Search Results - "Feeney, Stephen"

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  1. 1

    Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology by Alsing, Justin, Wandelt, Benjamin, Feeney, Stephen

    “…Abstract Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us…”
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  2. 2

    Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder by Feeney, Stephen M, Mortlock, Daniel J, Dalmasso, Niccolò

    “…Abstract Estimates of the Hubble constant, H0, from the local distance ladder and from the cosmic microwave background (CMB) are discrepant at the ∼3σ level,…”
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  3. 3

    Prospects for Resolving the Hubble Constant Tension with Standard Sirens by Feeney, Stephen M, Peiris, Hiranya V, Williamson, Andrew R, Nissanke, Samaya M, Mortlock, Daniel J, Alsing, Justin, Scolnic, Dan

    Published in Physical review letters (15-02-2019)
    “…The Hubble constant (H_{0}) estimated from the local Cepheid-supernova distance ladder is in 3-σ tension with the value extrapolated from cosmic microwave…”
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  4. 4

    How Isotropic is the Universe? by Saadeh, Daniela, Feeney, Stephen M, Pontzen, Andrew, Peiris, Hiranya V, McEwen, Jason D

    Published in Physical review letters (23-09-2016)
    “…A fundamental assumption in the standard model of cosmology is that the Universe is isotropic on large scales. Breaking this assumption leads to a set of…”
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  5. 5

    Exploiting Network Topology for Accelerated Bayesian Inference of Grain Surface Reaction Networks by Heyl, Johannes, Viti, Serena, Holdship, Jonathan, Feeney, Stephen M.

    Published in The Astrophysical journal (01-12-2020)
    “…In the study of grain-surface chemistry in the interstellar medium, there exists much uncertainty regarding the reaction mechanisms with few constraints on the…”
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  6. 6

    Prospects for Measuring the Hubble Constant with Neutron-Star-Black-Hole Mergers by Feeney, Stephen M, Peiris, Hiranya V, Nissanke, Samaya M, Mortlock, Daniel J

    Published in Physical review letters (30-04-2021)
    “…Gravitational wave (GW) and electromagnetic (EM) observations of neutron-star-black-hole (NSBH) mergers can provide precise local measurements of the Hubble…”
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  7. 7

    SSSpaNG! stellar spectra as sparse, data-driven, non-Gaussian processes by Feeney, Stephen M, Wandelt, Benjamin D, Ness, Melissa K

    “…ABSTRACT Upcoming million-star spectroscopic surveys have the potential to revolutionize our view of the formation and chemical evolution of the Milky Way…”
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  8. 8

    A framework for testing isotropy with the cosmic microwave background by Saadeh, Daniela, Feeney, Stephen M., Pontzen, Andrew, Peiris, Hiranya V., McEwen, Jason D.

    “…We present a new framework for testing the isotropy of the Universe using cosmic microwave background data, building on the nested-sampling anicosmo code…”
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  9. 9

    First observational tests of eternal inflation by Feeney, Stephen M, Johnson, Matthew C, Mortlock, Daniel J, Peiris, Hiranya V

    Published in Physical review letters (08-08-2011)
    “…The eternal inflation scenario predicts that our observable Universe resides inside a single bubble embedded in a vast inflating multiverse. We present the…”
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  10. 10
  11. 11

    Hierarchical Bayesian detection algorithm for early-universe relics in the cosmic microwave background by Feeney, Stephen M., Johnson, Matthew C., McEwen, Jason D., Mortlock, Daniel J., Peiris, Hiranya V.

    “…A number of theoretically well-motivated additions to the standard cosmological model predict weak signatures in the form of spatially localized sources…”
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  12. 12

    Robust constraint on cosmic textures from the cosmic microwave background by Feeney, Stephen M, Johnson, Matthew C, Mortlock, Daniel J, Peiris, Hiranya V

    Published in Physical review letters (15-06-2012)
    “…Fluctuations in the cosmic microwave background (CMB) contain information which has been pivotal in establishing the current cosmological model. These data can…”
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  13. 13

    Fast likelihood-free cosmology with neural density estimators and active learning by Alsing, Justin, Charnock, Tom, Feeney, Stephen, Wandelt, Benjamin

    “…ABSTRACT Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all…”
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  14. 14

