arXiv subjects

Siyang Li

Publications and source records attributed to Siyang Li.

At least 19 recordsLinked to original sources

arXiv2026

SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval

Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potential for neural communication. However, current EEG-to-image retrieval methods perform far below their within-subject counterparts for new users without labeled calibration, limiting real-world deployment. To understand this gap, we analyze EEG features across subjects and find that different subjects preserve similar relationships among concepts but express them along different coordinate directions. We therefore propose Subject Coordinate Recovery (SCORE), a target label-free framework combining recovery-aware source training with coordinate alignment at deployment. During training, SCORE aligns source subject EEG with a common image space and simulates unseen-subject recovery through source-only episodes. At deployment, with both encoders frozen, SCORE selects reliable EEG-image landmarks through hubness-corrected matching and estimates an orthogonal transformation to recover target EEG coordinates without source data or target labels. In 200-way retrieval on two public benchmarks, SCORE outperforms the unadapted baseline for every target subject and achieves the best overall accuracy. It reaches 53.23%/83.55% and 12.01%/32.16% Top-1/Top-5 on THINGS-EEG2 and Alljoined-1.6M, respectively, surpassing the strongest baselines by 17.45/15.70 and 3.08/4.62 percentage points. Without target labels or encoder updates, SCORE brings brain-based visual decoding closer to robust, practical, low-latency deployment across users.

cs.LG↗

arXiv2026

Temporal Out-of-Distribution Detection for Asynchronous Motor Imagery Brain-Computer Interfaces

Real online brain--computer interfaces operate on continuous electroencephalography (EEG) streams, where users are usually at rest and enter motor-imagery task states only intermittently. EEG windows may also arise from OOD MI activity outside the predefined control set. Conventional closed-set motor-imagery classifiers tend to assign such inputs to ID classes, which can cause erroneous control. To address this issue, this paper proposes a two-stage EEG detection framework for asynchronous motor-imagery brain--computer interfaces. A sliding-window mechanism continuously monitors EEG signals. The first stage uses an EEGNet-based rest/task gate to determine whether the current window should enter the control-decision process. The second stage performs ID MI classification and out-of-distribution detection only for task-state samples. To improve OOD rejection, we further propose TempDens, which combines classification-output energy, deep-feature density, and temporal-consistency scores to characterize distributional deviation from output, feature, and temporal-dynamic perspectives. Experimental results show that the proposed method effectively supports task-state detection and OOD MI recognition in continuous EEG streams, outperforming multiple conventional OOD baselines. This study reframes online motor-imagery control as a hierarchical decision problem involving continuous monitoring, state discrimination, ID classification, and OOD rejection.

cs.HC↗

arXiv2026

TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining. However, adapting TSFMs to downstream forecasting tasks remains challenging due to temporal distribution shifts and varying data availability. Specifically, the non-stationary and uncertain nature of time series data leads to discrepancies between historical training and future forecasting distributions, making existing Supervised FineTuning (SFT)-based adaptation vulnerable to overfitting and limited generalization. Moreover, forecasting tasks often operate under varying data regimes, requiring TSFMs to extract generalizable temporal patterns from limited training samples. To address these challenges, we propose Time series Reinforcement FineTuning (TimeRFT), a reinforcement learning-based adaptation paradigm for TSFMs. TimeRFT introduces two forecasting-oriented training recipes: (i) A quality-aware temporal reward mechanism providing fine-grained credit assignment by holistically evaluating the contribution of each prediction step to overall forecasting performance. (ii) A difficulty-aware data selection strategy prioritizing informative time series samples with generalizable forecasting patterns. Extensive experiments on diverse real-world forecasting benchmarks demonstrate that TimeRFT consistently surpasses SFT-based adaptation methods across various real-world forecasting tasks with different data regimes, achieving improved prediction accuracy and enhanced generalization against unforeseen distribution shifts.

