Search

The mass accretion rates of young stellar objects (YSOs) are key to understanding how stars form, how their circumstellar disks evolve, and even how planets form. We develop a Bayesian framework to determine the accretion rates of a sample of 15 YSOs using archival data from the VIRUS spectrograph ($R \sim 800$, 3500-5500\r{A}) on the Hobby-Eberly Telescope. We are publicly releasing our developed tool, dubbed nuts-for-ysos, as a Python package which can also be applied to other spectroscopic datasets. The nuts-for-ysos code fits a simple accretion model to the near-UV and optical continuum of each VIRUS spectrum. Our Bayesian approach aims to identify correlations between model parameters using the No U-Turn Sampler (NUTS). Moreover, this approach self-consistently incorporates all parameter uncertainties, allowing for a thorough estimation of the probability distribution for accretion rate not accomplished in previous works. Using nuts-for-ysos, we derive accretion rates of each YSO. We then verify the reliability of our method by comparing to results separately derived from only the spectral emission lines, and to results from earlier studies of the Lupus, Chamaeleon I, and NGC1333 regions. Finally, we discuss what qualitative trends, covariances, and degeneracies were found among model parameters. The technique developed in this paper is a useful improvement that can be applied in the future to larger samples of YSOs observed by VIRUS or other spectrographs., Comment: 51 pages, 50 figures, accepted for publication in The Astronomical Journal. New version with typo/formatting corrections

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms.

Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, the apparent source structure will depend on survey sensitivity, resolution and the observing frequency. Associated discrete radio components and their originating host galaxy are typically identified through a visual comparison of radio and mid-infrared survey images. We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in 3.6 um infrared images from the Wide-field Infrared Survey Explorer (WISE) and the Spitzer Space Telescope. This first data release consists of 100,185 classifications for 99,146 radio sources from the FIRST survey and 583 radio sources from the ATLAS survey. We include two tables for each of the FIRST and ATLAS surveys: 1)the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. The reliability of the DR1 catalogue has been further demonstrated through several parallel studies which used the pre-release versions of this catalogue to train and prototype machine learning-based classifiers. We also include a brief description of the radio source populations catalogued by RGZ DR1., Comment: 20 pages, 19 figures, accepted for publication in MNRAS

We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particularly for images of complex scenes. Our approach employs a series of diffusion models to progressively generate latent variables at different semantic levels. Each model in this series is conditioned on the output of the preceding higher-level models, culminating in image generation. Hierarchical latent variables guide the generation process along predefined semantic pathways, allowing our approach to capture intricate structural details while significantly improving image quality. To construct these latent variables, we leverage a pre-trained visual encoder, which learns strong semantic visual representations, and modulate its capacity via dimensionality reduction and noise injection. Across multiple datasets, our system demonstrates significant enhancements in image quality for both unconditional and class/text conditional generation. Moreover, our unconditional generation system substantially outperforms the baseline conditional system. These advancements incur minimal computational overhead as the more abstract levels of our hierarchy work with lower-dimensional representations.

Model selection is the process of choosing from a class of candidate models given data. For instance, methods such as the LASSO and sparse identification of nonlinear dynamics (SINDy) formulate model selection as finding a sparse solution to a linear system of equations determined by training data. However, absent strong assumptions, such methods are highly unstable: if a single data point is removed from the training set, a different model may be selected. This paper presents a new approach to stabilizing model selection that leverages a combination of bagging and an "inflated" argmax operation. Our method selects a small collection of models that all fit the data, and it is stable in that, with high probability, the removal of any training point will result in a collection of selected models that overlaps with the original collection. In addition to developing theoretical guarantees, we illustrate this method in (a) a simulation in which strongly correlated covariates make standard LASSO model selection highly unstable and (b) a Lotka-Volterra model selection problem focused on identifying how competition in an ecosystem influences species' abundances. In both settings, the proposed method yields stable and compact collections of selected models, outperforming a variety of benchmarks.

