1 code implementation • 30 Nov 2024 • Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina J. Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam H. Parker, Miles Cranmer, Shirley Ho
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows.
no code implementations • 30 Oct 2024 • Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junnichiro Makino, Ulrich P. Steinwandel, Shirley Ho
We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent.
2 code implementations • 16 Aug 2024 • Caleb Lammers, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, Daniel Tamayo
By combining with a model for predicting long-term stability, we create an ML-based giant impact emulator, which can predict the outcomes of giant impact simulations with reasonable accuracy and a speedup of up to four orders of magnitude.
no code implementations • 30 May 2024 • Siavash Golkar, Alberto Bietti, Mariel Pettee, Michael Eickenberg, Miles Cranmer, Keiya Hirashima, Geraud Krawezik, Nicholas Lourie, Michael McCabe, Rudy Morel, Ruben Ohana, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications.
no code implementations • 14 Nov 2023 • Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino, Shirley Ho
SNe release a substantial amount of matter and energy to the interstellar medium, resulting in significant feedback to star formation and gas dynamics in a galaxy.
no code implementations • 23 Oct 2023 • Pablo Lemos, Liam Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Regaldo-Saint Blancard, David Spergel
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
1 code implementation • 4 Oct 2023 • Liam Parker, Francois Lanusse, Siavash Golkar, Leopoldo Sarra, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Geraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Regaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification.
2 code implementations • 4 Oct 2023 • Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets.
1 code implementation • 4 Oct 2023 • Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers.
1 code implementation • 28 Sep 2023 • Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles Cranmer, Zijun Zhang, Shirley Ho
This suggests that different neural architectures are sensitive to different aspects of the data, an important yet under-explored challenge for clinical prediction tasks.
1 code implementation • 24 Jul 2023 • Christian Pedersen, Michael Eickenberg, Shirley Ho
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well.
no code implementations • 23 Mar 2023 • Vaibhav Jindal, Albert Liang, Aarti Singh, Shirley Ho, Drew Jamieson
Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive.
1 code implementation • 24 Nov 2022 • Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, Shirley Ho
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.
no code implementations • 8 Nov 2022 • Thomas Pfeil, Miles Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr
Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1. 5\times 10^8 \,\mathrm{km}$).
1 code implementation • 24 Oct 2022 • Christian Kragh Jespersen, Miles Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics.
1 code implementation • 5 Oct 2022 • Yan-Mong Chan, Natascha Manger, Yin Li, Chao-Chin Yang, Zhaohuan Zhu, Philip J. Armitage, Shirley Ho
The simulation data are used to train a U-Net deep learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields.
1 code implementation • 5 Sep 2022 • Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles Cranmer, Daisuke Nagai, Daniel Anglés-Alcázar, Shirley Ho, Lars Hernquist
Our results can be useful for using upcoming SZ surveys (e. g., SO, CMB-S4) and galaxy surveys (e. g., DESI and Rubin) to constrain the nature of baryonic feedback.
1 code implementation • 18 Jul 2022 • Pablo Lemos, Miles Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
1 code implementation • 9 Jun 2022 • Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho, Renan Alves de Oliveira, David N. Spergel
We find our model generalizes well to these well understood scenarios, demonstrating that the networks have inferred general physical principles and learned the nonlinear mode couplings from the complex, random Gaussian training data.
1 code implementation • 9 Jun 2022 • Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime.
1 code implementation • 28 Feb 2022 • Leander Thiele, Miles Cranmer, William Coulton, Shirley Ho, David N. Spergel
We train neural networks on the IllustrisTNG-300 cosmological simulation to predict the continuous electron pressure field in galaxy clusters from gravity-only simulations.
no code implementations • 4 Feb 2022 • Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.
1 code implementation • 4 Jan 2022 • Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, Miles Cranmer, David N. Spergel, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, Shirley Ho
Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$.
1 code implementation • 31 Dec 2021 • Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
1 code implementation • 11 Nov 2021 • David Schaurecker, Yin Li, Jeremy Tinker, Shirley Ho, Alexandre Refregier
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology.
no code implementations • ICLR 2022 • Kim Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
no code implementations • 2 Jun 2021 • Ji Won Park, Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter.
1 code implementation • 22 Mar 2021 • V. Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.
