Search Results for author: Brian Nord

Found 21 papers, 8 papers with code

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

no code implementations29 Nov 2023 Franco Terranova, M. Voetberg, Brian Nord, Amanda Pagul

We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO).

Astronomy Offline RL +2

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

1 code implementation2 Nov 2023 Andrea Roncoli, Aleksandra Ćiprijanović, Maggie Voetberg, Francisco Villaescusa-Navarro, Brian Nord

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets.

Unsupervised Domain Adaptation

WavPool: A New Block for Deep Neural Networks

no code implementations14 Jun 2023 Samuel D. McDermott, M. Voetberg, Brian Nord

Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks.

Neural Inference of Gaussian Processes for Time Series Data of Quasars

1 code implementation17 Nov 2022 Egor Danilov, Aleksandra Ćiprijanović, Brian Nord

A baseline approach to these tasks is to interpolate a time series with a Damped Random Walk (DRW) model, in which the spectrum is inferred using Maximum Likelihood Estimation (MLE).

Gaussian Processes Time Series +1

A robust estimator of mutual information for deep learning interpretability

1 code implementation31 Oct 2022 Davide Piras, Hiranya V. Peiris, Andrew Pontzen, Luisa Lucie-Smith, Ningyuan Guo, Brian Nord

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models.


Interpretable Uncertainty Quantification in AI for HEP

no code implementations5 Aug 2022 Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra

Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty.

Decision Making Uncertainty Quantification

Discovering the building blocks of dark matter halo density profiles with neural networks

no code implementations16 Mar 2022 Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, Davide Piras

The additional dimension in the representation contains information about the infalling material in the outer profiles of dark matter halos, thus discovering the splashback boundary of halos without prior knowledge of the halos' dynamical history.


Learning Representation for Bayesian Optimization with Collision-free Regularization

no code implementations16 Mar 2022 Fengxue Zhang, Brian Nord, Yuxin Chen

We show that even with proper network design, such learned representation often leads to collision in the latent space: two points with significantly different observations collide in the learned latent space, leading to degraded optimization performance.

Bayesian Optimization

DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

no code implementations28 Dec 2021 Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild

On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise.

Domain Adaptation Image Compression +1

Unsupervised Resource Allocation with Graph Neural Networks

1 code implementation17 Jun 2021 Miles Cranmer, Peter Melchior, Brian Nord

We present an approach for maximizing a global utility function by learning how to allocate resources in an unsupervised way.

Astronomy Evolutionary Algorithms

DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning

no code implementations25 Feb 2021 Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu, Shubhendu Trivedi, João Caldeira, Brian Nord

We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN).

Learning Collision-free Latent Space for Bayesian Optimization

no code implementations1 Jan 2021 Fengxue Zhang, Yair Altas, Louise Fan, Kaustubh Vinchure, Brian Nord, Yuxin Chen

To address this issue, we propose Collision-Free Latent Space Optimization (CoFLO), which employs a novel regularizer to reduce the collision in the learned latent space and encourage the mapping from the latent space to objective value to be Lipschitz continuous.

Bayesian Optimization Experimental Design

Deep learning insights into cosmological structure formation

2 code implementations20 Nov 2020 Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam

We train a three-dimensional convolutional neural network (CNN) to predict the mass of dark matter halos from the initial conditions, and quantify in full generality the amounts of information in the isotropic and anisotropic aspects of the initial density field about final halo masses.

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

1 code implementation22 Apr 2020 João Caldeira, Brian Nord

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system.

Uncertainty Quantification

Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances and Weak Lensing

no code implementations25 Feb 2020 DES Collaboration, Tim Abbott, Michel Aguena, Alex Alarcon, Sahar Allam, Steve Allen, James Annis, Santiago Avila, David Bacon, Alberto Bermeo, Gary Bernstein, Emmanuel Bertin, Sunayana Bhargava, Sebastian Bocquet, David Brooks, Dillon Brout, Elizabeth Buckley-Geer, David Burke, Aurelio Carnero Rosell, Matias Carrasco Kind, Jorge Carretero, Francisco Javier Castander, Ross Cawthon, Chihway Chang, Xinyi Chen, Ami Choi, Matteo Costanzi, Martin Crocce, Luiz da Costa, Tamara Davis, Juan De Vicente, Joseph DeRose, Shantanu Desai, H. Thomas Diehl, Jörg Dietrich, Scott Dodelson, Peter Doel, Alex Drlica-Wagner, Kathleen Eckert, Tim Eifler, Jack Elvin-Poole, Juan Estrada, Spencer Everett, August Evrard, Arya Farahi, Ismael Ferrero, Brenna Flaugher, Pablo Fosalba, Josh Frieman, Juan Garcia-Bellido, Marco Gatti, Enrique Gaztanaga, David Gerdes, Tommaso Giannantonio, Paul Giles, Sebastian Grandis, Daniel Gruen, Robert Gruendl, Julia Gschwend, Gaston Gutierrez, Will Hartley, Samuel Hinton, Devon L. Hollowood, Klaus Honscheid, Ben Hoyle, Dragan Huterer, David James, Mike Jarvis, Tesla Jeltema, Margaret Johnson, Stephen Kent, Elisabeth Krause, Richard Kron, Kyler Kuehn, Nikolay Kuropatkin, Ofer Lahav, Ting Li, Christopher Lidman, Marcos Lima, Huan Lin, Niall MacCrann, Marcio Maia, Adam Mantz, Jennifer Marshall, Paul Martini, Julian Mayers, Peter Melchior, Juan Mena, Felipe Menanteau, Ramon Miquel, Joe Mohr, Robert Nichol, Brian Nord, Ricardo Ogando, Antonella Palmese, Francisco Paz-Chinchon, Andrés Plazas Malagón, Judit Prat, Markus Michael Rau, Kathy Romer, Aaron Roodman, Philip Rooney, Eduardo Rozo, Eli Rykoff, Masao Sako, Simon Samuroff, Carles Sanchez, Alexandro Saro, Vic Scarpine, Michael Schubnell, Daniel Scolnic, Santiago Serrano, Ignacio Sevilla, Erin Sheldon, J. Allyn Smith, Eric Suchyta, Molly Swanson, Gregory Tarle, Daniel Thomas, Chun-Hao To, Michael A. Troxel, Douglas Tucker, Tamas Norbert Varga, Anja von der Linden, Alistair Walker, Risa Wechsler, Jochen Weller, Reese Wilkinson, Hao-Yi Wu, Brian Yanny, Zhuowen Zhang, Joe Zuntz

We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.

Cosmology and Nongalactic Astrophysics

Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer

no code implementations14 Nov 2019 João Caldeira, Joshua Job, Steven H. Adachi, Brian Nord, Gabriel N. Perdue

We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems.

General Classification Image Classification +2

DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks

1 code implementation2 Oct 2018 João Caldeira, W. L. Kimmy Wu, Brian Nord, Camille Avestruz, Shubhendu Trivedi, Kyle T. Story

In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet.

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