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Superconducting traveling-wave parametric amplifiers have emerged as highly promising devices for near-quantum-limited broadband amplification of microwave signals and are essential for high quantum-efficiency microwave readout lines. Built-in isolation, as well as gain, would address their primary limitation: lack of true directionality due to potential backward travel of electromagnetic radiation to their input port. Here, we demonstrate a Josephson-junction-based traveling-wave parametric amplifier isolator. It utilizes third-order nonlinearity for amplification and second-order nonlinearity for frequency upconversion of backward propagating modes to provide reverse isolation. These parametric processes, enhanced by a novel phase matching mechanism, exhibit gain of up to 20~dB and reverse isolation of up to 30~dB over a static 3~dB bandwidth greater than 500~MHz, while keeping near-quantum limited added noise. This demonstration of a broadband truly directional amplifier ultimately paves the way towards broadband quantum-limited microwave amplification lines without bulky magnetic isolators and with inhibited back-action.

We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgrounds, and it explored multiple facets of their perspectives on astronomy. The topics included students' views on the incorporation of astronomy in curricula, their grasp of fundamental astronomical concepts, access to educational resources, cultural connections to astronomy, and their levels of interest and aspirations in the subject. We find notable deficiencies in students' knowledge of basic astronomical principles, with only a minority demonstrating proficiency in key areas such as celestial sizes, distances, and lunar phases. Furthermore, access to resources such as telescopes and planetariums remain limited across the country. Despite these challenges, a significant majority of students expressed a keen interest in astronomy. We further analyze the data along socioeconomic and gender lines. Particularly striking were the socioeconomic disparities, with students from resource-poor backgrounds often having lower levels of access and proficiency. Some differences were observed between genders, although not very pronounced. The insights gleaned from this study hold valuable implications for the development of a more robust astronomy curriculum and the design of effective teacher training programs in the future., Comment: 15 pages, 19 figures

Traveling Wave Parametric Amplifiers (TWPAs) are extensively employed in experiments involving weak microwave signals for their highly desirable quantum-limited and broadband characteristics. However, TWPAs' broadband nature comes with the disadvantage of admitting the activation of spurious nonlinear processes, such as harmonics generation, that can potentially degrade amplification performance. Here we experimentally investigate a Josephson TWPA device with SNAIL (Superconducting Nonlinear Asymmetric Inductive Element)-based unit cells focusing on the amplification behaviour along with the generation of second and third harmonics of the pump. By comparing experimental results with transient numerical simulations, we demonstrate the influence of Josephson junctions' fabrication imperfections on the occurrence of harmonics and on the gain behaviour., Comment: accepted version

Dataset condensation can be used to reduce the computational cost of training multiple models on a large dataset by condensing the training dataset into a small synthetic set. State-of-the-art approaches rely on matching the model gradients between the real and synthetic data. However, there is no theoretical guarantee of the generalizability of the condensed data: data condensation often generalizes poorly across hyperparameters/architectures in practice. This paper considers a different condensation objective specifically geared toward hyperparameter search. We aim to generate a synthetic validation dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable. We propose a novel hyperparameter-calibrated dataset condensation (HCDC) algorithm, which obtains the synthetic validation dataset by matching the hyperparameter gradients computed via implicit differentiation and efficient inverse Hessian approximation. Experiments demonstrate that the proposed framework effectively maintains the validation-performance rankings of models and speeds up hyperparameter/architecture search for tasks on both images and graphs.

We study spontaneous photon decay into multiple photons triggered by strong non-linearities in a superconducting quantum simulator of the boundary sine-Gordon impurity model. Previously, spectroscopic signatures of photon-conversion were reported and evidenced as resonances in the many-body spectrum of these systems. Here, we report on the observation of multi-mode fluorescence of a small Josephson junction embedded in a high impedance superconducting transmission line. Measurement of the down-converted photons is achieved using state-of-the-art broadband parametric amplifiers. Photon triplet emission is explicitly demonstrated at a given frequency as the counterpart of inelastic photon decay at three-times the emission frequency. These results open exciting prospects for the burgeoning field of many-body quantum optics and offer a direct signature of the ultra-strong light-matter coupling., Comment: 16 pages including supplementary material, 8 figures

