Tracing Quasar Narrow-Line Regions Across Redshift: A Library of High S/N Optical Spectra

In a single optical spectrum, the quasar narrow-line region (NLR) reveals low density, photoionized gas in the host galaxy interstellar medium, while the immediate vicinity of the central engine generates the accretion disk continuum and broad emission lines. To isolate these two components, we construct a library of high S/N optical composite spectra created from the Sloan Digital Sky Survey (SDSS-DR7). We divide the sample into bins of continuum luminosity and Hbeta FWHM that are used to construct median composites at different redshift steps up to 0.75. We measure the luminosities of the narrow-emission lines [NeV]3427, [NeIII]3870, [OIII]5007, and [OII]3728 with ionization potentials (IPs) of 97, 40, 35, and 13.6 eV respectively. The high IP lines' luminosities show no evidence of increase with redshift consistent with no evolution in the AGN SED or the host galaxy ISM illuminated by the continuum. In contrast, we find that the [OII] line becomes stronger at higher redshifts, and we interpret this as a consequence of enhanced star formation contributing to the [OII] emission in host galaxies at higher redshifts. The SFRs estimated from the [OII] luminosities show a flatter increase with z than non-AGN galaxies given our assumed AGN contribution to the [OII] luminosity. Finally, we confirm an inverse correlation between the strength of the FeII4570 complex and both the [OIII] EW (though not the luminosity) and the width of the Hbeta line as known from the eigenvector 1 correlations.Comment: 17 pages, colour figures, accepted for publication in MNRA

Insights into Quasar UV Spectra Using Unsupervised Clustering Analysis

Machine learning techniques can provide powerful tools to detect patterns in multidimensional parameter space. We use K-means - a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabelled data - to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data Release of the Sloan Digital Sky Survey (SDSS-DR10) of Paris et al. Detecting patterns in large data sets helps us gain insights into the physical conditions and processes giving rise to the observed properties of quasars. We use K-means to find clusters in the parameter space of the equivalent width (EW), the blue- and red-half-width at half-maximum (HWHM) of the Mg II 2800 Å line, the C IV 1549 Å line, and the C III] 1908 Å blend in samples of broad absorption line (BAL) and non-BAL quasars at redshift 1.6-2.1. Using this method, we successfully recover correlations well-known in the UV regime such as the anti-correlation between the EW and blueshift of the C IV emission line and the shape of the ionizing spectra energy distribution (SED) probed by the strength of He II and the Si III]/C III] ratio. We find this to be particularly evident when the properties of C III] are used to find the clusters, while those of Mg II proved to be less strongly correlated with the properties of the other lines in the spectra such as the width of C IV or the Si III]/C III] ratio. We conclude that unsupervised clustering methods (such as K-means) are powerful methods for finding `natural\u27 binning boundaries in multidimensional data sets and discuss caveats and future work

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License