A. Sundararajan | Searcharxiv

arXiv2025

QSHS: An Axion Dark Matter Resonant Search Apparatus

We describe a resonant cavity search apparatus for axion dark matter constructed by the Quantum Sensors for the Hidden Sector (QSHS) collaboration. The apparatus is configured to search for QCD axion dark matter, though also has the capability to detect axion-like particles (ALPs), dark photons, and some other forms of wave-like dark matter. Initially, a tuneable cylindrical oxygen-free copper cavity is read out using a low noise microwave amplifier feeding a heterodyne receiver. The cavity is housed in a dilution refrigerator and threaded by a solenoidal magnetic field, nominally 8T. The apparatus also houses a magnetic field shield for housing superconducting electronics, and several other fixed-frequency resonators for use in testing and commissioning various prototype quantum electronic devices sensitive at a range of axion masses in the range 2.0 to 40 micro-eV/c2. The apparatus as currently configured is intended as a test stand for electronics over the relatively wide frequency band attainable with the TM010 cavity mode used for axion searches. We present performance data for the resonator, dilution refrigerator, and magnet, and plans for the first science run.

physics.ins-det↗

arXiv2022

mechanoChemML: A software library for machine learning in computational materials physics

We present mechanoChemML, a machine learning software library for computational materials physics. mechanoChemML is designed to function as an interface between platforms that are widely used for machine learning on one hand, and others for solution of partial differential equations-based models of physics. Of special interest here, and the focus of mechanoChemML, are applications to computational materials physics. These typically feature the coupled solution of material transport, reaction, phase transformation, mechanics, heat transport and electrochemistry. Central to the organization of mechanoChemML are machine learning workflows that arise in the context of data-driven computational materials physics. The mechanoChemML code structure is described, the machine learning workflows are laid out and their application to the solution of several problems in materials physics is outlined.

cs.CE↗