arXiv subjects

Stefan van der Walt

Publications and source records attributed to Stefan van der Walt.

3 recordsLinked to original sources

arXiv2026

First Pre-peak Ultraviolet Spectrum of a Tidal Disruption Event: A Fast Outflow Revealed Prior to Maximum Light in TDE2025aarm

The tidal disruption and eventual accretion of a star by a massive black hole can lead to the launching of outflows and winds that encode important information about the tidal disruption and accretion processes. However, little is known about the existence and behavior of these outflows before optical light-curve peak, when processes such as stream-stream collisions and disk formation may be important. Here, we present the first pre-peak ultraviolet (UV) spectrum of a tidal disruption event (TDE), obtained $\sim 20$ days before optical maximum light of the nearby (redshift $z=0.0137$) TDE2025aarm, along with quasi-simultaneous infrared (IR) through X-ray observations. Our HST/STIS spectrum shows strong evidence for an early-time outflow through broad near-UV (NUV) and far-UV (FUV) absorption lines blueshifted by $\sim$10,000 km s$^{-1}$, including two new UV broad absorption features not yet identified in a TDE. We find that the FUV--IR continuum deviates significantly from a blackbody ($f_λ\propto λ^{-3.08}$), which we interpret as a signature of reprocessing through the outflow. This deviation implies that the bolometric luminosity in optical TDEs may be underestimated by a significant factor ($\sim$9 in this case) when inferred from single-temperature blackbody fits to NUV--optical photometry alone. This work further confirms that TDEs are capable of launching fast outflows at very early times and emphasizes the importance of prompt FUV spectroscopic observations of TDEs that can capture the full continuum emission and energetics.

astro-ph.HE↗

arXiv2015

Evaluating the accuracy of diffusion MRI models in white matter

Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking.

q-bio.QM↗

arXiv2014

scikit-image: Image processing in Python

scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image.

cs.MS↗