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  • Letter
  • Open Access

Dark matter-induced electron excitations in silicon and germanium with deep learning

Riccardo Catena* and Einar Urdshals†

  • *Contact author: catena@chalmers.se
  • †Contact author: urdshals@chalmers.se
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Phys. Rev. D 111, L011702 – Published 14 January, 2025

DOI: https://doi.org/10.1103/PhysRevD.111.L011702

Abstract

We train a deep neural network (DNN) to output rates of dark matter (DM) induced electron excitations in silicon and germanium detectors. Our DNN provides a massive speedup of around 5 orders of magnitude relative to existing methods (i.e., qedark-eft), allowing for extensive parameter scans in the event of an observed DM signal. The network is also lighter and simpler to use than alternative computational frameworks based on a direct calculation of the DM-induced excitation rate.

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References (38)

  1. Gianfranco Bertone and Dan Hooper, History of dark matter, Rev. Mod. Phys. 90, 045002 (2018).
  2. Nabila Aghanim et al. (Planck Collaboration), Planck 2018 results. VI. Cosmological parameters, Astron. Astrophys. 641, A6 (2020); 652, C4(E) (2021).
  3. Douglas Clowe, Marusa Bradac, Anthony H. Gonzalez, Maxim Markevitch, Scott W. Randall, Christine Jones, and Dennis Zaritsky, A direct empirical proof of the existence of dark matter, Astrophys. J. 648, L109 (2006).
  4. Massimo Persic, Paolo Salucci, and Fulvio Stel, The universal rotation curve of spiral galaxies: 1. The dark matter connection, Mon. Not. R. Astron. Soc. 281, 27 (1996).
  5. Andrzej Drukier and Leo Stodolsky, Principles and applications of a neutral current detector for neutrino physics and astronomy, Phys. Rev. D 30, 2295 (1984).
  6. Mark W. Goodman and Edward Witten, Detectability of certain dark matter candidates, Phys. Rev. D 31, 3059 (1985).
  7. Marc Schumann, Direct detection of WIMP dark matter: Concepts and status, J. Phys. G 46, 103003 (2019).
  8. Marco Battaglieri et al., US cosmic visions: New ideas in dark matter 2017; Community report, Report No. FERMILAB-CONF-17-282-AE-PPD-T, 2017, arXiv:1707.04591.
  9. Rouven Essig, Jeremy Mardon, and Tomer Volansky, Direct detection of sub-GeV dark matter, Phys. Rev. D 85, 076007 (2012).
  10. Rouven Essig, Tomer Volansky, and Tien-Tien Yu, New constraints and prospects for sub-GeV dark matter scattering off electrons in Xenon, Phys. Rev. D 96, 043017 (2017).
  11. P. Agnes et al. (DarkSide Collaboration), Constraints on sub-GeV dark-matter–electron scattering from the DarkSide-50 experiment, Phys. Rev. Lett. 121, 111303 (2018).
  12. R. Agnese et al. (SuperCDMS Collaboration), First dark matter constraints from a SuperCDMS single-charge sensitive detector, Phys. Rev. Lett. 121, 051301 (2018); 122, 069901(E) (2019).
  13. E. Aprile et al. (XENON Collaboration), Light dark matter search with ionization signals in XENON1T, Phys. Rev. Lett. 123, 251801 (2019).
  14. G. Cavoto, M. G. Betti, C. Mariani, F. Pandolfi, A. D. Polosa, I. Rago, and A. Ruocco, Carbon nanotubes as anisotropic target for dark matter, J. Phys. Conf. Ser. 1468, 012232 (2020).
  15. Q. Arnaud et al. (EDELWEISS Collaboration), First germanium-based constraints on sub-MeV dark matter with the EDELWEISS experiment, Phys. Rev. Lett. 125, 141301 (2020).
  16. Liron Barak et al. (SENSEI Collaboration), SENSEI: Direct-detection results on sub-GeV dark matter from a new skipper-CCD, Phys. Rev. Lett. 125, 171802 (2020).