eess.SP↗

arXiv2026

Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions

Deep learning has achieved transformative performance across diverse domains, largely driven by large-scale and high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by limited, heterogeneous, and privacy-sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a promising strategy to mitigate data scarcity, improve model generalization, and support data-efficient BCIs. This survey provides a comprehensive review of synthetic brain data generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, key applications, and future directions. We systematically categorize existing generation approaches into four types: signal-transformation-based, feature-based, model-based, and translation-based generation, and discuss their characteristics, advantages, and limitations. Furthermore, we benchmark representative brain signal generation approaches across four BCI paradigms, including motor imagery, epileptic seizure detection, steady-state visually evoked potentials, and auditory attention detection, to provide an objective comparison of their downstream utility. We also summarize evaluation principles for generated brain signals from multiple perspectives, including signal realism, physiological plausibility, downstream utility, and privacy preservation. Finally, we discuss the potential and challenges of current generation approaches and outline future research directions toward accurate, data-efficient, generalizable, and privacy-aware BCI systems. The benchmark codebase is available at https://github.com/wzwvv/DG4BCI.

cs.LG↗

arXiv2026

StackingNet: Collective Inference Across Independent AI Foundation Models

Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black-box foundation models is essential for trustworthy intelligent systems, yet no established method exists. Here we show that such coordination can be achieved through a meta-ensemble framework termed StackingNet, which aggregates the output predictions of independent models at inference. StackingNet improves accuracy, reduces individual-model error and group-wise disparities, ranks model reliability, and identifies or prunes models that degrade performance, all without access to internal parameters or training data. Across language comprehension, visual attribute estimation, and academic paper rating, it consistently outperforms individual models and classic ensembles, with gains that persist when the base models are uniformly strong. These gains stem from variance reduction and consensus alignment among independent models rather than from any emergent group cognition, and they widen as the model pool grows more diverse. By turning model diversity from a source of inconsistency into a resource for cooperation, StackingNet offers a practical path toward coordinated artificial intelligence, where progress emerges not only from larger single models but from principled cooperation among many specialized ones.

cs.AI↗

arXiv2026

RAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection

Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding methodologies remain heavily dependent on task-specific datasets to train specialized neural network architectures. Consequently, limited data availability impedes the development of generalizable large brain decoding models. In this work, we propose a paradigm shift from conventional signal-based decoding by leveraging large-scale vision-language models (VLMs) to analyze EEG waveform plots. By converting multivariate EEG signals into stacked waveform images and integrating neuroscience domain expertise into textual prompts, we demonstrate that foundational VLMs can effectively differentiate between different patterns in the human brain. To address the inherent non-stationarity of EEG signals, we introduce a Retrieval-Augmented In-Context Learning (RAICL) approach, which dynamically selects the most representative and relevant few-shot examples to condition the autoregressive outputs of the VLM. Experiments on EEG-based seizure detection indicate that state-of-the-art VLMs under RAICL achieved better or comparable performance with traditional time series based approaches. These findings suggest a new direction in physiological signal processing that effectively bridges the modalities of vision, language, and neural activities. Furthermore, the utilization of off-the-shelf VLMs, without the need for retraining or downstream architecture construction, offers a readily deployable solution for clinical applications.

cs.HC↗

arXiv2026

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints. While test-time adaptation (TTA) mitigates distribution shifts under online data streams without per-use calibration sessions, existing TTA approaches heavily rely on explicitly defined loss objectives that require backpropagation for updating model parameters, which incurs computational overhead, privacy risks, and sensitivity to noisy data streams. This paper proposes Backpropagation-Free Transformations (BFT), a TTA approach for EEG decoding that avoids these issues. BFT applies multiple sample-wise transformations, based on knowledge-guided augmentations or structured feature masking, to each test trial, producing multiple predictions for a single test sample using only forward passes. A learning-to-rank module, trained on source data, estimates the reliability of each transformed prediction, so that a weighted aggregation suppresses prediction uncertainty during online inference, with theoretical justification. Extensive experiments on five EEG datasets, covering motor imagery classification and driver drowsiness regression, demonstrate the effectiveness, versatility, robustness, and efficiency of BFT. This research enables lightweight plug-and-play BCIs on resource-constrained devices, broadening the real-world deployment of EEG-based BCIs.

cs.HC↗

arXiv2025

The Local Distance Network: a community consensus report on the measurement of the Hubble constant at 1% precision