Context. Asteroseismic investigations of solar-like oscillations in giant stars enable the derivation of their masses and radii. For mono-age mono-metallicity populations of stars this allows the integrated red giant branch (RGB) mass loss to be estimated by comparing the median mass of the low-luminosity RGB stars to that of the helium-core-burning stars (HeCB). Aims. We aim to exploit quasi mono-age mono-metallicity populations of field stars in the $\alpha$-rich sequence of the Milky Way (MW) to derive the integrated mass loss and its dependence on metallicity. By comparing to metal-rich globular clusters (GCs), we wish to determine whether the RGB mass loss differs in the two environments. Methods. Catalogues of asteroseismic parameters based on time-series photometry from the Kepler and K2 missions cross-matched to spectroscopic information from APOGEE-DR17, photometry from 2MASS, parallaxes from Gaia DR3 and reddening maps are utilised. The RGB mass loss is determined by comparing mass distributions of RGB and HeCB stars in three metallicity bins. For two GCs, the mass loss is derived from colour-magnitude diagrams. Results. Integrated RGB mass loss is found to increase with decreasing metallicity and/or mass in the [Fe/H] range from -0.9 to +0.0. At [Fe/H]=-0.50 the RGB mass loss of MW $\alpha$-rich field stars is compatible with that in GCs of the same metallicity. Conclusions. We provide novel empirical determinations of the integrated mass loss connecting field stars and GC stars at comparable metallicities. These show that mass loss cannot be accurately described by a Reimers mass-loss law with a single value of $\eta$. This should encourage further theoretical developments aimed at gaining a deeper understanding of the processes involved in mass loss., Comment: 14 pages, 12 figures, accepted for publication in A&A

Scientific modeling and engineering applications rely heavily on parameter estimation methods to fit physical models and calibrate numerical simulations using real-world measurements. In the absence of analytic statistical models with tractable likelihoods, modern simulation-based inference (SBI) methods first use a numerical simulator to generate a dataset of parameters and simulated outputs. This dataset is then used to approximate the likelihood and estimate the system parameters given observation data. Several SBI methods employ machine learning emulators to accelerate data generation and parameter estimation. However, applying these approaches to high-dimensional physical systems remains challenging due to the cost and complexity of training high-dimensional emulators. This paper introduces Embed and Emulate (E&E): a new SBI method based on contrastive learning that efficiently handles high-dimensional data and complex, multimodal parameter posteriors. E&E learns a low-dimensional latent embedding of the data (i.e., a summary statistic) and a corresponding fast emulator in the latent space, eliminating the need to run expensive simulations or a high dimensional emulator during inference. We illustrate the theoretical properties of the learned latent space through a synthetic experiment and demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task using the high-dimensional, chaotic Lorenz 96 system.

We investigate research challenges and opportunities for visualization in motion during outdoor physical activities via an initial corpus of real-world recordings that pair egocentric video, biometrics, and think-aloud observations. With the increasing use of tracking and recording devices, such as smartwatches and head-mounted displays, more and more data are available in real-time about a person's activity and the context of the activity. However, not all data will be relevant all the time. Instead, athletes have information needs that change throughout their activity depending on the context and their performance. To address this challenge, we describe the collection of a diverse corpus of information needs paired with contextualizing audio, video, and sensor data. Next, we propose a first set of research challenges and design considerations that explore the difficulties of visualizing those real data needs in-context and demonstrate a prototype tool for browsing, aggregating, and analyzing this information. Our ultimate goal is to understand and support embedding visualizations into outdoor contexts with changing environments and varying data needs.

