Uncovering delayed patterns in noisy and irregularly sampled time series: an astronomy application

We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm for the (hyper)parameter estimation of a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. Mixed types (integer and real) are used to represent variables within the evolutionary algorithm. We test the algorithm on several artificial data sets, and also on real astronomical observations of quasar Q0957+561. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates: for Q0957+561, we obtain 419.6 days for the time delay between images A and B. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.Comment: 36 pages, 10 figures, 16 tables, accepted for publication in Pattern Recognition. This is a shortened version of the article: interested readers are urged to refer to the published versio

Null stream analysis of Pulsar Timing Array data: localisation of resolvable gravitational wave sources

Super-massive black hole binaries are expected to produce a GW signal in the nano-Hertz frequency band which may be detected by PTAs in the coming years. The signal is composed of both stochastic and individually resolvable components. Here we develop a generic Bayesian method for the analysis of resolvable sources based on the construction of `null-streams' which cancel the part of the signal held in common for each pulsar (the Earth-term). For an array of $N$ pulsars there are $N-2$ independent null-streams that cancel the GW signal from a particular sky location. This method is applied to the localisation of quasi-circular binaries undergoing adiabatic inspiral. We carry out a systematic investigation of the scaling of the localisation accuracy with signal strength and number of pulsars in the PTA. Additionally, we find that source sky localisation with the International PTA data release one is vastly superior than what is achieved by its constituent regional PTAs.Comment: 13 pages, 7 figures, 1 appendix. Edited Figures 5, 6, 7 due to a bug in the plotting script (results unchanged). Additional edit to fix a type in equation

Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data

Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ER) and FOXA1 binding in their proximal promoter regions.Peer reviewe

Algorithms for approximate Bayesian inference with applications to astronomical data analysis

Bayesian inference is a theoretically well-founded and conceptually simple approach to data analysis. The computations in practical problems are anything but simple though, and thus approximations are almost always a necessity. The topic of this thesis is approximate Bayesian inference and its applications in three intertwined problem domains. Variational Bayesian learning is one type of approximate inference. Its main advantage is its computational efficiency compared to the much applied sampling based methods. Its main disadvantage, on the other hand, is the large amount of analytical work required to derive the necessary components for the algorithm. One part of this thesis reports on an effort to automate variational Bayesian learning of a certain class of models. The second part of the thesis is concerned with heteroscedastic modelling which is synonymous to variance modelling. Heteroscedastic models are particularly suitable for the Bayesian treatment as many of the traditional estimation methods do not produce satisfactory results for them. In the thesis, variance models and algorithms for estimating them are studied in two different contexts: in source separation and in regression. Astronomical applications constitute the third part of the thesis. Two problems are posed. One is concerned with the separation of stellar subpopulation spectra from observed galaxy spectra; the other is concerned with estimating the time-delays in gravitational lensing. Solutions to both of these problems are presented, which heavily rely on the machinery of approximate inference

Evidence for Short Temporal Atmospheric Variations Observed by Infrasonic Signals: 1. The Troposphere

Infrasound monitoring is used in the forensic analysis of events, studying the physical processes of sources of interest, and probing the atmosphere. The dynamical nature of the atmosphere and the use of infrasound as a forensic tool lead to the following questions; (1) what is the timescale of atmospheric variability that affects infrasonic signals? (2) how do infrasound signals vary as a function of time? This study addresses these questions by monitoring a repetitive infrasound source and its corresponding tropospheric returns 54 km away. Source-receiver empirical Green\u27s functions are obtained every 20 s and used to demonstrate the effect of atmospheric temporal variability on infrasound propagation. In addition, observations are compared to predicted simulated signals based on realistic atmospheric conditions. Based on 127 events over 3 days, it is shown that infrasound properties change within tens of seconds. Particularly, phases can appear and disappear, the propagation time varies, and the signals\u27 energy fluctuates. Such variations are attributed to changes in temperatures and winds. Furthermore, atmospheric models can partly explain the observed changes. Therefore, this study highlights the potential of high temporal infrasound-based atmospheric sounding