Search

Intensity mapping experiments will soon have surveyed large swathes of the sky, providing information about the underlying matter distribution of the early universe. The resulting maps can be used to recover statistical information, such as the power spectrum, about the measured spectral lines (for example, HI, [CII], and [OIII]). However precise power spectrum measurements, such as the 21 cm autocorrelation, continue to be challenged by the presence of bright foregrounds and non-trivial systematics. By crosscorrelating different data sets, it may be possible to mitigate the effects of both foreground uncertainty and uncorrelated instrumental systematics. Beyond their own merit, crosscorrelations could also be used to recover autocorrelation information. Such a technique was proposed in Beane et al. (2019) for recovering the 21 cm power spectrum. Generalizing their result, we develop a statistical framework for combining multiple crosscorrelation signals in order to infer information about the corresponding autocorrelations. We do this first within the Least Squares Estimator (LSE) framework, and show how one can derive their estimator, along with several alternative estimators. We also investigate the posterior distribution of recovered autocorrelation and associated model parameters. We find that for certain noise regimes and cosmological signal modeling assumptions this procedure is effective at recovering autospectra from a set of crosscorrelations. Finally, we showcase our framework in the context of several near-future line intensity mapping experiments., Comment: 18 pages, 13 figures, to be submitted to MNRAS

The immense popularity of spheroid-based scored events (colloquially ``football games'') motivates the desire to better understand the underlying mechanisms affecting their outcomes. By construction of these events, participants must distinguish the spheroidal ball from not only the background, but also their team and enemy players, which are marked by self-assigned linear combinations of specific frequencies of electromagnetic magnetic radiation, known as uniform color. We investigate chromatic effects on the outcome of such events. We do this by finding the correlation between the color contrast and the success of several key spheroidal ball match tactics. We perform this analysis for the 2020 NFL regular season, focusing on moves in which uniform colors may be a factor in performance. We conduct a primary analysis using each team's cumulative results over the season, but in doing so neglect non-uniformity in the chosen uniform color per individual. We then conduct a secondary analysis of the performance per game of a single team, the Seattle Seahawks, which exhibited large uniform color variability for the 2020 NFL regular season. In this work, tackles and completions are considered. The Pearson correlation coefficient is then calculated for both tactics. We find little evidence of chromaticity effects, with correlation values of $r_t=-0.0885\pm 0.1819$ and $r_c-0.0292\pm0.1825 $, respectively, for the primary analysis., Comment: 8 pages, 4 Figures

We report the most sensitive upper limits to date on the 21 cm epoch of reionization power spectrum using 94 nights of observing with Phase I of the Hydrogen Epoch of Reionization Array (HERA). Using similar analysis techniques as in previously reported limits (HERA Collaboration 2022a), we find at 95% confidence that $\Delta^2(k = 0.34$ $h$ Mpc$^{-1}$) $\leq 457$ mK$^2$ at $z = 7.9$ and that $\Delta^2 (k = 0.36$ $h$ Mpc$^{-1}) \leq 3,496$ mK$^2$ at $z = 10.4$, an improvement by a factor of 2.1 and 2.6 respectively. These limits are mostly consistent with thermal noise over a wide range of $k$ after our data quality cuts, despite performing a relatively conservative analysis designed to minimize signal loss. Our results are validated with both statistical tests on the data and end-to-end pipeline simulations. We also report updated constraints on the astrophysics of reionization and the cosmic dawn. Using multiple independent modeling and inference techniques previously employed by HERA Collaboration (2022b), we find that the intergalactic medium must have been heated above the adiabatic cooling limit at least as early as $z = 10.4$, ruling out a broad set of so-called "cold reionization" scenarios. If this heating is due to high-mass X-ray binaries during the cosmic dawn, as is generally believed, our result's 99% credible interval excludes the local relationship between soft X-ray luminosity and star formation and thus requires heating driven by evolved low-metallicity stars., Comment: 57 pages, 37 figures. Updated to match the accepted ApJ version. Corresponding author: Joshua S. Dillon

Galactic dust emission is often accounted for in cosmic microwave background (CMB) analyses by fitting a simple two-parameter modified blackbody (MBB) model in each pixel, which nominally accounts for the temperature and opacity of the dust. While this may be a good approximation for individual dust clouds, typically a number of such clouds are found along each line of sight and within each angular pixel, resulting in a superposition of their spectra. In this paper, we study the effects of this superposition on pixel-based foreground fitting strategies by modelling the spectral energy distribution (SED) in each pixel as the integral of individual MBB spectra over various physically-motivated statistical distributions of dust cloud properties. We show that fitting these SEDs with the simple two-parameter MBB model generally results in unbiased estimates of the CMB Stokes Q and U amplitudes in each pixel, unless there are significant changes in both the dust SED and polarization angle along the line of sight, in which case significant ($ > 10\sigma$) biases are observed in an illustrative model. We also find that the best-fit values of the dust temperature, $T_d$, and spectral index, $\beta_d$, are significantly biased away from the mean/median of the corresponding statistical distributions when the distributions are broad, suggesting that MBB model fits can give an unrepresentative picture of the physical properties of the dust at microwave wavelengths if not interpreted carefully. Using a Fisher matrix analysis, we also determine the experimental sensitivity required to recover the parameters of the $T_d$ and $\beta_d$ distributions themselves by fitting a probabilistic MBB model, finding that only the parameters of broad distributions can be measured by SED fitting on a single line of sight., Comment: 15 pages, 10 figures; see Table 3 for main results