The LOFAR Two-metre Sky Survey: the Nature of the Faint Source Population and SFR-radio Luminosity Relation Using Prospector
Monthly Notices of the Royal Astronomical Society(2024)
Univ Hertfordshire | Univ Edinburgh | Columbia Univ | Natl Ctr Nucl Res | Univ Durham | Ist Radioastron
Abstract
Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus that may be present. We show that it is possible to produce similar-quality classifications using a single energy-balance SED fitting code, PROSPECTOR, to model up to 26 bands of UV-far-infrared aperture-matched photometry for similar to 31 000 sources in the ELAIS-N1 field from the LOFAR Two-Metre Sky Survey (LoTSS) deep fields first data release. One of a new generation of SED-fitting codes, PROSPECTOR accounts for potential contributions from radiative active galactic nuclei (AGN) when estimating galaxy properties, including star formation rates (SFRs) derived using non-parametric star formation histories. Combining this information with radio luminosities, we classify 92 per cent of the radio sources as a star-forming galaxy, high-/low-excitation radio galaxy, or radio-quiet AGN and study the population demographics as a function of 150 MHz flux density, luminosity, SFR, stellar mass, redshift, and apparent r-band magnitude. Finally, we use PROSPECTOR SED fits to investigate the SFR-150 MHz luminosity relation for a sample of similar to 133 000 3.6 mu m-selected z < 1 sources, finding that the stellar mass dependence is significantly weaker than previously reported, and may disappear altogether at log(10)(SFR/M-circle dot yr(-1)) > 0.5. This approach makes it significantly easier to classify radio sources from LoTSS and elsewhere, and may have important implications for future studies of star-forming galaxies at radio wavelengths.
MoreTranslated text
Key words
catalogues,surveys,galaxies: active,galaxies: evolution,galaxies: star formation,radio continuum: galaxies
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
The Properties of AGN in Dwarf Galaxies Identified Via SED Fitting
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话