arXiv subjects

Charlotte Haley

Publications and source records attributed to Charlotte Haley.

6 recordsLinked to original sources

arXiv2025

Red noise-based false alarm thresholds for astrophysical periodograms via Whittle's approximation to the likelihood

Astronomers who search for periodic signals using Lomb-Scargle periodograms rely on false alarm level (FAL) estimates to identify statistically significant peaks. Although FALs are often calculated from white noise models, many astronomical time series suffer from red noise. Prewhitening is a statistical technique in which a continuum model is subtracted from log power spectrum estimate, after which the observer can proceed with a white-noise treatment. Here we present a prewhitening-based method of calculating frequency-dependent FALs. We fit power laws and autoregressive models of order 1 to each Lomb-Scargle periodogram by minimizing the Whittle approximation to the negative log-likelihood (NLL), then calculate FALs based on the best-fit model power spectrum. Our technique is a novel extension of the Whittle NLL to datasets with uneven time sampling. We demonstrate FAL calculations using observations of $\alpha$~Cen~B, GJ~581, HD 192310, synthetic data from the radial velocity (RV) Fitting Challenge, and {\it Kepler} observations of a differential rotator. The {\it Kepler} data analysis shows that only true rotation signals are detected by red-noise FALs, while white-noise FALs suggest all spurious peaks in the low-frequency range are significant. A high-frequency sinusoid injected into $\alpha$~Cen~B $\log R^{\prime}_{HK}$ observations exceeds the 1\% red-noise FAL despite having only 8.9\% of the power of the dominant rotation signal. In a periodogram of HD 192310 RVs, peaks associated with differential rotation and planets are detected against the 5\% red-noise FAL without iterative model fitting or subtraction. Software for calculating red noise-based FALs is available on GitHub.

astro-ph.IM↗

arXiv2025

Multitaper magnitude-squared coherence for time series with missing data: Understanding oscillatory processes traced by multiple observables

To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic datasets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman $\alpha$ flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.

astro-ph.SR↗

arXiv2022

Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator

While the Lomb-Scargle periodogram is foundational to astronomy, it has a significant shortcoming: the variance in the estimated power spectrum does not decrease as more data are acquired. Statisticians have a 60-year history of developing variance-suppressing power spectrum estimators, but most are not used in astronomy because they are formulated for time series with uniform observing cadence and without seasonal or daily gaps. Here we demonstrate how to apply the missing-data multitaper power spectrum estimator to spacecraft data with uniform time intervals between observations but missing data during thruster fires or momentum dumps. The F-test for harmonic components may be applied to multitaper power spectrum estimates to identify statistically significant oscillations that would not rise above a white noise-based false alarm probability. Multitapering improves the dynamic range of the power spectrum estimate and suppresses spectral window artifacts. We show that the multitaper - F-test combination applied to Kepler observations of KIC 6102338 detects differential rotation without requiring iterative sinusoid fitting and subtraction. Significant signals reside at harmonics of both fundamental rotation frequencies and suggest an antisolar rotation profile. Next we use the missing-data multitaper power spectrum estimator to identify the oscillation modes responsible for the complex "scallop shell" shape of the K2 light curve of EPIC 203354381. We argue that multitaper power spectrum estimators should be used for all time series with regular observing cadence.

astro-ph.IM↗

arXiv2022

Order-Disorder Transitions in (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$

The classification of structural phase transitions as displacive or order-disorder in character is usually based on spectroscopic data above the transition. We use single crystal x-ray diffraction to investigate structural correlations in the quasi-skutterudites, (Ca$_x$Sr$_{1-x}$)$_3$Rh$_4$Sn$_{13}$, which have a quantum phase transition at $x\sim0.9$. Three-dimensional pair distribution functions show that the amplitudes of local atomic displacements are temperature-independent below the transition and persist to well above the transition, a signature of order-disorder behavior. The implications for the associated electronic transitions are discussed.

cond-mat.str-el↗

arXiv2022

Magnitude-squared coherence: A powerful tool for disentangling Doppler planet discoveries from stellar activity

If Doppler searches for earth-mass, habitable planets are to succeed, observers must be able to identify and model out stellar activity signals. Here we demonstrate how to diagnose activity signals by calculating the magnitude-squared coherence $\hat{C}^2_{xy}(f)$ between an activity indicator time series $x_t$ and the radial velocity (RV) time series $y_t$. Since planets only cause modulation in RV, not in activity indicators, a high value of $\hat{C}^2_{xy}(f)$ indicates that the signal at frequency $f$ has a stellar origin. We use Welch's method to measure coherence between activity indicators and RVs in archival observations of GJ 581, alpha Cen B, and GJ 3998. High RV-H$α$ coherence at the frequency of GJ 3998 b, and high RV-S index coherence at the frequency of GJ 3998 c, indicate that the planets may actually be stellar signals. We also replicate previous results showing that GJ 581 d and g are rotation harmonics and demonstrate that alpha Cen B has activity signals that are not associated with rotation. Welch's power spectrum estimates have cleaner spectral windows than Lomb-Scargle periodograms, improving our ability to estimate rotation periods. We find that the rotation period of GJ 581 is 132 days, with no evidence of differential rotation. Welch's method may yield unacceptably large bias for datasets with $N < 75$ observations and works best on datasets with $N > 100$. Tapering the time-domain data can reduce the bias of the Welch's power spectrum estimator, but observers should not apply tapers to datasets with extremely uneven observing cadence. A software package for calculating magnitude-squared coherence and Welch's power spectrum estimates is available on github.

astro-ph.EP↗

arXiv2022

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme precision radial velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The EXPRES Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed RV correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV RMS than classic linear decorrelation, but no method is yet consistently reducing the RV RMS to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets -- such as solar data or data with known injected planetary and/or stellar signals -- to better understand method performance and whether planetary signals are preserved.

astro-ph.EP↗