SPARCL: SPectra Analysis and Retrievable Catalog Lab

(2) University of Utah); Adam S. Bolton (1); Alice Jacques (1); Benjamin A. Weaver (1); Knut Olsen (1) ((1) NSF's NOIRLab; Ragadeepika Pucha (2); Robert Nikutta (1); Sean McManus (1); St\'ephanie Juneau (1); Steve Pothier (1)

SPARCL: SPectra Analysis and Retrievable Catalog Lab

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.05576 v2 pith:WFQIVIWP submitted 2024-01-10 astro-ph.IM astro-ph.GAastro-ph.SR

SPARCL: SPectra Analysis and Retrievable Catalog Lab

St\'ephanie Juneau (1) , Alice Jacques (1) , Steve Pothier (1) , Adam S. Bolton (1) , Benjamin A. Weaver (1) , Ragadeepika Pucha (2) , Sean McManus (1) , Robert Nikutta (1)

show 2 more authors

Knut Olsen (1) ((1) NSF's NOIRLab (2) University of Utah)

This is my paper

classification astro-ph.IM astro-ph.GAastro-ph.SR

keywords spectrasparclnoirlabspectroscopicanalysiscatalogdatasetsexample

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

Share X Bluesky LinkedIn Reddit HN

read the original abstract

SPectra Analysis and Retrievable Catalog Lab (SPARCL) at NOIRLab's Astro Data Lab was created to efficiently serve large optical and infrared spectroscopic datasets. It consists of services, tools, example workflows and currently contains spectra for over 7.5 million stars, galaxies and quasars from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Spectroscopic Instrument (DESI) survey. We aim to eventually support the broad range of spectroscopic datasets that will be hosted at NOIRLab and beyond. Major elements of SPARCL include capabilities to discover and query for spectra based on parameters of interest, a fast web service that delivers desired spectra either individually or in bulk as well as documentation and example Jupyter Notebooks to empower users in their research. More information is available on the SPARCL website (https://astrosparcl.datalab.noirlab.edu).

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI
astro-ph.SR 2026-02 conditional novelty 6.0

Pre-trained MLPs on LAMOST low-resolution spectra generalize to DESI medium-resolution spectra for [Fe/H] and [α/Fe], outperforming the DESI SP pipeline in zero-shot and improving with modest fine-tuning.