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Register a new userThe needle in the haystack - Where to look for more isolated cooling neutron stars
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Context: Isolated cooling neutron stars with thermal X-ray emission remain rarely detected objects despite many searches investigating the ROSAT data. Aims: We simulate the population of close-by young cooling neutron stars to explain the current observational results. Given the inhomogeneity of the neutron star distribution on the sky it is particularly interesting to identify promising sky regions with respect to on-going and future searches. Methods: Applying a population synthesis model the inhomogeneity of the progenitor distribution and the inhomogeneity of the X-ray absorbing interstellar medium are considered for the first time. The total number of observable neutron stars is derived with respect to ROSAT count rates. In addition, we present sky maps of neutron star locations and discuss age and distance distributions of the simulated neutron stars. Implications for future searches are discussed. Results: With our advanced model we can successfully explain the observed logN - logS distribution of close-by neutron stars. Cooling neutron stars will be most abundant in the directions of rich OB associations. New candidates are expected to be identified behind the Gould Belt, in particular in the Cygnus-Cepheus region. They are expected to be on average younger and then hotter than the known population of isolated cooling neutron stars. In addition, we propose to use data on runaway stars to search for more radio-quiet cooling neutron stars.
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Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular in vision and NLP where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. We provide a formalization of this problem through a familiar notion of regret and introduce the predominant strategies, namely task-agnostic (e.g. picking the highest scoring ImageNet model) and task-aware search strategies (such as linear or kNN evaluation). We conduct a large-scale empirical study and show that both task-agnostic and task-aware methods can yield high regret. We then propose a simple and computationally efficient hybrid search strategy which outperforms the existing approaches. We highlight the practical benefits of the proposed solution on a set of 19 diverse vision tasks.
We present a quantitative analysis of superfluidity and superconductivity in dense matter from observations of isolated neutron stars in the context of the minimal cooling model. Our new approach produces the best fit neutron triplet superfluid critical temperature, the best fit proton singlet superconducting critical temperature, and their associated statistical uncertainties. We find that the neutron triplet critical temperature is likely $2.09^{+4.37}_{-1.41} times 10^{8}$ K and that the proton singlet critical temperature is $7.59^{+2.48}_{-5.81} times 10^{9}$ K. However, we also show that this result only holds if the Vela neutron star is not included in the data set. If Vela is included, the gaps increase significantly to attempt to reproduce Velas lower temperature given its young age. Further including neutron stars believed to have carbon atmospheres increases the neutron critical temperature and decreases the proton critical temperature. Our method demonstrates that continued observations of isolated neutron stars can quantitatively constrain the nature of superfluidity in dense matter.
The Advanced LIGO and Virgo experiments are poised to detect gravitational waves (GWs) directly for the first time this decade. The ultimate prize will be joint observation of a compact binary merger in both gravitational and electromagnetic channels. However, GW sky locations that are uncertain by hundreds of square degrees will pose a challenge. I describe a real-time detection pipeline and a rapid Bayesian parameter estimation code that will make it possible to search promptly for optical counterparts in Advanced LIGO. Having analyzed a comprehensive population of simulated GW sources, we describe the sky localization accuracy that the GW detector network will achieve as each detector comes online and progresses toward design sensitivity. Next, in preparation for the optical search with the intermediate Palomar Transient Factory (iPTF), we have developed a unique capability to detect optical afterglows of gamma-ray bursts (GRBs) detected by the Fermi Gamma-ray Burst Monitor (GBM). Its comparable error regions offer a close parallel to the Advanced LIGO problem, but Fermis unique access to MeV-GeV photons and its near all-sky coverage may allow us to look at optical afterglows in a relatively unexplored part of the GRB parameter space. We present the discovery and broadband follow-up observations of eight GBM-iPTF afterglows. Two of the bursts are at low redshift, are sub-luminous with respect to standard cosmological bursts, and have spectroscopically confirmed broad-line type Ic supernovae. These two bursts are possibly consistent with mildly relativistic shocks breaking out from the progenitor envelopes rather than the standard mechanism of internal shocks within an ultra-relativistic jet. On a technical level, the GBM-iPTF effort is a prototype for locating and observing optical counterparts of GW events in Advanced LIGO with the Zwicky Transient Facility.
Important tasks like record linkage and extreme classification demonstrate extreme class imbalance, with 1 minority instance to every 1 million or more majority instances. Obtaining a sufficient sample of all classes, even just to achieve statistically-significant evaluation, is so challenging that most current approaches yield poor estimates or incur impractical cost. Where importance sampling has been levied against this challenge, restrictive constraints are placed on performance metrics, estimates do not come with appropriate guarantees, or evaluations cannot adapt to incoming labels. This paper develops a framework for online evaluation based on adaptive importance sampling. Given a target performance metric and model for $p(y|x)$, the framework adapts a distribution over items to label in order to maximize statistical precision. We establish strong consistency and a central limit theorem for the resulting performance estimates, and instantiate our framework with worked examples that leverage Dirichlet-tree models. Experiments demonstrate an average MSE superior to state-of-the-art on fixed label budgets.
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