arXiv subjects

Francesco Pistis

Publications and source records attributed to Francesco Pistis.

3 recordsLinked to original sources

arXiv2026

Extending the Little Red Dot population at intermediate redshift with VIPERS

Little red dots (LRDs) are a recently identified population of compact, red sources characterised by a distinctive V-shaped UV-to-optical continuum and broad emission lines. Their physical structure and evolution remain uncertain, while their properties at intermediate redshifts are largely unexplored. We identify and characterise LRD-like sources at 0.5 0.5, bridging local and high-redshift studies. Their abundance, compact morphology, environmental properties, and multi-wavelength emission provide new constraints on the evolution and physical origin of this population.

astro-ph.GA↗

arXiv2026

Forecast for the detectability of patchy hydrogen reionization in WEAVE-QSO measurements of the Lyman-$\alpha$ forest power spectrum at redshift $z \geq 4$

We present the first detailed forecasts for the detectability of patchy hydrogen reionization in the one-dimensional Ly$\alpha$ forest power spectrum to be measured by the WEAVE-QSO survey. Using the Sherwood-relics reionization simulations and a WEAVE-QSO survey configuration, we generate mock spectra in four redshift bins, $z=4.0,4.2,4.4,$ and $4.6$, in which relic ionization and temperature fluctuations from patchy hydrogen reionization enhance the Ly$\alpha$ forest power spectrum on large scales (i.e., at wavenumber $k\sim 10^{-3},\mathrm{s\,km^{-1}}$). Our Ly$\alpha$ forest pipeline forecasts the power spectrum covariance by considering sample size, spectral resolution, noise subtraction, continuum placement, metal contamination, and damping wings from high-column density absorbers. Applying our covariance forecast within a Bayesian parameter inference framework, we find that the signature of patchy hydrogen reionization should be detectable at a significance of $\simeq 4.5\sigma$. The forthcoming WEAVE-QSO 1D power spectrum measurements should therefore be able to directly detect and characterize the large-scale relic imprint of patchy hydrogen reionization in the Ly$\alpha$ forest power spectrum at $z\geq 4$.

astro-ph.CO↗

arXiv2025

Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures

Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\text{\AA}$ to $2000~\text{\AA}$. The ability to generalize and predict galaxy continua within the range of $3500~\text{\AA}$ to $5500~\text{\AA}$ is also tested. Methods. The performance of these architectures is evaluated using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey, and on real data from the Early Data Release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results. The autoencoder outperforms the U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Ly$\alpha$ optical depth evolution in DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.

astro-ph.GA↗