ). The resolve runs have been performed on a single node with five MPI tasks, each of which needs ≈2.2 GB main memory. Each MPI task uses four threads for the parallelization of the radio response and the Fast Fourier Transforms. The wall time for each resolve run is between 80 and 90 h. Single-scale CLEAN takes below 30 min for imaging each channel on a modern laptop. Thus, resolve is approximately 180 times slower that single-scale CLEAN here. This comparison does not include that the resolve had five times the number of CPUs available. Multi-scale CLEAN takes about 2 hours during the final round of imaging on the 13 GHz data set. This number does not account for the time taken during the initial rounds of imaging used to tune the hyper parameters and construct the mask which can be a time-consuming process. However, it should be kept in mind that CLEAN scales much better when the dimensionality of the image is much smaller than that of the data, which is not the case here. This is because CLEAN only requires about 10–30 applications of the full measurement operator and its adjoint, even including all preprocessing steps. Taking 90 min for the average multi-scale CLEAN run, resolve is 60 times slower than multi-scale CLEAN . 6. Conclusions This paper compares the output of two algorithms traditionally applied in the radio interferometry community (single-scale CLEAN and multi-scale CLEAN ) with a Bayesian approach to imaging called resolve . We demonstrate that resolve overcomes a variety of problems present in traditional imaging: The sky brightness distribution is a strictly positive quantity, the algorithm quantifies the uncertainty on the sky brightness distribution, and the weighting scheme is determined non-parametrically. Additionally, resolve provides varying resolution depending on the position on the sky into account, which enables super-resolution. We find that single-scale CLEAN and multi-scale CLEAN give similar results. In contrast, resolve produces images with higher resolution: The 4.8 GHz map has comparable resolution to the 13.4 GHz CLEAN maps. These advantages are at the cost of additional computational time, in our cases ≈90 h wall time on a single node. Future work may extend resolve to multi-frequency reconstructions where the correlation structure in frequency axis is taken into account as well in order to increase resolution. Additionally, direction-independent and antenna-based calibration may be integrated into resolve . Finally, the
Paper: 2020ApJ...890...46T (fulltext)
individual star. Tables 5 and 6 list the top 20 stars sorted by flare frequency f * and the number of flares N *flares , respectively. TIC43472154 is the most active star with an impressive flare frequency. If it is not coincidentally observed at an extraordinary active period, TIC43472154 can generate 233.17 flares per year. Besides, this star is not accompanied by any fainter stars or M dwarfs, according to the cross-match results from Gaia and Hipparcos data. This occurrence rate is much higher than that of KIC10422252, which is 41.6 flares per year (Shibayama et al. 2013 ). Figure 7 plots the light curve of TIC43472154. Superflares are marked with downward arrows. Figure 7. Light curve of TIC43472154. Small arrows mark those superflares selected from automatic software, and checked through a pixel-level pattern. Table 5 Stellar Properties of Flare Stars Sorted by Star Activity f * Note. Top 20 flare stars sorted by flare frequency ( f * ). The headers of this table are the same as in Table 1 . Table 6 Stellar Properties of Flare Stars Sorted by Number of Flares Note. Top 20 flare stars sorted by the number of flares on the corresponding star. The headers of this table are the same as in Table 5 . Another target, TIC364588501, was observed by all sectors of TESS and exhibits the largest number of flares (63). Additionally, like TIC43472154, this star is also a single star, according to the cross-match results from Gaia and Hipparcos data. TIC364588501 became more active than usual in the last 15 days of sector 5, and generated 15 superflares (Figure 8 ). Figure 8. Same as Figure 7 , but for TIC364588501. The most energetic superflare comes from TIC93277807, releasing 1.77 × 10 37 erg in around 1.5 hr. This value consists with the inference that energy releasing through a stellar flare is saturated at ∼2 × 10 37 erg (Wu et al. 2015 ). 3.4. Planets of Flare Stars Hot Jupiters are considered as one of the essential factors that can produce superflares on host stars (e.g., Rubenstein Schaefer 2000 ; Ip et al. 2004 ). According to these studies, it is not possible for the Sun to generate superflares. However, a lot of statistical works based on Kepler data did not detect hot Jupiters around flare stars (Maehara et al. 2012 ; Shibayama et al. 2013 ; Maehara et al. 2015 ). We made a cross-check between our 400 flare stars with the Exoplanet Follow-up Observing Program for TESS (ExoFOP– TESS 3 3 https://exofop.ipac.caltech.edu/tess/ ). Cross-matching results are listed in Table 7 . There are four stars (TIC25078924, 44797824, 257605131, and 373844472) that have planet candidates. These four stars are unlikely to affect our statistical results. We thus did not exclude them in the analysis. Besides, there is only one star (TIC410214986) that possesses a confirmed hot planet (Newton et al. 2019 ). This is an interesting planet, and we list its parameters in Table 7 , even if its hosting star (TIC410214986) is flagged as a binary system from the
