Engineering topics

Bowring, D.

Publications and source records attributed to Bowring, D..

5 records

DOE OSTI2024

Search for non-virialized axions with 3.3-4.2 $\mu$eV mass at selected resolving powers

The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Nonvirialized axion search sensitive to Doppler effects in the Milky Way halo

The Axion Dark Matter eXperiment (ADMX) has previously excluded Dine-Fischler-Srednicki- Zhitnisky (DFSZ) axions between 680-790 MHz under the assumption that the dark matter is described by the isothermal halo model. However, the precise nature of the velocity distribution of dark matter is still unknown, and alternative models have been proposed. Here, we report the results of a non-virialized axion search over the mass range 2.81–3.31 µeV, corresponding to the frequency range 680–800 MHz. This analysis marks the most sensitive search for non-virialized axions sensitive to Doppler effects in the Milky Way Halo to date. Accounting for frequency shifts due to the detector’s motion through the Galaxy, we exclude cold flow relic axions with a velocity dispersion of $\mathscr{O}$(10 -7 )c with 95% confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Axion Dark Matter eXperiment: Run 1A analysis details

The ADMX Collaboration gathered data for its Run 1A axion dark matter search from January 2017 to June 2017, scanning with an axion haloscope over the frequency range 645–680 MHz ( 2.66 – 2.81 μ eV in axion mass) at Dine-Fischler-Srednicki-Zhitnitskii (DFSZ) sensitivity. The resulting axion search found no axionlike signals comprising all the dark matter in the form of a virialized galactic halo over the entire frequency range, implying lower bound exclusion limits at or below DFSZ coupling at the 90% confidence level. This paper presents expanded details of the axion search analysis of Run 1A, including review of relevant experimental systems, data-taking operations, preparation and interpretation of raw data, axion search methodology, candidate handling, and final axion limits. Published by the American Physical Society 2024

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Non-Virialized Axion Search Sensitive to Doppler Effects in the Milky Way Halo

The Axion Dark Matter eXperiment (ADMX) has previously excluded Dine-Fischler-Srednicki-Zhitnisky (DFSZ) axions between 680-790 MHz under the assumption that the dark matter is described by the isothermal halo model. However, the precise nature of the velocity distribution of dark matter is still unknown, and alternative models have been proposed. We report the results of a non-virialized axion search over the mass range 2.81-3.31 μeV, corresponding to the frequency range 680-800 MHz. This analysis marks the most sensitive search for non-virialized axions sensitive to Doppler effects in the Milky Way Halo to date. Accounting for frequency shifts due to the detector's motion through the Galaxy, we exclude cold flow relic axions with a velocity dispersion of order 10^-7 c with 95% confidence.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Implementing machine learning methods on QICK hardware for qubit readout & control

Quantum readout and control is a fundamental aspect of quantum computing that requires accurate measurement of qubit states. Errors emerge in all stages, from initialization to readout, and identifying errors in post-processing necessitates resource-intensive statistical analysis. In our work, we use a lightweight fully-connected neural network (NN) to classify states of a transmon system with no prior processing. Our NN accelerator yields higher fidelities (92%) than the classical matched filter method (84%). By exploiting the natural parallelism of NNs and their placement near the source of data on field-programmable gate arrays (FPGAs), we can achieve ultra-low latency on the Quantum Instrumentation Control Kit (QICK). Integrating machine learning methods on QICK opens several pathways for efficient real-time processing of quantum circuits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