Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks.

We propose and demonstrate the use of a model-assisted generative adversarial network (GAN) to produce fake images that accurately match true images through the variation of the parameters of the model that describes the features of the images. The generator learns the model parameter values that produce fake images that best match the true images. Two case studies show excellent agreement between the generated best match parameters and the true parameters. The best match model parameter values can be used to retune the default simulation to minimize any bias when applying image recognition techniques to fake and true images. In the case of a real-world experiment, the true images are experimental data with unknown true model parameter values, and the fake images are produced by a simulation that takes the model parameters as input. The model-assisted GAN uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast fake-image production

3rd IML Machine Learning Workshop

We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. The best match parameter values that produce the most accurate simulated images can be extracted and used to re-tune the default simulation to minimise any bias when applying image recognition techniques to simulated and true images. In the case of a real-world experiment, the true data is replaced by experimental data with unknown true parameter values. The Model-Assisted Generative Adversarial Network uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast image production

Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers

For current and future neutrino oscillation experiments using large liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D submanifold sparse convolutional network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the short baseline neutrino program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying domain adversarial neural networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adversarial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.ISSN:1550-7998ISSN:0556-2821ISSN:1550-236

Fast inference using FPGAs for DUNE data reconstruction

The Deep Underground Neutrino Experiment (DUNE) will be a world-class neutrino observatory and nucleon decay detector aiming to address some of the most fundamental questions in particle physics. With a modular liquid argon time-projection chamber (LArTPC) of 40 kt fiducial mass, the DUNE far detector will be able to reconstruct neutrino interactions with an unprecedented resolution. With no triggering and no zero suppression or compression, the raw data volume for four modules would be of order 145 EB/year. Consequently, fast and affordable reconstruction methods are needed. Several state-of-the-art methods are focused on machine learning (ML) approaches to identify the signal within the raw data or to classify the neutrino interaction during the reconstruction. One of the main advantages of using those techniques is that they will reduce the computational cost and time compared to classical strategies. Our plan aims to go a bit further and test the implementation of those techniques on an accelerator board. In this work, we present the accelerator board used, a commercial off-the-shelf (COTS) hardware for fast deep learning inference based on an FPGA, and the experimental results obtained outperforming more traditional processing units