使用新型电极背景技术提高对Lux中低质量暗物质的敏感性

论文标题

使用新型电极背景技术提高对Lux中低质量暗物质的敏感性

Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique

论文作者

LUX Collaboration, Akerib, D. S., Alsum, S., Araújo, H. M., Bai, X., Balajthy, J., Bang, J., Baxter, A., Bernard, E. P., Bernstein, A., Biesiadzinski, T. P., Boulton, E. M., Boxer, B., Brás, P., Burdin, S., Byram, D., Carmona-Benitez, M. C., Chan, C., Cutter, J. E., de Viveiros, L., Druszkiewicz, E., Fan, A., Fiorucci, S., Gaitskell, R. J., Ghag, C., Gilchriese, M. G. D., Gwilliam, C., Hall, C. R., Haselschwardt, S. J., Hertel, S. A., Hogan, D. P., Horn, M., Huang, D. Q., Ignarra, C. M., Jacobsen, R. G., Jahangir, O., Ji, W., Kamdin, K., Kazkaz, K., Khaitan, D., Korolkova, E. V., Kravitz, S., Kudryavtsev, V. A., Leason, E., Lenardo, B. G., Lesko, K. T., Liao, J., Lin, J., Lindote, A., Lopes, M. I., Manalaysay, A., Mannino, R. L., Marangou, N., McKinsey, D. N., Mei, D. -M., Morad, J. A., Murphy, A. St. J., Naylor, A., Nehrkorn, C., Nelson, H. N., Neves, F., Nilima, A., Oliver-Mallory, K. C., Palladino, K. J., Rhyne, C., Riffard, Q., Rischbieter, G. R. C., Rossiter, P., Shaw, S., Shutt, T. A., Silva, C., Solmaz, M., Solovov, V. N., Sorensen, P., Sumner, T. J., Swanson, N., Szydagis, M., Taylor, D. J., Taylor, R., Taylor, W. C., Tennyson, B. P., Terman, P. A., Tiedt, D. R., To, W. H., Tvrznikova, L., Utku, U., Vacheret, A., Vaitkus, A., Velan, V., Webb, R. C., White, J. T., Whitis, T. J., Witherell, M. S., Wolfs, F. L. H., Woodward, D., Xiang, X., Xu, J., Zhang, C.

论文摘要

本文介绍了一种新型技术，用于减轻电极背景，该技术使用Xenon时间投影室限制了搜索低质量暗物质（DM）的敏感性。在Lux检测器中，低质量DM相互作用的特征将是非常低的能量（$ \ sim $ keV）散布在活动目标中，仅会使几个Xenon原子电离，很少会产生可检测到的闪烁信号。在此制度中，需要额外的预防措施才能拒绝一组在这类检测器中长期观察到的复杂的低能电子背景。注意到活动目标顶部和底部附近的电网电极的背景特别有害，我们基于电离脉冲形状开发机器学习技术，以识别和拒绝这些事件。我们证明，该技术可以将低质量DM交互的泊松限制提高到$ 2 $ -7 $的$ 2 $ - $ 7 $，而改进的量值很大，具体取决于电离信号的大小。我们使用该技术在有效的5美元$吨$ \ CDOT $ DAY中使用该技术从Lux的2013年科学运营中曝光，以对质量的低质量DM颗粒限制，质量为$M_χ\ in0.15 $ - $ 10 $ - $ 10 $ GEV。预计这种机器学习技术对于近距离实验（例如LZ和Xenonnt）有用，该实验希望通过进行发现的严格背景控制来执行低质量DM搜索。

This paper presents a novel technique for mitigating electrode backgrounds that limit the sensitivity of searches for low-mass dark matter (DM) using xenon time projection chambers. In the LUX detector, signatures of low-mass DM interactions would be very low energy ($\sim$keV) scatters in the active target that ionize only a few xenon atoms and seldom produce detectable scintillation signals. In this regime, extra precaution is required to reject a complex set of low-energy electron backgrounds that have long been observed in this class of detector. Noticing backgrounds from the wire grid electrodes near the top and bottom of the active target are particularly pernicious, we develop a machine learning technique based on ionization pulse shape to identify and reject these events. We demonstrate the technique can improve Poisson limits on low-mass DM interactions by a factor of $2$-$7$ with improvement depending heavily on the size of ionization signals. We use the technique on events in an effective $5$ tonne$\cdot$day exposure from LUX's 2013 science operation to place strong limits on low-mass DM particles with masses in the range $m_χ\in0.15$-$10$ GeV. This machine learning technique is expected to be useful for near-future experiments, such as LZ and XENONnT, which hope to perform low-mass DM searches with the stringent background control necessary to make a discovery.

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