arXiv subjects

Daniel B. Reisenfeld

Publications and source records attributed to Daniel B. Reisenfeld.

6 recordsLinked to original sources

arXiv2026

Capra: Scalable HEALPix-Native Intensity Reconstruction for High-Resolution IMAP Analyses

We present Capra, a HEALPix-native pipeline for reconstructing all-sky energetic neutral atom (ENA) intensity maps from the NASA Interstellar Boundary Explorer (IBEX) and Interstellar Mapping and Acceleration (IMAP) missions' event-counting data. Capra produces two products: (1) a baseline boresight-assigned map, and (2) an optional smoothed map on the HEALPix grid. This produces smooth, physically interpretable maps with consistent resolution changes and reference-frame transformations. The smoothed Capra maps recover the large-scale ENA morphology while reducing sampling-driven mottling and strip-like artifacts. Relative comparison with the Theseus reconstruction shows broadly consistent large-scale structure, with the discrepancies reflecting differences in reconstruction methods, rather than major morphological discrepancies. We verify invariance (preservation) of count numbers, rates, and intensities across tessellations under coordinate transformations, and demonstrate numerical convergence up to HEALPix resolution Nside = 64 for a representative dataset. We show that thread-based parallelism performance exhibits approximately linear strong scaling, with bounded memory usage, enabling efficient high-resolution reconstruction and making Capra well-suited for high-resolution IMAP reconstruction.

astro-ph.IM↗

arXiv2026

Statistical methods for partitioning ribbon and globally-distributed flux using data from the Interstellar Boundary Explorer

NASA's Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that can provide insight into the heliosphere boundary between our solar system and interstellar space. Using these data, scientists can construct maps of the ENA intensities (often, expressed in terms of flux) observed in all directions. The ENA flux observed in these maps is believed to come from at least two distinct sources: one source which manifests as a ribbon of concentrated ENA flux and one source (or possibly several) that results in a smoothly-varying globally-distributed flux. Each ENA source type and its corresponding ENA intensity map is of separate scientific interest. In this paper, we develop statistical algorithms for separating the total ENA intensity maps into two source-specific maps (ribbon and globally-distributed flux) and estimating corresponding uncertainty. Key advantages of the proposed method include enhanced model flexibility and improved propagation of estimation uncertainty. We evaluate the proposed methods on simulated data designed to mimic realistic data settings. We also propose new methods for estimating the center of the near-elliptical ribbon in the sky, which can be used in the future to study the location and variation of the local interstellar magnetic field.

stat.AP↗

arXiv2025

Bayesian Statistical Inversion for High-Dimensional Computer Model Output and Spatially Distributed Counts

Data collected by the Interstellar Boundary Explorer (IBEX) satellite, recording heliospheric energetic neutral atoms (ENAs), exhibit a phenomenon that has caused space scientists to revise hypotheses about the physical processes, and computer simulations under those models, in play at the boundary of our solar system. Evaluating the fit of these computer models involves tuning their parameters to observational data from IBEX. This would be a classic (Bayesian) inverse problem if not for three challenges: (1) the computer simulations are slow, limiting the size of campaigns of runs; so (2) surrogate modeling is essential, but outputs are high-resolution images, thwarting conventional methods; and (3) IBEX observations are counts, whereas most inverse problem techniques assume Gaussian field data. To fill that gap we propose a novel approach to Bayesian inverse problems coupling a Poisson response with a sparse Gaussian process surrogate using the Vecchia approximation. We demonstrate the capabilities of our proposed framework, which compare favorably to alternatives, through multiple simulated examples in terms of recovering "true" computer model parameters and accurate out-of-sample prediction. We then apply this new technology to IBEX satellite data and associated computer models developed at Los Alamos National Laboratory.

stat.AP↗

arXiv2024

Moving Towards Automated Interstellar Boundary Explorer Data Selection with LOTUS

The Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that provide insight into the heliosphere, the region surrounding our solar system and separating it from interstellar space. IBEX collects information on these particles and on extraneous ``background'' particles. While IBEX records how and when the different particles are observed, it does not distinguish between heliospheric ENA particles and incidental background particles. To address this issue, all IBEX data has historically been manually labeled as ``good'' ENA data, or ``bad'' background data. This manual culling process is incredibly time-intensive and contingent on subjective, manually-induced decision thresholds. In this paper, we develop a three-stage automated culling process, called LOTUS, that uses random forests to expedite and standardize the labelling process. In Stage 1, LOTUS uses random forests to obtain probabilities of observing true ENA particles on a per-observation basis. In Stage 2, LOTUS aggregates these probabilities to obtain predictions within small windows of time. In Stage 3, LOTUS refines these predictions. We compare the labels generated by LOTUS to those manually generated by the subject matter expert. We use various metrics to demonstrate that LOTUS is a useful automated process for supplementing and standardizing the manual culling process.

stat.AP↗

arXiv2023

Empirical Validation of a New Data Product from the Interstellar Boundary Explorer Satellite

Since 2008, the Interstellar Boundary Explorer (IBEX) satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data was in fact susceptible to contamination, having relatively low ENA counts and high background rates. Recently, the mission team considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to share the same signal rate as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics at an orbit, map, and full mission level provide additional confirmation of signal rate coherence across data products. These approaches are generalizable to other scenarios in which one wishes to test whether multiple observations could plausibly be generated by some underlying shared signal.

astro-ph.IM↗

arXiv2022

Towards Improved Heliosphere Sky Map Estimation with Theseus

The Interstellar Boundary Explorer (IBEX) satellite has been in orbit since 2008 and detects energy-resolved energetic neutral atoms (ENAs) originating from the heliosphere. Different regions of the heliosphere generate ENAs at different rates. It is of scientific interest to take the data collected by IBEX and estimate spatial maps of heliospheric ENA rates (referred to as sky maps) at higher resolutions than before. These sky maps will subsequently be used to discern between competing theories of heliosphere properties that are not currently possible. The data IBEX collects present challenges to sky map estimation. The two primary challenges are noisy and irregularly spaced data collection and the IBEX instrumentation's point spread function. In essence, the data collected by IBEX are both noisy and biased for the underlying sky map of inferential interest. In this paper, we present a two-stage sky map estimation procedure called Theseus. In Stage 1, Theseus estimates a blurred sky map from the noisy and irregularly spaced data using an ensemble approach that leverages projection pursuit regression and generalized additive models. In Stage 2, Theseus deblurs the sky map by deconvolving the PSF with the blurred map using regularization. Unblurred sky map uncertainties are computed via bootstrapping. We compare Theseus to a method closely related to the one operationally used today by the IBEX Science Operation Center (ISOC) on both simulated and real data. Theseus outperforms ISOC in nearly every considered metric on simulated data, indicating that Theseus is an improvement over the current state of the art.

astro-ph.IM↗