The past few days have seen Rubin Operations Rehearsal 4 (OR4), which has exercised more of the end-to-end observatory system, with a (simulated) data stream flowing from the summit to the US Data Facility at SLAC, where the Prompt Products pipeline was run on it, to generate alerts that were then sent out in near-real-time to the suite of Community Brokers, including our own Lasair. This provided the Lasair team with valuable experience of handling an alert stream flowing at a much greater rate than that provided by ZTF, which they have been consuming for some time. Hopefully, they will describe the lessons they learnt from this in next month’s Newsletter, where we might also hear from James Mullaney , who has been studying the quality of the coadded images produced during OR4, as part of his role as the UK’s Data Processing Scientist.

Elsewhere on the summit, ComCam has been removed from the Telescope Mount Assembly until the secondary mirror has been installed, which is expected to happen during July.

The 2024 Rubin Community Workshop (22-26 July) is taking shape, with a fairly final programme now available. Registration for in-person attendance has now closed, but further remote attendees are welcome to register still. Meanwhile, closer to home, we have published the near-final programme for the pair of LSST:UK sessions at NAM2024, which is taking place the week before that (14-19 July) in Hull. The current version of the programme is listed below, and an up-to-date version will remain available here.

A new paper from Xinyue Sheng and collaborators introduces NEEDLE, a tool for discovering large samples of rare events designed for the Lasair alert broker.

Published in the June 2024 edition of MNRAS, the paper is entitled NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images.

Known for their efficiency in analysing large data sets, machine learning-based classifiers have been widely used in wide-field sky survey pipelines. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate millions of real-time alerts every night, enabling the discovery of large samples of rare events. Identifying such objects soon after explosion will be essential to study their evolution. This requires a machine learning framework that makes use of all the information available, including light curve, host galaxy and other contextual data.

Using ~5400 transients from the ZTF Bright Transient Survey as training and testing data, we developed NEEDLE, a novel hybrid (convolutional neural network + dense neural network) classifier to select for two rare classes with strong environmental preferences: superluminous supernovae (SLSNe) preferring dwarf galaxies, and tidal disruption events (TDEs) occurring in the centres of nucleated galaxies.

Our network is designed with LSST in mind and we expect performance to improve further with the higher resolution images and more accurate transient and host photometry that will be available from Rubin. Our system is currently deployed as an annotator on the UK alert broker, Lasair, to provide predictions to the community in real time.

The NEEDLE pipeline design for the alert broker Lasair. NEEDLE receives ZTF (and ultimately LSST) alerts from Lasair via a customized SQL filter to remove old or bogus objects. The science and reference images are contained in the ZTF alerts, or requested from the Rubin Science Platform. If they pass the quality checker, the host metadata will be fetched from sherlock and Pan-STARRS. Finally, NEEDLE will return the probabilities for the three classes to the Lasair annotation database, allowing them to be used in subsequent alert filters by any user.

Access the paper: NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images

Xinyue Sheng, Matt Nicholl,Ken Smith , Dave Young ,Roy Williams, Heloise Stevance, Stephen Smartt, Shubham Srivastav and Thomas Moore.

Session 1: 09.00-11.00

• 09.00: Introduction and summary of UK involvement in the Rubin LSST (Bob Mann)

• 09.20: LSST survey strategy and early science plans (Stephen Smartt)

• 09.50: Preparations for Rubin Data Release productions (James Mullaney)

• 10.20: Photometric galaxy clustering with HSC DR3: an LSST precursor study (Tom Cornish)

• 10.40: Enabling early Rubin science with robust cross-matches in the crowded LSST sky (Tom Wilson)

Session 2: 15.00-17.00

• 15.00: Detecting the Galactic interstellar object population with LSST (Matthew Hopkins)

• 15.20: Eliciting astrophysical distributions from imperfect survey data (Dustin Mason)

• 15.40: Probing galaxy clusters with the XMM Cluster Survey and implications for LSST (Paul Giles)

• 16.00: TBC

• 16.20: Higher order statistics in Rubin data (Lea Harscouet)

• 16.40: Rubin Observatory update (Bob Blum)