Paper: 2016ApJS..223....8C (fulltext)
a zoom-in of the convolution kernel. Redder wavelengths have a larger kernel width due to . Positive redshifts move the position of the diagonal convolution to the right. The final model spectrum is shown compared to a low-resolution () spectrum of a solar-like star with S/N per pixel. The additional nuisance parameters f , p b , v b only contribute during the calculation of the likelihood , and do not affect the data generated by the model. Figure 2. A faux observed spectrum (black) of and S/N of ∼20 per pixel, which was used in the toy model test. The recovered maximum a posteriori model spectrum is shown in red. The construction of constitutes a non-negligible component to the total computational budget for each probability evaluation. For this reason a few (optional) approximations have been implemented to minimize this cost. A least-recently used cacher is employed by default to minimize the number of matrix constructions. This uses a small portion of random access memory to retain the N most common sets of . If is required and is sufficiently small (i.e., below some prescribed tolerance) such that , then future calls of within the range will return the previously calculated matrix instead of reconstructing it. The tolerances are configurable, and default to the sub-km s −1 level for and z , and . Alternatively, the construction of can be completely avoided by approximating the convolution with a single kernel width σ at all , where an interpolation routine is used to calculate the expected intensity at . The extent of approximation that can be afforded will vary on the scientific objectives, but the default behavior in sick is balanced to be computationally efficient, and suitable for most scientific applications. I have produced the expected intensities for some arbitrary that can fairly represent any rotational broadening of the source, the object’s redshift, as well as the resolving power of the instrument and the location of the CCD pixels. However, these intensities are not representative of real world data. The height (i.e., photon counts) and shape of an observed spectrum is a function of the source magnitude, exposure time, instrument sensitivities, atmospheric conditions, interstellar extinction, and a host of unaddressed effects. For these reasons a function is required to normalize 3 3 The normalization process is frequently abused by stellar spectroscopists in the literature. Wherever possible, data should not be transformed. One should seek to fit a model to the data, not the other way around. the model to the data. Although the function incorporates a number of effects (e.g., source blackbody temperature, dust, instrument sensitivities), they are phenomena that usually cannot be separated without additional information, and here I only care about their combined effect. The continuum is modeled as a polynomial that enters multiplicatively,where the maximum polynomial degree is specified by the user. For data spanning
Paper: 2019ApJ...882..165S (fulltext)
halos and therefore have different IGM densities, velocities, and temperature structures. These 100 independent model realizations are necessary to properly characterize the stochasticity of the IGM absorption. We forward model observational effects like finite spectral resolution, signal-to-noise ratio, and continuum-fitting errors, to make the model outputs directly comparable to observed spectra. Each spectrum extends for ±65 cMpc around the foreground quasars position, and we bin the spectra in chunks of 1 cMpc length. For each chunk, we obtain a kernel density estimate (KDE) of the probability distribution (PDF) of the transmission values in the chunk, based on the 100 independent realizations of that model. This results in smooth functions, , that describe the probability for measuring an IGM transmission F n , m in bin n , m , given the model parameter . Here, the index n identifies the different background sightlines within the sightline pattern while m denotes a certain chunk along the sightline. As long as we only focus on the quasar age, the parameter vector has only one component . However, we still denote it as a vector, because this approach allows for easy and straightforward generalization of the inference to additional parameters like quasar obscuration or orientation. Following this, each model of the H i proximity effect is fully described by a set of transmission probability functions . 