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The Probe far-Infrared Mission for Astrophysics (PRIMA) is a proposed space observatory which will use arrays of thousands of kinetic inductance detectors (KIDs) to perform low- and moderate-resolution spectroscopy throughout the far-infrared. The detectors must have noise equivalent powers (NEPs) at or below 0.1 aW/sqrt(Hz) to be subdominant to noise from sky backgrounds and thermal noise from PRIMA's cryogenically cooled primary mirror. Using a Radio Frequency System on a Chip for multitone readout, we measure the NEPs of detectors on a flight-like array designed to observe at a wavelength of 210 microns. We find that 92% of the KIDs measured have an NEP below 0.1 aW/sqrt(Hz) at a noise frequency of 10 Hz., Comment: 7 pages, 7 figures

We present the design and experimental characterization of a kinetic-inductance traveling-wave parametric amplifier (KI-TWPA) for sub-GHz frequencies. KI-TWPAs amplify signals through nonlinear mixing processes supported by the nonlinear kinetic inductance of a superconducting transmission line. The device described here utilizes a compactly meandered TiN microstrip transmission line to achieve the length needed to amplify sub-GHz signals. It is operated in a frequency translating mode where the amplified signal tone is terminated at the output of the amplifier, and the idler tone at approximately 2.5~GHz is brought out of the cryostat. By varying the pump frequency, a gain of up to 22 dB was achieved in a tunable range from about 450 to 850~MHz. Use of TiN as the nonlinear element allows for a reduction of the required pump power by roughly an order of magnitude relative to NbTiN, which has been used for previous KI-TWPA implementations. This amplifier has the potential to enable high-sensitivity and high-speed measurements in a wide range of applications, such as quantum computing, astrophysics, and dark matter detection.

We report measurements characterizing the performance of a kinetic inductance detector array designed for a wavelength of 25 microns and very low optical background level suitable for applications such as a far-infrared instrument on a cryogenically cooled space telescope. In a pulse counting mode of operation at low optical flux, the detectors can resolve individual 25-micron photons. In an integrating mode, the detectors remain photon noise limited over more than six orders of magnitude in absorbed power from 70 zW to 200 fW, with a limiting NEP of 4.6 x 10^-20 W/rtHz at 1 Hz. In addition, the detectors are highly stable with flat power spectra under optical load down to 1 mHz. Operational parameters of the detector are determined including the efficiency of conversion of the incident optical power into quasiparticles in the aluminum absorbing element and the quasiparticle self-recombination constant., Comment: 35 pages, 18 figures

Kinetic inductance traveling-wave parametric amplifiers (KI-TWPA) have a wide instantaneous bandwidth with near quantum-limited performance and a relatively high dynamic range. Because of this, they are suitable readout devices for cryogenic detectors and superconducting qubits and have a variety of applications in quantum sensing. This work discusses the design, fabrication, and performance of a KI-TWPA based on four-wave mixing in a NbTiN microstrip transmission line. This device amplifies a signal band from 4 to 8~GHz without contamination from image tones, which are produced in a separate higher frequency band. The 4 - 8~GHz band is commonly used to read out cryogenic detectors, such as microwave kinetic inductance detectors (MKIDs) and Josephson junction-based qubits. We report a measured maximum gain of over 20 dB using four-wave mixing with a 1-dB gain compression point of -58 dBm at 15 dB of gain over that band. The bandwidth and peak gain are tunable by adjusting the pump-tone frequency and power. Using a Y-factor method, we measure an amplifier-added noise of $ 0.5 \leq N_{added} \leq 1.5$ photons from 4.5 - 8 GHz.

Microwave Kinetic Inductance Detectors (MKIDs) have been demonstrated as capable phonon sensors when coupled to crystalline substrates, and have been proposed as detectors for next-generation rare-event searches such as for the direct detection of dark matter. These Kinetic Inductance Phonon Mediated (KIPM) detector designs, favoring large superconducting absorber volumes and high readout powers, are oftentimes limited in their sensitivity by low temperature amplifier noise introduced in the signal readout chain. We report here an effort to couple a wideband Kinetic Inductance Travelling Wave Parametric Amplifier (KI-TWPA), operated near the Standard Quantum Limit of minimal added amplifier noise, to sensors spanning a 70 MHz bandwidth at 3.5 GHz. This results in a ~5x improvement in the inferred detector energy resolution in the best sensor and highlights the potential of constructing O(100) meV resolving phonon-mediated particle detectors. We detail limitations introduced by lossy passive components, degraded RF responsivity, and microphysical noise sources like two-level systems (TLS), in achieving ultimate quantum-limited system noise levels., Comment: 7 pages, 4 figures

