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Register a new userWFIRST-2.4: What Every Astronomer Should Know
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The Astro2010 Decadal Survey recommended a Wide Field Infrared Survey Telescope (WFIRST) as its top priority for a new large space mission. The report of the WFIRST-AFTA Science Definition Team (SDT) presents a Design Reference Mission for WFIRST that employs one of the 2.4-m, Hubble-quality mirror assemblies recently made available to NASA. The 2.4-m primary mirror enables a mission with greater sensitivity and higher angular resolution than the smaller aperture designs previously considered for WFIRST, increasing both the science return of the primary surveys and the capabilities of WFIRST as a Guest Observer facility. The option of adding an on-axis, coronagraphic instrument would enable imaging and spectroscopic studies of planets around nearby stars. This short article, produced as a companion to the SDT report, summarizes the key points of the WFIRST-2.4 DRM. It highlights the remarkable opportunity that the 2.4-m telescope affords for advances in many fields of astrophysics and cosmology, including dark energy, the demographics and characterization of exoplanets, the evolution of galaxies and quasars, and the stellar populations of the Milky Way and its neighbors.
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We present our very recent results on the sub-mJy radio source populations at 1.4 GHz based on the Extended Chandra Deep Field South VLA survey, which reaches ~ 30 {mu}Jy, with details on their number counts, evolution, and luminosity functions. The sub-mJy radio sky turns out to be a complex mix of star-forming galaxies and radio-quiet AGN evolving at a similar, strong rate and declining radio-loud AGN. While the well-known flattening of the radio number counts below 1 mJy is mostly due to star-forming galaxies, these sources and AGN make up an approximately equal fraction of the sub-mJy sky. Our results shed also light on a fifty-year-old issue, namely radio emission from radio-quiet AGN, and suggest that it is closely related to star formation, at least at z ~ 1.5 - 2. The implications of our findings for future, deeper radio surveys, including those with the Square Kilometre Array, are also discussed. One of the main messages, especially to non-radio astronomers, is that radio surveys are reaching such faint limits that, while previously they were mainly useful for radio quasars and radio galaxies, they are now detecting mostly star-forming galaxies and radio-quiet AGN, i.e., the bulk of the extragalactic sources studied in the infrared, optical, and X-ray bands.
Deep generative models have been demonstrated as state-of-the-art density estimators. Yet, recent work has found that they often assign a higher likelihood to data from outside the training distribution. This seemingly paradoxical behavior has caused concerns over the quality of the attained density estimates. In the context of hierarchical variational autoencoders, we provide evidence to explain this behavior by out-of-distribution data having in-distribution low-level features. We argue that this is both expected and desirable behavior. With this insight in hand, we develop a fast, scalable and fully unsupervised likelihood-ratio score for OOD detection that requires data to be in-distribution across all feature-levels. We benchmark the method on a vast set of data and model combinations and achieve state-of-the-art results on out-of-distribution detection.
We propose a number of powerful dynamic-epistemic logics for multi-agent information sharing and acts of publicly or privately accessing other agents information databases. The static base of our logics is obtained by adding to standard epistemic logic comparative epistemic assertions, that can express epistemic superiority between groups or individuals, as well as a common distributed knowledge operator (that combines features of both common knowledge and distributed knowledge). On the dynamic side, we introduce actions by which epistemic superiority can be acquired: sharing all one knows (by e.g. giving access to ones information database to all or some of the other agents), as well as more complex informational events, such as hacking. We completely axiomatize several such logics and prove their decidability.
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as Obama is a _ by profession. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as Obama worked as a _ may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.
In the present paper, we investigate the cosmographic problem using the bias-variance trade-off. We find that both the z-redshift and the $y=z/(1+z)$-redshift can present a small bias estimation. It means that the cosmography can describe the supernova data more accurately. Minimizing risk, it suggests that cosmography up to the second order is the best approximation. Forecasting the constraint from future measurements, we find that future supernova and redshift drift can significantly improve the constraint, thus having the potential to solve the cosmographic problem. We also exploit the values of cosmography on the deceleration parameter and equation of state of dark energy $w(z)$. We find that supernova cosmography cannot give stable estimations on them. However, much useful information was obtained, such as that the cosmography favors a complicated dark energy with varying $w(z)$, and the derivative $dw/dz<0$ for low redshift. The cosmography is helpful to model the dark energy.
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