arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.06226astro-ph.IMcs.LG
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通过深度神经网络对CMB时序数据进行分类

Classifying CMB time-ordered data through deep neural networks

Pontificia Universidad Católica de Chile(智利天主教大学);Millennium Institute of Astrophysics(千年天体物理研究所);Université Paris-Saclay(巴黎-萨克雷大学);CNRS(法国国家科学研究中心)

Felipe Rojas, Loïc Maurin, Rolando Dünner, Karim Pichara

摘要:

我们在很宽的多极子范围内测量了宇宙微波背景。像阿塔卡马宇宙学望远镜(ACT)这样的弧分分辨率的实验有助于测量初级和次级各向异性,导致了显着的科学发现。这样的发现需要仔细的数据选择,以去除表现不佳的检测器和不需要的污染物。ACT目前使用的数据分类方法依赖于由专家评估和微调的若干统计参数。这种方法非常耗时,并且具有波段或季节特异性,这使得它对于未来的CMB实验来说可扩展性和效率较低。在这项工作中,我们提出了一个有监督的机器学习模型来分类检测器的CMB实验。该模型对应于深度卷积神经网络。我们测试了我们的方法在真正的ACT数据,使用2008年的季节,148 GHz,作为训练集与标签提供的ACT数据选择软件。该模型学习直接从原始数据开始对时间流进行分类。对于训练过程中考虑的季节和频率,我们发现我们的分类器达到了99.8%的精度。对于220和280 GHz的数据,2008年的季节,我们分别获得了99.4%和97.5%的精度。最后,我们对2009年和2010年的148 GHz数据进行了跨季节测试,我们的模型分别达到了99.8%和99.5%的精度。我们的模型比当前的流水线快10倍左右,这使得它可能适合实时实现。

英文摘要:

The Cosmic Microwave Background (CMB) has been measured over a wide range of multipoles. Experiments with arc-minute resolution like the Atacama Cosmology Telescope (ACT) have contributed to the measurement of primary and secondary anisotropies, leading to remarkable scientific discoveries. Such findings require careful data selection in order to remove poorly-behaved detectors and unwanted contaminants. The current data classification methodology used by ACT relies on several statistical parameters that are assessed and fine-tuned by an expert. This method is highly time-consuming and band or season-specific, which makes it less scalable and efficient for future CMB experiments. In this work, we propose a supervised machine learning model to classify detectors of CMB experiments. The model corresponds to a deep convolutional neural network. We tested our method on real ACT data, using the 2008 season, 148 GHz, as training set with labels provided by the ACT data selection software. The model learns to classify time-streams starting directly from the raw data. For the season and frequency considered during the training, we find that our classifier reaches a precision of 99.8%. For 220 and 280 GHz data, season 2008, we obtained 99.4% and 97.5% of precision, respectively. Finally, we performed a cross-season test over 148 GHz data from 2009 and 2010 for which our model reaches a precision of 99.8% and 99.5%, respectively. Our model is about 10x faster than the current pipeline, making it potentially suitable for real-time implementations.

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。

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