  17. E. Aprile et al. (XENON Collaboration), Search for new physics in electronic recoil data from XENONnT, Phys. Rev. Lett. 129, 161805 (2022).
  18. Shuaijie Li et al. (PandaX Collaboration), Search for light dark matter with ionization signals in the PandaX-4T experiment, Phys. Rev. Lett. 130, 261001 (2023).
  19. I. Arnquist et al. (DAMIC-M Collaboration), First constraints from DAMIC-M on sub-GeV dark-matter particles interacting with electrons, Phys. Rev. Lett. 130, 171003 (2023).
  20. V. Zema et al., Dark matter-electron scattering search using cryogenic light detectors, Phys. Rev. D 110, 123012 (2024).
  21. Riccardo Catena, Timon Emken, Nicola A. Spaldin, and Walter Tarantino, Atomic responses to general dark matter-electron interactions, Phys. Rev. Res. 2, 033195 (2020).
  22. Riccardo Catena, Timon Emken, Marek Matas, Nicola A. Spaldin, and Einar Urdshals, Crystal responses to general dark matter-electron interactions, Phys. Rev. Res. 3, 033149 (2021).
  23. Riccardo Catena and Taylor R. Gray, Spin-1 thermal targets for dark matter searches at beam dump and fixed target experiments, J. Cosmol. Astropart. Phys. 11 (2023) 058.
  24. Riccardo Catena, Timon Emken, Marek Matas, Nicola A. Spaldin, and Einar Urdshals, Direct searches for general dark matter-electron interactions with graphene detectors: Part II. Sensitivity studies, Phys. Rev. Res. 5, 043258 (2023).
  25. Riccardo Catena and Nicola Spaldin, Linear response theory for light dark matter-electron scattering in materials, Phys. Rev. Res. 6, 033230 (2024).
  26. Einar Urdshals and Marek Matas, qedark-eft, Zenodo, 10.5281/zenodo.4739187 (2021).
  27. Paolo Giannozzi et al., quantum espresso: A modular and open-source software project for quantum, J. Phys. Condens. Matter 21, 395502 (2009).
  28. Rouven Essig, Marivi Fernandez-Serra, Jeremy Mardon, Adrian Soto, Tomer Volansky, and Tien-Tien Yu, Direct detection of sub-GeV dark matter with semiconductor targets, J. High Energy Phys. 05 (2016) 046.
  29. D. Baxter et al., Recommended conventions for reporting results from direct dark matter searches, Eur. Phys. J. C 81, 907 (2021).
  30. B. G. Streetman and Sanjay Banerjee, Solid State Electronic Devices (Prentice Hall, Englewood Cliffs, NJ, 2005).
  31. C. A. Klein, Bandgap dependence and related features of radiation ionization energies in semiconductors, J. Appl. Phys. 39, 2029 (1968).
  32. JiJi Fan, Matthew Reece, and Lian-Tao Wang, Non-relativistic effective theory of dark matter direct detection, J. Cosmol. Astropart. Phys. 11 (2010) 042.
  33. A. Liam Fitzpatrick, Wick Haxton, Emanuel Katz, Nicholas Lubbers, and Yiming Xu, The effective field theory of dark matter direct detection, J. Cosmol. Astropart. Phys. 02 (2013) 004.
  34. Abien Fred Agarap, Deep learning using rectified linear units (ReLU), arXiv:1803.08375.
  35. Martín Abadi et al., tensorflow: Large-scale machine learning on heterogeneous systems, software available from https://www.tensorflow.org/ (2015).
  36. Diederik P. Kingma and Jimmy Ba, Adam: A method for stochastic optimization, arXiv:1412.6980.
  37. K. Ramanathan and N. Kurinsky, Ionization yield in silicon for eV-scale electron-recoil processes, Phys. Rev. D 102, 063026 (2020).
  38. https://github.com/urdshals/DEDD

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Phys. Rev. D 111, L011702– Published 14 January, 2025
  • Received 12 April 2024
  • Accepted 13 December 2024
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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