Timezone: »
Image coaddition is of critical importance to observational astronomy. This family of methods consisting of several processing steps such as image registration, resampling, deconvolution, and artifact removal is used to combine images into a single higher-quality image. An alternative to these methods that are built upon vectorized operations is the representation of an image function as a neural network, which has had considerable success in machine learning image processing applications. We propose a deep learning method employing gradient-based planar alignment with Bundle-Adjusting Radiance Fields (BARF) to combine, de-noise, and remove obstructions from observations of cosmological objects at different resolutions, seeing, and noise levels -- tasks not currently possible within a single process in astronomy. We test our algorithm on artificial images of star clusters, demonstrating powerful artifact removal and de-noising.
Author Information
Harlan Hutton (Flatiron Institute Center for Computational Astrophysics)
Harshitha Palegar (Flatiron Institute CCA)
Shirley Ho (Flatiron Institute)
Shirley Ho is a group leader and acting director at Flatiron Institute at Simons foundation, a research professor of physics and an affiliated faculty at Center for Data Science at NYU. Ho also holds associate (adjunct) professorship at Carnegie Mellon University and visiting appointment at Princeton University. She was a senior scientist at Berkeley National Lab from 2016-2018 and a Cooper-Siegel Development chair professor at Carnegie Mellon University before that. Ho was a Seaborg and Chamberlain Fellow from 2008-2011 at Berkeley Lab, after receiving her PhD in Astrophysics from Princeton University in 2008 under supervision of David Spergel. Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science from UC Berkeley. A cited expert in cosmology, machine learning applications in astrophysics and data science,her interests are using deep learning accelerated simulations to understand the Universe, and other astrophysical phenomena. She tries her best to balance her love for the Universe, the machine and life especially during these crazy times.
Miles Cranmer (Princeton University)
Miles Cranmer is an Astro PhD candidate trying to accelerate astrophysics with AI. Miles is from Canada and did his undergraduate in Physics at McGill. He is deeply interested in the automation of science, particularly aspects that are not yet tractable with existing machine learning, such as experiment planning, simulation, and theory. He works on symbolic regression, graph neural networks, normalizing flows, and learned simulation. He is hugely interested in symbolic ML, since, as he argues, symbolic models seem to be a surprisingly efficient basis for describing our universe.
Peter Melchior (Princeton University)
Jenna Eubank (Flatiron Institute)
More from the Same Authors
-
2021 : Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars »
Ho-Sang Chan · Siu Hei Cheung · Victoria Villar · Shirley Ho -
2021 : A Convolutional Autoencoder-Based Pipeline For Anomaly Detection And Classification Of Periodic Variables »
Ho-Sang Chan · Siu Hei Cheung · Shirley Ho -
2021 : Equivariant and Modular DeepSets with Applications in Cluster Cosmology »
Leander Thiele · Miles Cranmer · Shirley Ho · David Spergel -
2021 : Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars »
Ho-Sang Chan · Siu Hei Cheung · Victoria Villar · Shirley Ho -
2022 : A Neural Network Subgrid Model of the Early Stages of Planet Formation »
Thomas Pfeil · Miles Cranmer · Shirley Ho · Philip Armitage · Tilman Birnstiel · Hubert Klahr -
2022 : Interpretable Encoding of Galaxy Spectra »
Yan Liang · Peter Melchior · Sicong Lu -
2022 : Recovering Galaxy Cluster Convergence from Lensed CMB with Generative Adversarial Networks »
Liam Parker · Dongwon Han · Shirley Ho · Pablo Lemos -
