Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics 3rd ed.

Session

Flash talks #2

21 mai 2025, 10:15

Buffon Amphitheater (Université Paris Cité)

2. Deep K-Correct: Estimating K-Corrections and Absolute Magnitudes from Galaxy Images

M. Jeremias Rodriguez

21/05/2025 10:15

Uncertainties

Poster + lightning talk

The estimation of K-corrections and absolute magnitudes are essential in extragalactic astronomy for comparing galaxy properties across different redshifts. The state-of-the-art methods rely on deterministic template-fitting techniques: Blanton’s KCorrect, which estimates K-corrections and absolute magnitudes from photometry and redshift, and FastSpecFit, which refines these estimates by...

3. Bayesian neural network for active learning and applications to gravitational-wave source population inference

Theo Courty (Université Paris Dauphine)

22/05/2025 10:00

Time Domain Astrophysics

Poster + lightning talk

LIGO and Virgo have detected about a hundred of merging compact binaries in their first three science runs. This number will grow by a factor of 2 to 3 with the current run O4. This will allow a detailed analysis of the source population and possibly identify their origin and formation channel(s). The complex sequence leading to the formation of a compact binary from an isolated binary of...

5. Representation Learning for Anomaly Detection and Unsupervised Classification of Variable X-ray Sources

Steven Dillmann (Stanford University)

22/05/2025 10:05

Time Domain Astrophysics

Poster + lightning talk

We present a novel representation learning method for downstream tasks like anomaly detection, unsupervised classification, and similarity searches in high-energy data sets. This enabled the discovery of a new extragalactic fast X-ray transient (FXT) in Chandra archival data, XRT 200515, a needle-in-the-haystack event and the first Chandra FXT of its kind. Recent serendipitous discoveries in...

1. Approximate Bayesian Computation via Surrogate Posteriors

Florence Forbes (Inria)

Poster + lightning talk

A key ingredient in approximate Bayesian computation (ABC) procedures is the choice of a discrepancy that describes how different the simulated and observed data are, often based on a set of summary statistics when the data cannot be compared directly. Unless discrepancies and summaries are available from experts or prior knowledge, which seldom occurs, they have to be chosen, and thus their...