The direct, empirical determination of the local value of the Hubble constant (H0) has markedly advanced thanks to improved instrumentation, measurement techniques, and distance estimators. However, combining determinations from different estimators is non-trivial, due to correlated calibrations and different analysis methodologies. Using covariance weighting and leveraging the broad and comprehensive community of experts, we constructed a rigorous and transparent Distance Network (DN) to find a consensus value and uncertainty for the local H0. All critically reviewed the available data sets, spanning parallaxes, detached eclipsing binaries, masers, Cepheids, the TRGB, Miras, JAGB stars, SN Ia, Surface Brightness Fluctuations, SN II, the Fundamental Plane, and Tully-Fisher relations and voted for indicators to define a `baseline' DN and others to assess robustness and sensitivity of the results. We provide open-source software and data products to support full transparency and future extensions of this effort. Our conclusions: 1) Local H0 is robustly determined, with first-rank indicators internally consistent within their uncertainties; 2) A covariance-weighted combination yields an uncertainty of 1.1% (baseline) or 0.9% (all estimators); 3) The contribution from SNe Ia is consistent across four current compilations of optical magnitudes or using NIR-only magnitudes; 4) Removing either Cepheids or TRGB has minimal effect; 5) Replacing SNe Ia with galaxy-based indicators changes H0 by less than 0.1 km/s/Mpc, while doubling its uncertainty; 6) The baseline result is H0=73.50+/-0.81 km/s/Mpc. Compared to early Universe results, our result differs by 7.1sigma from flat {\Lambda}CDM with Planck+SPT+ACT and 5.0 sigma with BBN+BAO (DESI2). A networked approach is invaluable for enabling further progress in accuracy and precision without overreliance on any single method, sample or group.

astro-ph.CO↗

arXiv2025

Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning

Advanced deep learning-based approaches have been actively applied to forecast the spatiotemporal physical dynamics governed by partial differential equations (PDEs), which acts as a critical procedure in tackling many science and engineering problems. As real-world physical environments like PDE system parameters are always capricious, how to generalize across unseen out-of-distribution (OOD) forecasting scenarios using limited training data is of great importance. To bridge this barrier, existing methods focus on discovering domain-generalizable representations across various PDE dynamics trajectories. However, their zero-shot OOD generalization capability remains deficient, since extra test-time samples for domain-specific adaptation are still required. This is because the fundamental physical invariance in PDE dynamical systems are yet to be investigated or integrated. To this end, we first explicitly define a two-fold PDE invariance principle, which points out that ingredient operators and their composition relationships remain invariant across different domains and PDE system evolution. Next, to capture this two-fold PDE invariance, we propose a physics-guided invariant learning method termed iMOOE, featuring an Invariance-aligned Mixture Of Operator Expert architecture and a frequency-enriched invariant learning objective. Extensive experiments across simulated benchmarks and real-world applications validate iMOOE's superior in-distribution performance and zero-shot generalization capabilities on diverse OOD forecasting scenarios.

cs.LG↗

arXiv2025

The Perfect Host: JWST Cepheid Observations in a Background-Free SN Ia Host Confirm No Bias in Hubble-Constant Measurements

Cycle 1 JWST observations of Cepheids in SN Ia hosts resolved their red-giant-dominated NIR backgrounds, sharply reducing crowding and showing that photometric bias in lower-resolution HST data does not account for the Hubble tension. We present Cycle 2 JWST observations of >100 Cepheids in NGC 3447, a unique system that pushes this test to the limit by transitioning from low to no background contamination. NGC 3447, an SN Ia host at D~25 Mpc, is an interacting pair comprising (i) a spiral with mixed stellar populations, typical of H0 calibrators, and (ii) a young, star-forming companion (NGC 3447A) devoid of old stars and hence stellar crowdinga rare "perfect host" for testing photometric bias. We detect ~60 long-period Cepheids in each, enabling a "three-way comparison" across HST, JWST, and background-free conditions. We find no component-to-component offset (sigma<0.03 mag; a calibration independent test), and a 50% reduction in scatter to ~0.12 mag in the background-free case, the tightest seen for any SN Ia host. Across Cycles 1-2 we also measure Cepheids in all SH0ES hosts observed by JWST (19 hosts of 24 SNe Ia; >50% of the sample) and find no evidence of bias relative to HST photometry, including for the most crowded, distant hosts. These observations constitute the most rigorous test yet of Cepheid distances and provide strong evidence for their reliability. Combining JWST Cepheid measurements in 19 hosts (24 SNe Ia) with HST data (37 hosts, 42 SNe Ia) yields H0 = 73.49 +/- 0.93 km/s/Mpc. Including 35 TRGB-based calibrations (from HST and JWST) totals 55 SNe Ia and gives H0 = 73.18 +/- 0.88 km/s/Mpc, ~6 sigma above the LambdaCDM+CMB expectation.

