A Strategy for Low-Mass Dark Matter Searches with Cryogenic Detectors in the SuperCDMS SNOLAB Facility
Abstract
The SuperCDMS Collaboration is currently building SuperCDMS SNOLAB, a dark matter search focused on nucleon-coupled dark matter in the 1-5 GeV/c mass range. Looking to the future, the Collaboration has developed a set of experience-based upgrade scenarios, as well as novel directions, to extend the search for dark matter using the SuperCDMS technology in the SNOLAB facility. The experienced-based scenarios are forecasted to probe many square decades of unexplored dark matter parameter space below 5 GeV/c, covering over 6 decades in mass: 1-100 eV/c for dark photons and axion-like particles, 1-100 MeV/c for dark-photon-coupled light dark matter, and 0.05-5 GeV/c for nucleon-coupled dark matter. They will reach the neutrino fog in the 0.5-5 GeV/c mass range and test a variety of benchmark models and sharp targets. The novel directions involve greater departures from current SuperCDMS technology but promise even greater reach in the long run, and their development must begin now for them to be available in a timely fashion. The experienced-based upgrade scenarios rely mainly on dramatic improvements in detector performance based on demonstrated scaling laws and reasonable extrapolations of current performance. Importantly, these improvements in detector performance obviate significant reductions in background levels beyond current expectations for the SuperCDMS SNOLAB experiment. Given that the dominant limiting backgrounds for SuperCDMS SNOLAB are cosmogenically created radioisotopes in the detectors, likely amenable only to isotopic purification and an underground detector life-cycle from before crystal growth to detector testing, the potential cost and time savings are enormous and the necessary improvements much easier to prototype.
Keywords
Cite
@article{arxiv.2203.08463,
title = {A Strategy for Low-Mass Dark Matter Searches with Cryogenic Detectors in the SuperCDMS SNOLAB Facility},
author = {SuperCDMS Collaboration and M. F. Albakry and I. Alkhatib and D. W. P. Amaral and T. Aralis and T. Aramaki and I. J. Arnquist and I. Ataee Langroudy and E. Azadbakht and S. Banik and C. Bathurst and D. A. Bauer and R. Bhattacharyya and P. L. Brink and R. Bunker and B. Cabrera and R. Calkins and R. A. Cameron and C. Cartaro and D. G. Cerdeno and Y. -Y. Chang and M. Chaudhuri and R. Chen and N. Chott and J. Cooley and H. Coombes and J. Corbett and P. Cushman and F. De Brienne and S. Dharani and M. L. di Vacri and M. D. Diamond and E. Fascione and E. Figueroa-Feliciano and C. W. Fink and K. Fouts and M. Fritts and G. Gerbier and R. Germond and M. Ghaith and S. R. Golwala and J. Hall and N. Hassan and B. A. Hines and M. I. Hollister and Z. Hong and E. W. Hoppe and L. Hsu and M. E. Huber and V. Iyer and A. Jastram and V. K. S. Kashyap and M. H. Kelsey and A. Kubik and N. A. Kurinsky and R. E. Lawrence and M. Lee and A. Li and J. Liu and Y. Liu and B. Loer and P. Lukens and D. B. MacFarlane and R. Mahapatra and V. Mandic and N. Mast and A. J. Mayer and H. Meyer zu Theenhausen and E. Michaud and E. Michielin and N. Mirabolfathi and B. Mohanty and S. Nagorny and J. Nelson and H. Neog and V. Novati and J. L. Orrell and M. D. Osborne and S. M. Oser and W. A. Page and R. Partridge and D. S. Pedreros and R. Podviianiuk and F. Ponce and S. Poudel and A. Pradeep and M. Pyle and W. Rau and E. Reid and R. Ren and T. Reynolds and A. Roberts and A. E. Robinson and T. Saab and B. Sadoulet and I. Saikia and J. Sander and A. Sattari and B. Schmidt and R. W. Schnee and S. Scorza and B. Serfass and S. S. Poudel and D. J. Sincavage and C. Stanford and J. Street and H. Sun and F. K. Thasrawala and D. Toback and R. Underwood and S. Verma and A. N. Villano and B. von Krosigk and S. L. Watkins and O. Wen and Z. Williams and M. J. Wilson and J. Winchell and K. Wyko and S. Yellin and B. A. Young and T. C. Yu and B. Zatschler and S. Zatschler and A. Zaytsev and E. Zhang and L. Zheng and S. Zuber},
journal= {arXiv preprint arXiv:2203.08463},
year = {2023}
}
Comments
contribution to Snowmass 2021; v2 updated (assorted corrections and improvements to forecasts) October 2022; v3 updated (corrected SuperCDMS SNOLAB sensitivity curves in upgrade forecast plots in body of text) April 2023