Search for 21-cm absorption systems with CHIME

Event Time: Thursday, July 23, 2026 | 1:00 pm - 2:00 pm
Event Location:
HENN 309
Add to Calendar 2026-07-23T13:00:00 2026-07-23T14:00:00 Search for 21-cm absorption systems with CHIME Event Information: Abstract: The 21-cm hyperfine transition of neutral hydrogen is a powerful probe of cold gas across cosmic time. When a radio source lies behind a cloud of neutral hydrogen, the cold gas imprints a characteristic absorption feature in the spectrum of the background source at the redshifted 21-cm frequency. Unlike optical tracers, this feature is detectable independently of the distance to the absorber. Despite this advantage, the known sample of extragalactic 21-cm absorbers at redshifts z > 1 has remained small: only 42 systems were known prior to this work, reflecting the limitations of targeted surveys and the scarcity of suitable radio facilities at the relevant frequencies. In this thesis, I present the CHIME Absorber Project: a blind survey for 21-cm absorption conducted with CHIME, a transit radio telescope operating at 400-800 MHz, corresponding to the redshift range 0.78 < z < 2.55. I describe the design and implementation of an offline processing pipeline that processes raw high-spectral-resolution data from the CHIME Absorber backend, corrects for instrumental systematics, and applies a search algorithm to the resulting spectra. Candidate absorption features identified by the algorithm then undergo visual inspection for confirmation. This pipeline represents the primary technical contribution of the thesis.I then present a pilot spectrally blind survey using four months of CHIME Absorber data, which yielded a new 21-cm absorber at z = 2.327, only the fifth detection of an absorber associated with a quasar at z > 2. The survey also recovered two previously known intervening absorbers, validating the pipeline.Finally, I describe a follow-up search that applied the pipeline to absorption candidates identified in CHIME intensity mapping data, confirming three new absorbers, all at z > 1. Together, these four new absorbers represent a roughly 10 per cent increase in the known sample at z > 1, obtained from a small fraction of the available data. This demonstrates the potential of CHIME to substantially expand the census of high-redshift 21-cm absorbers through a fully blind survey.    Event Location: HENN 309

Probing Brain Tissue Microstructure with Magnetic Resonance Imaging through Simulation and Bayesian Learning of Signal Dynamics

Event Time: Tuesday, July 28, 2026 | 1:00 am - 1:00 pm
Event Location:
DMCBH 3502 (Djavad Mowafaghian Centre for Brain Health - 2215 Wesbrook Mall)
Add to Calendar 2026-07-28T01:00:00 2026-07-28T13:00:00 Probing Brain Tissue Microstructure with Magnetic Resonance Imaging through Simulation and Bayesian Learning of Signal Dynamics Event Information: Abstract: Magnetic resonance imaging (MRI) resolves brain structures at the millimetre scale, up to three orders of magnitude coarser than the blood vessels, perivascular spaces, and myelinated axons of white matter. This microstructure cannot, therefore, be imaged directly; in this thesis, I combine physics-based simulation with statistical and deep-learning inference to develop methods for inferring these structures' properties from their imprint on the magnetic resonance signal. First, I developed an operator-splitting solver for the Bloch-Torrey equation that is one to two orders of magnitude faster than direct methods, enabling simulation of spin-echo and gradient-echo dynamic susceptibility contrast in three-dimensional white-matter voxels containing blood vessels and perivascular spaces. These simulations suggested that roughly half of the white-matter blood volume resides in anisotropic vessels aligned with the fibre tracts, and that perivascular diffusion enhances the signal's orientation dependence. Next, I built a finite-element framework for simulating multi-spin-echo signals from myelinated axons. These simulated signals were used to train a conditional variational autoencoder to estimate axon-scale tissue parameters, targeting myelin water fraction, g-ratio, and relaxation times. Turning to whole-brain analysis, I created DECAES, an open-source regularized nonnegative least squares toolbox that reduces myelin water mapping time from hours to minutes to seconds. Because MRI magnitude data is Rician-distributed and quantized, I developed fast, numerically stable routines for the (quantized-)Rician log-likelihood and its derivatives, improving signal modelling at low signal-to-noise ratio and enabling differentiation through likelihood-based inference. Finally, these tools enabled Bayesian inference of bi- and multi-exponential Rician signal models. For the former, I developed a semi-supervised approach that embeds a Metropolis-Hastings step to incorporate real MRI signals into training alongside simulated signals, improving real-data inference performance. For the latter, I trained a conditional normalizing flow over the probability simplex to precondition scalable Markov chain Monte Carlo sampling. On synthetic and in vivo data, this framework reduced myelin water fraction error by roughly 30-40% relative to DECAES, produced well-calibrated credible intervals, improved sampling efficiency by one to three orders of magnitude, and enabled further applications such as Bayesian flip-angle estimation and inter-voxel spatial regularization. Event Location: DMCBH 3502 (Djavad Mowafaghian Centre for Brain Health - 2215 Wesbrook Mall)