Due to their narrow band gap and compatibility with silicon processing, germanium-tin (Ge$_{1-x}$Sn$_x$) alloys are a versatile platform for scalable integrated mid-infrared photonics. These semiconductors are typically grown on silicon wafers using Ge as an interlayer. However, the large lattice mismatch in this heteroepitaxy protocol leads to the build-up of compressive strain in the grown layers. This compressive strain limits the material quality and its thermal stability besides expanding the band gap, thereby increasing the Sn content needed to cover a broader range in the mid-infrared. Released Ge$_{1-x}$Sn$_x$ membranes provide an effective way to mitigate these harmful effects of the epitaxial strain and control the band gap energy while enabling the hybrid integration onto different substrates. With this perspective, herein strain-relaxed Ge$_{0.89}$Sn$_{0.11}$ membranes are fabricated and subsequently transfer-printed with metal contacts to create multiple photodetectors in a single transfer step. The resulting photodetectors exhibit an extended photodetection cutoff reaching a wavelength of $3.1 \,\mu$m, coupled with a significant reduction in the dark current of two orders of magnitude as compared to as-grown photoconductive devices. The latter yields a reduced cutoff of $2.8 \,\mu$m due to the inherent compressive strain. Furthermore, the impact of chemical treatment and annealing on the device performance was also investigated showing a further reduction in the dark current. The demonstrated transfer printing, along with the use of an adhesive layer, would allow the transfer of multiple GeSn membranes onto virtually any substrate. This approach paves the way for scalable fabrication of hybrid optoelectronic devices leveraging the tunable band gap of Ge$_{1-x}$Sn$_x$ in the mid-wave infrared range.

This work culminates in a demonstration of an alternative Frequency Domain Multiplexing (FDM) scheme for Superconducting Nanowire Single-Photon Detectors (SNSPDs) using the Kinetic inductance Parametric UP-converter (KPUP) made out of NbTiN. There are multiple multiplexing architectures for SNSPDs that are already in use, but FDM could prove superior in applications where the operational bias currents are very low, especially for mid- and far-infrared SNSPDs. Previous FDM schemes integrated the SNSPD within the resonator, while in this work we use an external resonator, which gives more flexibility to optimize the SNSPD architecture. The KPUP is a DC-biased superconducting resonator in which a nanowire is used as its inductive element to enable sensitivity to current perturbations. When coupled to an SNSPD, the KPUP can be used to read out current pulses on the few $\mu$A scale. The KPUP is made out of NbTiN, which has high non-linear kinetic inductance for increased sensitivity at higher current bias and high operating temperature. Meanwhile, the SNSPD is made from WSi, which is a popular material for broadband SNSPDs. To read out the KPUP and SNSPD array, a software-defined radio platform and a graphics processing unit are used. Frequency Domain Multiplexed SNSPDs have applications in astronomy, remote sensing, exoplanet science, dark matter detection, and quantum sensing.

In recent years, the rapid evolution of computer vision has seen the emergence of various foundation models, each tailored to specific data types and tasks. In this study, we explore the adaptation of these models for few-shot semantic segmentation. Specifically, we conduct a comprehensive comparative analysis of four prominent foundation models: DINO V2, Segment Anything, CLIP, Masked AutoEncoders, and of a straightforward ResNet50 pre-trained on the COCO dataset. We also include 5 adaptation methods, ranging from linear probing to fine tuning. Our findings show that DINO V2 outperforms other models by a large margin, across various datasets and adaptation methods. On the other hand, adaptation methods provide little discrepancy in the obtained results, suggesting that a simple linear probing can compete with advanced, more computationally intensive, alternatives

Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challenging to seamlessly adapt these solutions to practical deployment scenarios, typically necessitating extensive data for fine-tuning while grappling with domain adaptation and generalization issues. In response, we propose a novel approach combining Meta-Learning Domain Generalization (MLDG) with novel data augmentation techniques during fine-tuning. This approach not only accelerates adaptation to new channel environments but also significantly reduces the data requirements for fine-tuning, thereby enhancing the practicality and efficiency of DL-based mMIMO systems. The proposed approach is validated by simulating the performance of a backbone model when deployed in a new channel environment, and with different antenna configurations, path loss, and base station height parameters. Our proposed approach demonstrates superior zero-shot performance compared to existing methods and also achieves near-optimal performance with significantly fewer fine-tuning data samples., Comment: This preprint comprises 6 pages and features 5 figures. It has been accepted to the IEEE International Conference on Machine Learning and Computer Networking (ICMLCN) 2024