A quasi-representation of a group is a map from the group into a matrix algebra (or similar object) that approximately satisfies the relations needed to be a representation. Work of many people starting with Kazhdan and Voiculescu, and recently advanced by Dadarlat, Eilers-Shulman-S\o{}rensen and others, has shown that there are topological obstructions to approximating unitary quasi-representations of groups by honest representations, where `approximation' is understood to be with respect to the operator norm. The purpose of this paper is to explore whether approximation is possible if the known obstructions vanish, partially generalizing work of Gong-Lin and Eilers-Loring-Pedersen for the free abelian group of rank two, and the Klein bottle group. We show that this is possible, at least in a weak sense, for some `low-dimensional' groups including fundamental groups of closed surfaces, certain Baumslag-Solitar groups, free-by-cyclic groups, and many fundamental groups of three manifolds. The techniques used in the paper are $K$-theoretic: they have their origin in Baum-Connes-Kasparov type assembly maps, and in the Elliott program to classify $C^*$-algebras; Kasparov's bivariant KK-theory is a crucial tool. The key new technical ingredients are: a stable uniqueness theorem in the sense of Dadarlat-Eilers and Lin that works for non-exact $C^*$-algebras; and an analysis of maps on $K$-theory with finite coefficients in terms of the relative eta invariants of Atiyah-Patodi-Singer. Despite the proofs going through $K$-theoretic machinery, the main theorems can be stated in elementary terms that do not need any $K$-theory., Comment: Version 2 has some more details, minor corrections, and improved references

We present morphological classifications based on Galaxy Zoo analysis of 71,052 galaxies with imaging from the United Kingdom Infrared Telescope Infrared Deep Sky Survey (UKIDSS). Galaxies were selected out of the Galaxy Zoo 2 (GZ2) sample, so also have gri imaging from the Sloan Digital Sky Survey. An identical classification tree, and vote weighting/aggregation was applied to both UKIDSS and GZ2 classifications enabling direct comparisons. With this Research Note we provide a public release of the GZ:UKIDSS morphologies and discuss some initial comparisons with GZ2., Comment: 3 pages, 1 figure

Prior coarticulation studies focus mainly on limited phonemic sequences and specific articulators, providing only approximate descriptions of the temporal extent and magnitude of coarticulation. This paper is an initial attempt to comprehensively investigate coarticulation. We leverage existing Electromagnetic Articulography (EMA) datasets to develop and train a phoneme-to-articulatory (P2A) model that can generate realistic EMA for novel phoneme sequences and replicate known coarticulation patterns. We use model-generated EMA on 9K minimal word pairs to analyze coarticulation magnitude and extent up to eight phonemes from the coarticulation trigger, and compare coarticulation resistance across different consonants. Our findings align with earlier studies and suggest a longer-range coarticulation effect than previously found. This model-based approach can potentially compare coarticulation between adults and children and across languages, offering new insights into speech production., Comment: To be published in Interspeech 2024

We study iterative signal reconstruction in computed tomography (CT), wherein measurements are produced by a linear transformation of the unknown signal followed by an exponential nonlinear map. Approaches based on pre-processing the data with a log transform and then solving the resulting linear inverse problem are tempting since they are amenable to convex optimization methods; however, such methods perform poorly when the underlying image has high dynamic range, as in X-ray imaging of tissue with embedded metal. We show that a suitably initialized subgradient method applied to a natural nonsmooth, nonconvex loss function produces iterates that converge to the unknown signal of interest at a geometric rate under the statistical model proposed by Fridovich-Keil et al. (arXiv:2310.03956). Our recovery program enjoys improved conditioning compared to the formulation proposed by the latter work, enabling faster iterative reconstruction from substantially fewer samples., Comment: 45 pages, 8 figures

Planets orbiting binary systems are relatively unexplored compared to those around single stars. Detections of circumbinary planets and planetary systems offer a first detailed view into our understanding of circumbinary planet formation and dynamical evolution. The BEBOP (Binaries Escorted by Orbiting Planets) radial velocity survey plays a special role in this adventure as it focuses on eclipsing single-lined binaries with an FGK dwarf primary and M dwarf secondary allowing for the highest-radial velocity precision using the HARPS and SOPHIE spectrographs. We obtained 4512 high-resolution spectra for the 179 targets in the BEBOP survey which we used to derive the stellar atmospheric parameters using both equivalent widths and spectral synthesis. We furthermore derive stellar masses, radii, and ages for all targets. With this work, we present the first homogeneous catalogue of precise stellar parameters for these eclipsing single-lined binaries., Comment: accepted for publication in MNRAS

