Search Results for author: Ofer Lahav

Found 11 papers, 6 papers with code

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

GEOMAX: beyond linear compression for 3pt galaxy clustering statistics

1 code implementation2 Dec 2019 Davide Gualdi, Héctor Gil-Marín, Marc Manera, Benjamin Joachimi, Ofer Lahav

By applying GEOMAX to bispectrum monopole measurements from BOSS DR12 CMASS redshift-space galaxy clustering data, we reduce the $68\%$ credible intervals for the inferred parameters $\left(b_1, b_2, f,\sigma_8\right)$ by $\left(50. 4\%, 56. 1\%, 33. 2\%, 38. 3\%\right)$ with respect to standard MCMC on the full data vector.

Cosmology and Nongalactic Astrophysics

Deep learning dark matter map reconstructions from DES SV weak lensing data

2 code implementations1 Aug 2019 Niall Jeffrey, François Lanusse, Ofer Lahav, Jean-Luc Starck

With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent.

Cosmology and Nongalactic Astrophysics

Geometrical compression: a new method to enhance the BOSS galaxy bispectrum monopole constraints

1 code implementation4 Jan 2019 Davide Gualdi, Héctor Gil-Marín, Marc Manera, Benjamin Joachimi, Ofer Lahav

We present a novel method to compress galaxy clustering three-point statistics and apply it to redshift space galaxy bispectrum monopole measurements from BOSS DR12 CMASS data considering a $k$-space range of $0. 03-0. 12\, h/\mathrm{Mpc}$.

Cosmology and Nongalactic Astrophysics

Science-Driven Optimization of the LSST Observing Strategy

1 code implementation14 Aug 2017 LSST Science Collaboration, Phil Marshall, Timo Anguita, Federica B. Bianco, Eric C. Bellm, Niel Brandt, Will Clarkson, Andy Connolly, Eric Gawiser, Zeljko Ivezic, Lynne Jones, Michelle Lochner, Michael B. Lund, Ashish Mahabal, David Nidever, Knut Olsen, Stephen Ridgway, Jason Rhodes, Ohad Shemmer, David Trilling, Kathy Vivas, Lucianne Walkowicz, Beth Willman, Peter Yoachim, Scott Anderson, Pierre Antilogus, Ruth Angus, Iair Arcavi, Humna Awan, Rahul Biswas, Keaton J. Bell, David Bennett, Chris Britt, Derek Buzasi, Dana I. Casetti-Dinescu, Laura Chomiuk, Chuck Claver, Kem Cook, James Davenport, Victor Debattista, Seth Digel, Zoheyr Doctor, R. E. Firth, Ryan Foley, Wen-fai Fong, Lluis Galbany, Mark Giampapa, John E. Gizis, Melissa L. Graham, Carl Grillmair, Phillipe Gris, Zoltan Haiman, Patrick Hartigan, Suzanne Hawley, Renee Hlozek, Saurabh W. Jha, C. Johns-Krull, Shashi Kanbur, Vassiliki Kalogera, Vinay Kashyap, Vishal Kasliwal, Richard Kessler, Alex Kim, Peter Kurczynski, Ofer Lahav, Michael C. Liu, Alex Malz, Raffaella Margutti, Tom Matheson, Jason D. McEwen, Peregrine McGehee, Soren Meibom, Josh Meyers, Dave Monet, Eric Neilsen, Jeffrey Newman, Matt O'Dowd, Hiranya V. Peiris, Matthew T. Penny, Christina Peters, Radoslaw Poleski, Kara Ponder, Gordon Richards, Jeonghee Rho, David Rubin, Samuel Schmidt, Robert L. Schuhmann, Avi Shporer, Colin Slater, Nathan Smith, Marcelles Soares-Santos, Keivan Stassun, Jay Strader, Michael Strauss, Rachel Street, Christopher Stubbs, Mark Sullivan, Paula Szkody, Virginia Trimble, Tony Tyson, Miguel de Val-Borro, Stefano Valenti, Robert Wagoner, W. Michael Wood-Vasey, Bevin Ashley Zauderer

The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics

Estimating the Mass of the Local Group using Machine Learning Applied to Numerical Simulations

no code implementations8 Jun 2016 Michael McLeod, Noam Libeskind, Ofer Lahav, Yehuda Hoffman

The resulting estimate for the Local Group mass, when shear information is included, is $4. 9 \times 10^{12} M_\odot$, with an error of $\pm0. 8 \times 10^{12} M_\odot$ from the 68% uncertainty in observables, and a 68% confidence interval of $^{+1. 3}_{-1. 4} \times 10^{12}M_\odot$ from the intrinsic scatter from the differences between the model and simulation masses.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

ANNz2 - photometric redshift and probability distribution function estimation using machine learning

1 code implementation2 Jul 2015 Iftach Sadeh, Filipe B. Abdalla, Ofer Lahav

We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister and Lahav (2004), which now includes generation of full probability distribution functions (PDFs).

Cosmology and Nongalactic Astrophysics

PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks

no code implementations7 Dec 2013 Shankar Agarwal, Filipe B. Abdalla, Hume A. Feldman, Ofer Lahav, Shaun A. Thomas

The overall precision of PkANN is set by the accuracy of our N-body simulations, at 5 per cent level for cosmological models with $\sum m_\nu<0. 5$ eV for all redshifts $z\leq2$.

Cosmology and Nongalactic Astrophysics

PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks

no code implementations8 Mar 2012 Shankar Agarwal, Filipe B. Abdalla, Hume A. Feldman, Ofer Lahav, Shaun A. Thomas

We show that an optimally trained artificial neural network (ANN), when presented with a set of cosmological parameters (Omega_m h^2, Omega_b h^2, n_s, w_0, sigma_8, m_nu and redshift z), can provide a worst-case error <=1 per cent (for z<=2) fit to the non-linear matter power spectrum deduced through N-body simulations, for modes up to k<=0. 7 h/Mpc.

Cosmology and Nongalactic Astrophysics

ANNz: estimating photometric redshifts using artificial neural networks

no code implementations3 Nov 2003 Adrian A. Collister, Ofer Lahav

We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks.

astro-ph

Massive Lossless Data Compression and Multiple Parameter Estimation from Galaxy Spectra

1 code implementation6 Nov 1999 Alan Heavens, Raul Jimenez, Ofer Lahav

We show that, if the noise in the data is independent of the parameters, we can form $M$ linear combinations of the data which contain as much information about all the parameters as the entire dataset, in the sense that the Fisher information matrices are identical; i. e. the method is lossless.

astro-ph Rings and Algebras Data Analysis, Statistics and Probability

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