There is an increasing need for silicon-compatible high bandwidth extended-short wave infrared (e-SWIR) photodetectors (PDs) to implement cost-effective and scalable optoelectronic devices. These systems are quintessential to address several technological bottlenecks in detection and ranging, surveillance, ultrafast spectroscopy, and imaging. In fact, current e-SWIR high bandwidth PDs are predominantly made of III-V compound semiconductors and thus are costly and suffer a limited integration on silicon besides a low responsivity at wavelengths exceeding $2.3 \,\mu$m. To circumvent these challenges, Ge$_{1-x}$Sn$_{x}$ semiconductors have been proposed as building blocks for silicon-integrated high-speed e-SWIR devices. Herein, this study demonstrates a vertical all-GeSn PIN PDs consisting of p-Ge$_{0.92}$Sn$_{0.08}$/i-Ge$_{0.91}$Sn$_{0.09}$/n-Ge$_{0.89}$Sn$_{0.11}$ and p-Ge$_{0.91}$Sn$_{0.09}$/i-Ge$_{0.88}$Sn$_{0.12}$/n-Ge$_{0.87}$Sn$_{0.13}$ heterostructures grown on silicon following a step-graded temperature-controlled epitaxy protocol. The performance of these PDs was investigated as a function of the device diameter in the $10-30 \,\mu$m range. The developed PD devices yield a high bandwidth of 12.4 GHz at a bias of 5V for a device diameter of $10 \,\mu$m. Moreover, these devices show a high responsivity of 0.24 A/W, a low noise, and a $2.8 \,\mu$m cutoff wavelength thus covering the whole e-SWIR range.

The Balloon Experiment for Galactic INfrared Science (BEGINS) is a concept for a sub-orbital observatory that will operate from $\lambda$ = 25-250 $\mu$m to characterize dust in the vicinity of high-mass stars. The mission's sensitivity requirements will be met by utilizing arrays of 1,840 lens-coupled, lumped-element kinetic inductance detectors (KIDs) operating at 300 mK. Each KID will consist of a titanium nitride (TiN) parallel strip absorbing inductive section and parallel plate capacitor (PPC) deposited on a silicon (Si) substrate. The PPC geometry allows for reduction of the pixel spacing. At the BEGINS focal plane the detectors require optical NEPs from $2\times10^{-16}$ W/$\sqrt{\textrm{Hz}}$ to $6\times10^{-17}$ W/$\sqrt{\textrm{Hz}}$ from 25-250 $\mu$m for optical loads ranging from 4 pW to 10 pW. We present the design, optical performance and quasiparticle lifetime measurements of a prototype BEGINS KID array at 25 $\mu$m when coupled to Fresnel zone plate lenses. For our optical set up and the absorption efficiency of the KIDs, the electrical NEP requirement at 25 $\mu$m is $7.6\times10^{-17}$ W/$\sqrt{\textrm{Hz}}$ for an absorbed optical power of 0.36 pW. We find that over an average of five resonators the the detectors are photon noise limited down to about 200 fW, with a limiting NEP of about $7.4\times10^{-17}$ W/$\sqrt{\textrm{Hz}}$.

This study presents a comprehensive investigation into the exceptional superconducting attributes of titanium nitride (TiN) achieved through plasma-enhanced atomic layer deposition (PEALD) on both planar and intricate three-dimensional (3D) structures. We introduced an additional substrate biasing cycle to densify the film and remove ligand residues, augmenting the properties while minimizing impurities. While reactive-sputtered TiN films exhibit high quality, our technique ensures superior uniformity by consistently maintaining a desired sheet resistance greater than 95 percent across a 6inch wafer, a critical aspect for fabricating extensive arrays of superconducting devices and optimizing wafer yield. Moreover, our films demonstrate exceptional similarity to conventional reactive-sputtered films, consistently reaching a critical temperature (Tc) of 4.35 K with a thickness of around 40 nm. This marks a notable achievement compared to previously reported ALD-based superconducting TiN. Using the same process as for planar films, we obtained Tc for aspect ratios (ARs) ranging from 2 to 40, observing a Tc of approximately 2 K for ARs between 2 and 10.5. We elucidate the mechanisms contributing to the limitations and degradation of superconducting properties over these aggressive 3D structures. Our results seamlessly align with both current and next-generation superconducting technologies, meeting stringent criteria for thin-film constraints, large-scale deposition, conformality, 3D integration schemes, and yield optimization.

