Search

Searches for high frequency gravitational waves using cavities based on the Gertsenshtein effect were recently proposed, building off existing axion dark matter experiments. In particular, the sensitivity of axion dark matter experiments using metamaterial plasmas (tunable plasma haloscopes) to gravitational waves has not been explored in detail. Here we perform a full analysis of gravitational wave detection in plasma haloscopes, showing that the baseline design of experiments such as ALPHA is several orders of magnitude less sensitive than previously thought. We show how simple changes to the experiment can recover that sensitivity and lead to a powerful gravitational wave detector in the order of $(10-50)$ GHz frequency range., Comment: 15 pages, 7 figures

Wire media (WM) resonators have emerged as promising realization for plasma haloscopes -- devices designed to detect axions, a potential component of dark matter. Key factors influencing the detection probability include cavity volume, resonance quality factor, and form factor. While the form factor has been explored for resonant frequency tuning, its optimization for axion detection remains unexplored. In this work, we present a novel approach to significantly enhance the form factor of WM plasma haloscopes. By shifting the metal walls of the resonator by a quarter wavelength, we effectively convert an electric wall boundary condition into a magnetic wall one, allowing for an almost uniform mode. Theoretical analysis and numerical simulations confirm that this modification improves the electric field profile and boosts the form factor. We validate these findings through experimental results from two prototype resonators: one with a standard geometry and another with a quarter-wave air gap between the WM and the walls. Additionally, our method provides a simple way to control the field profile within WM cavities, which can be explored for further applications.

The Coronagraphic Instrument onboard the Nancy Grace Roman Space Telescope is an important stepping stone towards the characterization of habitable, rocky exoplanets. In a technology demonstration phase conducted during the first 18 months of the mission (expected to launch in late 2026), novel starlight suppression technology may enable direct imaging of a Jupiter analog in reflected light. Here we summarize the current activities of the Observation Planning working group formed as part of the Community Participation Program. This working group is responsible for target selection and observation planning of both science and calibration targets in the technology demonstration phase of the Roman Coronagraph. We will discuss the ongoing efforts to expand target and reference catalogs, and to model astrophysical targets (exoplanets and circumstellar disks) within the Coronagraph's expected sensitivity. We will also present preparatory observations of high priority targets., Comment: Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 1309255 (2024)

We analyse continuum and molecular emission, observed with ALMA, from the dust-enshrouded intermediate-mass AGB star OH 30.1 -0.7. We find a secondary peak in the continuum maps, "feature B", separated by 4.6" from the AGB star, which corresponds to a projected separation of $1.8 \times 10^4$ au, placing a lower limit on the physical separation. This feature is most likely composed of cold dust and is likely to be ejecta associated with the AGB star, though we cannot rule out that it is a background object. The molecular emission we detect includes lines of CO, SiS, CS, SO$_2$, NS, NaCl, and KCl. We find that the NS emission is off centre and arranged along an axis perpendicular to the direction of feature B, indicative of a UV-emitting binary companion (e.g. a G-type main sequence star or hotter), perhaps on an eccentric orbit, contributing to its formation. However, the NaCl and KCl emission constrain the nature of that companion to not be hotter than a late B-type main sequence star. We find relatively warm emission arising from the inner wind and detect several vibrationally excited lines of SiS (3 = 1), NaCl (up to 3 = 4) and KCl (up to 3 = 2), and emission from low energy levels in the mid to outer envelope, as traced by SO$_2$. The CO emission is abruptly truncated around 3.5" or 14,000 au from the continuum peak, suggesting that mass loss at a high rate may have commenced as little as 2800 years ago., Comment: Accepted for publication in MNRAS

We present significant upgrades to the VAMPIRES instrument, a visible-light (600 nm to 800 nm) high-contrast imaging polarimeter integrated within SCExAO on the Subaru telescope. Key enhancements include new qCMOS detectors, coronagraphs, polarization optics, and a multiband imaging mode, improving sensitivity, resolution, and efficiency. These upgrades position VAMPIRES as a powerful tool for studying sub-stellar companions, accreting protoplanets, circumstellar disks, stellar jets, stellar mass-loss shells, and solar system objects. The instrument achieves angular resolutions from 17 mas to 21 mas and Strehl ratios up to 60\%, with 5$\sigma$ contrast limits of $10^{\text{-}4}$ at 0.1'' to $10^{\text{-}6}$ beyond 0.5''. We demonstrate these capabilities through spectro-polarimetric coronagraphic imaging of the HD 169142 circumstellar disk, ADI+SDI imaging of the sub-stellar companion HD 1160B, narrowband H$\alpha$ imaging of the R Aqr emission nebula, and spectro-polarimetric imaging of Neptune., Comment: 36 pages, 33 figures, accepted to PASP

