arXiv subjects

William Roster

Publications and source records attributed to William Roster.

3 recordsLinked to original sources

arXiv2026

A FLASH HI absorption search in compact sources in the pilot ASKAP interplanetary scintillation field

In this pilot study, we use data from the First Large Absorption Survey in HI (FLASH) to search for redshifted HI 21cm absorption at $0.4<z<1$ towards 157 bright radio sources with Interplanetary Scintillation (IPS) measurements at 820MHz in a single field observed with the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope. The ASKAP IPS measurements, which use only 2.5 minutes of observing time in total, allow us to estimate the compactness of these radio sources on sub-arcsecond scales at a frequency within the 712-1000MHz FLASH band. In particular, the Normalised Scintillation Index (NSI) for each source reflects the fraction of the flux density arising from compact components less than 0.1 arcsec in diameter. We find that $18\pm5$% of ASKAP sources with flux densities above 150mJy are highly compact with NSI $\geq0.8$ - implying that at least 80% of their radio emission arises from a single region smaller than about 800pc in size. The compactness of these sources makes them ideal probes for an HI absorption search, since the covering factor for any HI gas clouds along the line of sight is likely to be high. About half of the sources with NSI $\geq0.8$ also have peaked radio spectral energy distributions (SEDs), consistent with previous IPS studies at lower frequencies. These pilot results imply that IPS measurements with ASKAP can provide a simple and powerful tool for identifying uniform samples of compact radio sources at frequencies of a few hundred MHz across large areas of sky. With FLASH, we detect two new HI absorption lines against compact sources in the $\sim30$deg$^2$ region of sky covered by the IPS data; an associated HI line at redshift $z=0.9540$ (with a matching optical redshift) in MRC 2125-237 (NSI = 0.98), and a likely intervening line at $z=0.4632$ towards MRC 2131-241 (NSI = 0.84).

astro-ph.GA↗

arXiv2026

DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach

The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate classifications and photometric redshifts (photo-z) for these sources are crucial to maximizing the value of these data. In this work, we perform source classification and photo-z estimation for the Euclid Deep Field North (EDF-N) around the North Ecliptic Pole, using a deep learning framework (DeepDISC) that learns and infers using 9-band images simultaneously. We train three dedicated models for (1) source detection and classification, (2) galaxy photo-z, and (3) quasar photo-z. The Euclid Q1 input source catalog, and classifications and spectroscopic redshifts (spec-z) from the Dark Energy Spectroscopic Instrument Data Release 1 are adopted as our training data. DeepDISC source detection achieves overall completeness of ~93% and purity of ~80% if using the Euclid source catalog as the ground truth. Using a JWST source catalog within EDF-N as the reference, we estimate a true purity of ~ 90% for DeepDISC sources. About 99.2%, 99.0%, and 84.8% of stars, galaxies, and quasars, respectively, are correctly recovered with their spectroscopic classifications. The DeepDISC photo-zs show good agreement with spectroscopic redshifts, for both galaxies and quasars. Comparisons with other Euclid Q1 products demonstrate that DeepDISC provides comparable or improved performance in source detection/deblending, classification and photo-z, especially for quasars. These results demonstrate the potential of pixel-level deep learning approaches for large-scale sky surveys such as Euclid and Roman, which will continue to improve with better training labels. We release the full DeepDISC source catalog (~13 million objects) for EDF-N with classifications and photo-zs, including photo-z probability distributions.

astro-ph.GA↗

arXiv2024

PICZL: Image-based Photometric Redshifts for AGN

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

astro-ph.GA↗