New 13 meV Sensors Probe Dark Matter at Lower Energies

A new dark matter search at the Jet Propulsion Laboratory, a collaboration spanning Caltech, Washington University in St. Louis, MIT, the University of Chicago, Oxford University, and JPL, is probing an elusive energy range previously out of reach. Researchers have developed a 13 meV threshold superconducting sensor array, designed to detect the incredibly faint energy deposits expected from interactions with dark matter particles at these low energies. This development addresses a critical need in the field, as many well-motivated dark matter models predict interactions at this scale. The experiment, named QUALIPHIDE, utilizes a pixel array and has already yielded among the first terrestrial limits on dark matter scattering off nuclei and electrons, down to MeV/c² and keV/c², respectively, with data from an initial run of several hours.

Dark Matter Candidates and the meV Mass Range

These detectors function by measuring changes in the kinetic inductance of a superconducting resonator when struck by a photon or phonon, with the amplitude of the signal scaling with the deposited energy. “MKIDs are superconducting microwave resonators whose kinetic inductance, and thus resonance frequency, responds to changes in the Cooper pair density within the inductor,” explains the research team. The array’s design incorporates both on- and off-focus pixels, a strategy that allows for a data-driven background model, enhancing its potential for discovery. This approach is particularly well-suited for searching for hidden photons, a compelling dark matter candidate that interacts with standard model electromagnetism. The experiment exploits the principle that hidden photons can convert into detectable photons at the surface of a conducting material. Initial analysis of data, totaling several hours, has not revealed any significant excess, but has already set the strongest constraints on the hidden photon kinetic mixing parameter over the mass range of, meV/c², reaching 10⁻¹² at meV/c².

Researchers at the California Institute of Technology are utilizing a highly sensitive array of superconducting sensors for a new approach to dark matter detection. Jacob Harris of Caltech, alongside colleagues from Washington University in St. Louis, Jet Propulsion Laboratory, MIT, the University of Chicago, and Oxford University, has achieved a 13 meV threshold, opening a new window for potential discoveries, as many theoretical models predict dark matter interactions at this low energy scale. This innovative approach is crucial, as the team aims to not only detect potential hidden photons, hypothetical particles interacting with electromagnetic forces, but also phonons resulting from direct dark matter particle interactions. The array serves as both detectors and target material, maximizing sensitivity.

Researchers from Caltech, Washington University in St. Louis, and the Jet Propulsion Laboratory collaborated on this work. The on-focus pixels collect converted photons, while the off-focus pixels provide a baseline for background noise, enhancing the signal-to-noise ratio. The data show a limit of 4 x 10⁻¹⁵ at 20 meV. The low threshold also enables future study of the low-energy excess limiting cryogenic detectors and, as the team projects, will allow for a terahertz-scale QCD axion search with a magnetic field.

Data Analysis and Background Modeling Techniques

Establishing a reliable signal amidst inherent noise is paramount in any dark matter search, and the QUALIPHIDE array tackles this challenge with a sophisticated data analysis strategy centered around its unique design. Researchers leveraged the array’s incorporation of both on- and off-focus pixels, not merely as a detection scheme, but as the foundation for a more accurate subtraction of noise and spurious signals. This approach is critical, as the experiment aims to detect incredibly faint energy deposits, potentially as low as 13 meV, from dark matter interactions. The team meticulously calibrated the array’s energy resolution, extending previous work at 25 µm wavelengths down to a 13 meV threshold. This precise calibration is essential for spectral discrimination, enabling the separation of potential dark matter signals from background events.

Data processing focused on identifying coincident pulses across multiple microwave kinetic inductance detectors (MKIDs); a likely non-dark matter origin was inferred when pulses appeared simultaneously on several sensors, as illustrated in the provided data timestreams. Crucially, the off-focus pixels, expected to receive minimal signal from potential dark matter interactions, provide a statistical baseline for background fluctuations. The researchers state that this technique demonstrates the power of their approach. This method is particularly important for the hidden photon search, where the expected signal is a faint conversion of dark matter into detectable photons.

Existing experiments, designed to detect heavier dark matter particles or relying on specific interaction types, struggle to probe the elusive meV-scale energy deposits predicted by many contemporary models. This instrument isn’t simply refining existing techniques; it represents a shift toward technology essential for registering the incredibly faint energy signatures expected from light dark matter.

Researchers affiliated with Caltech, Washington University in St. Louis, and the Jet Propulsion Laboratory have demonstrated a 13 meV energy threshold, previously a challenge for similar experiments, positioning the array to pursue a terahertz-scale search for QCD axions, hypothetical particles also considered strong dark matter candidates. This transition relies on the array’s sensitivity to the faint energy deposits expected from axion-photon conversion within a magnetic field, a technique already employed in other axion searches. The researchers explain that this approach yields among the first terrestrial limits on dark matter scattering off nuclei and electrons, rather than one able to set only upper limits. The low-energy sensitivity achieved by QUALIPHIDE-FIR offers a unique opportunity to characterize limitations in existing cryogenic detectors, and the array’s performance, detailed in the paper, demonstrates a pathway toward probing previously inaccessible regions of the dark matter parameter space and expanding the search for these elusive particles.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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