End-to-End Encryption (E2EE) ensures that only the intended recipient(s) can read messages. Popular instant messaging (IM) applications such as Signal, WhatsApp, Apple's iMessage, and Telegram claim to offer E2EE. However, client-side scanning (CSS) undermines these claims by scanning all messages, including text, images, audio, and video files, on both sending and receiving ends. Industry and government parties support CSS to combat harmful content such as child pornography, terrorism, and other illegal activities. In this paper, we introduce ARSecure, a novel end-to-end encryption messaging solution utilizing augmented reality glasses. ARSecure allows users to encrypt and decrypt their messages before they reach their phone devices, effectively countering the CSS technology in E2EE systems.

We present Subaru/SCExAO + CHARIS broadband (JHK) integral field spectroscopy of HD 34700 A in polarized light. CHARIS has the unique ability to obtain polarized integral field images at 22 wavelength channels in broadband, as the incoming light is first split into different polarization states before passing though the lenslet array. We recover the transition disk around HD 34700 A in multiband polarized light in our data. We combine our polarized intensity data with previous total intensity data to examine the scattering profiles, scattering phase functions and polarized fraction of the disk at multiple wavelengths. We also carry out 3D Monte Carlo radiative transfer simulations of the disk using MCFOST, and make qualitative comparisons between our models and data to constrain dust grain properties. We find that in addition to micron-sized dust grains, a population of sub-micron grains is needed to match the surface brightness in polarized light and polarized fraction. This could indicate the existence of a population of small grains in the disk, or it could be caused by Mie theory simulations using additional small grains to compensate for sub-micron structures of real dust aggregates. We find models that match the polarized fraction of the data but the models do not apply strong constraints on the dust grain type or compositions. We find no models that can match all observed properties of the disk. More detailed modeling using realistic dust aggregates with irregular surfaces and complex structures is required to further constrain the dust properties.

In-situ sensing devices need to be deployed in remote environments for long periods of time; minimizing their power consumption is vital for maximising both their operational lifetime and coverage. We introduce Terracorder -- a versatile multi-sensor device -- and showcase its exceptionally low power consumption using an on-device reinforcement learning scheduler. We prototype a unique device setup for biodiversity monitoring and compare its battery life using our scheduler against a number of fixed schedules; the scheduler captures more than 80% of events at less than 50% of the number of activations of the best-performing fixed schedule. We then explore how a collaborative scheduler can maximise the useful operation of a network of devices, improving overall network power consumption and robustness., Comment: Preprint

Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path methods are one such class of attribution methods susceptible to adversarial attacks. Adversarial learning is typically phrased as a constrained optimisation problem. In this work, we propose algebraic adversarial examples and study the conditions under which one can generate adversarial examples for integrated gradients. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples.

Binary interactions have been proposed to explain a variety of circumstellar structures seen around evolved stars, including asymptotic giant branch (AGB) stars and planetary nebulae. Studies resolving the circumstellar envelopes of AGB stars have revealed spirals, discs and bipolar outflows, with shaping attributed to interactions with a companion. For the first time, we have used a combined chemical and dynamical analysis to reveal a highly eccentric and long-period orbit for W Aquilae, a binary system containing an AGB star and a main sequence companion. Our results are based on anisotropic SiN emission, the first detections of NS and SiC towards an S-type star, and density structures observed in the CO emission. These features are all interpreted as having formed during periastron interactions. Our astrochemistry-based method can yield stringent constraints on the orbital parameters of long-period binaries containing AGB stars, and will be applicable to other systems., Comment: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature's AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41550-023-02154-y

The 16 Myr-old A0V star HD 156623 in the Scorpius--Centaurus association hosts a high-fractional-luminosity debris disk, recently resolved in scattered light for the first time by the Gemini Planet Imager (GPI) in polarized intensity. We present new analysis of the GPI H-band polarimetric detection of the HD 156623 debris disk, with particular interest in its unique morphology. This debris disk lacks a visible inner clearing, unlike the majority of low-inclination disks in the GPI sample and in Sco-Cen, and it is known to contain CO gas, positioning it as a candidate ``hybrid'' or ``shielded'' disk. We use radiative transfer models to constrain the geometric parameters of the disk based on scattered light data and thermal models to constrain the unresolved inner radius based on the system's spectral energy distribution (SED). We also compute a measurement of the polarized scattering phase function, adding to the existing sample of empirical phase function measurements. We find that HD 156623's debris disk inner radius is constrained to less than 26.6 AU from scattered light imagery and less than 13.4 AU from SED modeling at a 99.7% confidence interval, and suggest that gas drag may play a role in retaining sub-blowout size dust grains so close to the star., Comment: 18 pages, 10 figures. Accepted for publication in AJ

