Rachel Mandelbaum, Ph.D. - US grants

This goal of this proposal is to develop advanced techniques in weak lensing for cosmology, in both data analysis and theoretical understanding. The work focuses on mitigating several systematic uncertainties that affect dark energy measurements to below the 1% level. The methods developed will be important for interpreting the results of upcoming lensing surveys, such as the one planned for NSF?s Large Synoptic Survey Telescope (LSST), the top ground-based priority of the 2010 Decadal Survey.

Broader impacts of the work include training of a postdoc and a graduate student, and release of weak lensing analysis software to the astronomical public. The PI will give lectures at Alleghany Observatory and to minority middle-school students.

Understanding how matter is distributed in the Universe is key to developing accurate models of how it evolved. The investigators will use mapping of filamentary structures in large samples of galaxies as a new indicator of large scale structure that can then be compared to other data. They seek to trace the cosmic web from these data. Broader impacts of the work include training of a graduate student, and engagement of the broader community through public lectures at the Allegheny Observatory, online educational games, and existing programs for middle school students.

The research will cross correlate the new data with other cosmological observables like Baryon Acoustic Oscillations and study intrinsic galaxy shapes with filaments. The group has developed a method for identifying filaments in purely photometric data and, with this award, will develop a pre-existing prototype into a reconstruction tool. They will then apply the tool to simulated data.

The purpose of this work is to i) develop and validate new, efficient machine learning methods for making inferences and predictions in a massively parallel and distributed way on large-scale complex data sets coming from upcoming sky surveys, and ii) help answer important fundamental questions in cosmology and astrophysics using those new methods. Theoretical properties of these algorithms will also be investigated. The proposed cosmology and astrophysics applications will include a) building a probabilistic model for light intensity signals from stars, b) evolving the matter density of the Universe at a speed much higher than the traditional method of N-body simulations, and c) creating "mock catalogs" with all commonly observable galaxy properties. The methods that are developed will have far broader applicability than the examples listed here, both for other problems in astrophysics and problems in completely different domains (e.g. bioinformatics, climatology, social sciences), where complex scientific simulations require large-scale learning methods. The software developed in this work (including documentation, examples, and case studies) will be made publicly available. The PIs will also include the results in their course materials for graduate and undergraduate students.


The aim of this proposal is to develop new machine learning methods that can work directly on large-scale, high-dimensional functions and continuous distributions as inputs or outputs in a regression problem, and can process large-scale scientific data in a massively parallel distributed way. Important theoretical properties, such as computational efficiency, sample complexity, generalization accuracy, consistency, lower and upper bounds on the convergence rates will also be investigated. Gaussian processes (GPs) are among the most popular nonparametric Bayesian function approximation methods. However, the standard GP methods are limited to at most a few thousands data points, and not applicable for large datasets. Kernel learning for GPs is an even more challenging problem. The question of how to scale up GP kernel learning methods for large datasets will be addressed as part of this project. Using the machine learning methods developed in this proposal, the following cosmology and astrophysics problems will be investigated: a) Scalable Gaussian processes with spectral mixture kernels will be used to build a probabilistic generative model for light intensity signals from stars to extract fundamental properties such as density profiles. b) New machine learning algorithms will be used to evolve the matter density of the Universe at a speed much higher than the traditional method of N-body simulations. This will enable a completely new way of generating a large number of cosmological simulations in order to compare the cosmological observations to our understanding of the Universe. c) Simulated galaxy catalogs are a powerful tool for testing cosmological analysis methods, since the cosmological parameters in the simulation are known and thus our ability to recover them can be tested perfectly. For a single cosmological simulation, the properties and alignments of galaxies are not fully determined, but rather must be added probabilistically to the dark matter distribution as an extra layer of modeling typically with many parameters. The new machine learning tools developed in this proposal will be used to make "mock catalogs" with all commonly observable galaxy properties.

One of the key mysteries in modern cosmology is the nature of the dark energy that is responsible for the accelerated expansion rate of our Universe. Weak gravitational lensing, the coherent distortion of galaxy shapes due to deflection of light by mass, is the most promising observational method for uncovering the nature of dark energy. As a result, large upcoming surveys such as the Large Synoptic Survey Telescope (LSST) have been planned to measure weak lensing very precisely. The objective of this project is to provide accurate theoretical predictions that will eliminate a key source of uncertainty in weak lensing measurements and test their use in analysis of data from an ongoing sky survey. Work such as this, that will contribute to the solution of the biggest mystery in modern cosmology, will clearly promote the progress of science. As an additional benefit, the investigators will extend an existing, highly successful educational outreach program ("Space Public Outreach Team" or SPOT) to the Pittsburgh area, focusing on schools with a significant fraction of under-represented minorities. This project will have an impact both on K-12 education and undergraduate and graduate education.

Weak gravitational lensing has great potential to solve some of the major outstanding issues in cosmology, such as the nature of dark energy. However, a major source of uncertainty is that these measurements are typically interpreted assuming that all coherent galaxy shape alignments are due to weak lensing, but unfortunately, galaxies do exhibit coherent "intrinsic alignments" with large-scale density fields. The alignments contaminate weak lensing measurements at a level that will significantly exceed the statistical error bars on weak lensing measurements with the LSST. This project has three main objectives. The first objective is to use large volume, extremely high resolution hydrodynamic simulations to further our understanding of the physics behind intrinsic alignments and build improved intrinsic alignments models. The second objective is to produce mock galaxy catalogs with intrinsic alignments for use in testing intrinsic alignments mitigation schemes. The final objective is to carry out cosmological weak lensing analysis including strategies to marginalize over intrinsic alignments using improved intrinsic alignment models that are motivated by state-of-the-art simulations, analytic theory, and observations.