This article presents a new methodology for extracting intervals when a home is vacant from low-frequency electricity consumption data. The approach combines multiple algorithms, including change point detection, classification, period detection, and periodic spikes retrieval. It shows encouraging results on both simulated and real consumption curves. This approach offers practical insights for optimizing energy use and holds potential benefits for residential consumers and utility companies in terms of energy cost reduction and sustainability. Further research is needed to enhance its applicability in diverse settings and with larger datasets., Comment: Comments welcome!

A new apparatus has been developed to measure the conductivity of rock samples. The probe, which consists of multi-coil transmitters and receivers doesn't require physical contact with the samples. The measurement is based on the induction principle. The measurement system is validated by using saline solutions and water-saturated sands of known conductivity. This work presents details of the development of a system of magnetic resistivity measurements by magnetic induction for petrophysical applications. The first application consists of measuring the resistivity of the core plug which is 0.038 m in diameter. Currently the system is operating properly at a frequency of 50 kHz with a current of up to 500 mA at 20 {\deg}C. During the study two types of samples were investigated: aqueous solutions with conductivities between 1 to 100 mS/cm and rocks. Several tests were carried out with the objective of investigating the performance of the instrument, such as the experiment to obtain sensitivity for the measurement system as a function of the current applied to the transmitter coil., Comment: 14 pages, 5 figures

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is working to develop kinetic inductance detectors (KIDs) that can meet the sensitivity targets of a far-infrared spectrometer on a cryogenically cooled space telescope. An important ingredient for achieving high sensitivity is increasing the fractional-frequency responsivity. Here we present a study of the responsivity of aluminum KIDs fabricated at the Jet Propulsion Laboratory. Specifically, we model the KID's temporal response to pair-breaking excitations in the framework of the Mattis-Bardeen theory, incorporating quasiparticle recombination dynamics and the pair-breaking efficiency. Using a near-IR laser, we measure time-resolved photon pulses and fit them to our model, extracting the time-resolved quasiparticle density and the quasiparticle recombination lifetime. Comparing the fit to the known energy of the laser provides a measurement of the pair-breaking efficiency. In addition to photon-sourced excitations, it is important to understand the KID's response to phonon-sourced excitations from cosmic rays. We measure the rate of secondary cosmic rays detected by our devices, and predict the dead time due to cosmic rays for an array in L2 orbit. This work provides confidence in KIDs' robustness to cosmic ray events in the space environment., Comment: 8 pages, 4 figures

The PRobe far-Infrared Mission for Astrophysics (PRIMA) is under study as a potential far-IR space mission, featuring actively cooled optics, and both imaging and spectroscopic instrumentation. To fully take advantage of the low background afforded by a cold telescope, spectroscopy with PRIMA requires detectors with a noise equivalent power (NEP) better than $1 \times 10^{-19}$ W Hz$^{-1/2}$. To meet this goal we are developing large format arrays of kinetic inductance detectors (KIDs) to work across the $25-250$ micron range. Here we present the design and characterization of a single pixel prototype detector optimized for $210$ micron. The KID consists of a lens-coupled aluminum inductor-absorber connected to a niobium interdigitated capacitor to form a 2 GHz resonator. We have fabricated a small array with 28 KIDs, and we measure the performance of one of these detectors with an optical loading in the $0.01 - 300$ aW range. At low loading the detector achieves an NEP of $9\times10^{-20}$ W Hz$^{-1/2}$ at a 10 Hz readout frequency, and the lens-absorber system achieves a good optical efficiency. An extrapolation of these measurements suggest this detector may remain photon noise limited at up to 20 fW of loading, offering a high dynamic range for PRIMA observations of bright astronomical sources., Comment: revised submission to IEEE Transactions on Terahertz Science and Technology

