Abstract
In the astrophysics domain, the detection and description of gamma rays is a research direction for our understanding of the universe. Gamma-ray reconstruction from Cherenkov telescope data is multi-task by nature. The image recorded in the Cherenkov camera pixels relates to the type, energy, incoming direction and distance of a particle from a telescope observation. We propose \(\gamma \)-PhysNet, a physically inspired multi-task deep neural network for gamma/proton particle classification, and gamma energy and direction reconstruction. As ground truth does not exist for real data, \(\gamma \)-PhysNet is trained and evaluated on large-scale Monte Carlo simulations. Robustness is then crucial for the transfer of the performance to real data. Relying on a visual explanation method, we evaluate the influence of attention on the variability due to weight initialization, and how it helps improve the robustness of the model. All the experiments are conducted in the context of single telescope analysis for the Cherenkov Telescope Array simulated data analysis.
We gratefully acknowledge financial support from the agencies and organizations listed here: www.cta-observatory.org/consortium_acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 653477, and from the Fondation Université Savoie Mont Blanc. This work has been done thanks to the facilities offered by the Univ. Savoie Mont Blanc - CNRS/IN2P3 MUST computing center and HPC resources from GENCI-IDRIS (Grant 2020-AD011011577) and 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.
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Ambrosi, G., Awane, Y., Baba, H., et al.: For the CTA Consortium: the Cherenkov telescope array large size telescope. In: Proceedings of the 33rd International Cosmic Ray Conference, pp. 8–11 (2013). https://doi.org/10.1117/12.2054605
Bernlöhr, K., et al.: Monte Carlo design studies for the Cherenkov telescope array. Astropart. Phys. 43, 171–188 (2013)
Cao, C., Liu, X., Yang, Y., et al.: Look and think twice: capturing top-down visual attention with feedback convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2956–2964 (2015)
Cao, J., Li, Y., Zhang, Z.: Partially shared multi-task convolutional neural network with local constraint for face attribute learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4290–4299 (2018)
Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 794–803. PMLR (2018)
Guo, M., Haque, A., Huang, D.A., Yeung, S., Fei-Fei, L.: Dynamic task prioritization for multitask learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 270–287 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hillas, A.: Cerenkov light images of EAS produced by primary gamma. In: International Cosmic Ray Conference, vol. 3 (1985)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Jacquemont, M., et al.: Indexed operations for non-rectangular lattices applied to convolutional neural networks. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 5, no. VISAPP, pp. 362–371. INSTICC, SciTePress (2019). https://doi.org/10.5220/0007364303620371
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482–7491 (2018)
Kim, B., Brill, A., Miener, T., Nieto, D., Feng, Q.: DL1-Data-Handler: DL1 HDF5 writer, reader, and processor for IACT data, v0.8.1-legacy (2019). https://doi.org/10.5281/zenodo.3336561
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)
Luvizon, D.C., Picard, D., Tabia, H.: 2D/3D pose estimation and action recognition using multitask deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Mangano, S., Delgado, C., Bernardos, M.I., Lallena, M., Rodríguez Vázquez, J.J.: Extracting gamma-ray information from images with convolutional neural network methods on simulated Cherenkov telescope array data. In: Pancioni, L., Schwenker, F., Trentin, E. (eds.) ANNPR 2018. LNCS (LNAI), vol. 11081, pp. 243–254. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99978-4_19
Morcos, A.S., Barrett, D.G.T., Rabinowitz, N.C., Botvinick, M.: On the importance of single directions for generalization. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April–3 May 2018, Conference Track Proceedings. OpenReview.net (2018)
Nieto Castaño, D., Brill, A., Kim, B., Humensky, T.B., Consortium, C.: Exploring deep learning as an event classification method for the Cherenkov Telescope Array. In: 35th International Cosmic Ray Conference. ICRC, vol. 301, p. 809 (2017)
Olah, C., Mordvintsev, A., Schubert, L.: Feature visualization. Distill 2(11), e7 (2017)
Parsons, R.D., Ohm, S.: Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks. Eur. Phys. J. C 80(5), 1–11 (2020). https://doi.org/10.1140/epjc/s10052-020-7953-3
Ren, Z., Jae Lee, Y.: Cross-domain self-supervised multi-task feature learning using synthetic imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–771 (2018)
Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Advances in Neural Information Processing Systems (2018)
Shilon, I., et al.: Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data. Astropart. Phys. 105, 44–53 (2019)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Workshop Track Proceedings (2015)
Srinivas, S., Fleuret, F.: Full-gradient representation for neural network visualization. In: Advances in Neural Information Processing Systems, pp. 4126–4135 (2019)
Sun, J., Darbeha, F., Zaidi, M., Wang, B.: Saunet: Shape attentive u-net for interpretable medical image segmentation. arXiv preprint arXiv:2001.07645 (2020)
Thrun, S.: Is learning the n-th thing any easier than learning the first? In: Advances in Neural Information Processing Systems, pp. 640–646 (1996)
Völk, H.J., Bernlöhr, K.: Imaging very high energy gamma-ray telescopes. Exp. Astron. 25(13), 173–191 (2009)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Zhou, B., Sun, Y., Bau, D., Torralba, A.: Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 (2018)
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Jacquemont, M., Vuillaume, T., Benoit, A., Maurin, G., Lambert, P. (2021). Deep Learning for Astrophysics, Understanding the Impact of Attention on Variability Induced by Parameter Initialization. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_13
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