arXiv subjects

Hugo Camacho

Publications and source records attributed to Hugo Camacho.

5 recordsLinked to original sources

arXiv2026

Estimating LuSEE-Night's Sensitivity to Cα Radio Recombination Lines

Carbon radio recombination lines (CRRLs) arising from the cold neutral medium (CNM) of the interstellar medium are potential foregrounds in the 1-50 MHz band that LuSEE-Night will observe from the lunar far side. CRRLs are sensitive probes of the temperature, density, and radiation field of the CNM, and LuSEE-Night will provide sky-averaged measurements in a poorly explored frequency range. We model CRRLs across this band, accounting for doppler, collisional, and radiation broadening, and we estimate line strengths using two independent methods - fixing either the peak or the frequency-integrated optical depth - applied both uniformly across the sky and with region-dependent values informed by prior low-frequency observations. We simulate LuSEE-Night's expected response to these CRRLs and further convolve the beam-diluted spectra with LuSEE-Night's polyphase-filter-bank spectrometer response, including its zoomed-mode frequency resolution of $\sim$390 Hz, to obtain the expected observed brightness temperature. Using these simulated spectra, we estimate the integration time required to detect individual CRRLs at SNR=5 across the six sky models considered. Required integration times range from as short as 30 hours (in the most optimistic model where the optical depth across the sky is $\sim$10$^{-3}$) to 27.6 years (in the most conservative models where the optical depth across the sky is $\sim$10$^{-4}$). Stacking the 361 simulated C$α$ lines between 10 and 50 MHz reduces the integration time needed for an SNR=5 detection to $\sim$11 minutes and $\sim$151 hours respectively, indicating that LuSEE-Night has realistic chances of making a stacked detection of C$α$ RRLs within a realistic mission lifetime, even under the most conservative optical-depth assumptions.

astro-ph.GA↗

arXiv2026

Joint Estimation of Properties of the Lunar Subsurface and Galactic Foregrounds with LuSEE-Night

The Lunar Surface Electromagnetics Experiment (LuSEE-Night) is a joint NASA-DOE-ESA low-frequency radio telescope that will reach the lunar far side in 2027. The unknown dielectric properties of the subsurface at the LuSEE-Night landing site impose the most significant limitation for precision instrument calibration, as reflections from the lunar subsurface can change the primary beam at the 10-20% level. Simulations of these effects have provided insight and concern, showing that the lunar subsurface modeled as a lossy dielectric can absorb a large amount of the power of the sky signal. While this absorption may not strongly impact the signal-to-noise ratio in a sky-noise-dominated regime, it could complicate the beam pattern and make the signal more difficult to model and interpret. We have simulated the far-field properties of the LuSEE-Night beam for varying dielectric profiles of the lunar subsurface. We find that varying the properties of the lunar subsurface has the most significant impact around the antenna resonance, impacting its amplitude, position and width. Conversely, changing the properties of the foreground impacts the data across the band. We use a Bayesian inference pipeline to jointly estimate parameters of a galactic foreground model and dielectric properties of the lunar subsurface around the LuSEE-Night landing site and find that parameters of both the galaxy and subsurface properties can be estimated jointly. While the modeling is somewhat idealized, we believe that the results are largely robust owing to the fact that spectral variations for plausible subsurface and galaxy models have very different spectral signatures.

astro-ph.IM↗

arXiv2026

Linear map-making with LuSEE-Night

LuSEE-Night is a pathfinder radio telescope on the lunar far side employing four 3-m monopole antennas arranged as two horizontal cross pseudo-dipoles on a rotational stage and sensitive to the radio sky in the 1-50 MHz frequency band. LuSEE-Night measures the corresponding 16 correlation products as a function of frequency. While each antenna combination measures radiation coming from a large area of the sky, their aggregate information as a function of phase in the lunar cycle and rotational stage position can be deconvolved into a low-resolution map of the sky. We study this deconvolution using linear map-making based on the Wiener filter algorithm. We illustrate how systematic effects can be effectively marginalised over as contributions to the noise covariance and demonstrate this technique on beam knowledge uncertainty and gain fluctuations. With reasonable assumptions about instrument performance, we show that LuSEE-Night should be able to map the sub-50 MHz sky at a ~5-degree resolution.

astro-ph.IM↗

arXiv2025

Fast Projected Bispectra: the filter-square approach

The study of third-order statistics in large-scale structure analyses has been hampered by the increased complexity of bispectrum estimators (compared to power spectra), the large dimensionality of the data vector, and the difficulty in estimating its covariance matrix. In this paper we present the filtered-squared bispectrum (FSB), an estimator of the projected bispectrum effectively consisting of the cross-correlation between the square of a field filtered on a range of scales and the original field. Within this formalism, we are able to recycle much of the infrastructure built around power spectrum measurement to construct an estimator that is both fast and robust against mode-coupling effects caused by incomplete sky observations. Furthermore, we demonstrate that the existing techniques for the estimation of analytical power spectrum covariances can be used within this formalism to calculate the bispectrum covariance at very high accuracy, naturally accounting for the most relevant Gaussian and non-Gaussian contributions in a model-independent manner.

astro-ph.CO↗

arXiv2023

Primordial non-Gaussianity with Angular correlation function: Integral constraint and validation for DES

Local primordial non-Gaussianity (PNG) is a promising observable of the underlying physics of inflation, characterised by $f_{\rm NL}^{\rm loc}$. We present the methodology to measure $f_{\rm NL}^{\rm loc}$ from the Dark Energy Survey (DES) data using the 2-point angular correlation function (ACF) with scale-dependent bias. One of the focuses of the work is the integral constraint. This condition appears when estimating the mean number density of galaxies from the data and is key in obtaining unbiased $f_{\rm NL}^{\rm loc}$ constraints. The methods are analysed for two types of simulations: $\sim 246$ GOLIAT-PNG N-body small area simulations with $f_{\rm NL}$ equal to -100 and 100, and 1952 Gaussian ICE-COLA mocks with $f_{\rm NL}=0$ that follow the DES angular and redshift distribution. We use the ensemble of GOLIAT-PNG mocks to show the importance of the integral constraint when measuring PNG, where we recover the fiducial values of $f_{\rm NL}$ within the $1σ$ when including the integral constraint. In contrast, we found a bias of $Δf_{\rm NL}\sim 100$ when not including it. For a DES-like scenario, we forecast a bias of $Δf_{\rm NL} \sim 23$, equivalent to $1.8σ$, when not using the IC for a fiducial value of $f_{\rm NL}=100$. We use the ICE-COLA mocks to validate our analysis in a realistic DES-like setup finding it robust to different analysis choices: best-fit estimator, the effect of IC, BAO damping, covariance, and scale choices. We forecast a measurement of $f_{\rm NL}$ within $σ(f_{\rm NL})=31$ when using the DES-Y3 BAO sample, with the ACF in the $1\ {\rm deg}<θ<20\ {\rm deg}$ range.

astro-ph.CO↗