High-dimensional sparse data emerge in many critical application domains such as cybersecurity, healthcare, anomaly detection, and trend analysis. To quickly extract meaningful insights from massive volumes of these multi-dimensional data, scientists employ unsupervised analysis tools based on tensor decomposition (TD) methods. However, real-world sparse tensors exhibit highly irregular shapes, data distributions, and sparsity, which pose significant challenges for making efficient use of modern parallel architectures. This study breaks the prevailing assumption that compressing sparse tensors into coarse-grained structures (i.e., tensor slices or blocks) or along a particular dimension/mode (i.e., mode-specific) is more efficient than keeping them in a fine-grained, mode-agnostic form. Our novel sparse tensor representation, Adaptive Linearized Tensor Order (ALTO), encodes tensors in a compact format that can be easily streamed from memory and is amenable to both caching and parallel execution. To demonstrate the efficacy of ALTO, we accelerate popular TD methods that compute the Canonical Polyadic Decomposition (CPD) model across a range of real-world sparse tensors. Additionally, we characterize the major execution bottlenecks of TD methods on multiple generations of the latest Intel Xeon Scalable processors, including Sapphire Rapids CPUs, and introduce dynamic adaptation heuristics to automatically select the best algorithm based on the sparse tensor characteristics. Across a diverse set of real-world data sets, ALTO outperforms the state-of-the-art approaches, achieving more than an order-of-magnitude speedup over the best mode-agnostic formats. Compared to the best mode-specific formats, which require multiple tensor copies, ALTO achieves more than 5.1x geometric mean speedup at a fraction (25%) of their storage., Comment: We extend the results of our previous ICS paper to significantly improve the parallel performance of the Canonical Polyadic Alternating Least Squares (CP-ALS) algorithm for normally distributed data and the Canonical Polyadic Alternating Poisson Regression (CP-APR) algorithm for non-negative count data

We utilize a superconducting qubit processor to experimentally probe non-Markovian dynamics of an entangled qubit pair. We prepare an entangled state between two qubits and monitor the evolution of entanglement over time as one of the qubits interacts with a small quantum environment consisting of an auxiliary transmon qubit coupled to its readout cavity. We observe the collapse and revival of the entanglement as a signature of quantum memory effects in the environment. We then engineer the non-Markovianity of the environment by populating its readout cavity with thermal photons to show a transition from non-Markovian to Markovian dynamics, ultimately reaching a regime where the quantum Zeno effect creates a decoherence-free subspace that effectively stabilizes the entanglement between the qubits., Comment: 13 pages, 8 figures

Ising machines are a form of quantum-inspired processing-in-memory computer which has shown great promise for overcoming the limitations of traditional computing paradigms while operating at a fraction of the energy use. The process of designing Ising machines is known as the reverse Ising problem. Unfortunately, this problem is in general computationally intractable: it is a nonconvex mixed-integer linear programming problem which cannot be naively brute-forced except in the simplest cases due to exponential scaling of runtime with number of spins. We prove new theoretical results which allow us to reduce the search space to one with quadratic scaling. We utilize this theory to develop general purpose algorithmic solutions to the reverse Ising problem. In particular, we demonstrate Ising formulations of 3-bit and 4-bit integer multiplication which use fewer total spins than previously known methods by a factor of more than three. Our results increase the practicality of implementing such circuits on modern Ising hardware, where spins are at a premium., Comment: 14 pages, 3 figures, submitted to IEEE International Conference on Rebooting Computing 2023

It is commonly observed that deep networks trained for classification exhibit class-selective neurons in their early and intermediate layers. Intriguingly, recent studies have shown that these class-selective neurons can be ablated without deteriorating network function. But if class-selective neurons are not necessary, why do they exist? We attempt to answer this question in a series of experiments on ResNet-50s trained on ImageNet. We first show that class-selective neurons emerge during the first few epochs of training, before receding rapidly but not completely; this suggests that class-selective neurons found in trained networks are in fact vestigial remains of early training. With single-neuron ablation experiments, we then show that class-selective neurons are important for network function in this early phase of training. We also observe that the network is close to a linear regime in this early phase; we thus speculate that class-selective neurons appear early in training as quasi-linear shortcut solutions to the classification task. Finally, in causal experiments where we regularize against class selectivity at different points in training, we show that the presence of class-selective neurons early in training is critical to the successful training of the network; in contrast, class-selective neurons can be suppressed later in training with little effect on final accuracy. It remains to be understood by which mechanism the presence of class-selective neurons in the early phase of training contributes to the successful training of networks.

