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Register a new userA closer look at the flaring feature in the M87 jet
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C. S. Chang
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The radio-loud active galactic nucleus in M 87 hosts a powerful jet fueled by a super-massive black hole in its center. A bright feature 80 pc away from the M 87 core has been reported to show superluminal motions, and possibly to be connected with a TeV flare observed around 2005. To complement these studies and to understand the nature of this feature, we analyzed 2 cm VLBI data from 15 observing runs between 2000 and 2009. This feature is successfully detected at the milli-Jansky level from 2003 to 2007. Our detections show that its milli-arcsecond structure appears to be extended with a steep spectrum, and no compact or rapidly moving features are observed. Our results do not favor a blazar scenario for this feature.
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A bright feature 80 pc away from the core in the powerful jet of M87 shows highly unusual properties. Earlier radio, optical and X-ray observations have shown that this feature, labeled HST-1, is superluminal, and is possibly connected with the TeV flare detected by HESS in 2005. It has been claimed that this feature might have a blazar nature, due to these properties. To examine the possible blazar-like nature of HST-1, we analyzed lambda 2 cm VLBA archival data from dedicated full-track observations and the 2 cm survey/MOJAVE VLBI monitoring programs obtained between 2000 and 2009. Applying VLBI wide-field imaging techniques, the HST-1 region was imaged at milliarcsecond resolution. Here we present the first 2 cm VLBI detection of this feature in observations from early 2003 to early 2007, and analyze its evolution over this time. Using the detections of HST-1, we find that the projected apparent speed is 0.61 +/- 0.31 c. A comparison of the VLA and VLBA flux densities of this feature indicate that is mostly resolved on molliarcsecond scales. This feature is optically thin between lambda 2 cm and lambda 20 cm. We do not find evidence of a blazar nature for HST-1.
Filaments in Herschel molecular cloud images are found to exhibit a characteristic width. This finding is in tension with spatial power spectra of the data, which show no indication of this characteristic scale. We demonstrate that this discrepancy is a result of the methodology adopted for measuring filament widths. First, we perform the previously used analysis technique on artificial scale-free data, and obtain a peaked width distribution of filament-like structures. Next, we repeat the analysis on three Herschel maps and reproduce the narrow distribution of widths found in previous studies $-$ when considering the average width of each filament. However, the distribution of widths measured at all points along a filament spine is broader than the distribution of mean filament widths, indicating that the narrow spread (interpreted as a characteristic width) results from averaging. Furthermore, the width is found to vary significantly from one end of a filament to the other. Therefore, the previously identified peak at 0.1 pc cannot be understood as representing the typical width of filaments. We find an alternative explanation by modelling the observed width distribution as a truncated power-law distribution, sampled with uncertainties. The position of the peak is connected to the lower truncation scale and is likely set by the choice of parameters used in measuring filament widths. We conclude that a characteristic width of filaments is not supported by the available data.
Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. With this property in mind, we then prove that robustness and accuracy should both be achievable for benchmark datasets through locally Lipschitz functions, and hence, there should be no inherent tradeoff between robustness and accuracy. Through extensive experiments with robustness methods, we argue that the gap between theory and practice arises from two limitations of current methods: either they fail to impose local Lipschitzness or they are insufficiently generalized. We explore combining dropout with robust training methods and obtain better generalization. We conclude that achieving robustness and accuracy in practice may require using methods that impose local Lipschitzness and augmenting them with deep learning generalization techniques. Code available at https://github.com/yangarbiter/robust-local-lipschitz
We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development. To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes. Our results show that the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict: the surrogate objective does not match the true reward landscape, learned value estimators fail to fit the true value function, and gradient estimates poorly correlate with the true gradient. The mismatch between predicted and empirical behavior we uncover highlights our poor understanding of current methods, and indicates the need to move beyond current benchmark-centric evaluation methods.
The GD-1 stream is one of the longest and coldest stellar streams discovered to date, and one of the best objects for constraining the dark matter properties of the Milky Way. Using data from {it Gaia} DR2 we study the proper motions, distance, morphology and density of the stream to uncover small scale perturbations. The proper motion cleaned data shows a clear distance gradient across the stream, ranging from 7 to 12 kpc. However, unlike earlier studies that found a continuous gradient, we uncover a distance minimum at $varphi_{1}approx$-50 deg, after which the distance increases again. We can reliably trace the stream between -85$<varphi_{1}<$15 deg, showing an even further extent to GD-1 beyond the earlier extension of citet{Price-Whelan18a}. We constrain the stream track and density using a Boolean matched filter approach and find three large under densities and find significant residuals in the stream track lining up with these gaps. In particular, a gap is visible at $varphi_{1}$=-3 deg, surrounded by a clear sinusoidal wiggle. We argue that this wiggle is due to a perturbation since it has the wrong orientation to come from a progenitor. We compute a total initial stellar mass of the stream segment of 1.58$pm$0.07$times$10$^{4}$ M$_{odot}$. With the extended view of the spur in this work, we argue that the spur may be unrelated to the adjacent gap in the stream. Finally, we show that an interaction with the Sagittarius dwarf can create features similar to the spur.
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