Presenter: Dr. Mahdi Qezlou, HETDEX-Cosmology Postdoctoral Fellow, University of Texas at Austin
Date: Wednesday, February 26, 2025
Time: 1:00 pm CST
Venue: POB 4.304
Zoom: https://utexas.zoom.us/j/96542873118
Meeting ID: 965 4287 3118
Passcode: 750886
One tap mobile +13462487799,,96542873118# US (Houston) +16694449171,,96542873118# US

Abstract:
Continuing the recent theme at the CosmicAI talks, I discuss “surrogate modeling” for inferring cosmological and astrophysical parameters with the help of machine learning. The available and upcoming observations require accurate simulations of the complex physical processes, including the non-linear gravity and gas dynamics. Using these detailed simulations at each Monte Carlo Markov Chain (MCMC) step in an inferential framework is computationally prohibitive. To overcome this, machine learning (surrogate) models can be trained to learn the map from the parameters of interest to the observables. 

In particular, I will highlight a multi-fidelity Gaussian Process framework designed to maximize the constraining power on a high-dimensional input parameter space while minimizing the computational cost. I will also demonstrate how these emulators enable unprecedented accuracy in parameter inference, paving the way for robust scientific discoveries with current and future cosmological surveys.

CosmicAI Hybrid Seminar Series

Title: Some advances in simulation-based inference: Calibration, aggregation, and model check
Presenter: Dr. Yuling Yao, Assistant Professor in the Department of Statistics and Data Sciences at the University of Texas at Austin. yulingyao.com. 
Venue: POB 4.304 (UT Austin)
Join Zoom Meeting: utexas.zoom.us/j/95502700680 (Meeting ID: 955 0270 0680, Passcode: 745761)

Abstract:
Simulation is a powerful way to specify models in modern scientific computing, while the likelihood-free setting imposes challenges for inference and calibration. To start, I present a cosmology example of galaxy clustering analysis using simulation-based inference and normalizing flows. I present three recent advances in simulation-based inference:
(1) “discriminative calibration” develops a general classifier approach to check Bayesian computation including simulation-based inference and Markov chain Monte Carlo. The classifier performance is a consistent estimate of a family of divergence measures, including the classical classifier two-sample test as a special case.
(2) To incorporate posterior approximations from different inference algorithms or flow architectures and improve the final inference quality, I present “simulation-based stacking”,  a general framework to combine probabilistic inferences.
(3) Yet even when the inference is perfect, the simulation model is often an approximation to the nature. I present “simulation-based posterior predictive check”, a framework to check if the simulation model does a good job of capturing relevant aspects of the data, such as means, standard deviations, and quantiles. This new predictive check p-value is ensured to be frequentist-calibrated under the null, making it particularly suitable for rigorously testing scientific hypothesis.

Title: Learning how Stars Form: Harnessing AI to Identify Structures in Noisy Spectral Cubes.
Dr. Stella Offner and Dr. Josh Taylor

Abstract:
Star formation is messy! The process spans many orders of magnitude in scale and involves a variety of physical processes: gravity, magnetic fields, radiation, and turbulence. Young stars announce their presence by emitting radiation and ejecting high-velocity material, “stellar feedback,” which in turn shapes the surrounding natal environment.
Part 1: Stella will present the results from a 3-D convolutional neural network model, trained on full-physics numerical simulations, which can accurately identify stellar feedback features in molecular line spectral cubes. She will discuss the pros and cons of supervised approaches based on numerical simulations for data segmentation.
Part 2: Josh will present a new approach to identifying cohesive spectral structures in molecular line cubes. Multiview Prototype Embedding and Clustering (MPEC) is an integrated approach to simultaneously:

• Learn prototypes of spectral data for efficient sample size reduction,

• Embed these prototypes in a lower dimensional latent space generated by a Self-Organizing variant of UMAP, and

• Cluster a graph representation of (learned) prototype similarity in both feature and latent spaces.

All facets of MPEC are self-parameterized, making it feasible for pipeline processing of spectral cubes. Josh will also discuss noisy cluster identification for spectral data using standard tools from statistical time series analysis.

This spring NSF-Simons AI Institute for Cosmic Origins (CosmicAI) is launching a new hybrid online/in-person seminar series for the community working at the intersection of AI and astronomy.

This seminar is intended to showcase research that is of broad interest and importance to AI developments in astrophysical research. This spring the primary in-person location will be at UT Austin, with some rotating guest seminars from researchers at other institutions.

Stella Offner, Director of NSF-Simons CosmicAI, and Josh Taylor will kick off the series on January 29: “Learning how Stars Form: Harnessing AI to Identify Structures in Noisy Spectral Cubes.”

Image Credit: KPNO/NOIRLab/NSF/AURA/Blythe Guvenen