A traveling wave parametric amplifier has been integrated in the haloscope of the QUAX experiment. A search for dark matter axions has been performed with a high Q dielectric cavity immersed in a 8 T magnetic field and read by a detection chain having a system noise temperature of about 2.1 K at the frequency of 10.353 GHz. Scanning has been conducted by varying the cavity frequency using sapphire rods immersed into the cavity. At multiple operating frequencies, the sensitivity of the instrument was at the level of viable axion models.

Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person., Comment: Published in Tackling Climate Change with Machine Learning workshop, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)

In recent decades, the laws of thermodynamics have been pushed down to smaller and smaller scales, within the field of stochastic thermodynamics and state-of-art experiments performed on mesoscopic systems. These measurements concern electrons, photons, and mesoscopic mechanical objects. Here we report on the measurements of thermal fluctuations of a single mechanical mode in-equilibrium with a heat reservoir. The device under study is a nanomechanical beam with a first flexure resonating at 3.8MHz, cooled down to temperatures in the range from 100mK to 400mK. The technique is constructed around a microwave opto-mechanical setup using a cryogenic High Electron Mobility Transistor, and is based on two parametric amplifications implemented in series: an in-built opto-mechanical 'blue-detuned' pumping plus a Traveling Wave Parametric Amplifier stage. We demonstrate our ability to resolve energy fluctuations of the mechanical mode in real-time up to the fastest relevant speed given by the mechanical relaxation rate. The energy probability distribution is then exponential, matching the expected Boltzmann distribution. The variance of fluctuations is found to be $(k_B T)^2$ with no free parameters. Our microwave detection floor is about 3 Standard Quantum Limit at 6GHz; the resolution of our fastest acquisition tracks reached about 100 phonons, and is related to the rather poor opto-mechanical coupling of the device ($g_0/2\pi\approx 0.5~$Hz). This result is deeply in the classical regime, but shall be extended to the quantum case in the future with systems presenting a much larger $g_0$ (up to $2\pi\times 250~$Hz), potentially reaching the resolution of a single mechanical quantum. We believe that it will open a new experimental field: phonon-based quantum stochastic thermodynamics, with fundamental implications for quantum heat transport and macroscopic mechanical quantum coherence., Comment: With appendix

In this paper we will describe the characterisation of a rf detection chain based on a travelling wave parametric amplifier (TWPA). The detection chain is meant to be used for dark matter axion searches and thus it is mounted coupled to a high Q microwave resonant cavity. A system noise temperature $T_{\rm sys} = (3.3 \pm 0.1$) K has been measured at a frequency of 10.77 GHz, using a novel scheme allowing measurement of $T_{\rm sys} $ exactly at the cavity output port., Comment: 8 pages

With a large portfolio of elemental quantum components, superconducting quantum circuits have contributed to dramatic advances in microwave quantum optics. Of these elements, quantum-limited parametric amplifiers have proven to be essential for low noise readout of quantum systems whose energy range is intrinsically low (tens of $\mu$eV ). They are also used to generate non classical states of light that can be a resource for quantum enhanced detection. Superconducting parametric amplifiers, like quantum bits, typically utilize a Josephson junction as a source of magnetically tunable and dissipation-free nonlinearity. In recent years, efforts have been made to introduce semiconductor weak links as electrically tunable nonlinear elements, with demonstrations of microwave resonators and quantum bits using semiconductor nanowires, a two dimensional electron gas, carbon nanotubes and graphene. However, given the challenge of balancing nonlinearity, dissipation, participation, and energy scale, parametric amplifiers have not yet been implemented with a semiconductor weak link. Here we demonstrate a parametric amplifier leveraging a graphene Josephson junction and show that its working frequency is widely tunable with a gate voltage. We report gain exceeding 20 dB and noise performance close to the standard quantum limit. Our results complete the toolset for electrically tunable superconducting quantum circuits and offer new opportunities for the development of quantum technologies such as quantum computing, quantum sensing and fundamental science.

Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectures. In contrast to prior work, the proposed Blocked Linearized Coordinate (BLCO) format enables efficient out-of-memory computation of tensor algorithms using a unified implementation that works on a single tensor copy. Our adaptive blocking and linearization strategies not only meet the resource constraints of GPU devices, but also accelerate data indexing, eliminate control-flow and memory-access irregularities, and reduce kernel launching overhead. To address the substantial synchronization cost on GPUs, we introduce an opportunistic conflict resolution algorithm, in which threads collaborate instead of contending on memory access to discover and resolve their conflicting updates on-the-fly, without keeping any auxiliary information or storing non-zero elements in specific mode orientations. As a result, our framework delivers superior in-memory performance compared to prior state-of-the-art, and is the only framework capable of processing out-of-memory tensors. On the latest Intel and NVIDIA GPUs, BLCO achieves 2.12-2.6X geometric-mean speedup (with up to 33.35X speedup) over the state-of-the-art mixed-mode compressed sparse fiber (MM-CSF) on a range of real-world sparse tensors., Comment: Accepted to ICS 2022

Traveling wave parametric amplifiers (TWPAs) have recently emerged as essential tools for broadband near quantum-limited amplification. However, their use to generate microwave quantum states still misses an experimental demonstration. In this letter, we report operation of a TWPA as a source of two-mode squeezed microwave radiation. We demonstrate broadband entanglement generation between two modes separated by up to 400 MHz by measuring logarithmic negativity between 0.27 and 0.51 and collective quadrature squeezing below the vacuum limit between 1.5 and 2.1 dB. This work opens interesting perspectives for the exploration of novel microwave photonics experiments with possible applications in quantum sensing and continuous variable quantum computing., Comment: version accepted for publication

Quantum-limited microwave parametric amplifiers are genuine key pillars for rising quantum technologies and in general for applications that rely on the successful readout of weak microwave signals by adding only the minimum amount of noise allowed by quantum mechanics. In this perspective, after providing a brief overview on the different families of parametric microwave amplifiers, we focus on traveling wave parametric amplifiers (TWPAs), underlining the key achievements of the last years and the present open challenges. We discuss also possible new research directions beyond amplification such as exploring these devices as a platform for multi-mode entanglement generation and for the development of single photon detectors., Comment: version accepted for publication

Blood oxygen saturation (SpO$_2$) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO$_2$. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO$_2$ monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO$_2$ information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO$_2$ prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.

We present 3D calculations for dielectric haloscopes such as the currently envisioned MADMAX experiment. For ideal systems with perfectly flat, parallel and isotropic dielectric disks of finite diameter, we find that a geometrical form factor reduces the emitted power by up to $30\,\%$ compared to earlier 1D calculations. We derive the emitted beam shape, which is important for antenna design. We show that realistic dark matter axion velocities of $10^{-3} c$ and inhomogeneities of the external magnetic field at the scale of $10\,\%$ have negligible impact on the sensitivity of MADMAX. We investigate design requirements for which the emitted power changes by less than $20\,\%$ for a benchmark boost factor with a bandwidth of $50\,{\rm MHz}$ at $22\,{\rm GHz}$, corresponding to an axion mass of $90\,\mu{\rm eV}$. We find that the maximum allowed disk tilt is $100\,\mu{\rm m}$ divided by the disk diameter, the required disk planarity is $20\,\mu{\rm m}$ (min-to-max) or better, and the maximum allowed surface roughness is $100\,\mu{\rm m}$ (min-to-max). We show how using tiled dielectric disks glued together from multiple smaller patches can affect the beam shape and antenna coupling., Comment: 25 pages, 12 figures; added detail, updated references, matches published version

The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their irregular shapes and data distributions. We propose the Adaptive Linearized Tensor Order (ALTO) format, a novel mode-agnostic (general) representation that keeps neighboring nonzero elements in the multi-dimensional space close to each other in memory. To generate the indexing metadata, ALTO uses an adaptive bit encoding scheme that trades off index computations for lower memory usage and more effective use of memory bandwidth. Moreover, by decoupling its sparse representation from the irregular spatial distribution of nonzero elements, ALTO eliminates the workload imbalance and greatly reduces the synchronization overhead of tensor computations. As a result, the parallel performance of ALTO-based tensor operations becomes a function of their inherent data reuse. On a gamut of tensor datasets, ALTO outperforms an oracle that selects the best state-of-the-art format for each dataset, when used in key tensor decomposition operations. Specifically, ALTO achieves a geometric mean speedup of 8X over the best mode-agnostic (coordinate and hierarchical coordinate) formats, while delivering a geometric mean compression ratio of 4.3X relative to the best mode-specific (compressed sparse fiber) formats., Comment: Accepted to ICS 2021

