Bayesian time series analysis of terrestrial impact cratering

Giant impacts by comets and asteroids have probably had an important influence on terrestrial biological evolution. We know of around 180 high velocity impact craters on the Earth with ages up to 2400Myr and diameters up to 300km. Some studies have identified a periodicity in their age distribution, with periods ranging from 13 to 50Myr. It has further been claimed that such periods may be causally linked to a periodic motion of the solar system through the Galactic plane. However, many of these studies suffer from methodological problems, for example misinterpretation of p-values, overestimation of significance in the periodogram or a failure to consider plausible alternative models. Here I develop a Bayesian method for this problem in which impacts are treated as a stochastic phenomenon. Models for the time variation of the impact probability are defined and the evidence for them in the geological record is compared using Bayes factors. This probabilistic approach obviates the need for ad hoc statistics, and also makes explicit use of the age uncertainties. I find strong evidence for a monotonic decrease in the recorded impact rate going back in time over the past 250Myr for craters larger than 5km. The same is found for the past 150Myr when craters with upper age limits are included. This is consistent with a crater preservation/discovery bias modulating an otherwise constant impact rate. The set of craters larger than 35km (so less affected by erosion and infilling) and younger than 400Myr are best explained by a constant impact probability model. A periodic variation in the cratering rate is strongly disfavoured in all data sets. There is also no evidence for a periodicity superimposed on a constant rate or trend, although this more complex signal would be harder to distinguish.Comment: Minor typos corrected in arXiv v2. Erratum (minor notation corrections) corrected in arXiv v3. (Erratum available from http://www.mpia-hd.mpg.de/~calj/craterTS_erratum.pdf

Non-Compositional Term Dependence for Information Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR

Analysis of bilinear oscillators under harmonic loading using nonlinear output frequency response functions

In this paper, the new concept of Nonlinear Output Frequency Response Functions (NOFRFs) is extended to the harmonic input case, an input-independent relationship is found between the NOFRFs and the Generalized Frequency Response Functions (GFRFs). This relationship can greatly simplify the application of the NOFRFs. Then, beginning with the demonstration that a bilinear oscillator can be approximated using a polynomial type nonlinear oscillator, the NOFRFs are used to analyze the energy transfer phenomenon of bilinear oscillators in the frequency domain. The analysis provides insight into how new frequency generation can occur using bilinear oscillators and how the sub-resonances occur for the bilinear oscillators, and reveals that it is the resonant frequencies of the NOFRFs that dominate the occurrence of this well-known nonlinear behaviour. The results are of significance for the design and fault diagnosis of mechanical systems and structures which can be described by a bilinear oscillator model

Bayesian joint analysis of cluster weak lensing and Sunyaev-Zel'dovich effect data

As the quality of the available galaxy cluster data improves, the models fitted to these data might be expected to become increasingly complex. Here we present the Bayesian approach to the problem of cluster data modelling: starting from simple, physically motivated parameterised functions to describe the cluster's gas density, gravitational potential and temperature, we explore the high-dimensional parameter spaces with a Markov-Chain Monte-Carlo sampler, and compute the Bayesian evidence in order to make probabilistic statements about the models tested. In this way sufficiently good data will enable the models to be distinguished, enhancing our astrophysical understanding; in any case the models may be marginalised over in the correct way when estimating global, perhaps cosmological, parameters. In this work we apply this methodology to two sets of simulated interferometric Sunyaev-Zel'dovich effect and gravitational weak lensing data, corresponding to current and next-generation telescopes. We calculate the expected precision on the measurement of the cluster gas fraction from such experiments, and investigate the effect of the primordial CMB fluctuations on their accuracy. We find that data from instruments such as AMI, when combined with wide-field ground-based weak lensing data, should allow both cluster model selection and estimation of gas fractions to a precision of better than 30 percent for a given cluster.Comment: 13 pages, 7 figures, submitted to MNRAS; accepted 14/8/03 after minor revisio

A Bayesian study of the primordial power spectrum from a novel closed universe model

We constrain the shape of the primordial power spectrum using recent measurements of the cosmic microwave background (CMB) from the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data and other high-resolution CMB experiments. We also include observations of the matter power spectrum from the luminous red galaxy (LRG) subset DR7 of the Sloan Digital Sky Survey (SDSS). We consider two different models of the primordial power spectrum. The first is the standard nearly scale-invariant spectrum in the form of a generalised power-law parameterised in terms of the spectral amplitude $A_{\rm s}$, the spectral index $n_{\rm s}$ and (possibly) the running parameter $n_{\rm run}$. The second spectrum is derived from the Lasenby and Doran (LD) model. The LD model is based on the restriction of the total conformal time available in a closed Universe and the predicted primordial power spectrum depends upon just two parameters. An important feature of the LD spectrum is that it naturally incorporates an exponential fall-off on large scales, which might provide a possible explanation for the lower-than-expected power observed at low multipoles in the CMB. In addition to parameter estimation, we compare both models using Bayesian model selection. We find there is a significant preference for the LD model over a simple power-law spectrum for a CMB-only dataset, and over models with an equal number of parameters for all the datasets considered.Comment: minor corrections to match accepted version to MNRA

Through a Glass, Darkly:The CIA and Oral History

This article broaches the thorny issue of how we may study the history of the CIA by utilizing oral history interviews. This article argues that while oral history interviews impose particular demands upon the researcher, they are particularly pronounced in relation to studying the history of intelligence services. This article, nevertheless, also argues that while intelligence history and oral history each harbour their own epistemological perils and biases, pitfalls which may in fact be pronounced when they are conjoined, the relationship between them may nevertheless be a productive one. Indeed, each field may enrich the other provided we have thought carefully about the linkages between them: this article's point of departure. The first part of this article outlines some of the problems encountered in studying the CIA by relating them to the author's own work. This involved researching the CIA's role in US foreign policy towards Afghanistan since a landmark year in the history of the late Cold War, 1979 (i.e. the year the Soviet Union invaded that country). The second part of this article then considers some of the issues historians must confront when applying oral history to the study of the CIA. To bring this within the sphere of cognition of the reader the author recounts some of his own experiences interviewing CIA officers in and around Washington DC. The third part then looks at some of the contributions oral history in particular can make towards a better understanding of the history of intelligence services and the CIA