arXiv subjects

O. Bait

Publications and source records attributed to O. Bait.

5 recordsLinked to original sources

arXiv2025

The Lyman-alpha and Continuum Origins Survey II: the connection between the escape of ionizing radiation and Lyman-alpha halos in star-forming galaxies

One of the current challenges in galaxy evolution studies is to establish the mechanisms that govern the escape of ionizing radiation from galaxies. Here, we investigate the connection between Lyman Continuum (LyC) escape and the conditions of the Circumgalactic Medium (CGM), as probed by Ly$\alpha$ halos (LAHs) in emission. We use Ly$\alpha$ and UV continuum imaging data from the Lyman alpha and Continuum Origins Survey (LaCOS), targeting 42 nearby ($z \simeq 0.3$), star-forming galaxies with LyC observations (escape fractions of $f_{\rm esc}^{\rm LyC} \simeq 0.01-0.49$). LaCOS galaxies show extended Ly$\alpha$ emission ubiquitously, with LyC emitters (LCEs) having more compact Ly$\alpha$ morphologies than non-LCEs, and Ly$\alpha$ spatial offsets that do not exceed the extent of the UV continuum. We model the diffuse LAHs using a combined S\'ersic plus exponential 2D profile, and find that the characteristic scale length of the Ly$\alpha$ halo is ten times larger than the UV, on average. We unveil a significant anti-correlation between $f_{\rm esc}^{\rm LyC}$ and the Ly$\alpha$ Halo Fraction (HF, or contribution of the halo to the total Ly$\alpha$ luminosity), that we propose as a new LyC indicator. Our observations show that halo scale lengths and HFs both scale positively with the optical depth of the neutral gas in the ISM, revealing a picture in which Ly$\alpha$ and LyC photons in LCEs either emerge directly from the central starbursts or escape isotropically and, in the case of Ly$\alpha$, minimize the number of scattering interactions in a less-extended CGM.

astro-ph.GA↗

arXiv2025

Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

We present and analyse the results of the Science data challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an EoR foreground-removal community-wide exercise organised by the Square Kilometre Array Observatory (SKAO). The challenge ran for 8 months, from March to October 2023. Participants were provided with realistic simulations of SKA-Low data between 106 MHz and 196 MHz, including foreground contamination from extragalactic as well as Galactic emission, instrumental and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned from all corruptions, and the corresponding confidence levels. Here we describe the approaches taken by the 17 teams that completed the challenge, and we assess their performance using different metrics. The challenge results provide a positive outlook on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations. The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some cases there are some significant outliers. The smallest residual overall is $4.2_{-4.2}^{+20} \times 10^{-4}\,\rm{K}^2h^{-3}$cMpc$^{3}$ across all considered scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error being typically under-estimated, sometimes very significantly. The most accurate error bars account for $60 \pm 20$\% of the true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and improve error estimation.

astro-ph.IM↗

arXiv2024

Bridging the Gap: Examining Vision Foundation Models for Optical and Radio Astronomy Applications

Vision foundation models, which have demonstrated significant potential in many multimedia applications, are often underutilized in the natural sciences. This is primarily due to mismatches between the nature of domain-specific scientific data and the typical training data used for foundation models, leading to distribution shifts. Scientific data often differ substantially in structure and characteristics, and researchers frequently face the challenge of optimizing model performance with limited labeled data of only a few hundred or thousand images. This work evaluates the performance of vision foundation models in astrophysics, with a focus on identifying the best practices for adapting them to domain-specific datasets. We aim to establish a framework for selecting, fine-tuning, and optimizing these models for common tasks in optical and radio astronomy. We compared multiple foundation models, including self-supervised, weakly supervised, and distillation-based architectures, across two representative optical and radio datasets. Experiments involved different fine-tuning strategies, projector heads, and data preprocessing techniques, with performance evaluated on classification and detection metrics. Features extracted by specific foundation models improved classification accuracy for optical galaxy images compared to conventional supervised training. Similarly, these models achieved equivalent or superior performance in object detection tasks with radio images. However, classification performance for radio galaxy images was generally poor, often falling short of supervised approaches. These findings demonstrate that vision foundation models can be effectively adapted to astrophysical applications, provided practitioners iterate on model selection, training strategies, and data handling.

astro-ph.IM↗

arXiv2024

Ubiquitous broad-line emission and the relation between ionized gas outflows and Lyman continuum escape in Green Pea galaxies

We report observational evidence of highly turbulent ionized gas kinematics in a sample of 20 Lyman continuum (LyC) emitters (LCEs) at low redshift ($z\sim 0.3$). Detailed Gaussian modeling of optical emission line profiles in high-dispersion spectra consistently shows that both bright recombination and collisionally excited lines can be fitted as one or two narrow components with intrinsic velocity dispersion of $\sigma$ $\sim$ 40-100 km s$^{-1}$, in addition to a broader component with $\sigma \sim$ 100-300 km s$^{-1}$, which contributes up to $\sim$40% of the total flux and is preferentially blueshifted from the systemic velocity. We interpret the narrow emission as highly ionized gas close to the young massive star clusters and the broader emission as a signpost of unresolved ionized outflows, resulting from massive stars and supernova feedback. We find a significant correlation between the width of the broad emission and the LyC escape fraction, with strong LCEs exhibiting more complex and broader line profiles than galaxies with weaker or undetected LyC emission. We provide new observational evidence supporting predictions from models and simulations; our findings suggest that gas turbulence and outflows resulting from strong radiative and mechanical feedback play a key role in clearing channels through which LyC photons escape from galaxies. We propose that the detection of blueshifted broad emission in the nebular lines of compact extreme emission-line galaxies can provide a new indirect diagnostic of Lyman photon escape, which could be useful to identify potential LyC leakers in the epoch of reionization with the JWST.

astro-ph.GA↗

arXiv2023

Challenging interferometric imaging: Machine learning-based source localization from uv-plane observations

In our work, we examine, for the first time, the possibility of fast and efficient source localization directly from the uvobservations, omitting the recovering of the dirty or clean images. We propose a deep neural network-based framework that takes as its input a low-dimensional vector of sampled uvdata and outputs source positions on the sky. We investigated a representation of the complex-valued input uv-data via the real and imaginary and the magnitude and phase components. We provided a comparison of the efficiency of the proposed framework with the traditional source localization pipeline based on the state-of-the-art Python Blob Detection and Source Finder (PyBDSF) method. The investigation was performed on a data set of 9164 sky models simulated using the Common Astronomy Software Applications (CASA) tool for the Atacama Large Millimeter Array (ALMA) Cycle 5.3 antenna configuration. We investigated two scenarios: (i) noise-free as an ideal case and (ii) sky simulations including noise representative of typical extra-galactic millimeter observations. In the noise-free case, the proposed localization framework demonstrates the same high performance as the state-of-the-art PyBDSF method. For noisy data, however, our new method demonstrates significantly better performance, achieving a completeness level that is three times higher for sources with uniform signal-to-noise (S/N) ratios between 1 and 10, and a high increase in completeness in the low S/N regime. Furthermore, the execution time of the proposed framework is significantly reduced (by factors about 30) as compared to traditional methods that include image reconstructions from the uv-plane and subsequent source detections.

astro-ph.IM↗