Using Dark Energy Explorers and Machine Learning to Enhance the Hobby–Eberly Telescope Dark Energy Experiment
Using Dark Energy Explorers and Machine Learning to Enhance the Hobby–Eberly Telescope Dark Energy Experiment
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DOI：
10.3847/1538-4357/accdd0
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发表时间：
2023-04
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通讯作者：
Lindsay R. House;K. Gebhardt;K. Finkelstein;E. Cooper;D. Davis;R. Ciardullo;D. Farrow;S. Finkelstein;C. Gronwall;D. Jeong;L. Johnson;Chenxu Liu;B. Thomas;G. Zeimann
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作者：
Lindsay R. House;K. Gebhardt;K. Finkelstein;E. Cooper;D. Davis;R. Ciardullo;D. Farrow;S. Finkelstein;C. Gronwall;D. Jeong;L. Johnson;Chenxu Liu;B. Thomas;G. Zeimann
我们目前的分析使用公民科学运动，以改善宇宙学的措施，从霍比-埃伯利望远镜暗能量实验（HETDEX）。HETDEX的目标是测量哈勃膨胀率H（z）和角直径距离DA（z），在z = 2.4时，每个都达到1/2级的精度。这种准确性主要取决于探测到的Lyα发射体（LAE）的总数，由于噪声的假阳性率，以及由于[O ii]发射星系的污染。本文介绍了公民科学项目，暗能量探索者（https：//www.zooniverse.org/projects/erinmc/dark-energy-explorers），其目标是增加LAE的数量，减少由于噪声和[O ii]星系造成的误报数量。初步分析表明，公民科学是一种高效有效的工具，可以最准确地通过人眼进行分类，特别是与无监督机器学习相结合。公民科学运动中影响最大的三个方面是：（1）通过检测识别个体问题，（2）提供一个干净的样本，其100%的视觉识别高于信噪比，以及（3）为机器学习工作提供标签。自2022年底以来，暗能量探索者已在全球85个不同国家的11，000名志愿者中收集了超过350万个分类。通过引入暗能量探测器的结果，我们期望在z = 2.″4处将DA（z）和H（z）参数的精度提高10%-30%。虽然主要目标是改进HETDEX，但暗能量探索者已经被证明是科学进步和增加全球科学可及性的独特强大工具。
We present analysis using a citizen science campaign to improve the cosmological measures from the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the Hubble expansion rate, H(z), and angular diameter distance, D A(z), at z = 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyα emitters (LAEs), the false positive rate due to noise, and the contamination due to [O ii] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers (https://www.zooniverse.org/projects/erinmc/dark-energy-explorers), with the goal of increasing the number of LAEs and decreasing the number of false positives due to noise and the [O ii] galaxies. Initial analysis shows that citizen science is an efficient and effective tool for classification most accurately done by the human eye, especially in combination with unsupervised machine learning. Three aspects from the citizen science campaign that have the most impact are (1) identifying individual problems with detections, (2) providing a clean sample with 100% visual identification above a signal-to-noise cut, and (3) providing labels for machine-learning efforts. Since the end of 2022, Dark Energy Explorers has collected over three and a half million classifications by 11,000 volunteers in over 85 different countries around the world. By incorporating the results of the Dark Energy Explorers, we expect to improve the accuracy on the D A(z) and H(z) parameters at z = 2.″4 by 10%–30%. While the primary goal is to improve on HETDEX, Dark Energy Explorers has already proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.