发布日期: 2025-09-16

更新日期: 2025-09-17

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2025-09-16 更新

Joint X-ray, kinetic Sunyaev-Zeldovich, and weak lensing measurements: toward a consensus picture of efficient gas expulsion from groups and clusters

Authors:Jared Siegel, Alexandra Amon, Ian G. McCarthy, Leah Bigwood, Masaya Yamamoto, Esra Bulbul, Jenny E. Greene, Jamie McCullough, Matthieu Schaller, Joop Schaye

There is no consensus on how baryon feedback shapes the underlying matter distribution from either simulations or observations. We confront the uncertain landscape by jointly analyzing new measurements of the gas distribution around groups and clusters – DESI+ACT kinetic Sunyaev-Zel’dovich (kSZ) effect profiles and eROSITA X-ray gas masses – with mean halo masses characterized by galaxy-galaxy lensing. Across a wide range of halo masses ($M_{500}=10^{13-14}M_\odot$) and redshifts ($0<z<1$), we find evidence of more efficient gas expulsion beyond several $R_{500}$ than predicted by most state-of-the-art simulations. A like-with-like comparison reveals all kSZ and X-ray observations are inconsistent with the fiducial 1 Gpc$^{3}$ hydrodynamical FLAMINGO simulation, which was calibrated to reproduce pre-eROSITA X-ray gas fractions: eROSITA X-ray gas fractions are $2\times$ lower than the simulation, and the kSZ measurements are combined $>8 \sigma$ discrepant. The FLAMINGO simulation variant with the most gas expulsion, and therefore the most suppression of the matter power spectrum relative to a dark matter only simulation, provides a good description of how much gas is expelled and how far it extends; the enhanced gas depletion is achieved by more powerful but less frequent AGN outbursts. Joint kSZ, X-ray, and lensing measurements form a consistent picture of gas expulsion beyond several $R_{500}$, implying a more suppressed matter power spectrum than predicted by most recent simulations. Complementary observables and next-generation simulations are critical to understanding the physical mechanism behind this extreme gas expulsion and mapping its impact on the large-scale matter distribution.

关于重子反馈如何塑造基础物质分布的问题，无论是模拟还是观测，目前尚无共识。我们通过联合分析新的气体分布测量值来应对这一不确定的局面，这些测量值包括利用DES和ACT动力学Sunyaev-Zel’dovich（kSZ）效应轮廓和eROSITA X射线气体质量测量得到的值，以及通过星系透镜效应确定的平均晕质量。在一系列广泛的晕质量（$M_{500}=10^{13-14}M_\odot$）和红移（$0<z<1$）范围内，我们发现证据表明在多个$R_{500}$之外的气体排出效率比最先进的模拟预测的要高。同类比较显示，所有的kSZ和X射线观测结果与基准的1 Gpc$^{3}$流体动力学FLAMINGO模拟结果不一致，该模拟经过校准以再现pre-eROSITA X射线气体分数：eROSITA X射线气体分数比模拟结果低两倍，而kSZ测量值与模拟结果的差异大于8σ。FLAMINGO模拟变体展现了最强烈的气体排出，与仅包含暗物质的模拟相比，物质功率谱受到了最大的抑制，它很好地描述了排出的气体量及其延伸范围；强大的但较少频发的AGN爆发实现了气体的增强消耗。联合kSZ、X射线和透镜测量形成了一幅一致的图像，显示出了几个$R_{500}$之外的气体排出，意味着物质功率谱受到比最近模拟所预测的更强烈的抑制。为了理解这种极端的气体排出背后的物理机制和其对大规模物质分布的影响，我们需要补充观测数据和下一代模拟。