Large Language Models (LLMs) are gaining popularity as tools for reading and summarization aids. However, little is known about their potential benefits when integrated with mixed reality (MR) interfaces to support everyday reading assistants. We developed RealitySummary, an MR reading assistant that seamlessly integrates LLMs with always-on camera access, OCR-based text extraction, and augmented spatial and visual responses in MR interfaces. Developed iteratively, RealitySummary evolved across three versions, each shaped by user feedback and reflective analysis: 1) a preliminary user study to understand user perceptions (N=12), 2) an in-the-wild deployment to explore real-world usage (N=11), and 3) a diary study to capture insights from real-world work contexts (N=5). Our findings highlight the unique advantages of combining AI and MR, including an always-on implicit assistant, minimal context switching, and spatial affordances, demonstrating significant potential for future LLM-MR interfaces beyond traditional screen-based interactions.

This paper introduces the concept of augmented conversation, which aims to support co-located in-person conversations via embedded speech-driven on-the-fly referencing in augmented reality (AR). Today computing technologies like smartphones allow quick access to a variety of references during the conversation. However, these tools often create distractions, reducing eye contact and forcing users to focus their attention on phone screens and manually enter keywords to access relevant information. In contrast, AR-based on-the-fly referencing provides relevant visual references in real-time, based on keywords extracted automatically from the spoken conversation. By embedding these visual references in AR around the conversation partner, augmented conversation reduces distraction and friction, allowing users to maintain eye contact and supporting more natural social interactions. To demonstrate this concept, we developed \system, a Hololens-based interface that leverages real-time speech recognition, natural language processing and gaze-based interactions for on-the-fly embedded visual referencing. In this paper, we explore the design space of visual referencing for conversations, and describe our our implementation -- building on seven design guidelines identified through a user-centered design process. An initial user study confirms that our system decreases distraction and friction in conversations compared to smartphone searches, while providing highly useful and relevant information.

Context. Planets with radii of between 2-4 RE closely orbiting solar-type stars are of significant importance for studying the transition from rocky to giant planets. Aims. Our goal is to determine the mass of a transiting planet around the very bright F6 star HD 73344 . This star exhibits high activity and has a rotation period that is close to the orbital period of the planet. Methods. The transiting planet, initially a K2 candidate, is confirmed through TESS observations . We refined its parameters and rule out a false positive with Spitzer observations. We analyzed high-precision RV data from the SOPHIE and HIRES spectrographs. We conducted separate and joint analyses using the PASTIS software. We used a novel observing strategy, targeting the star at high cadence for two consecutive nights with SOPHIE to understand the short-term stellar variability. We modeled stellar noise with two Gaussian processes. Results. High-cadence RV observations provide better constraints on stellar variability and precise orbital parameters for the transiting planet. The derived mean density suggests a sub-Neptune-type composition, but uncertainties in the planet's mass prevent a detailed characterization. In addition, we find a periodic signal in the RV data that we attribute to the signature of a nontransiting exoplanet, without totally excluding the possibility of a nonplanetary origin. Dynamical analyses confirm the stability of the two-planet system and provide constraints on the inclination of the candidate planet; these findings favor a near-coplanar system. Conclusions. While the transiting planet orbits the bright star at a short period, stellar activity prevented us from precise mass measurements. Long-term RV tracking of this planet could improve this measurement, as well as our understanding of the activity of the host star., Comment: Accepted for publication in Astronomy and Astrophysics

We propose a new framework for algorithmic stability in the context of multiclass classification. In practice, classification algorithms often operate by first assigning a continuous score (for instance, an estimated probability) to each possible label, then taking the maximizer -- i.e., selecting the class that has the highest score. A drawback of this type of approach is that it is inherently unstable, meaning that it is very sensitive to slight perturbations of the training data, since taking the maximizer is discontinuous. Motivated by this challenge, we propose a pipeline for constructing stable classifiers from data, using bagging (i.e., resampling and averaging) to produce stable continuous scores, and then using a stable relaxation of argmax, which we call the "inflated argmax," to convert these scores to a set of candidate labels. The resulting stability guarantee places no distributional assumptions on the data, does not depend on the number of classes or dimensionality of the covariates, and holds for any base classifier. Using a common benchmark data set, we demonstrate that the inflated argmax provides necessary protection against unstable classifiers, without loss of accuracy.

