Unsupervised non-parametric change point detection in quasi-periodic signals

We propose a new unsupervised and non-parametric method to detect change points in intricate quasi-periodic signals. The detection relies on optimal transport theory combined with topological analysis and the bootstrap procedure. The algorithm is designed to detect changes in virtually any harmonic or a partially harmonic signal and is verified on three different sources of physiological data streams. We successfully find abnormal or irregular cardiac cycles in the waveforms for the six of the most frequent types of clinical arrhythmias using a single algorithm. The validation and the efficiency of the method are shown both on synthetic and on real time series. Our unsupervised approach reaches the level of performance of the supervised state-of-the-art techniques. We provide conceptual justification for the efficiency of the method and prove the convergence of the bootstrap procedure theoretically.Comment: 8 pages, 7 figures, 1 tabl

Systematic search for gamma-ray periodicity in active galactic nuclei detected by the Fermi Large Area Telescope

We use nine years of gamma-ray data provided by the Fermi Large Area Telescope (LAT) to systematically study the light curves of more than two thousand active galactic nuclei (AGN) included in recent Fermi-LAT catalogs. Ten different techniques are used, which are organized in an automatic periodicity-search pipeline, in order to search for evidence of periodic emission in gamma rays. Understanding the processes behind this puzzling phenomenon will provide a better view about the astrophysical nature of these extragalactic sources. However, the observation of temporal patterns in gamma-ray light curves of AGN is still challenging. Despite the fact that there have been efforts on characterizing the temporal emission of some individual sources, a systematic search for periodicities by means of a full likelihood analysis applied to large samples of sources was missing. Our analysis finds 11 AGN, of which 9 are identified for the first time, showing periodicity at more than 4sigma in at least four algorithms. These findings will help in solving questions related to the astrophysical origin of this periodic behavior.Comment: 16 pages, 5 figures, 4 tables. Accepted by Ap

Systemic: A Testbed For Characterizing the Detection of Extrasolar Planets. I. The Systemic Console Package

We present the systemic Console, a new all-in-one, general-purpose software package for the analysis and combined multiparameter fitting of Doppler radial velocity (RV) and transit timing observations. We give an overview of the computational algorithms implemented in the Console, and describe the tools offered for streamlining the characterization of planetary systems. We illustrate the capabilities of the package by analyzing an updated radial velocity data set for the HD128311 planetary system. HD128311 harbors a pair of planets that appear to be participating in a 2:1 mean motion resonance. We show that the dynamical configuration cannot be fully determined from the current data. We find that if a planetary system like HD128311 is found to undergo transits, then self-consistent Newtonian fits to combined radial velocity data and a small number of timing measurements of transit midpoints can provide an immediate and vastly improved characterization of the planet's dynamical state.Comment: 10 pages, 5 figures, accepted for publication on PASP. Additional material at http://www.ucolick.org/~smeschia/systemic.ph

An empirical Bayesian analysis applied to the globular cluster pulsar population

We describe an empirical Bayesian approach to determine the most likely size of an astronomical population of sources of which only a small subset are observed above some limiting flux density threshold. The method is most naturally applied to astronomical source populations at a common distance (e.g.,stellar populations in globular clusters), and can be applied even to populations where a survey detects no objects. The model allows for the inclusion of physical parameters of the stellar population and the detection process. As an example, we apply this method to the current sample of radio pulsars in Galactic globular clusters. Using the sample of flux density limits on pulsar surveys in 94 globular clusters published by Boyles et al., we examine a large number of population models with different dependencies. We find that models which include the globular cluster two-body encounter rate, $\Gamma$, are strongly favoured over models in which this is not a factor. The optimal model is one in which the mean number of pulsars is proportional to $\exp(1.5 \log \Gamma)$. This model agrees well with earlier work by Hui et al. and provides strong support to the idea that the two-body encounter rate directly impacts the number of neutron stars in a cluster. Our model predicts that the total number of potentially observable globular cluster pulsars in the Boyles et al. sample is 1070$^{+1280}_{-700}$, where the uncertainties signify the 95% confidence interval. Scaling this result to all Galactic globular clusters, and to account for radio pulsar beaming, we estimate the total population to be 2280$^{+2720}_{-1490}$.Comment: 8 pages, 6 figures, 3 tables, corrected a few minor formatting errors which have also been submitted as an erratum to MNRA

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.Comment: 28 pages, 4 figures, 3 table

Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data

We propose a resampling-based fast variable selection technique for selecting important Single Nucleotide Polymorphisms (SNP) in multi-marker mixed effect models used in twin studies. Due to computational complexity, current practice includes testing the effect of one SNP at a time, commonly termed as `single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect the relevant genetic variants, hence we adapt our recently proposed framework of $e$-values to address this. In this paper, we propose a computationally efficient approach for single SNP detection in families while utilizing information on multiple SNPs simultaneously. We achieve this through improvements in two aspects. First, unlike other model selection techniques, our method only requires training a model with all possible predictors. Second, we utilize a fast and scalable bootstrap procedure that only requires Monte-Carlo sampling to obtain bootstrapped copies of the estimated vector of coefficients. Using this bootstrap sample, we obtain the $e$-value for each SNP, and select SNPs having $e$-values below a threshold. We illustrate through numerical studies that our method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. We also use the $e$-values to perform gene-level analysis in nuclear families and detect several SNPs that have been implicated to be associated with alcohol consumption

Detecting Unspecified Structure in Low-Count Images

Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet