arXiv subjects

Jeffrey D. Scargle

Publications and source records attributed to Jeffrey D. Scargle.

At least 19 recordsLinked to original sources

arXiv2026

Studies in Astronomical Time Series Analysis: The Double Lomb-Scargle Periodogram and Super Resolution

Multiple-frequency periodograms -- based on time series models consisting of two or more independent sinusoids -- have long been discussed. What is new here is the presentation of a practical, simple-to-use computational framework implementing this concept. Our algorithms have super resolution that evades the Rayleigh criterion, as well as provision for statistical weighting and tapering. They can be used for essentially any time series (e.g. time-tagged events or point measurements) with arbitrary sampling -- even or uneven. Examples of super resolution of synthetic data, sunspot numbers, and the rich pulsations of white dwarf J0135+5722, demonstrate practical applications. Appendices derive generalized periodograms using an arbitrary number of arbitrary basis functions (following Bretthorst, 1988)and define several examples of non-sinusoidal bases for these ``omnigrams.'' Application beyond the frequency domain is demonstrated with an autoregressive model exhibiting super resolution in the time domain. A GitHub repository containing omnigram code, and symbolic algebra scripts for generating it, will soon be available.

astro-ph.IM↗

arXiv2025

High-energy variability of the gravitationally lensed blazar PKS 1830-211

The production site and process responsible for the highly variable high-energy emission observed from blazar jets are still debated. Gravitational lenses can be used as microscopes to investigate the nature of such sources. We study the broad-band spectral properties and the high-energy variability of the gravitationally-lensed blazar PKS 1830-211, for which radio observations have revealed two images, to put constraints on the jet physics and the existence of a gravitationally-induced time delay and magnification ratio between the images. We utilize Swift/XRT, Nustar, and Fermi-LAT observations from 2016 and 2019 to compare periods of low activity and high activity in PKS 1830-211. Short-timescale variability is elucidated with an unbinned power spectrum analysis of time-tagged NuSTAR photon data. To study the gravitationally-induced time delay in the gamma-ray light curve observed with Fermi-LAT, we improve existing autocorrelation function based methods. Our modified auto-correlation method yields a delay of t_0=21.1 +/- 0.1 d and magnification factor a=0.13 +/- 0.01. These parameters remain time-invariant. In data from 2016 and 2019, the X-ray spectra remain remarkably stable, contrasting with extreme changes in gamma-rays. Both states can be fitted with a single component from Comptonisation of infrared emission from the dusty torus, with different gamma-ray states arising solely from a shift in the break of the electron energy distribution. The detection of a consistent lag throughout the whole light curve suggests that they originate from a persistent location in the jet.

astro-ph.HE↗

arXiv2024

Detection of the Permanent Strain Offset Component of Gravitational-Wave Memory in Black Hole Mergers

We propose a novel approach to detecting the elusive gravitational-wave memory predicted by general relativity to accompany black hole mergers: direct measurement of the permanent space-time strain offset. Compared to previous techniques modeling and disentangling both the "chirp" and memory signals, this approach has several advantages: it targets the feature of the signal carrying nearly all its Shannon information, has great simplicity, circumvents the need for precise modeling of the time evolution of all components of the gravitational wave signal, and uses only data largely free of the more complicated chirp signal. The frequency spectrum of the predicted memory signal is roughly similar to that of the chirp signal. However its inclusion of lower frequencies, where noise and data calibration are problematic, makes detection difficult but not impossible. We applied this novel analysis, implemented with a template-like algorithm, to a selection of 67 observations of 41 black hole mergers in the LIGO/Virgo Gravitational Wave Transient Catalog. Statistical significance was assessed by analyzing many time-shifted intervals. The result: a few possible detections ($2σ-4σ$) and many upper limits. The probability that a random ensemble of 67 strain time series, with the same noise but no memory signals, will yield a particular figure-of-merit computed for the actual data is approximately 0.1. Several validation checks proved useless, partly due to large measurement and theoretical uncertainties, so these results should be viewed with reservation. Appendices contain MatLab code for various operations, including an algorithm for the complex Fourier transform of arbitrarily spaced data.

gr-qc↗

arXiv2022

Large-scale Volcanism and the Heat Death of Terrestrial Worlds

Large-scale volcanism has played a critical role in the long-term habitability of Earth. Contrary to widely held belief, volcanism, rather than impactors, has had the greatest influence on and bears most of the responsibility for large-scale mass extinction events throughout Earth's history. We examine the timing of large igneous provinces (LIPs) throughout Earth's history to estimate the likelihood of nearly simultaneous events that could drive a planet into an extreme moist or runaway greenhouse, leading to the end of volatile cycling and causing the heat death of formerly temperate terrestrial worlds. In one approach, we make a conservative estimate of the rate at which sets of near-simultaneous LIPs (pairs, triplets, and quartets) occur in a random history statistically the same as Earth's. We find that LIPs closer in time than 0.1-1 million yr are likely; significantly, this is less than the time over which terrestrial LIP environmental effects are known to persist. In another approach, we assess the cumulative effects with simulated time series consisting of randomly occurring LIP events with realistic time profiles. Both approaches support the conjecture that environmental impacts of LIPs, while narrowly avoiding grave effects on the climate history of Earth, could have been responsible for the heat death of our sister world Venus.

astro-ph.EP↗

arXiv2021

Statistical properties of flux variations in blazar light curves at GeV and TeV energies

Despite numerous detections of individual flares, the physical origin of the rapid variability observed from blazars remains uncertain. Using Bayesian blocks and the Eisenstein-Hut HOP algorithm, we characterize flux variations of high significance in the $γ$-ray light curves of two samples of blazars. Daily binned long-term light curves of TeV-bright blazars observed with the First G-APD Cherenkov Telescope (FACT) are compared to those of GeV-bright blazars observed with the Large Area Telescope on board the $Fermi$ Gamma-ray Space Telescope ($Fermi$-LAT). We find no evidence for systematic asymmetry of the flux variations based on the derived rise and decay time scales. Additionally, we show that the daily-binned blazar light curves can be described by an exponential stochastic Ornstein-Uhlenbeck (OU) process with parameters depending on energy. Our analysis suggests that the flux variability in both samples is a superposition of faster fluctuations. This is, for instance, challenging to explain by shock-acceleration but expected for magnetic reconnection.

astro-ph.HE↗

arXiv2020

Ornstein-Uhlenbeck parameter extraction from light curves of Fermi-LAT observed blazars

Context. Monthly-binned gamma-ray light curves of 236 bright gamma-ray sources, particularly blazars, selected from a sample of 2278 high-galactic latitude objects observed with Fermi-LAT, show flux variability characterized by power spectral densities consisting of a single power-law component, ranging from Brownian to white noise. Aims. The main goal here is to assess the Ornstein-Uhlenbeck (OU) model by studying the range of its three parameters that reproduces these statistical properties. Methods. We develop procedures for extracting values of the three OU model parameters (mean flux, correlation length, and random amplitude) from time series data, and apply them to compare numerical integrations of the OU process with the Fermi-LAT data. Results. The OU process fully describes the statistical properties of the flux variations of the 236 blazars. The distributions of the extracted OU parameters are narrowly peaked about well-defined values (sigma, mu, theta) = (0.2, -8.4, 0.5) with variances (0.004, 0.07, 0.13). The distributions of rise and decay time scales of flares in the numerical simulations, i.e. major flux variations fulfilling pre-defined criteria, are in agreement with the observed ones. The power spectral densities of the synthetic light curves are statistically indistinguishable from those of the measured light curves. Conclusions. Long-term gamma-ray flux variability of blazars on monthly time scales is well described by a stochastic model involving only three parameters. The methods described here are powerful tools to study randomness in light curves and thereby constrain the physical mechanisms responsible for the observed flux variations.

astro-ph.HE↗

arXiv2020

Studies in Astronomical Time Series Analysis: VII. An Enquiry Concerning Non-Linearity, the RMS-Mean Flux Relation, and log-Normal Flux Distributions

A broad and widely used class of stationary, linear, additive time series models can have statistical properties which many authors have asserted imply that the underlying process must be non-linear, non-stationary, multiplicative, or inconsistent with shot noise. This result is demonstrated with exact and numerical evaluation of the model flux distribution function and dependence of flux standard deviation on mean flux (here and in the literature called the \emph{rms-flux relation}). These models can: (1) exhibit normal, log-normal or other flux distributions; (2) show linear or slightly non-linear rms-mean flux dependencies; as well as (3) match arbitrary second order statistics of the time series data. Accordingly the above assertions cannot be made on the basis of statistical time series analysis alone. Also discussed are ambiguities in the meaning of terms relevant to this study -- \emph{linear}, \emph{stationary} and \emph{multiplicative} -- and functions that can transform observed fluxes to a normal distribution as well or better than the logarithm.

astro-ph.IM↗

arXiv2020

Characterizing the Gamma-Ray Variability of the Brightest Flat Spectrum Radio Quasars Observed with the Fermi LAT

Almost 10 yr of $γ$-ray observations with the Fermi Large Area Telescope (LAT) have revealed extreme $γ$-ray outbursts from flat spectrum radio quasars (FSRQs), temporarily making these objects the brightest $γ$-ray emitters in the sky. Yet, the location and mechanisms of the $γ$-ray emission remain elusive. We characterize long-term $γ$-ray variability and the brightest $γ$-ray flares of six FSRQs. Consecutively zooming in on the brightest flares, which we identify in an objective way through Bayesian blocks and a hill-climbing algorithm, we find variability on subhour time scales and as short as minutes for two sources in our sample (3C279, CTA102) and weak evidence for variability at time scales less than the Fermi satellite's orbit of 95 minutes for PKS1510-089 and 3C454.3. This suggests extremely compact emission regions in the jet. We do not find any signs for $γ$-ray absorption in the broad-line region (BLR), which indicates that $γ$-rays are produced at distances greater than hundreds of gravitational radii from the central black hole. This is further supported by a cross-correlation analysis between $γ$-ray and radio/millimeter light curves, which is consistent with $γ$-ray production at the same location as the millimeter core for 3C273, CTA102, and 3C454.3. The inferred locations of the $γ$-ray production zones are still consistent with the observed decay times of the brightest flares if the decay is caused by external Compton scattering with BLR photons. However, the minute-scale variability is challenging to explain in such scenarios.

astro-ph.HE↗

arXiv2019

Machine Vision and Deep Learning for Classification of Radio SETI Signals

We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of measured and simulated radio signals bearing the imprint of a technological origin. The studies are performed using archived narrow-band signal data captured from real-time SETI observations with the Allen Telescope Array and a set of digitally simulated signals designed to mimic real observed signals. By treating the 2D spectrogram as an image, we show that high quality parametric and non-parametric classifiers based on automated visual analysis can achieve high levels of discrimination and accuracy, as well as low false-positive rates. The (real) archived data were subjected to numerous feature-extraction algorithms based on the vertical and horizontal image moments and Huff transforms to simulate feature rotation. The most successful algorithm used a two-step process where the image was first filtered with a rotation, scale and shift-invariant affine transform followed by a simple correlation with a previously defined set of labeled prototype examples. The real data often contained multiple signals and signal ghosts, so we performed our non-parametric evaluation using a simpler and more controlled dataset produced by simulation of complex-valued voltage data with properties similar to the observed prototypes. The most successful non-parametric classifier employed a wide residual (convolutional) neural network based on pre-existing classifiers in current use for object detection in ordinary photographs. These results are relevant to a wide variety of research domains that already employ spectrogram analysis from time-domain astronomy to observations of earthquakes to animal vocalization analysis.

astro-ph.IM↗

arXiv2017

Order out of Randomness : Self-Organization Processes in Astrophysics

Self-organization is a property of dissipative nonlinear processes that are governed by an internal driver and a positive feedback mechanism, which creates regular geometric and/or temporal patterns and decreases the entropy, in contrast to random processes. Here we investigate for the first time a comprehensive number of 16 self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous {\sl order out of chaos}, during the evolution from an initially disordered system to an ordered stationary system, via quasi-periodic limit-cycle dynamics, harmonic mechanical resonances, or gyromagnetic resonances. The internal driver can be gravity, rotation, thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational instability, the Rayleigh-Bénard convection instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or loss-cone instability. Physical models of astrophysical self-organization processes involve hydrodynamic, MHD, and N-body formulations of Lotka-Volterra equation systems.

