Deep learning for the detection of γ-ray sources: Bridging the gap between
training simulations and real telescope observations using unsupervised domain adaptation
GammaLearn update status
Author
Under the supervision of
Michaël Dell'aiera (LAPP, LISTIC)
Thomas Vuillaume (LAPP) Alexandre Benoit (LISTIC)
LST meeting (Prague) - 21th May 2024
dellaiera.michael@gmail.com
Contextualisation
GammaLearn
Fosters innovative methods in AI for CTA
Evaluate the added value of deep learning
Fig. The detection workflow
Sensitivity of γ-PhysNet to NSB
Fig. Impact of NSB. Poisson rate difference δλ=λ_train-λ_test
Simulations and real data discrepencies
Simulations are approximations of the reality
Fig. Poisson rate λ(t) as a function of time t. Variation of NSB in Crab runs.
Poisson approximation
Independent pixels
Fig. Stars in the FoV, dysfunctioning pixels
Some pixels are correlated
Data adaptation (or data tuning)
Modify the simulations to fit the acquisitions
Fig. NSB distributions (Simulations, Crab, Markarian, and data adaptation)
Results with data adaptation on Crab (real data)
γ-PhysNet-CBN (Conditional Batch Norm)
Modify the model to make it robust to noise
Fig. γ-PhysNet-CBN architecture
Fig. BN module
Fig. CBN module
Setup
Fig. NSB (Simulations, Crab, Markarian, and data adaptation)
Results with multi-modality on simulations
Fig. Poisson rate difference δλ=λ_train-λ_test, with λ_train~Uniform(0,1)
Results with multi-modality on Crab (real data)
Multi-modality
Fig. γ-PhysNet-CBN architecture
Domain adaptation
Domain adaptation : Set of algorithms and techniques to reduce domain discrepancies
Take into account unknown differences between the source (labelled, simulations) and target (unlabelled, real data) domains
Somehow include unlabelled real data in the training (→ label shift)
Selection, implementation and validation of DANN (focus of this talk), DeepJDOT , DeepCORAL
Fig. Domain confusion in the feature space
Comparative study and validation
Traditional deep learning dataset (digits)
Most of the difficulty resides in the optimization of the model (conflicting gradients)
Automatic determination of the loss coefficients
Gradient weighting strategy
Best performance using DANN paired with Uncertainy Weighting
→ Paper #1 : Method-oriented with results on MC (will be send soon for your review)
Fig. Multi-task balancing
γ-PhysNet-DANN (Domain Adversarial Neural Network)
Modify the model to make it domain agnostic
Fig. γ-PhysNet-DANN architecture
Application of γ-PhysNet-DANN to simulations
Fig. Results on simulations (NSB, w/o label shift)(Published )
Conditional domain adaptation
Fig. Domain adaptation
Fig. Conditional domain adaptation
Setup
Fig. Conditional domain adaptation
Results with domain adaptation on simulations
Fig. γ-PhysNet-DANN vs γ-PhysNet-CDANN
Results with domain adaptation on Crab (real data)
Conclusion and perspectives
Conclusion & Perspectives
Comparison of Data adaptation, Multi-modality, Domain adaptation to solve simulations vs real data discreprency
Tested on simulations
Tested on real data (Crab), both moonlight and no moonlight conditions
Standard analysis and γ-PhysNet strongly affected by moonlight
Data adaptation and multi-modality increase the performance in degraded conditions
The benefits of domain adaptation are not well established yet
Tuning accurately the MC to data NSB is still the best performing approach
Domain adaptation can match the best performance on tuned simulations (but more variability)
γ-PhysNet-CBN with pedestal image conditioning
Two articles to be published:
Paper #1: Method-oriented with results on MC (will be send soon for your review)
Paper #2: Crab analysis
My PhD ends September 30th
Acknowledgments
This project is supported by the facilities offered by the Univ. Savoie Mont Blanc - CNRS/IN2P3 MUST computing center
This project was granted access to the HPC resources of IDRIS under the allocation 2020-AD011011577 made by GENCI
This project is supported by the computing and data processing ressources from the CNRS/IN2P3 Computing Center (Lyon - France)
We gratefully acknowledge the support of the NVIDIA Corporation with the donation of one NVIDIA P6000 GPU for this research.
We gratefully acknowledge financial support from the agencies and organizations listed here .
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 653477
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824064
Resume presentation
Deep learning for the detection of γ-ray sources: Bridging the gap between
training simulations and real telescope observations using unsupervised domain adaptation GammaLearn update status Author Under the supervision of Michaël Dell'aiera (LAPP, LISTIC) Thomas Vuillaume (LAPP) Alexandre Benoit (LISTIC) LST meeting (Prague) - 21th May 2024 dellaiera.michael@gmail.com