2022 : Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics »
Benjamin Horowitz · Peter Melchior -
2022 : Emulating cosmological growth functions with B-Splines »
Ngai Pok Kwan · Chirag Modi · Yin Li · Shirley Ho -
2022 : Adversarial Noise Injection for Learned Turbulence Simulations »
Jingtong Su · Julia Kempe · Drummond Fielding · Nikolaos Tsilivis · Miles Cranmer · Shirley Ho -
2022 : Learning Integrable Dynamics with Action-Angle Networks »
Ameya Daigavane · Arthur Kosmala · Miles Cranmer · Tess Smidt · Shirley Ho -
2023 : Predicting the Initial Conditions of the Universe using a Deterministic Neural Network »
Vaibhav Jindal · Albert Liang · Aarti Singh · Shirley Ho · Drew Jamieson -
2023 : Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations »
Keiya Hirashima · Kana Moriwaki · Michiko Fujii · Yutaka Hirai · Takayuki Saitoh · Junichiro Makino · Shirley Ho -
2023 : AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models »
Francois Lanusse · Liam Parker · Siavash Golkar · Alberto Bietti · Miles Cranmer · Michael Eickenberg · Geraud Krawezik · John McCabe · Ruben Ohana · Mariel Pettee · Bruno Régaldo-Saint Blancard · Tiberiu Tesileanu · Kyunghyun Cho · Shirley Ho -
2023 : Multiple Physics Pretraining for Physical Surrogate Models »
John McCabe · John McCabe · Bruno Régaldo-Saint Blancard · Bruno Régaldo-Saint Blancard · Liam Parker · Ruben Ohana · Ruben Ohana · Miles Cranmer · Alberto Bietti · Michael Eickenberg · Michael Eickenberg · Siavash Golkar · Siavash Golkar · Geraud Krawezik · Geraud Krawezik · Francois Lanusse · Francois Lanusse · Mariel Pettee · Tiberiu Tesileanu · Kyunghyun Cho · Kyunghyun Cho · Shirley Ho -
2023 : xVal: A Continuous Number Encoding for Large Language Models »
Siavash Golkar · Mariel Pettee · Michael Eickenberg · Alberto Bietti · Miles Cranmer · Geraud Krawezik · Francois Lanusse · John McCabe · Ruben Ohana · Liam Parker · Bruno Régaldo-Saint Blancard · Tiberiu Tesileanu · Tiberiu Tesileanu · Kyunghyun Cho · Kyunghyun Cho · Shirley Ho · Shirley Ho -
2023 : Multiscale Feature Attribution for Outliers »
Jeff Shen · Peter Melchior -
2023 Workshop: AI for Science: from Theory to Practice »
Yuanqi Du · Max Welling · Yoshua Bengio · Marinka Zitnik · Carla Gomes · Jure Leskovec · Maria Brbic · Wenhao Gao · Kexin Huang · Ziming Liu · Rocío Mercado · Miles Cranmer · Shengchao Liu · Lijing Wang -
2021 : AI X Cosmology »
Shirley Ho -
2021 : Coding Session 3 »
Shirley Ho · Miles Cranmer -
2021 : The Problem with Deep Learning for Physics (and how to fix it) »
Miles Cranmer · Shirley Ho -
2021 : Accelerating Simulations in Physics with Deep Learning »
Miles Cranmer · Shirley Ho -
2021 : Coding Session »
Shirley Ho · Miles Cranmer -
2021 : Physics-Informed Inductive Biases in Deep Learning »
Miles Cranmer · Shirley Ho -
2021 Tutorial: ML for Physics and Physics for ML »
Shirley Ho · Miles Cranmer -
2021 : The Intersection of ML and Physics »
Shirley Ho · Miles Cranmer -
2020 Workshop: Interpretable Inductive Biases and Physically Structured Learning »
Michael Lutter · Alexander Terenin · Shirley Ho · Lei Wang -
2020 : Contributed talk - Unsupervised Resource Allocation with Graph Neural Networks »
Miles Cranmer -
2020 Workshop: Machine Learning and the Physical Sciences »
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais -
2020 Poster: Discovering Symbolic Models from Deep Learning with Inductive Biases »
Miles Cranmer · Alvaro Sanchez Gonzalez · Peter Battaglia · Rui Xu · Kyle Cranmer · David Spergel · Shirley Ho -
2019 : Learning Symbolic Physics with Graph Networks »
Miles Cranmer -
2019 : Morning Coffee Break & Poster Session »
Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger -
2019 : Opening Remarks »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood -
2019 Workshop: Machine Learning and the Physical Sciences »
Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood