Introduces an auditory feedback tool called The Squealer that produces louder and more unpleasant squeals as model-data discrepancy increases during interactive curve adjustment.
MASTER of the CMB Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
We describe a fast and accurate method for estimation of the cosmic microwave background (CMB) anisotropy angular power spectrum --- Monte Carlo Apodised Spherical Transform EstimatoR. Originally devised for use in the interpretation of the Boomerang experimental data, MASTER is both a computationally efficient method suitable for use with the currently available CMB data sets (already large in size, despite covering small fractions of the sky, and affected by inhomogeneous and correlated noise), and a very promising application for the analysis of very large future CMB satellite mission products.
citation-role summary
background 1
method 1
citation-polarity summary
representative citing papers
Adding a fitted radio-emission component to tSZ×galaxy cross-spectra removes an apparent negative small-scale signal and yields spectra consistent with a standard halo model (9.5–11σ BIC preference for the radio term).
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
Reports f_NL = -20.5^{+19.0}_{-18.1} (68% CL) from combined Quaia quasar auto-correlation and CMB lensing cross-correlation assuming p_phi=1, or -28.7^{+26.1}_{-24.6} for p_phi=1.6.
Cosmological data can constrain only a handful of EFT parameters for single-scalar dark energy; extended models show modest preference over Λ but remain underdetermined and challenged by fifth forces and screening.
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
citing papers explorer
-
The Squealer: Sensification of model exploration and model misfit
Introduces an auditory feedback tool called The Squealer that produces louder and more unpleasant squeals as model-data discrepancy increases during interactive curve adjustment.
-
Thermal Sunyaev-Zel'dovich cross-correlations with unWISE galaxies: disentangling radio contamination, dust properties, and electron pressure
Adding a fitted radio-emission component to tSZ×galaxy cross-spectra removes an apparent negative small-scale signal and yields spectra consistent with a standard halo model (9.5–11σ BIC preference for the radio term).
-
A unified harmonic framework for dark siren cosmology
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
-
Blind mitigation of foreground-induced biases on primordial $B$ modes for ground-based CMB experiments
Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
-
Constraints on primordial non-Gaussianity from Quaia
Reports f_NL = -20.5^{+19.0}_{-18.1} (68% CL) from combined Quaia quasar auto-correlation and CMB lensing cross-correlation assuming p_phi=1, or -28.7^{+26.1}_{-24.6} for p_phi=1.6.
-
The Status of Single Scalar Field Dark Energy
Cosmological data can constrain only a handful of EFT parameters for single-scalar dark energy; extended models show modest preference over Λ but remain underdetermined and challenged by fifth forces and screening.
-
BROOM: a python package for model-independent analysis of microwave astronomical data
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.