astro-ph.CO↗

arXiv2025

AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding

Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limited neurophysiological prior integration. To address these issues, we propose a plug-and-play Alignment-Based Frame-Patch Modeling (AFPM) framework, which has two main components: 1) Spatial Alignment, which selects task-relevant channels based on brain-region priors, aligns EEG distributions across domains, and remaps the selected channels to a unified layout; and, 2) Frame-Patch Encoding, which models multi-dataset signals into unified spatiotemporal patches for EEG decoding. Compared to 17 state-of-the-art approaches that need dataset-specific tuning, the proposed calibration-free AFPM achieves performance gains of up to 4.40% on motor imagery and 3.58% on event-related potential tasks. To our knowledge, this is the first calibration-free cross-dataset EEG decoding framework, substantially enhancing the practicalness of BCIs in real-world applications.

cs.HC↗

arXiv2025

DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mainstream backbone for EEG decoding, their inherently short receptive field makes it difficult to capture long-range temporal dependencies and global inter-channel relationships. Recent CNN-Transformer (Conformer) hybrids partially address this issue, but most adopt a serial design, resulting in suboptimal integration of local and global features, and often overlook explicit channel-wise modeling. To address these limitations, we propose DBConformer, a dual-branch convolutional Transformer network tailored for EEG decoding. It integrates a temporal Conformer to model long-range temporal dependencies and a spatial Conformer to extract inter-channel interactions, capturing both temporal dynamics and spatial patterns in EEG signals. A lightweight channel attention module further refines spatial representations by assigning data-driven importance to EEG channels. Extensive experiments under four evaluation settings on three paradigms, including motor imagery, seizure detection, and steady-state visual evoked potential, demonstrated that DBConformer consistently outperformed 13 competitive baseline models, with over an eight-fold reduction in parameters than current high-capacity EEG Conformer architecture. Furthermore, the visualization results confirmed that the features extracted by DBConformer are physiologically interpretable and aligned with prior knowledge. The superior performance and interpretability of DBConformer make it reliable for accurate, robust, and explainable EEG decoding. Code is publicized at https://github.com/wzwvv/DBConformer.

cs.LG↗

arXiv2025

SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding

Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses. This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs, along with the corresponding decoding algorithms. Stereo-electroencephalography (SEEG) and synchronized audio data were collected from eight drug-resistant epilepsy patients as they conducted a word-level reading task. The proposed SEEG and Audio Contrastive Matching (SACM), a contrastive learning-based framework, achieved decoding accuracies significantly exceeding chance levels in both speech detection and speech decoding tasks. Electrode-wise analysis revealed that a single sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. These findings provide valuable insights for developing more accurate online speech decoding BCIs.

cs.HC↗

arXiv2025

The Complete Sample of Available SNe Ia Luminosity Calibrations from the TRGB Observed with either HST or JWST

