Engineering topics

Stefanazzi, L.

Publications and source records attributed to Stefanazzi, L..

3 records

DOE OSTI2025

First Search for Dark Photon Dark Matter with a madmax Prototype

We report the first result from a dark photon dark matter search in the mass range from 78.62 to 83.95 μ eV / c 2 with a dielectric haloscope prototype for (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons would convert to detectable photons within a stack consisting of three sapphire disks and a mirror. The emitted power of this system is received by an antenna and successively digitized using a low-noise receiver. No significant signal attributable to dark photons has been observed above the expected background. Assuming unpolarized dark photon dark matter with a local density of ρ χ = 0.3 G eV cm 3 we exclude a dark photon to photon mixing parameter χ > 2.7 × 10 − 12 over the full mass range and χ > 1.1 × 10 − 13 at a mass of 80.57 μ eV / c 2 with a 95% confidence level. This is the first physics result from a prototype and exceeds previous constraints on χ in this mass range by up to almost three orders of magnitude. Published by the American Physical Society 2025

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Confirmation of the spectral excess in DAMIC at SNOLAB with skipper CCDs

Here, we present results from a 3.25 kg–day target exposure of two silicon charge-coupled devices (CCDs), each with 24 megapixels and skipper readout, deployed in the DAMIC setup at SNOLAB. With a reduction in pixel readout noise of a factor of 10 relative to the previous detector, we investigate the excess population of low-energy events in the CCD bulk previously observed above expected backgrounds. We address the dominant systematic uncertainty of the previous analysis through a depth fiducialization designed to reject surface backgrounds on the CCDs. The measured bulk ionization spectrum confirms the presence of an excess population of low-energy events in the CCD target with characteristic rate of ~7 events per kg-day and electron-equivalent energies of ~80 eV, whose origin remains unknown.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

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↗