Josephson meta-materials have recently emerged as very promising platform for superconducting quantum science and technologies. Their distinguishing potential resides in ability to engineer them at sub-wavelength scales, which allows complete control over wave dispersion and nonlinear interaction. In this article we report a versatile Josephson transmission line with strong third order nonlinearity which can be tuned from positive to negative values, and suppressed second order non linearity. As an initial implementation of this multipurpose meta-material, we operate it to demonstrate reversed Kerr phase-matching mechanism in traveling wave parametric amplification. Compared to previous state of the art phase matching approaches, this reversed Kerr phase matching avoids the presence of gaps in transmission, can reduce gain ripples, and allows in situ tunability of the amplification band over an unprecedented wide range. Besides such notable advancements in the amplification performance with direct applications to superconducting quantum computing and generation of broadband squeezing, the in-situ tunability with sign reversal of the nonlinearity in traveling wave structures, with no counterpart in optics to the best of our knowledge, opens exciting experimental possibilities in the general framework of microwave quantum optics, single-photon detection and quantum limited amplification.

Dynamically triggered earthquakes and tremor generate two classes of weak seismic signals whose detection, identification, and authentication traditionally call for laborious analyses. Machine learning (ML) has grown in recent years to be a powerful efficiency-boosting tool in geophysical analyses, including the detection of specific signals in time series. However, detecting weak signals that are buried in noise challenges ML algorithms, in part because ubiquitous training data is not always available. Under these circumstances, ML can be as ineffective as human experts are inefficient. At this intersection of effectiveness and efficiency, we leverage a third tool that has grown in popularity over the past decade: Citizen science. Citizen science project Earthquake Detective leverages the eyes and ears of volunteers to detect and classify weak signals in seismograms from potentially dynamically triggered (PDT) events. Here, we present the Earthquake Detective data set - A crowd-sourced set of labels on PDT earthquakes and tremor. We apply Machine Learning to classify these PDT seismic events and explore the challenges faced in segregating and classifying such weak signals. We confirm that with an image- and wavelet-based algorithm, machine learning can detect signals from small earthquakes. In addition, we report that our ML algorithm can also detect signals from PDT tremor, which has not been previously demonstrated. The citizen science data set of classifications and ML code are available online., Comment: Updated version of the paper presented at AI for Earth Sciences Workshop, NeurIPS 2020

We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement learning research in computer audition, where agents must learn to listen to the world around them to navigate. OtoWorld is built on three open source libraries: OpenAI Gym for environment and agent interaction, PyRoomAcoustics for ray-tracing and acoustics simulation, and nussl for training deep computer audition models. OtoWorld is the audio analogue of GridWorld, a simple navigation game. OtoWorld can be easily extended to more complex environments and games. To solve one episode of OtoWorld, an agent must move towards each sounding source in the auditory scene and "turn it off". The agent receives no other input than the current sound of the room. The sources are placed randomly within the room and can vary in number. The agent receives a reward for turning off a source. We present preliminary results on the ability of agents to win at OtoWorld. OtoWorld is open-source and available., Comment: Published in Self Supervision in Audio and Speech Workshop, 37th International Conference on Machine Learning, Vienna, Austria (ICML 2020)

The Transiting Exoplanet Survey Satellite (TESS) is observing bright known planet-host stars across almost the entire sky. These stars have been subject to extensive ground-based observations, providing a large number of radial velocity (RV) measurements. In this work we use the new TESS photometric observations to characterize the star $\lambda^2$ Fornacis, and following this to update the parameters of the orbiting planet $\lambda^2$ For b. We measure the p-mode oscillation frequencies in $\lambda^2$ For, and in combination with non-seismic parameters estimate the stellar fundamental properties using stellar models. Using the revised stellar properties and a time series of archival RV data from the UCLES, HIRES and HARPS instruments spanning almost 20 years, we refit the orbit of $\lambda^2$ For b and search the RV residuals for remaining variability. We find that $\lambda^2$ For has a mass of $1.16\pm0.03$M$_\odot$ and a radius of $1.63\pm0.04$R$_\odot$, with an age of $6.3\pm0.9$Gyr. This and the updated RV measurements suggest a mass of $\lambda^2$ For b of $16.8^{+1.2}_{-1.3}$M$_\oplus$, which is $\sim5$M$_\oplus$ less than literature estimates. We also detect a periodicity at 33 days in the RV measurements, which is likely due to the rotation of the host star. While previous literature estimates of the properties of $\lambda^2$ are ambiguous, the asteroseismic measurements place the star firmly at the early stage of its subgiant evolutionary phase. Typically only short time series of photometric data are available from TESS, but by using asteroseismology it is still possible to provide tight constraints on the properties of bright stars that until now have only been observed from the ground. This prompts a reexamination of archival RV data from the past few decades to update the characteristics of the planet hosting systems observed by TESS for which asteroseismology is possible., Comment: Accepted for publication in A&A. 12 pages, 11 figures