论文及项目相关链接

PDF 28 pages, 8 figures, submitted to ApJ

摘要

新的气体分布测量值与透镜效应分析显示，大范围晕质量（$M_{500}=10^{13-14}M_\odot$）和红移（$0<z<1$）下的物质分布与当前最先进的模拟预测存在不一致。特别是eROSITA的X射线气体质量观测值比模拟值低两倍，而kSZ效应观测值与模拟结果存在大于8σ的偏差。FLAMINGO模拟中的变体，其气体排除最多，相对于仅暗物质模拟的物质功率谱抑制程度更大，提供了被排除气体的量和扩展范围的良好描述。联合kSZ、X射线和透镜测量技术形成一致的气体排除图像，这暗示着最近的模拟预测中物质功率谱受到更大程度的抑制。需要更多的观测数据和下一代模拟来理解这种极端气体排除的物理机制和它对大规模物质分布的影响。

关键要点

新测量与模拟之间存在物质分布的不一致性。
eROSITA的X射线气体质量观测结果与FLAMINGO模拟相比偏低。
kSZ效应观测结果与模拟结果存在显著偏差。
FLAMINGO模拟中的某种变体提供了被排除气体的量和范围的准确描述。
该模拟中的气体排除与更频繁但力量较小的AGN爆发形成对比。
联合测量技术显示气体排除现象的一致图像，暗示物质功率谱受到更大程度的抑制。

Cool Papers

点此查看论文截图

Polarization Denoising and Demosaicking: Dataset and Baseline Method

Authors:Muhamad Daniel Ariff Bin Abdul Rahman, Yusuke Monno, Masayuki Tanaka, Masatoshi Okutomi

A division-of-focal-plane (DoFP) polarimeter enables us to acquire images with multiple polarization orientations in one shot and thus it is valuable for many applications using polarimetric information. The image processing pipeline for a DoFP polarimeter entails two crucial tasks: denoising and demosaicking. While polarization demosaicking for a noise-free case has increasingly been studied, the research for the joint task of polarization denoising and demosaicking is scarce due to the lack of a suitable evaluation dataset and a solid baseline method. In this paper, we propose a novel dataset and method for polarization denoising and demosaicking. Our dataset contains 40 real-world scenes and three noise-level conditions, consisting of pairs of noisy mosaic inputs and noise-free full images. Our method takes a denoising-then-demosaicking approach based on well-accepted signal processing components to offer a reproducible method. Experimental results demonstrate that our method exhibits higher image reconstruction performance than other alternative methods, offering a solid baseline.

一个分焦平面（DoFP）偏振仪使我们能够在一次拍摄中获得多个偏振方向的图像，因此对于使用偏振信息的许多应用来说非常有价值。分焦平面偏振仪的图像处理流程包括两个关键任务：去噪和马赛克去除。虽然针对无噪声情况的偏振马赛克去除研究已经逐渐增多，但由于缺乏合适的评估数据集和可靠的基线方法，针对偏振去噪和马赛克去除的联合任务的研究仍然很少。在本文中，我们提出了一个用于偏振去噪和马赛克去除的新数据集和方法。我们的数据集包含40个真实世界场景和三种噪声水平条件，由噪声马赛克输入和无噪声全图像对组成。我们的方法基于公认的信号处理组件，采用先去噪后去马赛克的方法，以提供一种可复制的方法。实验结果表明，我们的方法在图像重建性能上优于其他替代方法，提供了一个可靠的基线。

论文及项目相关链接

PDF Published in ICIP2025; Project page: http://www.ok.sc.e.titech.ac.jp/res/PolarDem/PDD.html

Summary

本文主要介绍了一种分区焦平面（DoFP）偏振仪的图像处理方法，包括去噪和马赛克取样（demosaicking）。针对现有研究中缺乏联合执行偏振去噪和马赛克取样的方法的问题，本文提出了一种新的数据集和方法来实现偏振去噪和马赛克取样。新方法采用公认的信号处理组件，先降噪后马赛克取样，实验结果表明该方法具有较高的图像重建性能。

Key Takeaways

分区焦平面（DoFP）偏振仪可以一次获取多个偏振方向的图像。
图像处理的两个关键任务是去噪和马赛克取样（demosaicking）。
目前对于联合执行偏振去噪和马赛克取样的研究较少。
本文提出了一种新的数据集，包含40个真实场景和三种噪声水平条件。
提出的方法基于公认的信号处理组件，采用先去噪后马赛克取样的方式。
实验结果表明，该方法在图像重建性能上优于其他方法。