    A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey by Lin, Kiyam, von wietersheim-Kramsta, Maximilian, Joachimi, Benjamin, Feeney, Stephen

    “…ABSTRACT The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a…”
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  15. 15

    POLARBEAR constraints on cosmic birefringence and primordial magnetic fields by Ade, Peter A. R., Arnold, Kam, Atlas, Matt, Baccigalupi, Carlo, Barron, Darcy, Boettger, David, Borrill, Julian, Chapman, Scott, Chinone, Yuji, Cukierman, Ari, Dobbs, Matt, Ducout, Anne, Dunner, Rolando, Elleflot, Tucker, Errard, Josquin, Fabbian, Giulio, Feeney, Stephen, Feng, Chang, Gilbert, Adam, Goeckner-Wald, Neil, Groh, John, Hall, Grantland, Halverson, Nils W., Hasegawa, Masaya, Hattori, Kaori, Hazumi, Masashi, Hill, Charles, Holzapfel, William L., Hori, Yasuto, Howe, Logan, Inoue, Yuki, Jaehnig, Gregory C., Jaffe, Andrew H., Jeong, Oliver, Katayama, Nobuhiko, Kaufman, Jonathan P., Keating, Brian, Kermish, Zigmund, Keskitalo, Reijo, Kisner, Theodore, Kusaka, Akito, Le Jeune, Maude, Lee, Adrian T., Leitch, Erik M., Leon, David, Li, Yun, Linder, Eric, Lowry, Lindsay, Matsuda, Frederick, Matsumura, Tomotake, Miller, Nathan, Montgomery, Josh, Myers, Michael J., Navaroli, Martin, Nishino, Haruki, Okamura, Takahiro, Paar, Hans, Peloton, Julien, Pogosian, Levon, Poletti, Davide, Puglisi, Giuseppe, Raum, Christopher, Rebeiz, Gabriel, Reichardt, Christian L., Richards, Paul L., Ross, Colin, Rotermund, Kaja M., Schenck, David E., Sherwin, Blake D., Shimon, Meir, Shirley, Ian, Siritanasak, Praween, Smecher, Graeme, Stebor, Nathan, Steinbach, Bryan, Suzuki, Aritoki, Suzuki, Jun-ichi, Tajima, Osamu, Takakura, Satoru, Tikhomirov, Alexei, Tomaru, Takayuki, Whitehorn, Nathan, Wilson, Brandon, Yadav, Amit, Zahn, Alex, Zahn, Oliver

    “…Here, we constrain anisotropic cosmic birefringence using four-point correlations of even-parity E-mode and odd-parity B-mode polarization in the cosmic…”
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  16. 16

    Cosmic microwave background science at commercial airline altitudes by Feeney, Stephen M., Gudmundsson, Jon E., Peiris, Hiranya V., Verde, Licia, Errard, Josquin

    “…Abstract Obtaining high-sensitivity measurements of degree-scale cosmic microwave background (CMB) polarization is the most direct path to detecting primordial…”
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  17. 17

    The importance of local measurements for cosmology by Verde, Licia, Jimenez, Raul, Feeney, Stephen 

    Published in PHYSICS OF THE DARK UNIVERSE (01-06-2013)
    “…We explore how local, cosmology-independent measurements of the Hubble constant and the age of the Universe help to provide a powerful consistency check of the…”
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  18. 18

    Unbiased likelihood-free inference of the Hubble constant from light standard sirens by Gerardi, Francesca, Feeney, Stephen M, Alsing, Justin

    Published 06-04-2021
    “…Multi-messenger observations of binary neutron star mergers offer a promising path towards resolution of the Hubble constant ($H_0$) tension, provided their…”
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  19. 19

    Measuring the nuclear equation of state with neutron star-black hole mergers by Sarin, Nikhil, Peiris, Hiranya V, Mortlock, Daniel J, Alsing, Justin, Nissanke, Samaya M, Feeney, Stephen M

    Published 09-11-2023
    “…Gravitational-wave (GW) observations of neutron star-black hole (NSBH) mergers are sensitive to the nuclear equation of state (EOS). We present a new…”
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  20. 20

    A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey by Lin, Kiyam, von Wietersheim-Kramsta, Maximilian, Joachimi, Benjamin, Feeney, Stephen

    Published 23-08-2023
    “…MNRAS 524 (2023) 6167-6180 The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic…”
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