2 code implementations • 11 Jan 2021 • Miles Cranmer, Daniel Tamayo, Hanno Rein, Peter Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel
Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three.
no code implementations • 10 Dec 2020 • Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, Anthony Pullen
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe.
no code implementations • 11 Nov 2020 • Francisco Villaescusa-Navarro, Benjamin D. Wandelt, Daniel Anglés-Alcázar, Shy Genel, Jose Manuel Zorrilla Mantilla, Shirley Ho, David N. Spergel
For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
1 code implementation • 29 Oct 2020 • T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault-Levasseur, David N. Spergel
We seek to remove foreground contaminants from 21cm intensity mapping observations.
no code implementations • 21 Oct 2020 • V. Ashley Villar, Miles Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
1 code implementation • 1 Oct 2020 • Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Rachel S. Somerville, Romeel Dave, Annalisa Pillepich, Lars Hernquist, Dylan Nelson, Paul Torrey, Desika Narayanan, Yin Li, Oliver Philcox, Valentina La Torre, Ana Maria Delgado, Shirley Ho, Sultan Hassan, Blakesley Burkhart, Digvijay Wadekar, Nicholas Battaglia, Gabriella Contardo
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
no code implementations • 20 Jul 2020 • Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho, Laurence Perreault-Levasseur
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
1 code implementation • 13 Jul 2020 • Daniel Tamayo, Miles Cranmer, Samuel Hadden, Hanno Rein, Peter Battaglia, Alysa Obertas, Philip J. Armitage, Shirley Ho, David Spergel, Christian Gilbertson, Naireen Hussain, Ari Silburt, Daniel Jontof-Hutter, Kristen Menou
Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first $10^4$ orbits, thus achieving speed-ups of up to $10^5$ over full simulations.
Earth and Planetary Astrophysics
1 code implementation • 8 Jul 2020 • Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo
This paper presents a meta-learning framework for few-shots One-Class Classification (OCC) at test-time, a setting where labeled examples are only available for the positive class, and no supervision is given for the negative example.
3 code implementations • NeurIPS 2020 • Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho
The technique works as follows: we first encourage sparse latent representations when we train a GNN in a supervised setting, then we apply symbolic regression to components of the learned model to extract explicit physical relations.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, Shirley Ho
Accurate models of the world are built upon notions of its underlying symmetries.
1 code implementation • 17 Oct 2019 • Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
no code implementations • 9 Oct 2019 • Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro, Siyu He, Shirley Ho, ChangHoon Hahn
In this work, we propose a new method, based on a deep learning network, to quickly generate simulations with massive neutrinos from standard $\Lambda$CDM simulations without neutrinos.
no code implementations • 12 Sep 2019 • Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho
We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization.
3 code implementations • 11 Sep 2019 • Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara, Arka Banerjee, Ana Maria Delgado, Doogesh Kodi Ramanah, Tom Charnock, Elena Giusarma, Yin Li, Erwan Allys, Antoine Brochard, Chi-Ting Chiang, Siyu He, Alice Pisani, Andrej Obuljen, Yu Feng, Emanuele Castorina, Gabriella Contardo, Christina D. Kreisch, Andrina Nicola, Roman Scoccimarro, Licia Verde, Matteo Viel, Shirley Ho, Stephane Mallat, Benjamin Wandelt, David N. Spergel
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
2 code implementations • 21 Aug 2019 • Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho
We demonstrate an algorithm for learning a flexible color-magnitude diagram from noisy parallax and photometry measurements using a normalizing flow, a deep neural network capable of learning an arbitrary multi-dimensional probability distribution.
1 code implementation • 29 Apr 2019 • Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro, Seda Bilaloglu, Asena Derin Cengiz, Siyu He, Laurence Perreault Levasseur, Shirley Ho
One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes.
