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Pushing the Limits of Pulse Shape Discrimination in a Large Liquid Xenon Detector

D. S. Akerib, A. K. Al Musalhi, F. Alder, B. J. Almquist, C. S. Amarasinghe, A. Ames, T. J. Anderson, N. Angelides, H. M. Araújo, J. E. Armstrong, M. Arthurs, A. Baker, S. Balashov, J. Bang, J. W. Bargemann, E. E. Barillier, K. Beattie, A. Bhatti, T. P. Biesiadzinski, H. J. Birch, E. Bishop, G. M. Blockinger, C. A. J. Brew, P. Brás, S. Burdin, M. C. Carmona-Benitez, M. Carter, A. Chawla, H. Chen, Y. T. Chin, N. I. Chott, S. Contreras, M. V. Converse, R. Coronel, A. Cottle, G. Cox, D. Curran, C. E. Dahl, I. Darlington, S. Dave, A. David, J. Delgaudio, S. Dey, L. de Viveiros, L. Di Felice, C. Ding, J. E. Y. Dobson, E. Druszkiewicz, S. Dubey, C. L. Dunbar, S. R. Eriksen, N. M. Fearon, N. Fieldhouse, S. Fiorucci, H. Flaecher, E. D. Fraser, T. M. A. Fruth, P. W. Gaemers, R. J. Gaitskell, A. Geffre, J. Genovesi, C. Ghag, J. Ghamsari, A. Ghosh, S. Ghosh, R. Gibbons, S. Gokhale, J. Green, M. G. D. van der Grinten, J. J. Haiston, C. R. Hall, T. Hall, R. H. Hampp, S. J. Haselschwardt, M. A. Hernandez, S. A. Hertel, G. J. Homenides, M. Horn, D. Q. Huang, D. Hunt, E. Jacquet, R. S. James, K. Jenkins, A. C. Kaboth, A. C. Kamaha, M. K. Kannichankandy, D. Khaitan, A. Khazov, J. Kim, Y. D. Kim, D. Kodroff, E. V. Korolkova, H. Kraus, S. Kravitz, L. Kreczko, V. A. Kudryavtsev, C. Lawes, E. B. Leon, D. S. Leonard, K. T. Lesko, C. Levy, J. Lin, A. Lindote, W. H. Lippincott, J. Long, M. I. Lopes, W. Lorenzon, C. Lu, S. Luitz, W. Ma, V. Mahajan, P. A. Majewski, A. Manalaysay, R. L. Mannino, R. J. Matheson, C. Maupin, M. E. McCarthy, D. N. McKinsey, J. McLaughlin, J. B. McLaughlin, R. McMonigle, B. Mitra, E. Mizrachi, M. E. Monzani, K. Morå, E. Morrison, B. J. Mount, M. Murdy, A. St. J. Murphy, H. N. Nelson, F. Neves, A. Nguyen, C. L. O'Brien, F. H. O'Shea, I. Olcina, K. C. Oliver-Mallory, J. Orpwood, K. Y. Oyulmaz, K. J. Palladino, N. J. Pannifer, S. J. Patton, B. Penning, G. Pereira, E. Perry, T. Pershing, A. Piepke, S. S. Poudel, Y. Qie, J. Reichenbacher, C. A. Rhyne, G. R. C. Rischbieter, E. Ritchey, H. S. Riyat, R. Rosero, N. J. Rowe, T. Rushton, D. Rynders, S. Saltão, D. Santone, A. B. M. R. Sazzad, R. W. Schnee, G. Sehr, B. Shafer, S. Shaw, W. Sherman, K. Shi, T. Shutt, C. Silva, G. Sinev, J. Siniscalco, A. M. Slivar, A. M. Softley-Brown, V. N. Solovov, P. Sorensen, J. Soria, T. J. Sumner, A. Swain, M. Szydagis, D. R. Tiedt, D. R. Tovey, J. Tranter, M. Trask, K. Trengove, M. Tripathi, A. Usón, A. C. Vaitkus, O. Valentino, V. Velan, A. Wang, J. J. Wang, Y. Wang, L. Weeldreyer, T. J. Whitis, K. Wild, M. Williams, J. Winnicki, L. Wolf, F. L. H. Wolfs, S. Woodford, D. Woodward, C. J. Wright, Q. Xia, J. Xu, Y. Xu, M. Yeh, D. Yeum, J. Young, W. Zha, H. Zhang, T. Zhang, Y. Zhou