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method -- a commonly used technique for stably inverting scattered wavefield data -- that progressively refines the estimate with higher frequency content., Comment: 25 pages, 8 figures

Modern data-driven surrogate models for weather forecasting provide accurate short-term predictions but inaccurate and nonphysical long-term forecasts. This paper investigates online weather prediction using machine learning surrogates supplemented with partial and noisy observations. We empirically demonstrate and theoretically justify that, despite the long-time instability of the surrogates and the sparsity of the observations, filtering estimates can remain accurate in the long-time horizon. As a case study, we integrate FourCastNet, a state-of-the-art weather surrogate model, within a variational data assimilation framework using partial, noisy ERA5 data. Our results show that filtering estimates remain accurate over a year-long assimilation window and provide effective initial conditions for forecasting tasks, including extreme event prediction.

Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of promoting stability in the resulting output. We provide a general, finite-sample theoretical result guaranteeing the stability of bagging when applied to algorithms that return outputs in a general space, so that the output is not necessarily a real-valued -- for example, an algorithm that estimates a vector of weights or a density function. We empirically assess the stability of bagging on synthetic and real-world data for a range of problem settings, including causal inference, nonparametric regression, and Bayesian model selection.

The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology., Comment: Paper accepted at the First Workshop on Imageomics (Imageomics-AAAI-24) - Discovering Biological Knowledge from Images using AI (https://sites.google.com/vt.edu/imageomics-aaai-24/home), held as part of the 38th Annual AAAI Conference on Artificial Intelligence (https://aaai.org/aaai-conference/)

We show that introducing a weighting factor to reduce the influence of identity shortcuts in residual networks significantly enhances semantic feature learning in generative representation learning frameworks, such as masked autoencoders (MAEs) and diffusion models. Our modification notably improves feature quality, raising ImageNet-1K K-Nearest Neighbor accuracy from 27.4% to 63.9% and linear probing accuracy from 67.8% to 72.7% for MAEs with a ViT-B/16 backbone, while also enhancing generation quality in diffusion models. This significant gap suggests that, while residual connection structure serves an essential role in facilitating gradient propagation, it may have a harmful side effect of reducing capacity for abstract learning by virtue of injecting an echo of shallower representations into deeper layers. We ameliorate this downside via a fixed formula for monotonically decreasing the contribution of identity connections as layer depth increases. Our design promotes the gradual development of feature abstractions, without impacting network trainability. Analyzing the representations learned by our modified residual networks, we find correlation between low effective feature rank and downstream task performance.

Silent Speech Interfaces (SSIs) offer a noninvasive alternative to brain-computer interfaces for soundless verbal communication. We introduce Multimodal Orofacial Neural Audio (MONA), a system that leverages cross-modal alignment through novel loss functions--cross-contrast (crossCon) and supervised temporal contrast (supTcon)--to train a multimodal model with a shared latent representation. This architecture enables the use of audio-only datasets like LibriSpeech to improve silent speech recognition. Additionally, our introduction of Large Language Model (LLM) Integrated Scoring Adjustment (LISA) significantly improves recognition accuracy. Together, MONA LISA reduces the state-of-the-art word error rate (WER) from 28.8% to 12.2% in the Gaddy (2020) benchmark dataset for silent speech on an open vocabulary. For vocal EMG recordings, our method improves the state-of-the-art from 23.3% to 3.7% WER. In the Brain-to-Text 2024 competition, LISA performs best, improving the top WER from 9.8% to 8.9%. To the best of our knowledge, this work represents the first instance where noninvasive silent speech recognition on an open vocabulary has cleared the threshold of 15% WER, demonstrating that SSIs can be a viable alternative to automatic speech recognition (ASR). Our work not only narrows the performance gap between silent and vocalized speech but also opens new possibilities in human-computer interaction, demonstrating the potential of cross-modal approaches in noisy and data-limited regimes.

