New Results from HAYSTAC's Phase II Operation with a Squeezed State Receiver
HAYSTAC Collaboration: M. J. Jewell, A. F. Leder, K. M. Backes, Xiran, Bai, K. van Bibber, B. M. Brubaker, S. B. Cahn, A. Droster, Maryam H. Esmat,, Sumita Ghosh, Eleanor Graham, Gene C. Hilton, H. Jackson, Claire Laffan, S., K. Lamoreaux, K. W. Lehnert, S. M. Lewis, M. Malnou

TL;DR
This paper reports on the HAYSTAC experiment's use of a squeezed state receiver to search for axion dark matter in higher mass ranges, achieving faster scanning and setting new exclusion limits without detecting axions.
Contribution
It introduces upgrades to the HAYSTAC experiment enabling faster scans at higher masses and demonstrates the use of a squeezed state receiver for dark matter searches.
Findings
No axion signal detected in the new mass range.
Achieved a 5-fold reduction in data acquisition dead time.
Set an upper limit on axion-photon coupling at 90% confidence.
Abstract
A search for dark matter axions with masses has been performed using the HAYSTAC experiment's squeezed state receiver to achieve sub-quantum limited noise. This report includes details of the design and operation of the experiment previously used to search for axions in the mass ranges and (GHz) and GHz) as well as upgrades to facilitate an extended search at higher masses. These upgrades include improvements to the data acquisition routine which have reduced the effective dead time by a factor of 5, allowing for the new region to be scanned 1.6 times faster with comparable sensitivity. No statistically significant evidence of an axion signal is found in the range (GHz), leading to an aggregate upper limit exclusion at the level on the axion-photon…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDark Matter and Cosmic Phenomena · Particle physics theoretical and experimental studies · Computational Physics and Python Applications