Abstract

The LUX-ZEPLIN (LZ) experiment is a direct-detection dark matter experiment, optimized to search for weakly interacting massive particles (WIMPs) through WIMP-nucleon interactions. The main challenge in dark matter detection is differentiating between WIMP signals and background events. In LZ, the ratio of ionization to scintillation signals (charge-to-light) is the primary method for rejecting electronic recoil (ER) background. Pulse shape discrimination (PSD) offers a method for additional ER backgrounds rejection in liquid xenon detectors. In this paper, the discrimination power of PSD with the LZ experiment is discussed. To precisely characterize the scintillation pulse shape, an analysis framework is developed to reconstruct the detection time of individual photons. Using LZ calibration data, the photon-timing prompt fraction discriminator is optimized and achieves ER leakage as low as $15\%$. For specific background processes such as $^{124}$Xe double electron capture, the leakage is reduced further to about $5\%$. PSD is combined with charge-to-light to form two-factor discrimination (TFD). The optimized TFD performance is compared with the performance of the charge-to-light method, with the corresponding false positive rate reduced by up to a factor of two for large scintillation pulses. Finally, PSD and TFD are applied to data from LZ's WS2024 run and their performance is summarized.

Pushing the Limits of Pulse Shape Discrimination in a Large Liquid Xenon Detector

Abstract

The LUX-ZEPLIN (LZ) experiment is a direct-detection dark matter experiment, optimized to search for weakly interacting massive particles (WIMPs) through WIMP-nucleon interactions. The main challenge in dark matter detection is differentiating between WIMP signals and background events. In LZ, the ratio of ionization to scintillation signals (charge-to-light) is the primary method for rejecting electronic recoil (ER) background. Pulse shape discrimination (PSD) offers a method for additional ER backgrounds rejection in liquid xenon detectors. In this paper, the discrimination power of PSD with the LZ experiment is discussed. To precisely characterize the scintillation pulse shape, an analysis framework is developed to reconstruct the detection time of individual photons. Using LZ calibration data, the photon-timing prompt fraction discriminator is optimized and achieves ER leakage as low as . For specific background processes such as Xe double electron capture, the leakage is reduced further to about . PSD is combined with charge-to-light to form two-factor discrimination (TFD). The optimized TFD performance is compared with the performance of the charge-to-light method, with the corresponding false positive rate reduced by up to a factor of two for large scintillation pulses. Finally, PSD and TFD are applied to data from LZ's WS2024 run and their performance is summarized.

Paper Structure

This paper contains 24 sections, 14 equations, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Theory
  3. Photon Timing
  4. Relative Timing
  5. $N$-photon Model
  6. Pulse Shape Discrimination
  7. ER/NR Photon Timing Spectra
  8. Prompt Fraction Discriminator
  9. $z$-correction of the Prompt Fraction
  10. NEST Simulation
  11. Photon Timing Model
  12. Photon Emission Model
  13. Photon Transit Model
  14. PSD with NEST
  15. $z$-correction of the Prompt Fraction with NEST
  16. ...and 9 more sections

Figures (14)

  • Figure 1: An example of a waveform (blue, dotted) from a WS2024 event processed with a 2-photon model. The result of the fit is shown by the two photons (green and orange). The sum of the two photon distributions is also shown (red). The arrival time of each photon is represented by the peak time.
  • Figure 2: Normalized photon timing distributions for NR (orange) and ER (blue) events with pulse areas between $70-80$ phd and $z= 100-140$ cm. The mean photon arrival time is used to align individual events, defined as $t=0$. Shaded regions represent the statistical uncertainties in each bin. The observed difference between the ER and NR photon timing distributions is exploited to develop the pulse shape discriminator.
  • Figure 3: Optimization pipeline for the pulse shape discriminator. The optimization begins with a raster scan over the parameter space, followed by refinement using stochastic gradient descent (SGD). The procedure is repeated 100 times with a $75\%/25\%$ split between optimization and verification. Random sampling is performed in each iteration.
  • Figure 4: Normalized prompt fraction distributions for ER (blue) and NR (red) events with S1$c= 70-80$ phd and $z= 100-140$ cm. The dash lines indicate the median values for ER ($0.914 \pm 0.001$) and NR ($0.956\pm 0.001$). Shaded regions represent the statistical uncertainties in each bin. A clear separation between ER and NR populations is observed in prompt fraction space, with an ER leakage of $14.4\pm 1.0 \%$.
  • Figure 5: Receiver operating characteristic (ROC) curves for the pulse shape discriminator, evaluated using calibration events with $z= 100-140$ cm, and with S1$c$ of $70-80$ phd (black), $50-60$ phd (blue), and $20-30$ phd (pink). The corresponding areas under the curves are 0.76, 0.72, and 0.59, respectively. The curves are constructed using the optimized prompt fraction parameters for the respective bin. The dashed grey line represents the random classifier baseline, for which points above this line indicate performance better than random.
  • ...and 9 more figures