Astrophysics & Machine Learning
The automation of optical transient discovery and classification in Rubin-era time-domain astronomy
This article reviews the current state of automated transient discovery and classification in time-domain astronomy, particularly in the context of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). It highlights the increasing automation driven by machine learning and AI, from alert filtering and real/bogus classification to automated follow-up observations and photometric classification. The Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS) is presented as a case study for a fully automated end-to-end supernova discovery-to-classification workflow. The authors emphasize the need for continued investment in automation, standardized benchmarks, and interdisciplinary collaboration to handle the expected order-of-magnitude increase in transient discoveries in the Rubin era, accelerate scientific returns, and enable rapid-response multi-messenger astronomy.
Accelerating Automation for Rubin-Era Astronomy
The Vera C. Rubin Observatory will produce an unprecedented volume of astronomical data, requiring a paradigm shift towards highly automated workflows for transient discovery and classification. Current ML/AI tools are effective but need further generalization, standardization, and robust integration into real-time pipelines to maximize scientific return.
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Increased Alert Volume
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ML Accuracy (Real/Bogus)
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Automated Supernovae
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Modern wide-field surveys like ZTF and ATLAS use image differencing to identify new sources, generating 'alerts'. These alerts are then processed by 'alert brokers' (e.g., ALeRCE, Fink, ANTARES) which augment and filter the data. Machine learning is crucial here for filtering 'bogus' alerts from real astrophysical events, with current models achieving over 99% accuracy. The Rubin Observatory's LSST will dramatically increase this alert volume, making automation indispensable.
Automated follow-up observations are critical for efficiency and minimizing latency, especially in multi-messenger astronomy (e.g., gamma-ray bursts, gravitational-wave counterparts) and for characterizing infant supernovae. Facilities like the Palomar 60-inch telescope have successfully deployed automated follow-up utilities, with tools like BTSbot-nearby enabling rapid-response space-based observations. Photometric transient classification, using mock light curves (e.g., PLASTICC, ELASTICC), also plays a vital role given that most transients will not receive spectroscopic observations. The challenge remains to bridge the gap between simulations and real-time data streams.
The upcoming Rubin era demands significant investment in workflow automation. Key challenges include managing the massive alert stream (10^7 alerts/night), developing more generalized and robust ML/AI models, establishing standardized benchmarks for photometric classification and anomaly detection, and fostering interdisciplinary collaboration between astronomers, ML/AI practitioners, and software engineers. The success of projects like ZTF BTS highlights the potential of end-to-end automation but also underscores the time and collaboration required.
10 Million
Alerts per night from LSST
ZTF Bright Transient Survey Workflow
Survey Scheduling
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Observations
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Data Reduction & Alert Creation
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Alert Augmentation & Filtering
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Automated Scanning (BTSbot)
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Automated Follow-up Request (SEDM)
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Automated Classification (SNIascore)
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ZTF Bright Transient Survey (BTS): A Success Story
The ZTF Bright Transient Survey (BTS) exemplifies the power of full automation. It has achieved a fully automated end-to-end supernova discovery-to-classification workflow, enabling the rapid identification and spectroscopic classification of over 200 Type Ia supernovae. This was made possible by integrating tools like BTSbot for automated scanning, SkyPortal for scheduling, and SNIascore for spectral classification. This success highlights the critical role of interdisciplinary collaboration and sustained investment in infrastructure development over several years.
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Your AI Implementation Roadmap
A phased approach to integrating advanced AI into your operations, designed for measurable impact and seamless adoption.
Phase 1: Foundation & Data Integration
Establish robust data pipelines for LSST alert streams, integrate with existing brokers (e.g., ALeRCE, Fink), and develop initial real/bogus ML models with domain adaptation.
Phase 2: Automated Filtering & Candidate Selection
Implement advanced ML filters for science-specific transients, develop and deploy automated scanning tools (e.g., enhanced BTSbot) to reduce human workload significantly.
Phase 3: Rapid-Response Follow-up & Photometric Classification
Integrate automated scheduling for follow-up facilities (e.g., P60, AEON), develop and benchmark robust photometric classifiers for various transient types, and expand anomaly detection capabilities.
Phase 4: End-to-End Automation & Multi-Messenger Integration
Achieve fully automated discovery-to-classification for specific transient classes, seamlessly integrate with multi-messenger alerts (gravitational waves, neutrinos) for rapid, autonomous follow-up.
Phase 5: Continuous Improvement & Community Engagement
Establish standardized benchmarks, foster open-source development, and build a collaborative ecosystem for sharing tools, models, and labeled datasets to ensure long-term sustainability and scientific impact.
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