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The Vera C. Rubin Observatory, currently in construction in Chile, will start performing the Legacy Survey of Space and Time (LSST) in 2025 for 10 years. Its 8.4-meter telescope will survey the southern sky in less than 4 nights in six optical bands, and repeatedly generate about 2 000 exposures per night, corresponding to a data volume of about 20 TiB every night. Three data facilities are preparing to contribute to the production of the annual data releases: the US Data Facility will process 35% of the raw data, the UK data facility will process 25% of the raw data and the French data facility, operated by CC-IN2P3, will locally process the remaining 40% of the raw data. In the context of the Data Preview 0.2 (DP0.2), the Data Release Production pipelines have been executed on the DC-2 simulated dataset (generated by the Dark Energy Science Collaboration, DESC). This dataset includes 20 000 simulated exposures, representing 300 square degrees of Rubin images with a typical depth of 5 years. DP0.2 ran at the Interim Data Facility (based on Google cloud), and the full exercise was independently replicated at CC-IN2P3. During this exercise, 3 PiB of data and more than 200 million files were produced. In this contribution we will present a detailed description of the system that we set up to perform this processing campaign using CC-IN2P3’s computing and storage infrastructure. Several topics will be addressed: workflow generation and execution, batch job submission, memory and I/O requirements, etc. We will focus on the issues that arose during this campaign and how we addressed them and will present some perspectives after this exercise.

We present lightcurves, rotation periods, and colors for the first asteroid discoveries made with the NSF-DOE Vera C. Rubin Observatory. These are the first science results derived from the 2103 asteroid discoveries released as part of the Rubin First Look (RFL) media event on 2025 June 23, in which the first LSST Camera commissioning images were released. The ∼340,000 observations in which the discoveries were made span nine nights between 2025 April 21 and May 5. With a limiting single-epoch 5σ depth of ∼23–25 mag and dense temporal sampling under an irregular, commissioning-driven cadence, the RFL observations provide an ideal test bed for determination of rotation periods, including sensitivity to rapid rotation. We model lightcurves and derive rotation periods and colors for the ∼2000 objects. We find 75 main-belt asteroids (MBAs) and one near-Earth object (NEO) with reliable rotation periods spanning 0.031–21.3 hr and a photometric precision in the range of 0.05–0.15 mag. We find 19 superfast rotators with periods shorter than the 2.2 hr spin barrier. Rubin-discovered MBA 2025 MN45 is the fastest-rotating d > 0.5 km known asteroid with a rotation period of 1.9 minutes; along with NEO 2025 MJ71 (1.9 minutes) and Rubin-discovered MBAs 2025 MK41 (3.8 minutes), 2025 MV71 (13 minutes), and 2025 MG56 (16 minutes), these five super- to ultrafast rotators join a couple of NEOs as the fastest-spinning subkilometer asteroids known. As this study demonstrates, even in early commissioning, Rubin is successfully probing a previously sparsely sampled region of the subkilometer size−spin rate regime for MBAs.

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time-domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a Legacy Survey of Space and Time (LSST) Commissioning Camera observing campaign between 2024 November and December with multiband imaging of seven fields, covering roughly 0.4 deg2 each, providing a first glimpse into the data products that will become available once the LSST begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae (SNe), and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns subclasses with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with SN detection rate predictions of 15 ± 4 SNe from simulations using simsurvey. The SN detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory’s LSST is well placed to fulfill its discovery potential in time-domain astronomy.

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile and due to start operations in 2025 for 10 years, its 8.4-meter telescope will nightly scan the southern sky and collect images of the entire visible sky every 4 nights using a 3.2 Gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this paper, we briefly present the infrastructure deployed at the French Rubin data facility (operated by IN2P3 computing center, CC-IN2P3) to deploy the Rubin Science Platform, a set of web-based services to provide effective and convenient access to LSST data for scientific analysis. We describe the main services of the platform, the components that provide those services and our deployment model. We also present the Kubernetes-based infrastructure we are experimenting with for hosting the LSST astronomical catalog, a petabyte-scale relational database developed for the specific needs of the project.

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.

