IN2P3/IRFU Machine Learning workshop 2026

Session

Signal Processing, Calibration and Reconstruction

16 nov. 2026, 14:10

Amphitheatre (LPSC)

Description

AI methods for detector-level signal processing and detector response modeling.

Examples include:
- Signal reconstruction and denoising
- Waveform analysis and deconvolution
- Detector calibration and alignment
- Differentiable calibration
- Detector response correction

184. Multi-Parameter Calibration and Uncertainty Propagation in LArTPCs Using Differentiable Simulation

Pierre Granger (CERN)

16/11/2026 14:10

Signal Processing, Calibration and Reconstruction

Standard talks

Precision measurements in Liquid Argon Time Projection Chambers (LArTPCs) rely on accurate modeling of detector response. In standard calibration workflows, effects such as space charge, recombination, electron lifetime, and electronics response are usually measured independently. This segmented approach ignores correlations among parameters and can introduce systematic biases into the...

198. Deep Learning for Extracting Spectra form JWST Spectrograms

Soorya Narayan Rajeshkumar (Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France)

16/11/2026 14:30

Signal Processing, Calibration and Reconstruction

Standard talks

With telescopes like the Euclid in orbit and CSST in the works, we are looking at an emerging era of astronomy with surveys producing millions and millions of slitless spectrograms. While the analytical extraction techniques are sufficient for the current amount of data, the community will require faster pipelines for future data releases. Added to the large amount of expected data, slitless...

196. A plug-and-play approach for tomographic weak-lensing mass mapping

Hubert Leterme (CEA IRFU/DAp)

16/11/2026 14:50

Signal Processing, Calibration and Reconstruction

Standard talks

Upcoming surveys such as Euclid and Rubin will probe the matter distribution through weak lensing with unprecedented precision. However, reconstructing mass maps from noisy shear measurements is fundamentally an ill-posed inverse problem. While current deep-learning approaches achieve high reconstruction accuracy, they either must require retraining for each new sky region (due to varying mask...