We study depth separation in infinite-width neural networks, where complexity is controlled by the overall squared $\ell_2$-norm of the weights (sum of squares of all weights in the network). Whereas previous depth separation results focused on separation in terms of width, such results do not give insight into whether depth determines if it is possible to learn a network that generalizes well even when the network width is unbounded. Here, we study separation in terms of the sample complexity required for learnability. Specifically, we show that there are functions that are learnable with sample complexity polynomial in the input dimension by norm-controlled depth-3 ReLU networks, yet are not learnable with sub-exponential sample complexity by norm-controlled depth-2 ReLU networks (with any value for the norm). We also show that a similar statement in the reverse direction is not possible: any function learnable with polynomial sample complexity by a norm-controlled depth-2 ReLU network with infinite width is also learnable with polynomial sample complexity by a norm-controlled depth-3 ReLU network.

We prove the rational HK-conjecture for a large class of transformation groupoids in the case when the relevant action has torsion-free stabilizers. A revised version of the rational HK-conjecture in the case of (possibly) torsion stabilizers is introduced and proved for a large class of transformation groupoids. In particular, this revised version holds for Scarparo's counterexamples to the original rational HK-conjecture. The key tools used are the Baum-Connes conjecture and a Chern character defined by Raven., Comment: 43 pages

Observations of low-mass stars have frequently shown a disagreement between observed stellar radii and radii predicted by theoretical stellar structure models. This ``radius inflation'' problem could have an impact on both stellar and exoplanetary science. We present the final results of our observation programme with the CHEOPS satellite to obtain high-precision light curves of eclipsing binaries with low mass stellar companions (EBLMs). Combined with the spectroscopic orbits of the solar-type companion, we can derive the masses, radii and effective temperatures of 23 M-dwarf stars. We use the PYCHEOPS data analysis software to analyse their primary and secondary occultations. For all but one target, we also perform analyses with TESS light curves for comparison. We have assessed the impact of starspot-induced variation on our derived parameters and account for this in our radius and effective temperature uncertainties using simulated light curves. We observe trends for inflation with both metallicity and orbital separation. We also observe a strong trend in the difference between theoretical and observational effective temperatures with metallicity. There is no such trend with orbital separation. These results are not consistent with the idea that observed inflation in stellar radius combines with lower effective temperature to preserve the luminosity predicted by low-mass stellar models. Our EBLM systems are high-quality and homogeneous measurements that can be used in further studies into radius inflation., Comment: 21 pages, 10 figures, accepted for publication in MNRAS, Supplementary material provided as ancillary files

The origin of apparently young $\alpha$-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution and merger/mass accretion. We aim to shed light on the nature of young $\alpha$-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an unprecedented sample of giant stars that cover different Galactic regions and have precise asteroseismic ages, chemical, and kinematic measurements. We analyze a new sample of $\sim$ 6000 stars with precise ages coming from asteroseismology. Our sample combines the global asteroseismic parameters measured from light curves obtained by the K2 mission with stellar parameters and chemical abundances obtained from APOGEE DR17 and GALAH DR3, then cross-matched with Gaia DR3. We define our sample of young $\alpha$-rich stars and study their chemical, kinematic, and age properties. We investigate young $\alpha$-rich stars in different parts of the Galaxy and we find that the fraction of young $\alpha$-rich stars remains constant with respect to the number of high-$\alpha$ stars at $\sim$ 10%. Furthermore, young $\alpha$-rich stars have kinematic and chemical properties similar to high-$\alpha$ stars, except for [C/N] ratios. This suggests that these stars are not genuinely young, but products of binary evolution and merger/mass accretion. Under that assumption, we find the fraction of these stars in the field to be similar to that found recently in clusters. This fact suggests that $\sim$ 10% of the low-$\alpha$ field stars could also have their ages underestimated by asteroseismology. This should be kept in mind when using asteroseismic ages to interpret results in Galactic archaeology., Comment: 13 pages, 7 figures. Accepted by A&A