Collaborative Research: Capturing Salient Features in Point Process Models via Stochastic Process Discrepancies

协作研究：通过随机过程差异捕获点过程模型中的显着特征

• 批准号: 2015386
• 负责人: Thomas Loredo
• 金额: $ 13.54万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015386&HistoricalAwards=false
• 关键词: Collaborative; Research; Capturing; Salient; Features

In the 21st century, mathematical modeling has emerged as a primary tool for scientific investigation. Scientists construct mathematical and computer models intended to capture the important features of a complex physical phenomenon, and learn more about the phenomenon by exploring what values of any uncertain model parameters lead to model predictions that mimic closely what is observed in nature. Still, at best the models typically cannot capture all of nature's complexity – or, as George Box famously put it, "All models are wrong, but some are useful." This is really a good thing for discovery – often scientists can get new insights and develop deeper understanding by studying precisely why their models fail to match reality. In this research the PIs will develop a new approach to quantifying this "discrepancy" between mathematical models and observations, intended specifically for problems in which the data are numerical counts of events or objects. Examples arise in nearly every scientific field – counts of volcanoes or earthquakes or disease cases; of galaxies or stars or exoplanets; of photons or gamma ray burst pulses or neutrinos. This project will specifically address two classes of problems in astronomy. One class concerns how astronomers can convert raw data measuring light from astrophysical objects (such as stars and galaxies) into estimates of properties of the sources (such as brightness and color) with accurately quantified uncertainties, even when the precise shapes of the objects are not known, and their images overlap. A second class concerns using astronomical survey catalogs to learn the dominant demographic properties of stars, galaxies, or minor planets (such as asteroids), such as the distribution of their luminosities or masses. The NSF-funded Vera Rubin Observatory will produce data for both types of problems. The research will include developing fast, open-sourcecomputational algorithms implementing the new approaches.The project is motivated by application areas in which salient feature discovery is threatened by model misspecification. In applications with real-valued magnitude data with additive Gaussian errors, statisticians have addressed misspecification by introducing additive discrepancy processes into models, often using Gaussian processes. The two problem areas addressed here require analysis of discrete count or point process data: photon counting data comprising images and time series from cosmic sources, or demographic data in astronomical survey catalogs. Both areas rely on Poisson point process models, with an intensity function describing, say, the photon arrival rate (per unit area) as a function of direction and time, or the density of galaxies as a function of spatial location and luminosity. Additive discrepancy models are not applicable to such discrete-data settings. The team will develop new semiparametric methods that supplement parametric salient feature models with nonparametric discrepancy processes that flexibly model the departure of salient models from the true data generating process. An approach serving as a starting point represents the true underlying intensity function as the product of the salient feature model and a stochastic multiplicative discrepancy process. The salient feature model will be a parametricintensity function (e.g., with location, amplitude, scale, and shape parameters), sometimes in a superposition of multiple components (e.g., stars in an image, pulses in a transient burst, or population components). To model discrepancy from the salient model, a natural choice with appealing theoretical properties is a multiplicative gamma discrepancy process; composition with the Poisson point process leads to an overall negative binomial point process for the observations. The team will implement this approach, and generalize it in several directions. For demographic models, the discrepancy models will be embedded in a hierarchical model that accounts for measurement error and selection effects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在21世纪，数学建模已经成为科学研究的主要工具。科学家们构建了数学和计算机模型，旨在捕捉复杂物理现象的重要特征，并通过探索任何不确定的模型参数的值导致模型预测接近模拟自然界中观察到的东西来了解更多关于该现象的信息。尽管如此，这些模型通常充其量也不能捕捉到自然界的全部复杂性--或者，正如George Box著名的话所说，“所有的模型都是错误的，但有些模型是有用的。”这对发现来说真的是一件好事--科学家通常可以通过精确研究他们的模型与现实不符的原因，获得新的见解并加深理解。在这项研究中，PI将开发一种新的方法来量化数学模型和观测之间的“差异”，专门用于数据是事件或对象的数字计数的问题。几乎在每个科学领域都有这样的例子--火山、地震或疾病病例的计数；星系、恒星或系外行星的计数；光子或伽马射线爆发脉冲或中微子的计数。这个项目将专门解决天文学中的两类问题。其中一类涉及天文学家如何将测量天体(如恒星和星系)的光的原始数据转换为具有精确量化不确定性的源(如亮度和颜色)的属性估计，即使在对象的精确形状未知且它们的图像重叠的情况下也是如此。第二类涉及使用天文观测星表来了解恒星、星系或小行星(如小行星)的主要人口统计特性，例如它们的光度或质量的分布。美国国家科学基金会资助的维拉·鲁宾天文台将为这两种类型的问题提供数据。这项研究将包括开发实现新方法的快速、开源的经济计算算法。该项目的动机是应用领域，在这些领域中，显著特征发现受到模型错误指定的威胁。在具有加性高斯误差的实值震级数据的应用中，统计学家通过在模型中引入加性差异过程来解决错误说明问题，通常使用高斯过程。这里涉及的两个问题领域需要分析离散计数或点过程数据：包括来自宇宙来源的图像和时间序列的光子计数数据，或天文测量星表中的人口统计数据。这两个区域都依赖于泊松过程模型，强度函数描述了光子到达速率(单位面积)作为方向和时间的函数，或者星系的密度作为空间位置和光度的函数。加性差异模型不适用于这种离散数据设置。该团队将开发新的半参数方法，用非参数差异过程补充参数显著特征模型，从而灵活地对显著模型与真实数据生成过程的偏离进行建模。作为起点的方法将真实的潜在强度函数表示为显著特征模型和随机乘法差异过程的乘积。显著特征模型将是参数强度函数(例如，具有位置、幅度、比例和形状参数)，有时是多个分量(例如，图像中的恒星、瞬变猝发中的脉冲或人口分量)的叠加。为了从显著模型模拟差异，具有吸引人的理论属性的自然选择是乘性伽马差异过程；与泊松点过程合成导致观测的总体负二项点过程。该团队将实现这种方法，并在几个方向上推广它。对于人口统计模型，差异模型将嵌入到考虑测量误差和选择影响的分层模型中。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。