Distance ladders which calibrate the luminosity of Type Ia supernovae (SNe Ia) currently provide the strongest constraints on the local value of H0. Recent studies from the Hubble Space Telescope (HST) and James Webb Space Telescope (JWST) show good consistency between measurements of SNe Ia host distances. These are calibrated to NGC 4258 using different primary distance indicators (Cepheids, Tip of the Red Giant Branch (TRGB), J-region Asymptotic Giant Branch, and Miras). However, some sub-samples of calibrated SNe Ia employed to measure H0 yield noteworthy differences due to small sample statistics but also due to differences in sample selection. This issue is particularly important for TRGB-derived calibrations owing to the smaller volume they reach compared to Cepheids, reducing sample size and enhancing the size of statistical fluctuations. To mitigate this issue, we compile the largest and complete (as currently available) sample of HST or JWST measurements of the TRGB in the hosts of normal SNe Ia for a total of N=35, 50% larger than the previous largest. Most are present in the literature, and we compile multiple measures when available. We also add 5 SNe Ia hosts from the HST archive not previously published. The full sample together with the Pantheon+ SN catalog gives H0=72.1-73.3 +/- 1.8 km/s/Mpc (depending on methodology), in good agreement with the value of 72.5 +/- 1.5 km/s/Mpc from HST Cepheids in hosts of 42 SNe Ia calibrated by the same anchor, NGC 4258. We trace the difference in the result of H0=70.4 +/- 1.9 km/s/Mpc from Freedman et al. 2025 to 11 hosts not selected for that CCHP compilation (of N=24) which alone yield H0=74.1 km/s/Mpc, 2$\sigma$ higher than the selected sample. A smaller increase of 0.6 km/s/Mpc comes from a commonly employed correction for peculiar velocities.

astro-ph.CO↗

arXiv2025

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics

The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged]

astro-ph.CO↗

arXiv2025

Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.

cs.LG↗

arXiv2025

MVCNet: Multi-View Contrastive Network for Motor Imagery Classification

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to its intuitive mechanism. However, most existing models rely on single-stream architectures and overlook the multi-view nature of EEG signals, leading to limited performance and generalization. We propose a multi-view contrastive network (MVCNet), a dual-branch architecture that parallelly integrates CNN and Transformer blocks to capture both local spatial-temporal features and global temporal dependencies. To enhance the informativeness of training data, MVCNet incorporates a unified augmentation pipeline across time, frequency, and spatial domains. Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both branches. Final representations are fused and jointly optimized by contrastive and classification losses. Experiments on five public MI datasets across three scenarios demonstrate that MVCNet consistently outperforms nine state-of-the-art MI decoding networks, highlighting its effectiveness and generalization ability. MVCNet provides a robust solution for MI decoding by integrating multi-view information and dual-branch modeling, contributing to the development of more reliable BCI systems.

eess.SP↗

arXiv2025

JAGB 2.0: Improved Constraints on the J-region Asymptotic Giant Branch-based Hubble Constant from an Expanded Sample of JWST Observations

The J-region Asymptotic Giant Branch (JAGB) is an overdensity of stars in the near-infrared, attributed to carbon-rich asymptotic giant branch stars, and recently used as a standard candle for measuring extragalactic distances and the Hubble constant. Using JWST in Cycle 2, we extend JAGB measurements to 6 hosts of 9 Type Ia supernovae (SNe Ia) (NGC 2525, NGC 3147, NGC 3370, NGC 3447, NGC 5468, and NGC 5861), with two at $D \sim 40$ Mpc, all calibrated by the maser host NGC 4258. We investigate the effects of incompleteness and find that we are unable to recover a robust JAGB measurement in one of the two most distant hosts at $R \sim 40$ Mpc, NGC 3147. We compile all JWST JAGB observations in SNe Ia hosts, 15 galaxies hosting 18 SNe Ia, from the SH0ES and CCHP programs and employ all literature measures (mode, mean, median, model). We find no significant mean difference between these distances and those from HST Cepheids, $-0.03\pm0.02$ (stat) $\pm$ 0.05 (sys) mag. We find a difference of 0.11 $\pm$ 0.02 mag between JAGB mode measurements in the CCHP analyses of two fields in NGC 4258, a feature also seen in two SH0ES fields (see field-to-field variations in Li et al. 2024a), indicating significant field-to-field variation of JAGB measurements in NGC 4258 which produce a large absolute calibration uncertainty. Variations are also seen in the shape of the JAGB LF across galaxies so that different measures produce different values of the Hubble constant. We look for but do not (yet) find a standardizing relation between JAGB LF skew or color dependence and the apparent variation. Using the middle result of all JAGB measures to calibrate SNe Ia yields a Hubble constant of $H_0$ = 73.3 $\pm$ 1.4 (stat) $\pm$ 2.0 (sys) km/s/Mpc with the systematic dominated by apparent differences across NGC 4258 calibrating fields or their measures.

astro-ph.CO↗