The Vera C. Rubin Observatory, currently in construction in Chile, will start performing the Legacy Survey of Space and Time (LSST) in 2025 for 10 years. Its 8.4-meter telescope will survey the southern sky in less than 4 nights in six optical bands, and repeatedly generate about 2 000 exposures per night, corresponding to a data volume of about 20 TiB every night. Three data facilities are preparing to contribute to the production of the annual data releases: the US Data Facility will process 35% of the raw data, the UK data facility will process 25% of the raw data and the French data facility, operated by CC-IN2P3, will locally process the remaining 40% of the raw data. In the context of the Data Preview 0.2 (DP0.2), the Data Release Production pipelines have been executed on the DC-2 simulated dataset (generated by the Dark Energy Science Collaboration, DESC). This dataset includes 20 000 simulated exposures, representing 300 square degrees of Rubin images with a typical depth of 5 years. DP0.2 ran at the Interim Data Facility (based on Google cloud), and the full exercise was independently replicated at CC-IN2P3. During this exercise, 3 PiB of data and more than 200 million files were produced. In this contribution we will present a detailed description of the system that we set up to perform this processing campaign using CC-IN2P3's computing and storage infrastructure. Several topics will be addressed: workflow generation and execution, batch job submission, memory and I/O requirements, etc. We will focus on the issues that arose during this campaign and how we addressed them and will present some perspectives after this exercise.

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile and due to start operations in 2025 for 10 years, its 8.4-meter telescope will nightly scan the southern sky and collect images of the entire visible sky every 4 nights using a 3.2 Gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this paper, we briefly present the infrastructure deployed at the French Rubin data facility (operated by IN2P3 computing center, CC-IN2P3) to deploy the Rubin Science Platform, a set of web-based services to provide effective and convenient access to LSST data for scientific analysis. We describe the main services of the platform, the components that provide those services and our deployment model. We also present the Kubernetes-based infrastructure we are experimenting with for hosting the LSST astronomical catalog, a petabyte-scale relational database developed for the specific needs of the project.

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory's ten-year science mission scheduled to begin in 2025. Rubin's 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320-1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties. In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way. Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.

We report on the observation and measurement of astrometry, photometry, morphology, and activityof the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS) with the NSF-DOE Vera C. Rubin Observatory. Comet 3I/ATLAS, the third known interstellar object, was discovered on UT 2025 July 1. Rubin Observatory had coincidentally collected images of the object's region of the sky during routine commissioning. Facilitated by Rubin's high resolution and large aperture, we successfully recovered object detections from Rubin observations spanning UT 2025 June 21 (10 days before discovery, when 3I/ATLAS was 4.5 au from the Sun) through the date of discovery, and we acquired additional images through UT 2025 July 20 as part of commissioning. We measure on-sky locations of 3I/ATLAS in Rubin ugrizy bands, with a typical precision of about 70 mas, and briefly describe the reason this is coarser than our measured static source astrometric precision of about 3 mas in Rubin images. We measure grizy magnitudes of 3I/ATLAS photometry at about 0.01 mag precision, detecting no short-term photometric variability above 0.01 mag. We derive an estimated near-nucleus dust-to-nucleus scattering cross-section ratio of eta >= 13 on UT 2025 July 2 based on Rubin photometry and an upper limit nucleus size computed from Hubble Space Telescope observations. We find Rubin colors of g - r = (0.657 +/- 0.013) mag, r - i = (0.235 +/- 0.018) mag, i - z = (0.147 +/- 0.042) mag, z - y = (0.047 +/- 0.052) mag. These data represent the earliest observations of this object by a large (>=8-meter class) telescope and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide after it begins in early 2026.

We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical near infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera LSSTComCam on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón Chile in late 2024. DP1 covers \\(\\)15 deg\\(^2\\) distributed across seven roughly equal-sized non-contiguous fields, each independently observed in six broad photometric bands \\(ugrizy\\). The median FWHM of the point spread function across all bands is approximately 1.14 arcseconds, with the sharpest images reaching about 0.58 arcseconds. The 5\\(\\) point source depths for coadded images in the deepest field the Extended Chandra Deep Field South are \\(u\\) = 24.55, \\(g\\) = 26.18, \\(r\\) = 25.96, \\(i\\) = 25.71, \\(z\\) = 25.07, \\(y\\) = 23.1. Other fields are no more than 2.2 magnitudes shallower in any band where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds and 431 solar system objects of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Rubin data rights holders via the Rubin Science Platform a cloud based environment for the analysis of petascale astronomical data. While small compared to future LSST releases its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.