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Register a new userPolarized CMB Foregrounds: What do we know and how bad is it?
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Clive Dickinson PhD
Publication date
2010
fields
Physics
and research's language is
English
Authors
Clive Dickinson
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Polarized foregrounds are going to be a serious challenge for detecting CMB cosmological B-modes. Both diffuse Galactic emission and extragalactic sources contribute significantly to the power spectrum on large angular scales. At low frequencies, Galactic synchrotron emission will dominate with fractional polarization $sim 20-40%$ at high latitudes while radio sources can contribute significantly even on large ($sim 1^{circ}$) angular scales. Nevertheless, simulations suggest that a detection at the level of $r=0.001$ might be achievable if the foregrounds are not too complex.
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Temperature, the central concept of thermal physics, is one of the most frequently employed physical quantities in common practice. Even though the operative methods of the temperature measurement are described in detail in various practical instructions and textbooks, the rigorous treatment of this concept is almost lacking in the current literature. As a result, the answer to a simple question of what the temperature is is by no means trivial and unambiguous. There is especially an appreciable gap between the temperature as introduced in the frame of statistical theory and the only experimentally observable quantity related to this concept, phenomenological temperature. Just the logical and epistemological analysis of the present concept of phenomenological temperature is the kernel of the contribution.
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.
The bright, well-known K5 giant Aldebaran, alpha Tau, is probably the star with the largest number of direct angular diameter determinations, achieved over a long time by several authors using various techniques. In spite of this wealth of data, or perhaps as a direct result of it, there is not a very good agreement on a single angular diameter value. This is particularly unsettling if one considers that Aldebaran is also used as a primary calibrator for some angular resolution methods, notably for optical and infrared long baseline interferometry. Directly connected to Aldebarans angular diameter and its uncertainties is its effective temperature, which also has been used for several empirical calibrations. Among the proposed explanations for the elusiveness of an accurate determination of the angular diameter of Aldebaran are the possibility of temporal variations as well as a possible dependence of the angular diameter on the wavelength. We present here a few, very accurate new determinations obtained by means of lunar occultations and long baseline interferometry. We derive an average value of 19.96+-0.03 milliarcseconds for the uniform disk diameter. The corresponding limb-darkened value is 20.58+-0.03 milliarcseconds, or 44.2+-0.9 R(sun). We discuss this result, in connection with previous determinations and with possible problems that may affect such measurements.
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.
The participants in this discussion session of the QCHS 9 meeting were each asked the following question: What would be the most useful piece of information that you could obtain, by whatever means, that would advance your own program, and/or our general understanding of confinement? This proceedings contains a brief summary of each panel members contribution to the discussion, provided by the panel members themselves.
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