astro-ph.SR↗

arXiv2017

Structure in Galaxy Distribution. III. Fourier Transforming the Universe

We demonstrate the effectiveness of a relatively straightforward analysis of the complex 3D Fourier transform of galaxy coordinates derived from redshift surveys. Numerical demonstrations of this approach are carried out on a volume-limited sample of the Sloan Digital Sky Survey redshift survey. The direct unbinned transform yields a complex 3D data cube quite similar to that from the Fast Fourier Transform (FFT) of finely binned galaxy positions. In both cases deconvolution of the sampling window function yields estimates of the true transform. Simple power spectrum estimates from these transforms are roughly consistent with those using more elaborate methods. However we concentrate on the less often studied Fourier phase spectrum, a simple and general framework for characterizing non-Gaussianity, more easily interpretable than the tangled, incomplete multi-point methods conventionally used. No significant signature of non-Gaussianity has been found in the relatively small data set analyzed, but we identify some threads of modern large scale inference methodology that will presumably yield detections in new wider and deeper surveys.

astro-ph.CO↗

arXiv2015

Correlations of atmospheric water ice and dust in the Martian Polar regions

We report on the interannual variability of the atmospheric ice/dust cycle in the Martian polar regions for Mars Years 28-30. We used CRISM emission phase function measurements to derive atmospheric dust optical depths and data from the MARCI instrument to derive atmospheric water ice optical depths. We have used autocorrelation and cross correlation functions in order to quantify the degree to which dust and ice are correlated throughout both polar regions during Mars Years 28-29. We find that in the south polar region, dust has the tendency to "self clear", demonstrated by negative autocorrelation around the central peak. This does not occur in the north polar region. In the south polar region, dust and ice are temporally and spatially anti correlated. In the north polar region, this relationship is reversed, however temporal correlation of northern dust and ice clouds is weak - 6 times weaker than the anticorrelation in the south polar region. Our latitudinal autocorrelation functions allow us to put average spatial sizes of event cores and halos. Dust events in the south are largest, affecting almost the entire pole, whereas dust storms are smaller in the north. Ice clouds in north are similar in latitudinal extent to those in the south (both have halos < 10°). Using cross-correlation functions of water ice and dust, we find that dust events temporally lag ice events by 35-80 degrees of solar longitude in the north and south poles, which is likely due to seasonality of dust and ice events.

astro-ph.EP↗

arXiv2014

Structure in the 3D Galaxy Distribution: II. Voids and Watersheds of Local Maxima and Minima

The major uncertainties in studies of the multi-scale structure of the Universe arise not from observational errors but from the variety of legitimate definitions and detection methods for individual structures. To facilitate the study of these methodological dependencies we have carried out 12 different analyses defining structures in various ways. This has been done in a purely geometrical way by utilizing the HOP algorithm as a unique parameter-free method of assigning groups of galaxies to local density maxima or minima. From three density estimation techniques (smoothing kernels, Bayesian Blocks and self organizing maps) applied to three data sets (the Sloan Digital Sky Survey Data Release 7, the Millennium Simulation and randomly distributed points) we tabulate information that can be used to construct catalogs of structures connected to local density maxima and minima. The resulting sizes follow continuous multi-scale distributions with no indication of the presence of a discrete hierarchy. We also introduce a novel void finder that utilizes a method to assemble Delaunay tetrahedra into connected structures and characterizes regions very nearly empty of galaxies in the source catalog.

astro-ph.CO↗

arXiv2013

The Bayesian Block Algorithm

This presentation describes the Bayesian Block algorithm in the context of its application to analysis of time series data from the Fermi Gamma Ray Space Telescope. More generally this algorithm performs optimal segmentation analysis on sequential data in any mode, with arbitrary sampling and in the presence of gaps and exposure variations. A new procedure for correcting for backgrounds is also described.

