标题： 机器学习 delta-T 噪声用于温度偏差估计 显示英文标题

标题： Machine learning delta-T noise for temperature bias estimation

摘要： 在温度偏置的电子结中，Delta-T 散粒噪声被激活，甚至达到原子尺度。 其特点是与温度差呈二次依赖关系，并且与部分开启的传导通道的透射系数呈非线性关系。 在本工作中，我们证明，通过测量一组原子尺度结的 Delta-T 噪声，可以用来估算这些系统中的温度偏置。 我们的方法采用监督式机器学习算法，以归一化的电导、Delta-T 噪声和平均温度作为输入特征来训练神经网络。 由于实验数据有限，我们生成了合成数据集，旨在模拟实验。 该神经网络在这些合成数据上进行训练后，随后被用于从实验数据集中预测温度偏置。 使用性能指标，我们证明了对于电导高达 4$G_0$的结，平均偏差——即预测温度差与其真实值的偏差——小于 1 K。 我们的研究强调，尽管单次 Delta-T 噪声测量不足以准确估算施加的温度偏置（因为其他来源的噪声会带来干扰），但对一组结进行平均可以实现处于实验不确定度范围内的预测。 这表明，机器学习方法可用于温度偏置的估算，同样也可用于电子结中其他刺激的估算。 显示英文摘要

摘要： Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It is characterized by a quadratic dependence on the temperature difference and a nonlinear relationship with the transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble of atomic-scale junctions, can be utilized to estimate the temperature bias in these systems. Our approach employs a supervised machine learning algorithm to train a neural network with input features being the scaled electrical conductance, the delta-T noise, and the mean temperature. Due to limited experimental data, we generate synthetic datasets, designed to mimic experiments. The neural network, trained on these synthetic data, was subsequently applied to predict temperature biases from experimental datasets. Using performance metrics, we demonstrate that the mean bias -- the deviation of predicted temperature differences from their true value -- is less than 1 K for junctions with conductance up to 4$G_0$. Our study highlights that, while a single delta-T noise measurement is insufficient for accurately estimating the applied temperature bias due to noise contributions from other sources, averaging over an ensemble of junctions enables predictions within experimental uncertainties. This demonstrates that machine learning approaches can be utilized for estimation of temperature biases, and similarly other stimuli in electronic junctions.