The SDSS-V Local Volume Mapper (LVM): Technical Overview
Abstract
The Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) is an ultra-wide integral field spectroscopic survey of the Milky Way, the Magellanic Clouds, and a sample of galaxies in the local volume. The LVM spatially resolved spectroscopy covers the bulk of the optical range (3600-9800 A) at medium spectral-resolution (R = 2200-5500), and maps the disks of these galaxies at high physical spatial resolution (< 1 pc in the MW and ~10 pc in the Magellanic Clouds). Observations are carried out with the LVM Instrument (LVM-I), a custom-built robotic telescope, instrument, and facility located at Las Campanas Observatory (LCO) in Chile. In this article we present a technical overview of the LVM project. We describe the hardware, the survey observing strategy, and the details of how science and calibration observations are carried out. We provide a high-level overview of the survey data flow, including the reduction and calibration of the LVM spectra, and the production of the main data products that are released by the project. We also present an assessment of the LVM data quality and instrumental stability based on science observations. The main goal of this article is to serve as a primer for LVM users, providing the technical information required for the adequate analysis, modeling, and interpretation of the LVM data.
Related
- SDSS-V Local Volume Mapper (LVM): The Integrated Light and Internal Rotation of Omega Centauri
- Unveiling the Origins of Local Group Galaxies with ELT-SHARP: An Overview of Science Cases
- Radial Stellar Age Gradients in 42 Local Volume Dwarf Galaxies
- Constraints on the Cosmic Expansion Rate Using the DECam Local Volume Exploration Survey and the Fifth Gravitational Wave Transient Catalog
- ELVES-Dwarf. II. A Systematic Search for Satellite Systems of Dwarf Galaxies in the Local Volume
BibTeX
@misc{blanc2026sdssv,
title = {{The SDSS-V Local Volume Mapper (LVM): Technical Overview}},
author = {Guillermo A. Blanc and Niv Drory and Sebastián F. Sánchez and Nicholas P. Konidaris and Kathryn Kreckel and Tony Wong and Eric W. Pellegrini and Cynthia S. Froning and Tom Herbst and Evelyn J. Johnston and Amy M. Jones and Ivan Yu. Katkov and Alfredo Mejía-Narváez and Solange Ramirez and José Sánchez-Gallego and Stefanie Wachter and Hans-Walter Rix and Dmitry Bizyaev and Brian Cherinka and Enrico Congiu and Maren Cosens and Bruno Dias and John Donor and Oleg Egorov and Evgeniya Egorova and Maximilian Häberle and Hector Ibarra and Knox S. Long and José Eduardo Méndez-Delgado and Amrita Singh and Jorge K. Barrera-Ballesteros and Michelle Berg and Mar Canal i Saguer and Maria-Rosa Cioni and Hugo Cortés-Muñoz and Rodrigo Cuellar and Florence de Almeida and Rodolfo de J. Zermeño and Mariia Demianenko and Pablo García and Simon C. O. Glover and Hannah Greve and Thomas Hilder and Kevin Hoy and Jing Li and Ralf S. Klessen and Dhanesh Krishnarao and Nimisha Kumari and Sorya Lambert and Fu-Heng Liang and Aurora Mata Sánchez and Dante Minniti and Aparna Nair and Silvia Anastasia Popa and Ravi Sankrit and Natascha Sattler and Anna Lena Schaible and Guy Stringfellow and Luciano Vargas Herrera and Monica Alejandra Villa Durango},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31936},
eprint = {2610.31936},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31936},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The SDSS-V Local Volume Mapper (LVM): Technical Overview
AU - Guillermo A. Blanc
AU - Niv Drory
AU - Sebastián F. Sánchez
AU - Nicholas P. Konidaris
AU - Kathryn Kreckel
AU - Tony Wong
AU - Eric W. Pellegrini
AU - Cynthia S. Froning
AU - Tom Herbst
AU - Evelyn J. Johnston
AU - Amy M. Jones
AU - Ivan Yu. Katkov
AU - Alfredo Mejía-Narváez
AU - Solange Ramirez
AU - José Sánchez-Gallego
AU - Stefanie Wachter
AU - Hans-Walter Rix
AU - Dmitry Bizyaev
AU - Brian Cherinka
AU - Enrico Congiu
AU - Maren Cosens
AU - Bruno Dias
AU - John Donor
AU - Oleg Egorov
AU - Evgeniya Egorova
AU - Maximilian Häberle
AU - Hector Ibarra
AU - Knox S. Long
AU - José Eduardo Méndez-Delgado
AU - Amrita Singh
AU - Jorge K. Barrera-Ballesteros
AU - Michelle Berg
AU - Mar Canal i Saguer
AU - Maria-Rosa Cioni
AU - Hugo Cortés-Muñoz
AU - Rodrigo Cuellar
AU - Florence de Almeida
AU - Rodolfo de J. Zermeño
AU - Mariia Demianenko
AU - Pablo García
AU - Simon C. O. Glover
AU - Hannah Greve
AU - Thomas Hilder
AU - Kevin Hoy
AU - Jing Li
AU - Ralf S. Klessen
AU - Dhanesh Krishnarao
AU - Nimisha Kumari
AU - Sorya Lambert
AU - Fu-Heng Liang
AU - Aurora Mata Sánchez
AU - Dante Minniti
AU - Aparna Nair
AU - Silvia Anastasia Popa
AU - Ravi Sankrit
AU - Natascha Sattler
AU - Anna Lena Schaible
AU - Guy Stringfellow
AU - Luciano Vargas Herrera
AU - Monica Alejandra Villa Durango
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.31936
UR - https://arxiv.org/abs/2610.31936
AB - The Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) is an ultra-wide integral field spectroscopic survey of the Milky Way, the Magellanic Clouds, and a sample of galaxies in the local volume. The LVM spatially resolved spectroscopy covers the bulk of the optical range (3600-9800 A) at medium spectral-resolution (R = 2200-5500), and maps the disks of these galaxies at high physical spatial resolution (< 1 pc in the MW and ~10 pc in the Magellanic Clouds). Observations are carried out with the LVM Instrument (LVM-I), a custom-built robotic telescope, instrument, and facility located at Las Campanas Observatory (LCO) in Chile. In this article we present a technical overview of the LVM project. We describe the hardware, the survey observing strategy, and the details of how science and calibration observations are carried out. We provide a high-level overview of the survey data flow, including the reduction and calibration of the LVM spectra, and the production of the main data products that are released by the project. We also present an assessment of the LVM data quality and instrumental stability based on science observations. The main goal of this article is to serve as a primer for LVM users, providing the technical information required for the adequate analysis, modeling, and interpretation of the LVM data.
ER -