Over the course of its history, the Milky Way has ingested multiple smaller satellite galaxies. While these accreted stellar populations can be forensically identified as kinematically distinct structures within the Galaxy, it is difficult in general to precisely date the age at which any one merger occurred. Recent results have revealed a population of stars that were accreted via the collision of a dwarf galaxy, called \textit{Gaia}-Enceladus, leading to a substantial pollution of the chemical and dynamical properties of the Milky Way. Here, we identify the very bright, naked-eye star $\nu$\,Indi as a probe of the age of the early in situ population of the Galaxy. We combine asteroseismic, spectroscopic, astrometric, and kinematic observations to show that this metal-poor, alpha-element-rich star was an indigenous member of the halo, and we measure its age to be $11.0 \pm 0.7$ (stat) $\pm 0.8$ (sys)$\,\rm Gyr$. The star bears hallmarks consistent with it having been kinematically heated by the \textit{Gaia}-Enceladus collision. Its age implies that the earliest the merger could have begun was 11.6 and 13.2 Gyr ago at 68 and 95% confidence, respectively. Input from computations based on hierarchical cosmological models tightens (i.e. reduces) slightly the above limits., Comment: Accepted for publication as a Letter in Nature Astronomy (26 pages, 7 figures, including main article and methods section)

An amplifier combining noise performances as close as possible to the quantum limit with large bandwidth and high saturation power is highly desirable for many solid state quantum technologies such as high fidelity qubit readout or high sensitivity electron spin resonance for example. Here we introduce a new Traveling Wave Parametric Amplifier based on Superconducting QUantum Interference Devices. It displays a 3 GHz bandwidth, a -102 dBm 1-dB compression point and added noise near the quantum limit. Compared to previous state-of-the-art, it is an order of magnitude more compact, its characteristic impedance is in-situ tunable and its fabrication process requires only two lithography steps. The key is the engineering of a gap in the dispersion relation of the transmission line. This is obtained using a periodic modulation of the SQUID size, similarly to what is done with photonic crystals. Moreover, we provide a new theoretical treatment to describe the non-trivial interplay between non-linearity and such periodicity. Our approach provides a path to co-integration with other quantum devices such as qubits given the low footprint and easy fabrication of our amplifier., Comment: 6 pages, 4 figures, Appendixes

We present the discovery of TOI-197.01, the first transiting planet identified by the Transiting Exoplanet Survey Satellite (TESS) for which asteroseismology of the host star is possible. TOI-197 (HIP116158) is a bright (V=8.2 mag), spectroscopically classified subgiant which oscillates with an average frequency of about 430 muHz and displays a clear signature of mixed modes. The oscillation amplitude confirms that the redder TESS bandpass compared to Kepler has a small effect on the oscillations, supporting the expected yield of thousands of solar-like oscillators with TESS 2-minute cadence observations. Asteroseismic modeling yields a robust determination of the host star radius (2.943+/-0.064 Rsun), mass (1.212 +/- 0.074 Msun) and age (4.9+/-1.1 Gyr), and demonstrates that it has just started ascending the red-giant branch. Combining asteroseismology with transit modeling and radial-velocity observations, we show that the planet is a "hot Saturn" (9.17+/-0.33 Rearth) with an orbital period of ~14.3 days, irradiance of 343+/-24 Fearth, moderate mass (60.5 +/- 5.7 Mearth) and density (0.431+/-0.062 gcc). The properties of TOI-197.01 show that the host-star metallicity - planet mass correlation found in sub-Saturns (4-8 Rearth) does not extend to larger radii, indicating that planets in the transition between sub-Saturns and Jupiters follow a relatively narrow range of densities. With a density measured to ~15%, TOI-197.01 is one of the best characterized Saturn-sized planets to date, augmenting the small number of known transiting planets around evolved stars and demonstrating the power of TESS to characterize exoplanets and their host stars using asteroseismology., Comment: 12 pages (excluding author list and references), 9 figures, 4 tables, accepted for publication in AJ. An electronic version of Table 3 is available as an ancillary file (sidebar on the right)