Polarization aberrations originating from the telescope and high-contrast imaging instrument optics introduce polarization-dependent speckles and associated errors in the image plane, affecting the measured exoplanet signal. Understanding this effect is critical for future space-based high-contrast imaging instruments that aim to image the Earth analogs with 1e-10 raw contrast and characterize their atmospheres. We present end-to-end modeling of the polarization aberrations for a high-contrast imaging testbed, SCoOB. We use a vector vortex coronagraph (VVC) as the focal plane mask, incorporate polarization filtering, and estimate the peak contrast in the dark hole region. The dominant polarization aberrations in the system are retardance defocus and tilt due to the OAPs and fold mirrors. Although the mean contrast in the dark hole region remains unaffected by the polarization aberrations, we see brighter speckles limiting the contrast to 1e-9 at smaller inner working angles. We extend the simulations using the measured retardance maps for the VVC. We find that the mean contrast in SCoOB is more sensitive to the VVC and the QWP retardance errors than the polarization aberrations., Comment: 11 pages, 10 figures, SPIE Astronomical Telescopes and Instruments conference 2024, Paper no: 13092-189

High-contrast imaging in the next decade aims to image exoplanets at smaller angular separations and deeper contrasts than ever before. A problem that has recently garnered attention for telescopes equipped with high-contrast coronagraphs is polarization aberration arising from the optics. These aberrations manifest as low-order aberrations of different magnitudes for orthogonal polarization states and spread light into the dark hole of the coronagraph that cannot be fully corrected. The origin of polarization aberrations has been modeled at the telescope level. However, we don't fully understand how polarization aberrations arise at the instrument level. To directly measure this effect, we construct a dual-rotating-retarder polarimeter around the SCoOB high-contrast imaging testbed to measure its Mueller matrix. With this matrix, we directly characterize the diattenuation, retardance, and depolarization of the instrument as a function of position in the exit pupil. We measure the polarization aberrations in the Lyot plane to understand how polarization couples into high-contrast imaging residuals., Comment: 16 pages, 13 figures

Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumour microenvironment at the cellular level. Current state-of-the-art multiplexed immunofluorescence image analysis pipelines depend on cell feature representations characterised by morphological and stain intensity-based metrics generated using simple statistical and machine learning-based tools. However, these methods are not capable of generating complex representations of cells. We propose a deep learning-based cell feature extraction model using a variational autoencoder with supervision using a latent subspace to extract cell features in mIF images. We perform cell phenotype classification using a cohort of more than 44,000 multiplexed immunofluorescence cell image patches extracted across 1,093 tissue microarray cores of breast cancer patients, to demonstrate the success of our model against current and alternative methods.

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Using homological techniques we show that a pin-anchored frame that involves only moments and shears provides a conceptual bridge between the statics of moment frames and the kinematics of pin-jointed trusses. One immediate result is a long exact sequence whose alternating sum of dimensions gives a novel counting rule for self-stresses and mechanisms. This combines the Maxwell-Calladine count for pin-jointed trusses with the circuit rank (first Betti number) associated with self-stresses in moment frames. These relations apply to frames in 2, 3 or any dimensions. This work heralds a shift towards a deeper study of the relationships and dualities that exist between structural equilibria and kinematics., Comment: 5 figures, submitted to IASS 2024 conference

The observed diversity of exoplanets can possibly be traced back to the planet formation processes. Planet-disk interactions induce sub-structures in the circumstellar disk that can be revealed via scattered light observations. However, a high-contrast imaging technique such as polarimetric differential imaging (PDI) must first be applied to suppress the stellar diffraction halo. In this work we present the PDI PiPelIne for NACO data (PIPPIN), which reduces the archival polarimetric observations made with the NACO instrument at the Very Large Telescope. Prior to this work, such a comprehensive pipeline to reduce polarimetric NACO data did not exist. We identify a total of 243 datasets of 57 potentially young stellar objects observed before NACO's decommissioning. The PIPPIN pipeline applies various levels of instrumental polarisation correction and is capable of reducing multiple observing setups, including half-wave plate or de-rotator usage and wire-grid observations. A novel template-matching method is applied to assess the detection significance of polarised signals in the reduced data. In 22 of the 57 observed targets, we detect polarised light resulting from a scattering of circumstellar dust. The detections exhibit a collection of known sub-structures, including rings, gaps, spirals, shadows, and in- or outflows of material. Since NACO was equipped with a near-infrared wavefront sensor, it made unique polarimetric observations of a number of embedded protostars. This is the first time detections of the Class I objects Elia 2-21 and YLW 16A have been published. Alongside the outlined PIPPIN pipeline, we publish an archive of the reduced data products, thereby improving the accessibility of these data for future studies., Comment: Accepted for publication in A&A. For more information on PIPPIN, see: https://pippin-naco.readthedocs.io/en/latest . The reduced images are available on Zenodo: https://zenodo.org/doi/10.5281/zenodo.8348803