no code implementations • 26 Feb 2019 • Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, Arya Farahi, Doug Finkbeiner, Shy Genel, Alyssa Goodman, Andy Goulding, Shirley Ho, Arthur Kosowsky, Paul La Plante, Francois Lanusse, Michelle Lochner, Rachel Mandelbaum, Daisuke Nagai, Jeffrey A. Newman, Brian Nord, J. E. G. Peek, Austin Peel, Barnabas Poczos, Markus Michael Rau, Aneta Siemiginowska, Dougal J. Sutherland, Hy Trac, Benjamin Wandelt
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
1 code implementation • 15 Feb 2019 • Xinyue Zhang, Yanfang Wang, Wei zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
1 code implementation • 15 Nov 2018 • Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
1 code implementation • 22 Aug 2018 • The Simons Observatory Collaboration, Peter Ade, James Aguirre, Zeeshan Ahmed, Simone Aiola, Aamir Ali, David Alonso, Marcelo A. Alvarez, Kam Arnold, Peter Ashton, Jason Austermann, Humna Awan, Carlo Baccigalupi, Taylor Baildon, Darcy Barron, Nick Battaglia, Richard Battye, Eric Baxter, Andrew Bazarko, James A. Beall, Rachel Bean, Dominic Beck, Shawn Beckman, Benjamin Beringue, Federico Bianchini, Steven Boada, David Boettger, J. Richard Bond, Julian Borrill, Michael L. Brown, Sarah Marie Bruno, Sean Bryan, Erminia Calabrese, Victoria Calafut, Paolo Calisse, Julien Carron, Anthony Challinor, Grace Chesmore, Yuji Chinone, Jens Chluba, Hsiao-Mei Sherry Cho, Steve Choi, Gabriele Coppi, Nicholas F. Cothard, Kevin Coughlin, Devin Crichton, Kevin D. Crowley, Kevin T. Crowley, Ari Cukierman, John M. D'Ewart, Rolando Dünner, Tijmen de Haan, Mark Devlin, Simon Dicker, Joy Didier, Matt Dobbs, Bradley Dober, Cody J. Duell, Shannon Duff, Adri Duivenvoorden, Jo Dunkley, John Dusatko, Josquin Errard, Giulio Fabbian, Stephen Feeney, Simone Ferraro, Pedro Fluxà, Katherine Freese, Josef C. Frisch, Andrei Frolov, George Fuller, Brittany Fuzia, Nicholas Galitzki, Patricio A. Gallardo, Jose Tomas Galvez Ghersi, Jiansong Gao, Eric Gawiser, Martina Gerbino, Vera Gluscevic, Neil Goeckner-Wald, Joseph Golec, Sam Gordon, Megan Gralla, Daniel Green, Arpi Grigorian, John Groh, Chris Groppi, Yilun Guan, Jon E. Gudmundsson, Dongwon Han, Peter Hargrave, Masaya Hasegawa, Matthew Hasselfield, Makoto Hattori, Victor Haynes, Masashi Hazumi, Yizhou He, Erin Healy, Shawn W. Henderson, Carlos Hervias-Caimapo, Charles A. Hill, J. Colin Hill, Gene Hilton, Matt Hilton, Adam D. Hincks, Gary Hinshaw, Renée Hložek, Shirley Ho, Shuay-Pwu Patty Ho, Logan Howe, Zhiqi Huang, Johannes Hubmayr, Kevin Huffenberger, John P. Hughes, Anna Ijjas, Margaret Ikape, Kent Irwin, Andrew H. Jaffe, Bhuvnesh Jain, Oliver Jeong, Daisuke Kaneko, Ethan D. Karpel, Nobuhiko Katayama, Brian Keating, Sarah S. Kernasovskiy, Reijo Keskitalo, Theodore Kisner, Kenji Kiuchi, Jeff Klein, Kenda Knowles, Brian Koopman, Arthur Kosowsky, Nicoletta Krachmalnicoff, Stephen E. Kuenstner, Chao-Lin Kuo, Akito Kusaka, Jacob Lashner, Adrian Lee, Eunseong Lee, David Leon, Jason S. -Y. Leung, Antony Lewis, Yaqiong Li, Zack Li, Michele Limon, Eric Linder, Carlos Lopez-Caraballo, Thibaut Louis, Lindsay Lowry, Marius Lungu, Mathew Madhavacheril, Daisy Mak, Felipe Maldonado, Hamdi Mani, Ben Mates, Frederick Matsuda, Loïc Maurin, Phil Mauskopf, Andrew May, Nialh McCallum, Chris McKenney, Jeff McMahon, P. Daniel Meerburg, Joel Meyers, Amber Miller, Mark Mirmelstein, Kavilan Moodley, Moritz Munchmeyer, Charles Munson, Sigurd Naess, Federico Nati, Martin Navaroli, Laura Newburgh, Ho Nam Nguyen, Michael Niemack, Haruki Nishino, John Orlowski-Scherer, Lyman Page, Bruce Partridge, Julien Peloton, Francesca Perrotta, Lucio Piccirillo, Giampaolo Pisano, Davide Poletti, Roberto Puddu, Giuseppe Puglisi, Chris Raum, Christian L. Reichardt, Mathieu Remazeilles, Yoel Rephaeli, Dominik Riechers, Felipe Rojas, Anirban Roy, Sharon Sadeh, Yuki Sakurai, Maria Salatino, Mayuri Sathyanarayana Rao, Emmanuel Schaan, Marcel Schmittfull, Neelima Sehgal, Joseph Seibert, Uros Seljak, Blake Sherwin, Meir Shimon, Carlos Sierra, Jonathan Sievers, Precious Sikhosana, Maximiliano Silva-Feaver, Sara M. Simon, Adrian Sinclair, Praween Siritanasak, Kendrick Smith, Stephen R. Smith, David Spergel, Suzanne T. Staggs, George Stein, Jason R. Stevens, Radek Stompor, Aritoki Suzuki, Osamu Tajima, Satoru Takakura, Grant Teply, Daniel B. Thomas, Ben Thorne, Robert Thornton, Hy Trac, Calvin Tsai, Carole Tucker, Joel Ullom, Sunny Vagnozzi, Alexander van Engelen, Jeff Van Lanen, Daniel D. Van Winkle, Eve M. Vavagiakis, Clara Vergès, Michael Vissers, Kasey Wagoner, Samantha Walker, Jon Ward, Ben Westbrook, Nathan Whitehorn, Jason Williams, Joel Williams, Edward J. Wollack, Zhilei Xu, Byeonghee Yu, Cyndia Yu, Fernando Zago, Hezi Zhang, Ningfeng Zhu
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
no code implementations • 14 Aug 2018 • Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee
Deep learning is a promising tool to determine the physical model that describes our universe.
