5 papers
Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier
S. A. K. Leeney, W. J. Handley, H. T. J. Bevins +1
Engineered robust losses such as Huber, Student-, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupt…
Calibrating Bayesian Tension Statistics using Neural Ratio Estimation
Harry T. J. Bevins, William J. Handley, Thomas Gessey-Jones
When fits of the same physical model to two different datasets disagree, we call this tension. Several apparent tensions in cosmology have occupied researchers in recent years, and…
PolySwyft: sequential simulation-based nested sampling
Kilian H. Scheutwinkel, Will Handley, Christoph Weniger +1
We present PolySwyft, a novel, non-amortised simulation-based inference framework that unites the strengths of nested sampling (NS) and neural ratio estimation (NRE) to tackle chal…
Rapid and Late Cosmic Reionization Driven by Massive Galaxies: a Joint Analysis of Constraints from 21-cm, Lyman Line & CMB Data Sets
Peter H. Sims, Harry T. J. Bevins, Anastasia Fialkov +6
Observations of the Epoch of Reionization (EoR) have the potential to answer long-standing questions of astrophysical interest regarding the nature of the first luminous sources an…
Accounting for Noise and Singularities in Bayesian Calibration Methods for Global 21-cm Cosmology Experiments
Christian J. Kirkham, William J. Handley, Jiacong Zhu +6
Due to the large dynamic ranges involved with separating the cosmological 21-cm signal from the Cosmic Dawn from galactic foregrounds, a well-calibrated instrument is essential to…