Speaker
Description
In particle physics, the search for phenomena outside the well-established predictions of the Standard Model (SM) is of great importance. For more than four decades, the SM has been the established theory of fundamental particles and their interactions. However, some aspects of nature remain elusive to the explanatory power of the SM. Thus, researchers' attention turns to the pursuit of new processes that can shed light on missing pieces of the model, potentially unveiling entirely new fundamental particles [1].
Within the context of the CERN Large Hadron Collider (LHC), most efforts to unveil new physics are directed toward specific experimental signatures. This strategy has proven exceptionally effective when hunting for preconceived, theoretically motivated particles. However, in cases where a predefined target is absent, the strength of this approach can also become its limitation. To overcome this potential hurdle, researchers engage in model-independent searches, and machine learning (ML) has emerged as the favored path for these explorations [2].
In the vast landscape of ML, Variational Autoencoders (VAEs) have emerged as a powerful tool for detecting anomalies across diverse domains. Their ability to capture the underlying data distribution and reconstruct input samples makes VAEs adept at identifying anomalies or outliers. Nonetheless, the conventional Gaussian distributions that underpin traditional VAEs may not be well-suited for the intricate nature of High Energy Physics (HEP) data. To address this challenge, we propose alternative VAE implementations, including [3]:
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Multi-mode Non-Gaussian VAE (MNVAE):
This approach, previously used on complex electromechanical equipment, enhances the encoder's architecture to generate a latent variable governed by a Gaussian mixture model (GMM). The GMM is a linear combination of multiple Gaussian distributions, and it can characterize arbitrarily complex distributions if the number of Gaussian components is large enough. Subsequently, the Householder flow (HF) is employed to endow the latent variable with the full covariance matrix. [3]. -
Monte Carlo-based Approach:
In this method, the latent vector can assume a non-Gaussian distribution, offering a broader range of choices for the posterior distribution while ensuring a tighter Evidence Lower Bound (ELBO). This can result in VAEs capable of capturing finer details within the data distribution, thereby enhancing their generative capabilities and data reconstruction prowess [4].
Our aim is to thoroughly examine the underlying data distributions and subsequently introduce suitable modifications to the VAE framework. Creating a latent space that closely mirrors the data's shape holds the potential to enhance the VAE's ability to capture semantic content, making it better suited for anomaly detection purposes [5]. Later, hls4ml may be employed to synthesize VHDL code, enabling the implementation of the network on an FPGA.
References
[1] @misc{golling2023massive, title={The Mass-ive Issue: Anomaly Detection in Jet Physics}, author={Tobias Golling and Takuya Nobe and Dimitrios Proios and John Andrew Raine and Debajyoti Sengupta and Slava Voloshynovskiy and Jean-Francois Arguin and Julien Leissner Martin and Jacinthe Pilette and Debottam Bakshi Gupta and Amir Farbin}, year={2023}, eprint={2303.14134}, archivePrefix={arXiv}, primaryClass={hep-ph}}
[2] @ARTICLE{10.3389/fdata.2022.803685, AUTHOR={Jawahar, Pratik and Aarrestad, Thea and Chernyavskaya, Nadezda and Pierini, Maurizio and Wozniak, Kinga A. and Ngadiuba, Jennifer and Duarte, Javier and Tsan, Steven}, TITLE={Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows}, JOURNAL={Frontiers in Big Data}, VOLUME={5}, YEAR={2022}, URL={https://www.frontiersin.org/articles/10.3389/fdata.2022.803685}, DOI={10.3389/fdata.2022.803685}, ISSN={2624-909X}}
[3] @article{LUO2023144, title = {Multi-mode non-Gaussian variational autoencoder network with missing sources for anomaly detection of complex electromechanical equipment}, journal = {ISA Transactions}, volume = {134}, pages = {144-158}, year = {2023}, issn = {0019-0578}, doi = {https://doi.org/10.1016/j.isatra.2022.09.009}, url = {https://www.sciencedirect.com/science/article/pii/S0019057822004669}, author = {Qinyuan Luo and Jinglong Chen and Yanyang Zi and Yuanhong Chang and Yong Feng}, keywords = {Electromechanical equipment, Fault diagnosis, Anomaly detection, Neural network, Variational autoencoder}}
[4] @misc{thin2021monte, title={Monte Carlo Variational Auto-Encoders}, author={Achille Thin and Nikita Kotelevskii and Arnaud Doucet and Alain Durmus and Eric Moulines and Maxim Panov}, year={2021}, eprint={2106.15921}, archivePrefix={arXiv}, primaryClass={stat.ML}}
[5] @Article{app12083839, AUTHOR = {Ciușdel, Costin Florian and Itu, Lucian Mihai and Cimen, Serkan and Wels, Michael and Schwemmer, Chris and Fortner, Philipp and Seitz, Sebastian and Andre, Florian and Buß, Sebastian Johannes and Sharma, Puneet and Rapaka, Saikiran}, TITLE = {Normalizing Flows for Out-of-Distribution Detection: Application to Coronary Artery Segmentation}, JOURNAL = {Applied Sciences}, VOLUME = {12}, YEAR = {2022}, NUMBER = {8}, ARTICLE-NUMBER = {3839}, URL = {https://www.mdpi.com/2076-3417/12/8/3839}, ISSN = {2076-3417}, DOI = {10.3390/app12083839}}