Far-infrared (far-IR) astrophysics missions featuring actively cooled telescopes will offer orders of magnitude observing speed improvement at wavelengths where galaxies and forming planetary systems emit most of their light. The PRobe far-Infrared Mission for Astrophysics (PRIMA), which is currently under study, emphasizes low and moderate resolution spectroscopy throughout the far-IR. Full utilization of PRIMA's cold telescope requires far-IR detector arrays with per-pixel noise equivalent powers (NEPs) at or below 1 x 10-19 W/rtHz. We are developing low-volume Aluminum kinetic inductance detector (KID) arrays to reach these sensitivities. We will present on the development of our long-wavelength (210 um) array approach, with a focus on multitone measurements of our 1,008-pixel arrays. We measure an NEP below 1 x 10-19 W/rtHz for 73 percent of our pixels., Comment: 9 pages, 5 figures, 20th International Workshop on Low Temperature Detectors, submitted to the Journal of Low Temperature Physics

Future space-based far-infrared astrophysical observatories will require exquis-itely sensitive detectors consistent with the low optical backgrounds. The PRobe far-Infrared Mission for Astrophysics (PRIMA) will deploy arrays of thousands of superconducting kinetic inductance detectors (KIDs) sensitive to radiation between 25 and 265 $\mu$m. Here, we present laboratory characterization of prototype, 25 -- 80 $\mu$m wavelength, low-volume, aluminum KIDs designed for the low-background environment expected with PRIMA. A compact parallel plate capacitor is used to minimize the detector footprint and suppress TLS noise. A novel resonant absorber is designed to enhance response in the band of interest. We present noise and optical efficiency measurements of these detectors taken with a low-background cryostat and a cryogenic blackbody. A microlens-hybridized KID array is found to be photon noise limited down to about 50 aW with a limiting detector NEP of about $6.5 \times 10^{-19}~\textrm{W/Hz}^{1/2}$. A fit to an NEP model shows that our optical system is well characterized and understood down to 50 aW. We discuss future plans for low-volume aluminum KID array development as well as the testbeds used for these measurements., Comment: 8 pages, 4 figures, 20th International Workshop on Low Temperature Detectors, submitted to the Journal of Low Temperature Physics

Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation. However, their performance relative to non-natural images, like radar-based satellite data, remains largely unknown. Generating large amounts of synthetic (and especially labelled) satellite data is crucial to implement deep-learning approaches for the processing and analysis of (interferometric) satellite aperture radar data. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue. Indeed, accelerated sampling strategies, which work well on simple image datasets like MNIST, fail on our radar datasets. We provide a simple and versatile open-source https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and evaluate PDMs using any dataset on a single GPU.

Methane is one of the most potent greenhouse gases, and its short atmospheric half-life makes it a prime target to rapidly curb global warming. However, current methane emission monitoring techniques primarily rely on approximate emission factors or self-reporting, which have been shown to often dramatically underestimate emissions. Although initially designed to monitor surface properties, satellite multispectral data has recently emerged as a powerful method to analyze atmospheric content. However, the spectral resolution of multispectral instruments is poor, and methane measurements are typically very noisy. Methane data products are also sensitive to absorption by the surface and other atmospheric gases (water vapor in particular) and therefore provide noisy maps of potential methane plumes, that typically require extensive human analysis. Here, we show that the image recognition capabilities of deep learning methods can be leveraged to automatize the detection of methane leaks in Sentinel-2 satellite multispectral data, with dramatically reduced false positive rates compared with state-of-the-art multispectral methane data products, and without the need for a priori knowledge of potential leak sites. Our proposed approach paves the way for the automated, high-definition and high-frequency monitoring of point-source methane emissions across the world.

Biological functions stem from coordinated interactions among proteins, nucleic acids and small molecules. Mass spectrometry technologies for reliable, high throughput single-cell proteomics will add a new modality to genomics and enable data-driven modeling of the molecular mechanisms coordinating proteins and nucleic acids at single-cell resolution. This promising potential requires estimating the reliability of measurements and computational analysis so that models can distinguish biological regulation from technical artifacts. We highlight different measurement modes that can support single-cell proteogenomic analysis and how to estimate their reliability. We then discuss approaches for developing both abstract and mechanistic models that aim to biologically interpret the measured differences across modalities, including specific applications to directed stem cell differentiation and to inferring protein interactions in cancer cells from the buffing of DNA copy-number variations. Single-cell proteogenomic data will support mechanistic models of direct molecular interactions that will provide generalizable and predictive representations of biological systems.

Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop an unsupervised deep learning method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, to ensure that the proposed method can be implemented in practice, we investigate the ability of the model to be trained exclusively using imperfect channel state information~(CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods., Comment: This preprint comprises 15 pages and features 15 figures. Copyright may be transferred without notice