astro-ph.IM↗

arXiv2012

Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations

This paper addresses the problem of detecting and characterizing local variability in time series and other forms of sequential data. The goal is to identify and characterize statistically significant variations, at the same time suppressing the inevitable corrupting observational errors. We present a simple nonparametric modeling technique and an algorithm implementing it - an improved and generalized version of Bayesian Blocks (Scargle 1998) - that finds the optimal segmentation of the data in the observation interval. The structure of the algorithm allows it to be used in either a real-time trigger mode, or a retrospective mode. Maximum likelihood or marginal posterior functions to measure model fitness are presented for events, binned counts, and measurements at arbitrary times with known error distributions. Problems addressed include those connected with data gaps, variable exposure, extension to piecewise linear and piecewise exponential representations, multi-variate time series data, analysis of variance, data on the circle, other data modes, and dispersed data. Simulations provide evidence that the detection efficiency for weak signals is close to a theoretical asymptotic limit derived by (Arias-Castro, Donoho and Huo 2003). In the spirit of Reproducible Research (Donoho et al. 2008) all of the code and data necessary to reproduce all of the figures in this paper are included as auxiliary material.

astro-ph.IM↗

arXiv2011

Heterogeneity in Short Gamma-ray Bursts

We analyze the Swift/BAT sample of short gamma-ray bursts, using an objective Bayesian Block procedure to extract temporal descriptors of the bursts' initial pulse complexes (IPCs). The sample comprises 12 and 41 bursts with and without extended emission (EE) components, respectively. IPCs of non-EE bursts are dominated by single pulse structures, while EE bursts tend to have two or more pulse structures. The medians of characteristic timescales - durations, pulse structure widths, and peak intervals - for EE bursts are factors of ~ 2-3 longer than for non-EE bursts. A trend previously reported by Hakkila and colleagues unifying long and short bursts - the anti-correlation of pulse intensity and width - continues in the two short burst groups, with non-EE bursts extending to more intense, narrower pulses. In addition we find that preceding and succeeding pulse intensities are anti-correlated with pulse interval. We also examine the short burst X-ray afterglows as observed by the Swift/XRT. The median flux of the initial XRT detections for EE bursts (~ 6 x 10^-10 erg cm^-2 s^-1) is ~> 20 x brighter than for non-EE bursts, and the median X-ray afterglow duration for EE bursts (~ 60,000 s) is ~ 30 x longer than for non-EE bursts. The tendency for EE bursts toward longer prompt-emission timescales and higher initial X-ray afterglow fluxes implies larger energy injections powering the afterglows. The longer-lasting X-ray afterglows of EE bursts may suggest that a significant fraction explode into more dense environments than non-EE bursts, or that the sometimes-dominant EE component efficiently powers the afterglow. Combined, these results favor different progenitors for EE and non-EE short bursts.

astro-ph.HE↗

arXiv2010

Structure in the 3D Galaxy Distribution: I. Methods and Example Results

Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using adaptive kernels. The first two methods are new, and allow detection and characterization of structures of arbitrary shape and at a wide range of spatial scales. These methods should elucidate not only clusters, but also the more distributed, wide-ranging filaments and sheets, and further allow the possibility of detecting and characterizing an even broader class of shapes. The methods are demonstrated and compared in application to three data sets: a carefully selected volume-limited sample from the Sloan Digital Sky Survey redshift data, a similarly selected sample from the Millennium Simulation, and a set of points independently drawn from a uniform probability distribution -- a so-called Poisson distribution. We demonstrate a few of the many ways in which these methods elucidate large scale structure in the distribution of galaxies in the nearby Universe.

astro-ph.CO↗

arXiv2010

Cross-Analyzing Radio and $γ$-Ray Time Series Data: Fermi Marries Jansky

A key goal of radio and $γ-$ray observations of active galactic nuclei is to characterize their time variability in order to elucidate physical processes responsible for the radiation. I describe algorithms for relevant time series analysis tools -- correlation functions, Fourier and wavelet amplitude and phase spectra, structure functions, and time-frequency distributions, all for arbitrary data modes and sampling schemes. For example radio measurements can be cross-analyzed with data streams consisting of time-tagged gamma-ray photons. Underlying these methods is the Bayesian block scheme, useful in its own right to characterize local structure in the light curves, and also prepare raw data for input to the other analysis algorithms. One goal of this presentation is to stimulate discussion of these methods during the workshop.

astro-ph.IM↗