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  1. AU="Bayer, Adrian E."
  2. AU="Boerke, A"
  3. AU="Brown, Guy C."
  4. AU=Ford Caleb A.
  5. AU="Hussain, Muhammad Afaq"
  6. AU="Werner Henkel"
  7. AU=Zellweger M J
  8. AU="Marasco, Michelangelo"
  9. AU="Landa-Moreno, Cinthia"
  10. AU="Kuntner, Matjaz"
  11. AU="Lemes, Robertha Mariana Rodrigues"
  12. AU="Riccioni, M E"
  13. AU="Traer, Colin J"
  14. AU="Cao, Xuejie"
  15. AU="Chen, Zishuo"
  16. AU="Kalachikov, Sergey"
  17. AU="Das, Tilak"
  18. AU="Bessat, Cécile"
  19. AU="Galina Velikova"
  20. AU="Greene, Sharrell"
  21. AU="Chen, Kallie J"
  22. AU="Schwab, Jörg O."
  23. AU="Ke Chen"
  24. AU="Hewei Liang"
  25. AU="Abreu, Cristina"
  26. AU="Mamani Ortiz, Yercin"
  27. AU="Castro, Lucíola de Fátima Albuquerque Almeida Peixoto"
  28. AU="Šimůnek, Tomáš"
  29. AU="Ong, Lizhen"
  30. AU="Chai, Chaoqing"
  31. AU="Maheswaran Kesavan"
  32. AU="Mehta, Mrunal"
  33. AU=Paredes Sergio D
  34. AU=Ghosh Nilanjan AU=Ghosh Nilanjan
  35. AU="Hofmann, Alexander"
  36. AU="Radici, Marco"
  37. AU="Noro, Fabrizia"
  38. AU="Wang, Jianzhao"
  39. AU="Divya Jeyam"
  40. AU="Wolf, Lisette"
  41. AU="Marjanovic, Nemanja Despot"
  42. AU="Jitxin, Lim"

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  1. Buch ; Online: The look-elsewhere effect from a unified Bayesian and frequentist perspective

    Bayer, Adrian E. / Seljak, Uros

    2020  

    Abstract: When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high significance will be found. This is known as the look-elsewhere effect and is ... ...

    Abstract When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high significance will be found. This is known as the look-elsewhere effect and is prevalent throughout cosmology, (astro)particle physics, and beyond. To avoid making false claims of detection, one must account for this effect when assigning the statistical significance of an anomaly. This is typically accomplished by considering the trials factor, which is generally computed numerically via potentially expensive simulations. In this paper we develop a continuous generalization of the Bonferroni and Sidak corrections by applying the Laplace approximation to evaluate the Bayes factor, and in turn relating the trials factor to the prior-to-posterior volume ratio. We use this to define a test statistic whose frequentist properties have a simple interpretation in terms of the global $p$-value, or statistical significance. We apply this method to various physics-based examples and show it to work well for the full range of $p$-values, i.e. in both the asymptotic and non-asymptotic regimes. We also show that this method naturally accounts for other model complexities such as additional degrees of freedom, generalizing Wilks' theorem. This provides a fast way to quantify statistical significance in light of the look-elsewhere effect, without resorting to expensive simulations.

    Comment: 21 pages, 7 figures
    Schlagwörter Physics - Data Analysis ; Statistics and Probability ; Astrophysics - Cosmology and Nongalactic Astrophysics ; Astrophysics - Instrumentation and Methods for Astrophysics ; High Energy Physics - Experiment ; Statistics - Applications
    Thema/Rubrik (Code) 306
    Erscheinungsdatum 2020-07-27
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: The DESI $N$-body Simulation Project -- II. Suppressing sample variance with fast simulations

    Ding, Zhejie / Chuang, Chia-Hsun / Yu, Yu / Garrison, Lehman H. / Bayer, Adrian E. / Feng, Yu / Modi, Chirag / Eisenstein, Daniel J. / White, Martin / Variu, Andrei / Zhao, Cheng / Zhang, Hanyu / Rizo, Jennifer Meneses / Brooks, David / Dawson, Kyle / Doel, Peter / Gaztanaga, Enrique / Kehoe, Robert / Krolewski, Alex /
    Landriau, Martin / Palanque-Delabrouille, Nathalie / Poppett, Claire

    2022  

    Abstract: Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim20\Gpchcube$. It is a great challenge to prepare high-resolution simulations with a much larger ...

    Abstract Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim20\Gpchcube$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. \textsc{AbacusSummit} is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\Gpchcube$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-$N$-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k < 0.3\hMpc$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in \textsc{AbacusSummit} suite to extend the effective volume $\sim 20$ times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

    Comment: Matched version accepted by MNRAS, should be clearer
    Schlagwörter Astrophysics - Cosmology and Nongalactic Astrophysics
    Thema/Rubrik (Code) 520 ; 612
    Erscheinungsdatum 2022-02-12
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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