arXiv subjects

Wendy F. Wallace

Publications and source records attributed to Wendy F. Wallace.

4 recordsLinked to original sources

arXiv2026

The swept-back multipolar magnetic field of neutron stars: Application to NICER MSP J0030+0451

NICER observations of millisecond pulsars (MSPs) suggest that non-dipolar magnetic fields are required to explain their surface X-ray hotspots. C. Kalapotharakos et al. modeled the NICER light curve of MSP J$0030+0451$ (J$0030$) using a static vacuum offset dipole-plus-quadrupole field and corresponding force-free (FF) solutions to jointly reproduce the X-ray and Fermi-LAT $γ$-ray emission. We substitute their static vacuum field model with a more realistic swept-back configuration that accounts for rotational effects. This field more closely resembles the corresponding FF solutions, making it a more physically motivated choice for future multiwavelength modeling. We adopt a centered swept-back vacuum multipolar magnetic field (SVM2F; J. Pétri), expressed as a complete expansion in vector spherical harmonics, enabling flexible descriptions of arbitrary magnetic field geometries. We introduce a metric to quantify the complexity among different field prescriptions, illustrated for the static offset vacuum field. To efficiently explore parameter space, we train a neural network surrogate (G. Olmschenk et al.) on SVM2F light curves including components up to the octupole, accelerating Markov chain Monte Carlo sampling by $\sim 10^3$ compared to direct physical model evaluations. Applying this framework to J$0030$, we constrain the field parameter space and find that a centered swept-back multipolar field including terms up to the octupole adequately reproduces the bolometric thermal X-ray light curve. Our study highlights the importance and inherent complexity of prescribing different multipolar magnetic field models for rotating stars, and can be extended to other MSPs to ultimately constrain the masses and radii of neutron stars, and hence their equation of state.

astro-ph.HE↗

arXiv2026

Multipolar Magnetic-Field Inference for PSR J0740+6620 with Neural-Network-Accelerated NICER Pulse-Profile Modeling

We investigate the multipolar surface magnetic-field structure of the high-mass millisecond pulsar PSR J0740+6620 using the 32-bin bolometric NICER pulse profile of Dittmann et al. (2024). Building on the neural-network surrogate framework of Olmschenk et al. (2025), we model the emitting regions as open-field-line footpoints of an offset dipole plus axisymmetric quadrupole static vacuum field, rather than as prescribed geometric hotspots. We fix the stellar mass, radius, observer inclination, and hotspot temperature ratio to the Dittmann et al. (2024) maximum-likelihood values and explore the resulting 11-dimensional magnetic-field space. To make this feasible, we train convolutional neural-network surrogates on $5.12\times10^7$ synthetic bolometric light curves and use them in a parallel ensemble Markov Chain Monte Carlo calculation on 4000 CPU cores, accelerating likelihood evaluations by a factor of $\gtrsim 400$. We perform independent inferences for two calibrated temperature-weight prescriptions, Tw=1.31 and Tw=1.41, encoding the relative bolometric weight associated with the hotspot temperature difference. The posteriors, posterior-predictive light curves, and maximum-likelihood values are very similar, indicating weak sensitivity to this choice. The offset model reproduces the observed double-peaked profile and yields broad, multimodal posteriors, reflecting both the background-dominated data and degeneracies of the multipolar parameterization. The hotspot-density map shows that pulse phases constrain the approximate azimuthal placement of the emission, while latitude, surface extent, and morphology remain weakly constrained. A restricted zero offset run is disfavored within the adopted field basis. This work extends neural-network-accelerated magnetic-field inference to PSR J0740+6620 and motivates future energy-dependent, force-free, and joint X-ray/$γ$-ray extensions.

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arXiv2025

A detailed dive into fitting strategies for GRB afterglows with contamination: A case study with kilonovae

Observations of gamma-ray burst afterglows have begun to readily reveal contamination from a kilonova or a supernova. This contamination presents significant challenges towards traditional methods of inferring the properties of these phenomena from observations. Given current knowledge of kilonova and afterglow modelling, observations (as expected) with near-infrared bands and at early observing times provide the greatest diagnostic power for both observing the presence of a kilonova and inferences on its properties in gamma-ray burst afterglows. However, contemporaneous observations in radio and X-ray are critical for reducing the afterglow parameter space and for more efficient parameter estimation. We compare different methods for fitting joint kilonova and afterglow observations under different scenarios. We find that ignoring the contribution of one source (even in scenarios where the source is sub-dominant) can lead to significantly biased estimated parameters but could still produce great light curve fits that do not raise suspicion. This bias is also present for analyses that fit data where one source is "subtracted". In most scenarios, the bias is smaller than the systematic uncertainty inherent to kilonova models but significant for afterglow parameters, particularly in the absence of high-quality radio and X-ray observations. Instead, we show that the most reliable method for inference in any scenario where contamination can not be confidently dismissed is to jointly fit for both an afterglow and kilonova/supernova, and showcase a Bayesian framework to make this joint analysis computationally feasible.

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arXiv2024

Redback: A Bayesian inference software package for electromagnetic transients

Fulfilling the rich promise of rapid advances in time-domain astronomy is only possible through confronting our observations with physical models and extracting the parameters that best describe what we see. Here, we introduce {\sc Redback}; a Bayesian inference software package for electromagnetic transients. {\sc Redback} provides an object-orientated {\sc python} interface to over 12 different samplers and over 100 different models for kilonovae, supernovae, gamma-ray burst afterglows, tidal disruption events, engine-driven transients among other explosive transients. The models range in complexity from simple analytical and semi-analytical models to surrogates built upon numerical simulations accelerated via machine learning. {\sc Redback} also provides a simple interface for downloading and processing data from various catalogs such as \textit{Swift} and Fink. The software can also serve as an engine to simulate transients for telescopes such as the Zwicky Transient Facility and Vera Rubin with realistic cadences, limiting magnitudes, and sky-coverage or a hypothetical user-constructed survey or a generic transient for target-of-opportunity observations with different telescopes. As a demonstration of its capabilities, we show how {\sc Redback} can be used to jointly fit the spectrum and photometry of a kilonova, enabling a more powerful, holistic probe into the properties of a transient. We also showcase general examples of how {\sc Redback} can be used as a tool to simulate transients for realistic surveys, fit models to real, simulated, or private data, multi-messenger inference with gravitational waves, and serve as an end-to-end software toolkit for parameter estimation and interpreting the nature of electromagnetic transients.

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