Astronomical time-series analysis faces a critical limitation: the scarcity of labeled observational data. We present a pre-training approach that leverages simulations, significantly reducing the need for labeled examples from real observations. Our models, trained on simulated data from multiple astronomical surveys (ZTF and LSST), learn generalizable representations that transfer effectively to downstream tasks. Using classifier-based architectures enhanced with contrastive and adversarial objectives, we create domain-agnostic models that demonstrate substantial performance improvements over baseline methods in classification, redshift estimation, and anomaly detection when fine-tuned with minimal real data. Remarkably, our models exhibit effective zero-shot transfer capabilities, achieving comparable performance on future telescope (LSST) simulations when trained solely on existing telescope (ZTF) data. Furthermore, they generalize to very different astronomical phenomena (namely variable stars from NASA's \textit{Kepler} telescope) despite being trained on transient events, demonstrating cross-domain capabilities. Our approach provides a practical solution for building general models when labeled data is scarce, but domain knowledge can be encoded in simulations.

Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey cannot be easily applied to another, and new surveys require prohibitively large amounts of labelled training data. These challenges are particularly pressing as we approach the era of the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), where existing classification models will need to be retrained using LSST observations. We demonstrate that transfer learning can overcome these challenges by repurposing existing models trained on either simulations or data from other surveys. Starting with a model trained on simulated Zwicky Transient Facility (ZTF) light curves, we show that transfer learning reduces the amount of labelled real ZTF transients needed by 75\% while maintaining equivalent performance to models trained from scratch. Similarly, when adapting ZTF models for LSST simulations, transfer learning achieves 95\% of the baseline performance while requiring only 30\% of the training data. These findings have significant implications for the early operations of LSST, suggesting that reliable automated classification will be possible soon after the survey begins, rather than waiting months or years to accumulate sufficient training data.

We hear about artificial intelligence all the time nowadays--but what is it doing for astronomy? New research papers are published almost every week using AI for some new investigation in astronomy: classifying galaxies, identifying solar flares, exploring exoplanet atmospheres, and more. AI's biggest strength is that it can sort through mountains of data much faster than a human--a skill that's particularly timely as new telescopes are generating more and more data for astronomers to handle. "We can use [AI] to tackle problems we couldn't tackle before because they're too computationally expensive," said Daniela Huppenkothen, astronomer and data scientist at the Netherlands Institute for Space Research, in MIT Technology Review. One telescope in particular has many astronomers abuzz about AI: the Vera C. Rubin Observatory, scheduled to be completed in January 2025, just a few short months away.

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time (LSST) data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g. Malmquist bias. Specifically, we show a 61x improvement in prediction bias on PLAsTiCC simulations and 5x improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well-constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

Stunning drone footage has revealed the incredible progress on the Large Synoptic Survey Telescope – a massive instrument that will one day produce the'deepest, widest image of the universe' ever captured. Construction on the telescope in Chile began in 2015, with plans for it to begin operations around 2022. Now, nearly three years later, the new video shows how the enormous mountaintop facility has begun to take shape. Stunning drone footage has revealed the incredible progress on the Large Synoptic Survey Telescope – a massive instrument that will one day produce the'deepest, widest image of the universe' ever captured The video, published this week by the LSST team, was submitted by Assembly Integration Verification Manager Jacques Sebag, who captured the amazing view using a drone. At the time, the team was working with subcontractor Besalco to move the facility's mobile roof to a flatter area on the north side of the building.