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arXiv 1808.08992hep-phhep-ex
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用深度自动编码器寻找新物理

Searching for New Physics with Deep Autoencoders

Marco Farina, Yuichiro Nakai, David Shih

摘要:

我们介绍了一种潜在的强大的新方法,使用自动编码器和无监督深度学习在LHC上搜索新物理。自动编码器的关键思想是,它学会将“正常”事件映射回自身,但无法重建以前从未遇到过的“异常”事件。然后,重建误差可以用作异常阈值。我们以QCD喷流为背景,以顶喷流和RPV胶子喷流为信号,证明了这一观点的有效性。我们表明,当仅在背景上训练时,或者甚至直接在包含少量信号混合物的数据上训练时,深度自动编码器可以显着改善背景上的信号。最后,我们检查了自动编码器与喷射质量的相关性,并展示了喷射质量分布如何在重建损失减少的情况下保持稳定。这对于从数据中估计QCD背景可能是重要的。作为一个测试案例,我们展示了如何可以合理地发现400 GeV的RPV胶子使用自动编码器结合在射流质量的碰撞狩猎。这开辟了直接在实际数据上进行训练的可能性,以发现新的物理学,而没有事先的期望或理论偏见。

英文摘要:

We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that it learns to map "normal" events back to themselves, but fails to reconstruct "anomalous" events that it has never encountered before. The reconstruction error can then be used as an anomaly threshold. We demonstrate the effectiveness of this idea using QCD jets as background and boosted top jets and RPV gluino jets as signal. We show that a deep autoencoder can significantly improve signal over background when trained on backgrounds only, or even directly on data which contains a small admixture of signal. Finally we examine the correlation of the autoencoders with jet mass and show how the jet mass distribution can be stable against cuts in reconstruction loss. This may be important for estimating QCD backgrounds from data. As a test case we show how one could plausibly discover 400 GeV RPV gluinos using an autoencoder combined with a bump hunt in jet mass. This opens up the exciting possibility of training directly on actual data to discover new physics with no prior expectations or theory prejudice.

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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