利用机器学习技术探索轻子领域的BSM景观
Rolling Down the Leptonic BSM Landscape Using Machine Learning Techniques
AI总结：
本文通过机器学习中的初始化和优化技术，最小化描述中微子质量矩阵纹理或条件的损失函数，以探索轻子领域中的标准模型外物理，并得到近似符合所需形式的矩阵及其优化参数。
摘要：
在这项工作中，我们调整并应用机器学习技术来探索轻子领域中标准模型之外的物理。即，我们采用机器学习中应用的初始化和优化方法，最小化描述中微子质量矩阵中所需纹理或条件的损失函数。在优化过程中探索模型自由参数，经过若干优化步骤的训练后，我们获得近似遵循所需形式的矩阵及其相应的优化参数。我们还讨论了本文提出的思想与其他基于人工智能的方法相结合的扩展和额外应用。
英文摘要：
In this work, we adapt and apply techniques from machine learning to the exploration of physics beyond the Standard Model in the leptonic sector. Namely, we employ initialization and optimization, as they are applied in machine learning, to minimize a loss function that describes textures or conditions which we want in the neutrino mass matrix. The model free parameters are explored during the optimization, and after training for a number of optimization steps, we obtain matrices that approximately follow the desired forms, as well as their corresponding optimized parameters. We also discuss extensions and additional applications of the ideas presented here in conjunction with other methods based on artificial intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
