Search Results for author: Peter Melchior

Found 14 papers, 11 papers with code

Multiscale Feature Attribution for Outliers

no code implementations30 Oct 2023 Jeff Shen, Peter Melchior

Machine learning techniques can automatically identify outliers in massive datasets, much faster and more reproducible than human inspection ever could.

Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

1 code implementation14 Mar 2022 ChangHoon Hahn, Peter Melchior

In this work, we present an alternative scalable approach to rigorous Bayesian inference using Amortized Neural Posterior Estimation (ANPE).

Bayesian Inference

Graph Neural Network-based Resource Allocation Strategies for Multi-Object Spectroscopy

1 code implementation27 Sep 2021 Tianshu Wang, Peter Melchior

But many concrete allocation problems in the experimental and observational sciences cannot or should not be expressed in the form of linear objective functions.

POS

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

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

Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems

no code implementations9 Dec 2019 Francois Lanusse, Peter Melchior, Fred Moolekamp

We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals.

BIG-bench Machine Learning

SCARLET: Source separation in multi-band images by Constrained Matrix Factorization

4 code implementations27 Feb 2018 Peter Melchior, Fred Moolekamp, Maximilian Jerdee, Robert Armstrong, Ai-Lei Sun, James Bosch, Robert Lupton

We present the source separation framework SCARLET for multi-band images, which is based on a generalization of the Non-negative Matrix Factorization to alternative and several simultaneous constraints.

Instrumentation and Methods for Astrophysics

Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints

3 code implementations30 Aug 2017 Fred Moolekamp, Peter Melchior

We introduce a generalization of the linearized Alternating Direction Method of Multipliers to optimize a real-valued function $f$ of multiple arguments with potentially multiple constraints $g_\circ$ on each of them.

Hyperspectral Unmixing

Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples

1 code implementation17 Nov 2016 Peter Melchior, Andy D. Goulding

We extend the common mixtures-of-Gaussians density estimation approach to account for a known sample incompleteness by simultaneous imputation from the current model.

Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Data Analysis, Statistics and Probability Methodology

GalSim: The modular galaxy image simulation toolkit

1 code implementation29 Jul 2014 Barnaby Rowe, Mike Jarvis, Rachel Mandelbaum, Gary M. Bernstein, James Bosch, Melanie Simet, Joshua E. Meyers, Tomasz Kacprzak, Reiko Nakajima, Joe Zuntz, Hironao Miyatake, Joerg P. Dietrich, Robert Armstrong, Peter Melchior, Mandeep S. S. Gill

GALSIM is a collaborative, open-source project aimed at providing an image simulation tool of enduring benefit to the astronomical community.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics 85-04

Dos and don'ts of reduced chi-squared

1 code implementation16 Dec 2010 Rene Andrae, Tim Schulze-Hartung, Peter Melchior

Concerning nonlinear models, the number of degrees of freedom is unknown, i. e., it is not possible to compute the value of reduced chi-squared.

Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Methodology

Weak gravitational lensing with DEIMOS

1 code implementation5 Aug 2010 Peter Melchior, Massimo Viola, Björn Malte Schäfer, Matthias Bartelmann

We introduce a novel method for weak-lensing measurements, which is based on a mathematically exact deconvolution of the moments of the apparent brightness distribution of galaxies from the telescope's PSF.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

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