Vortex fiber nulling (VFN) is a technique for detecting and characterizing faint companions at small separations from their host star. A near-infrared ($\sim2.3 \mu$m) VFN demonstrator mode was deployed on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck Observatory and presented earlier. In this paper, we present the first VFN companion detections. Three targets, HIP 21543 Ab, HIP 94666 Ab, and HIP 50319 B, were detected with host-companion flux ratios between 70 and 430 at and within one diffraction beamwidth ($\lambda/D$). We complement the spectra from KPIC VFN with flux ratio and position measurements from the CHARA Array to validate the VFN results and provide a more complete characterization of the targets. This paper reports the first direct detection of these three M dwarf companions, yielding their first spectra and flux ratios. Our observations provide measurements of bulk properties such as effective temperatures, radial velocities, and v$\sin{i}$, and verify the accuracy of the published orbits. These detections corroborate earlier predictions of the KPIC VFN performance, demonstrating that the instrument mode is ready for science observations., Comment: 13 pages, 2 figures; Accepted to ApJ Letters

The personalization of machine learning (ML) models to address data drift is a significant challenge in the context of Internet of Things (IoT) applications. Presently, most approaches focus on fine-tuning either the full base model or its last few layers to adapt to new data, while often neglecting energy costs. However, various types of data drift exist, and fine-tuning the full base model or the last few layers may not result in optimal performance in certain scenarios. We propose Target Block Fine-Tuning (TBFT), a low-energy adaptive personalization framework designed for resource-constrained devices. We categorize data drift and personalization into three types: input-level, feature-level, and output-level. For each type, we fine-tune different blocks of the model to achieve optimal performance with reduced energy costs. Specifically, input-, feature-, and output-level correspond to fine-tuning the front, middle, and rear blocks of the model. We evaluate TBFT on a ResNet model, three datasets, three different training sizes, and a Raspberry Pi. Compared with the $Block Avg$, where each block is fine-tuned individually and their performance improvements are averaged, TBFT exhibits an improvement in model accuracy by an average of 15.30% whilst saving 41.57% energy consumption on average compared with full fine-tuning., Comment: Accepetd to The 4th Workshop on Machine Learning and Systems (EuroMLSys '24)

We describe the results of a haloscope axion search performed with an 11.7 T dipole magnet at CERN. The search used a custom-made radio-frequency cavity coated with high-temperature superconducting tape. A set of 27 h of data at a resonant frequency of around 8.84 GHz was analysed. In the range of axion mass 36.5676 $\mu$eV to 36.5699 $\mu$eV, corresponding to a width of 554 kHz, no signal excess hinting at an axion-like particle was found. Correspondingly, in this mass range, a limit on the axion to photon coupling-strength was set in the range between g$_{a\gamma}\gtrsim$ 6.2e-13 GeV$^{-1}$ and g$_{a\gamma}\gtrsim$ 1.54e-13 GeV$^{-1}$ with a 95% confidence level., Comment: 19 pages, 8 figures

Explainability models are now prevalent within machine learning to address the black-box nature of neural networks. The question now is which explainability model is most effective. Probabilistic Lipschitzness has demonstrated that the smoothness of a neural network is fundamentally linked to the quality of post hoc explanations. In this work, we prove theoretical lower bounds on the probabilistic Lipschitzness of Integrated Gradients, LIME and SmoothGrad. We propose a novel metric using probabilistic Lipschitzness, normalised astuteness, to compare the robustness of explainability models. Further, we prove a link between the local Lipschitz constant of a neural network and its stable rank. We then demonstrate that the stable rank of a neural network provides a heuristic for the robustness of explainability models.

Survival risk stratification is an important step in clinical decision making for breast cancer management. We propose a novel deep learning approach for this purpose by integrating histopathological imaging, genetic and clinical data. It employs vision transformers, specifically the MaxViT model, for image feature extraction, and self-attention to capture intricate image relationships at the patient level. A dual cross-attention mechanism fuses these features with genetic data, while clinical data is incorporated at the final layer to enhance predictive accuracy. Experiments on the public TCGA-BRCA dataset show that our model, trained using the negative log likelihood loss function, can achieve superior performance with a mean C-index of 0.64, surpassing existing methods. This advancement facilitates tailored treatment strategies, potentially leading to improved patient outcomes., Comment: Keywords: Multimodal Fusion, Breast Cancer, Whole Slide Images, Survival Prediction

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88--4.78, p<0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95\% CI: 1.80--4.68, p<0.005)., Comment: Keywords: Multimodal Fusion, Breast Cancer, Whole Slide Images, Deep Neural Network, Survival Prediction

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for its high single-photon detection efficiency and low background. This makes them ideal for particle physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of events: electrical-noise, high-energy, and photonlike events. We introduce computational methods to automate the classification of events. For the first time, we experimentally verify and simulate the source of the high-energy events. We also isolate the photonlike events, the expected signal in dielectric haloscopes searching for dark matter dark photons, and achieve a record-low photonlike dark-count rate of $3.6 \times 10^{-4}$ Hz in the 0.8-3.2 eV energy range.

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust., Comment: 23+5 pages, 12+6 figures, 15 pages of Online Supplemental Material included; Accepted for publication in MNRAS

We present an unbiased $\lambda$ 3 mm spectral line survey (between 84.5 and 115.8 GHz), conducted by the Purple Mountain Observatory 13.7 meter radio telescope, together with updated modeling results, towards the carbon-rich Asymptotic Giant Branch star, IRC+10216 (CW Leo). A total of 75 spectral lines (96 transitions) are detected, and identified to arise from 19 molecules: C$_2$H, $l$-C$_3$H, C$_4$H, CN, C$_3$N, HC$_3$N, HC$_5$N, HCN, HNC, CH$_3$CN, MgNC, CO, $c$-C$_3$H$_2$, SiC$_2$, SiO, SiS, CS, C$_2$S, C$_3$S, and their isotopologues. Among them, one molecular emission line (H$^{13}$CCCN $J=13-12$) is discovered in IRC+10216 for the first time. The excitation temperature, column density, and fractional abundance of the detected species are deduced by assuming they are in local thermodynamic equilibrium. In addition, the isotopic ratios of [$^{12}$C]/[$^{13}$C], [$^{32}$S]/[$^{34}$S], [$^{28}$Si]/[$^{29}$Si], and [$^{12}$C$^{34}$S]/[$^{13}$C$^{32}$S] are obtained and found to be consistent with previous studies. Finally, we summarize all of the 106 species detected in IRC+10216 to date with their observed and modeled column densities for the convenience of future studies., Comment: 71 pages, 39 figures, 10 tables. Accepted for publication in ApJS

dCache (https://dcache.org) is a highly scalable storage system providing location-independent access to data. The data are stored across multiple data servers as complete files presented to the end-user via a single-rooted namespace. From its inception, dCache has been designed as a caching disk buffer to a tertiary tape storage system with the assumption that the latter has virtually unlimited capacity. dCache can also be configured as a disk-only storage system with no tape backend. Owing to the idea that a tape resource is infinite, or purely physically limited by budget considerations, the system has never provided for any restrictions on how much data can be stored on tape. Likewise, in the disk-only configuration, the capacity of the system is only limited by the aggregate disk capacity of the data servers. In a multi-user environment, however, this has become problematic. This presentation will describe the design and implementation of a user- and group-based quota system, that allows to manage tape and disk space allocations, as part of dCache namespace., Comment: 26th Intl Conf Computing High Energy & Nuclear Phys (CHEP 2023)

Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy., Comment: Paper as accepted on Quantum on 2024-09-18. The method showcased in this work is also available as a Jupyter notebook at https://github.com/qiskit-community/qiskit-machine-learning/tree/IEEE2024

In the presence of axion dark matter, fermion spins experience an "axion wind" torque and an "axioelectric" force. We investigate new experimental probes of these effects and find that magnetized analogs of multilayer dielectric haloscopes can explore orders of magnitude of new parameter space for the axion-electron coupling. We also revisit the calculation of axion absorption into in-medium excitations, showing that axioelectric absorption is screened in spin-polarized targets, and axion wind absorption can be characterized in terms of a magnetic energy loss function. Finally, our detailed theoretical treatment allows us to critically examine recent claims in the literature. We find that axioelectric corrections to electronic energy levels are smaller than previously estimated and that the purported electron electric dipole moment due to a constant axion field is entirely spurious., Comment: 58 pages, 8 figures. v2: discussion expanded, matches journal version