5.2. Likelihood Computation Due to the high dimensionality of the observable, , i.e., several thousand individual transmission measurements, determining the likelihood poses a very challenging task. The usual approach of approximating the likelihood as a multivariate Gaussian is inadequate for our problem, because the individual transmission PDFs are not described well by Gaussians. In addition, determining the significant correlations between all the elements of { F n , m } would likely require an excessive number of model realizations. Therefore, we follow a likelihood-free approach for which we never have to actually write down an analytic form for the likelihood function. In Schmidt et al. ( 2018 ), we solved a similar problem. There, however, the dimensionality of the problem was low (at most three transmission measurements and two model parameters), which made it feasible to simply map the full likelihood space by brute-force sampling. An approach like this would be completely impossible for our current case. A fully Bayesian treatment is only achievable if the dimensionality of the problem can be drastically reduced. We do this by first computing a so-called pseudo-likelihood , which acts in many ways like a proper likelihood—except that it ignores correlations between the { F n , m }. In a second step, we then map this pseudo-likelihood to determine a proper posterior probability distribution. Our approach is inspired in many ways by Alsing et al. ( 2018 ) and Davies et al. ( 2018b ), but is customized to the problem at hand and is
Paper: 2016A&A...587A..12W (fulltext)
of HO and CO bands. This closure-phase signal is interpreted as a signature of large-scale inhomogeneities or clumps at the photospheric layers and within the extended molecular layers. These inhomogeneities or clumps contribute a few percent of the total flux. Observations taken at two epochs separated by about two months indicate a variability of the detailed structure of the inhomogeneities, while a variability of the overall radius could not be detected within our calibration uncertainties. We compared our data with the latest 1D dynamic model atmospheres based on self-excited pulsation models (CODEX models) and for the first time also with 3D radiation hydrodynamic (RHD) simulations of the convective interiors and the atmospheres of AGB stars (CO5BOLD code). For the latter we chose to use azimuthally averaged intensity profiles because our main interest in this current investigation concerns the overall extension of the atmospheres and because we aim at comparing the 3D models to the results by 1D models. In addition, we have previously shown that visibilities above a flux level of about 10% are consistent with spherically symmetric models so that the detailed 3D structure mainly concerns only relatively low flux levels. Both model attempts provide satisfactory fits to our data and, in particular reproduce the wavelength dependence of the visibility data. This means that both models include a spatially extended molecular atmosphere that is consistent with our observations. A comparison between the intensity profiles of the best-fit 1D and 3D models show that indeed both model attempts include an extended atmosphere up to roughly two to three stellar radii and that the wavelength-dependent flux contributions of the extension are consistent with observations. This agreement of both the different 1D and 3D modeling attempts with our data supports the theoretical result by Freytag Höfner (2008) that shocks induced by convection and pulsation in 3D models are roughly spherically expanding and of similar nature to those of 1D self-excited pulsations. Moreover, our results show that indeed both modeling attempts, 1D pulsation models and 3D convection simulations, can levitate the atmosphere of Mira stars to at least two to three stellar radii, which is consistent with our observations. We note that, on the contrary the same modeling attempts failed to extend the atmospheres of red supergiant stars to similarly observed extensions ( Arroyo-Torres et al. 2015 ). In addition, observed and model-predicted broad-band and are consistent for both the 1D CODEX and 3D CO5BOLD models, while the color can only be re-produced by the 1D CODEX, which includes a simple dust description, and not by the dust-free 3D CO5BOLD models, indicating that dust starts to contribute to broad-band colors from the -band onward. The detailed intensity profile across the stellar disk could not be constrained by our current snapshot observations. This would require future