Cool Papers

点此查看论文截图

Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods

Authors:Carmen Jimenez-Mesa, Yizhou Wan, Guilio Sansone, Francisco J. Martinez-Murcia, Javier Ramirez, Pietro Lio, Juan M. Gorriz, Stephen J. Price, John Suckling, Michail Mamalakis

Brain tumor resection is a highly complex procedure with profound implications for survival and quality of life. Predicting patient outcomes is crucial to guide clinicians in balancing oncological control with preservation of neurological function. However, building reliable prediction models is severely limited by the rarity of curated datasets that include both pre- and post-surgery imaging, given the clinical, logistical and ethical challenges of collecting such data. In this study, we develop a novel framework that integrates explainable artificial intelligence (XAI) with neuroimaging-based feature engineering for survival assessment in brain tumor patients. We curated structural MRI data from 49 patients scanned pre- and post-surgery, providing a rare resource for identifying survival-related biomarkers. A key methodological contribution is the development of a global explanation optimizer, which refines survival-related feature attribution in deep learning models, thereby improving both the interpretability and reliability of predictions. From a clinical perspective, our findings provide important evidence that survival after oncological surgery is influenced by alterations in regions related to cognitive and sensory functions. These results highlight the importance of preserving areas involved in decision-making and emotional regulation to improve long-term outcomes. From a technical perspective, the proposed optimizer advances beyond state-of-the-art XAI methods by enhancing both the fidelity and comprehensibility of model explanations, thus reinforcing trust in the recognition patterns driving survival prediction. This work demonstrates the utility of XAI-driven neuroimaging analysis in identifying survival-related variability and underscores its potential to inform precision medicine strategies in brain tumor treatment.

脑肿瘤切除是一种对生存和生活质量有深远影响的复杂手术。预测患者的治疗效果对于指导临床医生平衡肿瘤控制与神经功能的保护至关重要。然而，由于收集此类数据面临的临床、后勤和伦理挑战，建立可靠的预测模型受到了严重限制，很少有经过筛选的数据集包含术前和术后的成像数据。在本研究中，我们开发了一种新型框架，融合了可解释的的人工智能（XAI）和基于神经成像的特征工程，用于脑肿瘤患者生存评估。我们从49名患者的术前和术后扫描中提取结构MRI数据，为识别与生存相关的生物标志物提供了宝贵的资源。一个重要的方法学贡献是开发了一种全局解释优化器，该优化器能优化深度学习模型中的生存相关特征属性，从而提高预测的可解释性和可靠性。从临床角度来看，我们的研究结果表明，肿瘤手术后的生存受到认知功能和感觉功能相关区域变化的影响。这些结果强调了在决策制定和情感调节中发挥作用的区域的重要性，旨在改善长期效果。从技术角度来看，所提出的优化器超越了最先进的XAI方法，提高了模型解释的保真度和易懂性，从而增强了人们对驱动生存预测的识别模式的信任。这项工作展示了XAI驱动的神经成像分析在识别与生存相关的变异性方面的实用性，并强调了其在脑肿瘤治疗的精准医学策略中的潜力。

论文及项目相关链接

PDF 18 pages, 6 figures

Summary

本研究结合解释性人工智能（XAI）与神经影像特征工程，开发了一种新型框架，用于脑肿瘤患者的生存评估。研究利用49例患者的术前和术后结构磁共振成像数据，识别与生存相关的生物标志物。开发了一种全局解释优化器，提高深度学习模型的生存相关特征归属的精准度和可靠性，提高预测的可解释性和可信度。研究发现，肿瘤手术后生存与认知和情感功能区域的改变有关。技术方面，所提出的优化器超越了现有的XAI方法，提高了模型解释的保真度和易懂性，增强了人们对驱动生存预测识别模式的信任。

Key Takeaways