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arXiv 2304.04785astro-ph.COastro-ph.IM
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包含内禀排列和重子效应的宇宙剪切场级推断

Field-level inference of cosmic shear with intrinsic alignments and baryons

Natalia Porqueres, Alan Heavens, Daniel Mortlock, Guilhem Lavaux, T. Lucas Makinen

AI总结:

构建了包含内禀排列和重子反馈的宇宙剪切场级贝叶斯分层模型,通过TATT和EGD方法验证了其不损失约束力且能打破弱透镜简并,适用于实际数据分析。

摘要:

我们构建了一个基于场的宇宙剪切贝叶斯分层模型,首次在引力模型之外,加入了内禀排列和重子反馈这两个重要的天体物理系统误差。我们在BORG-WL框架中加入了用于内禀排列的潮汐排列与潮汐力矩模型(TATT),并将其与非线性排列(NLA)模型进行比较。利用合成数据,我们表明加入内禀排列并对TATT参数进行采样不会降低该方法的约束力,且基于场的方法打破了弱引力透镜简并。我们使用焓梯度下降(EGD)模型在场级加入重子效应。该模型在不知晓暗物质粒子是否属于晕的情况下对其产生位移,并允许对从数据中推断的模型参数进行自校准。我们还说明了重子模型误设的影响。最终模型现包含了最重要的物理效应,适用于实际数据应用。

英文摘要:

We construct a field-based Bayesian Hierarchical Model for cosmic shear that includes, for the first time, the important astrophysical systematics of intrinsic alignments and baryon feedback, in addition to a gravity model. We add to the BORG-WL framework the tidal alignment and tidal torquing model (TATT) for intrinsic alignments and compare them with the non-linear alignment (NLA) model. With synthetic data, we have shown that adding intrinsic alignments and sampling the TATT parameters does not reduce the constraining power of the method and the field-based approach lifts the weak lensing degeneracy. We add baryon effects at the field level using the enthalpy gradient descent (EGD) model. This model displaces the dark matter particles without knowing whether they belong to a halo and allows for self-calibration of the model parameters, which are inferred from the data. We have also illustrated the effects of model misspecification for the baryons. The resulting model now contains the most important physical effects and is suitable for application to data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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