no code implementations • 6 Nov 2017 • Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos
A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.
2 code implementations • 11 Jul 2016 • Yuting Wang, Gong-Bo Zhao, Chia-Hsun Chuang, Ashley J. Ross, Will J. Percival, Héctor Gil-Marín, Antonio J. Cuesta, Francisco-Shu Kitaura, Sergio Rodriguez-Torres, Joel R. Brownstein, Daniel J. Eisenstein, Shirley Ho, Jean-Paul Kneib, Matt Olmstead, Francisco Prada, Graziano Rossi, Ariel G. Sánchez, Salvador Salazar-Albornoz, Daniel Thomas, Jeremy Tinker, Rita Tojeiro, Mariana Vargas-Magaña, Fangzhou Zhu
Splitting the sample into multiple overlapping redshift slices to extract the redshift information of galaxy clustering, we obtain a measurement of $D_A(z)/r_d$ and $H(z)r_d$ at nine effective redshifts with the full covariance matrix calibrated using MultiDark-Patchy mock catalogues.
Cosmology and Nongalactic Astrophysics
no code implementations • 11 Jul 2016 • Shadab Alam, Metin Ata, Stephen Bailey, Florian Beutler, Dmitry Bizyaev, Jonathan A. Blazek, Adam S. Bolton, Joel R. Brownstein, Angela Burden, Chia-Hsun Chuang, Johan Comparat, Antonio J. Cuesta, Kyle S. Dawson, Daniel J. Eisenstein, Stephanie Escoffier, Héctor Gil-Marín, Jan Niklas Grieb, Nick Hand, Shirley Ho, Karen Kinemuchi, David Kirkby, Francisco Kitaura, Elena Malanushenko, Viktor Malanushenko, Claudia Maraston, Cameron K. McBride, Robert C. Nichol, Matthew D. Olmstead, Daniel Oravetz, Nikhil Padmanabhan, Nathalie Palanque-Delabrouille, Kaike Pan, Marcos Pellejero-Ibanez, Will J. Percival, Patrick Petitjean, Francisco Prada, Adrian M. Price-Whelan, Beth A. Reid, Sergio A. Rodríguez-Torres, Natalie A. Roe, Ashley J. Ross, Nicholas P. Ross, Graziano Rossi, Jose Alberto Rubiño-Martín, Ariel G. Sánchez, Shun Saito, Salvador Salazar-Albornoz, Lado Samushia, Siddharth Satpathy, Claudia G. Scóccola, David J. Schlegel, Donald P. Schneider, Hee-Jong Seo, Audrey Simmons, Anže Slosar, Michael A. Strauss, Molly E. C. Swanson, Daniel Thomas, Jeremy L. Tinker, Rita Tojeiro, Mariana Vargas Magaña, Jose Alberto Vazquez, Licia Verde, David A. Wake, Yuting Wang, David H. Weinberg, Martin White, W. Michael Wood-Vasey, Christophe Yèche, Idit Zehavi, Zhongxu Zhai, Gong-Bo Zhao
When combined with supernova Ia data, we find H0 = 67. 3+/-1. 0 km/s/Mpc even for our most general dark energy model, in tension with some direct measurements.
Cosmology and Nongalactic Astrophysics
4 code implementations • 14 May 2016 • Roman Garnett, Shirley Ho, Simeon Bird, Jeff Schneider
We develop an automated technique for detecting damped Lyman-$\alpha$ absorbers (DLAs) along spectroscopic lines of sight to quasi-stellar objects (QSOs or quasars).
Cosmology and Nongalactic Astrophysics Data Analysis, Statistics and Probability
1 code implementation • 21 Oct 2015 • Emmanuel Schaan, Simone Ferraro, Mariana Vargas-Magaña, Kendrick M. Smith, Shirley Ho, Simone Aiola, Nicholas Battaglia, J. Richard Bond, Francesco De Bernardis, Erminia Calabrese, Hsiao-Mei Cho, Mark J. Devlin, Joanna Dunkley, Patricio A. Gallardo, Matthew Hasselfield, Shawn Henderson, J. Colin Hill, Adam D. Hincks, Renée Hlozek, Johannes Hubmayr, John P. Hughes, Kent D. Irwin, Brian Koopman, Arthur Kosowsky, Dale Li, Thibaut Louis, Marius Lungu, Mathew Madhavacheril, Loïc Maurin, Jeffrey John McMahon, Kavilan Moodley, Sigurd Naess, Federico Nati, Laura Newburgh, Michael D. Niemack, Lyman A. Page, Christine G. Pappas, Bruce Partridge, Benjamin L. Schmitt, Neelima Sehgal, Blake D. Sherwin, Jonathan L. Sievers, David N. Spergel, Suzanne T. Staggs, Alexander van Engelen, Edward J. Wollack
We use microwave temperature maps from two seasons of data from the Atacama Cosmology Telescope (ACTPol) at 146 GHz, together with the Constant Mass CMASS galaxy sample from the Baryon Oscillation Spectroscopic Survey to measure the kinematic Sunyaev-Ze\v{l}dovich (kSZ) effect over the redshift range z = 0. 4 - 0. 7.
Cosmology and Nongalactic Astrophysics
1 code implementation • 6 Oct 2015 • Ross O'Connell, Daniel Eisenstein, Mariana Vargas, Shirley Ho, Nikhil Padmanabhan
We introduce a new method for estimating the covariance matrix for the galaxy correlation function in surveys of large-scale structure.
Cosmology and Nongalactic Astrophysics
no code implementations • 22 Sep 2015 • Beth Reid, Shirley Ho, Nikhil Padmanabhan, Will J. Percival, Jeremy Tinker, Rita Tojeiro, Martin White, Daniel J. Eisenstein, Claudia Maraston, Ashley J. Ross, Ariel G. Sanchez, David Schlegel, Erin Sheldon, Michael A. Strauss, Daniel Thomas, David Wake, Florian Beutler, Dmitry Bizyaev, Adam S. Bolton, Joel R. Brownstein, Chia-Hsun Chuang, Kyle Dawson, Paul Harding, Francisco-Shu Kitaura, Alexie Leauthaud, Karen Masters, Cameron K. McBride, Surhud More, Matthew D. Olmstead, Daniel Oravetz, Sebastian E. Nuza, Kaike Pan, John Parejko, Janine Pforr, Francisco Prada, Sergio Rodriguez-Torres, Salvador Salazar-Albornoz, Lado Samushia, Donald P. Schneider, Claudia G. Scoccola, Audrey Simmons, Mariana Vargas-Magana
We also present the algorithms used to create large scale structure catalogues for the final Data Release (DR12) samples and the associated random catalogues that quantify the survey mask.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies
no code implementations • NeurIPS 2015 • Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
We introduce the concept of coverage risk as an error measure for density ridge estimation.
2 code implementations • 25 Jun 2013 • Nathalie Palanque-Delabrouille, Christophe Yèche, Arnaud Borde, Jean-Marc Le Goff, Graziano Rossi, Matteo Viel, Éric Aubourg, Stephen Bailey, Julian Bautista, Michael Blomqvist, Adam Bolton, James S. Bolton, Nicolás G. Busca, Bill Carithers, Rupert A. C. Croft, Kyle S. Dawson, Timothée Delubac, Andreu Font-Ribera, Shirley Ho, David Kirkby, Khee-Gan Lee, Daniel Margala, Jordi Miralda-Escudé, Demitri Muna, Adam D. Myers, Pasquier Noterdaeme, Isabelle Pâris, Patrick Petitjean, Matthew M. Pieri, James Rich, Emmanuel Rollinde, Nicholas P. Ross, David J. Schlegel, Donald P. Schneider, Anže Slosar, David H. Weinberg
We have developed two independent methods to measure the one-dimensional power spectrum of the transmitted flux in the Lyman-$\alpha$ forest.
Cosmology and Nongalactic Astrophysics
no code implementations • 5 Mar 2013 • Xiaoying Xu, Shirley Ho, Hy Trac, Jeff Schneider, Barnabas Poczos, Michelle Ntampaka
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties.
Cosmology and Nongalactic Astrophysics