Breakthroughs, discoveries, and DIY tips sent every weekday. While the Hubble and James Webb Space Telescopes continue to offer astronomers revolutionary glimpses of our universe, their upcoming sibling may very well upstage them. Scheduled to launch in 2027, NASA's Nancy Grace Roman Space Telescope is designed with a field of view at least 100 times larger than Hubble's, with the potential to document light from over a billion galaxies over its career. Combined with timelapse recording capabilities, Roman will help researchers to better understand exoplanets, infrared astrophysics, and the nature of dark matter. According to a study published on July 15 in The Astrophysics Journal, Roman is poised to eventually capture an estimated 100,000 celestial explosions over its lifetime.

Strongly lensed supernovae are a promising new probe to obtain independent measurements of the Hubble constant (${H_0}$). In this work, we employ simulated gravitationally lensed Type Ia supernovae (glSNe Ia) to train our machine learning (ML) pipeline to constrain $H_0$. We simulate image time-series of glSNIa, as observed with the upcoming Nancy Grace Roman Space Telescope, that we employ for training an ensemble of five convolutional neural networks (CNNs). The outputs of this ensemble network are combined with a simulation-based inference (SBI) framework to quantify the uncertainties on the network predictions and infer full posteriors for the $H_0$ estimates. We illustrate that the combination of multiple glSN systems enhances constraint precision, providing a $4.4\%$ estimate of $H_0$ based on 100 simulated systems, which is in agreement with the ground truth. This research highlights the potential of leveraging the capabilities of ML with glSNe systems to obtain a pipeline capable of fast and automated $H_0$ measurements.

Automating real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Modern survey telescopes are generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, are projected to increase this number dramatically. Currently, most anomaly detection algorithms for astronomical transients rely either on hand-crafted features extracted from light curves or on features generated through unsupervised representation learning, which are then coupled with standard machine learning anomaly detection algorithms. In this work, we introduce an alternative approach to detecting anomalies: using the penultimate layer of a neural network classifier as the latent space for anomaly detection. We then propose a novel method, named Multi-Class Isolation Forests (MCIF), which trains separate isolation forests for each class to derive an anomaly score for a light curve from the latent space representation given by the classifier. This approach significantly outperforms a standard isolation forest. We also use a simpler input method for real-time transient classifiers which circumvents the need for interpolation in light curves and helps the neural network model inter-passband relationships and handle irregular sampling. Our anomaly detection pipeline identifies rare classes including kilonovae, pair-instability supernovae, and intermediate luminosity transients shortly after trigger on simulated Zwicky Transient Facility light curves. Using a sample of our simulations that matched the population of anomalies expected in nature (54 anomalies and 12,040 common transients), our method was able to discover $41\pm3$ anomalies (~75% recall) after following up the top 2000 (~15%) ranked transients. Our novel method shows that classifiers can be effectively repurposed for real-time anomaly detection.

Artificial intelligence can accurately predict the age of supernovae and other rare stellar explosions within milliseconds of a telescope spotting them. That could prove useful for projects like the Vera C. Rubin Observatory's Legacy Survey of Space and Time, which is slated to begin observing in 2025. The 10-year survey of the southern sky will take 15 terabytes of data per night and could spot more than 10 million potential cosmic events each night. That may boost supernovae discoveries 100-fold. "It's going to observe potentially…

Astronomical transients, such as supernovae and other rare stellar explosions, have been instrumental in some of the most significant discoveries in astronomy. New astronomical sky surveys will soon record unprecedented numbers of transients as sparsely and irregularly sampled multivariate time series. To improve our understanding of the physical mechanisms of transients and their progenitor systems, early-time measurements are necessary. Prioritizing the follow-up of transients based on their age along with their class is crucial for new surveys. To meet this demand, we present the first method of predicting the age of transients in real-time from multi-wavelength time-series observations. We build a Bayesian probabilistic recurrent neural network. Our method can accurately predict the age of a transient with robust uncertainties as soon as it is initially triggered by a survey telescope. This work will be essential for the advancement of our understanding of the numerous young transients being detected by ongoing and upcoming astronomical surveys.

In astrophysics, a gravitational lens is a matter distribution (e.g., a black hole) able to bend the trajectory of transiting light, similar to an optical lens. Such apparent distortion is caused by the curvature of the geometry of space-time around the massive body acting as a lens, a phenomenon that forces the light to travel along the geodesics (i.e., the shortest paths in the curved space-time). Strong and weak gravitational lensing focus on the effects produced by particularly massive bodies (e.g., galaxies and black holes), while microlensing addresses the consequences produced by lighter entities (e.g., stars). This research proposes an approach to automatically classify strong gravitational lenses with respect to the lensed objects and to their evolution through time. Automatically finding and classifying gravitational lenses is a major challenge in astrophysics. As [103, 91, 39, 44] show, gravitational lensing systems can be complex, ubiquitous and hard to detect without computer-aided data processing. The volumes of data gathered by contemporary instruments make manual inspection unfeasible. As an example, the Vera C. Rubin Observatory is expected to collect petabytes of data [108]. Moreover, strong lensing is involved in major astrophysical problems: studying massive bodies that are too faint to be analyzed with current instrumentation; characterizing the geometry, content and kinematics of the universe; and investigating mass distribution in the galaxy formation process [103].

Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods. This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results. In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

Many current and exciting scientific questions that astronomers are trying to answer require them to collect large samples of different cosmic events. As a result, modern astronomical observatories have become relentless data-generating machines that throw thousands of alerts and images at astronomers every night. Using a machine learning algorithm, astronomers from the Zwicky Transient Facility collaboration at Caltech successfully classified 1000 supernovae autonomously. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech's Palomar Observatory. Every night, ZTF analyses the night sky for alterations known as transient events.

Currently, SNIascore can classify what are known as Type Ia supernovae, or the "standard candles" in the sky. A machine learning algorithm developed by astronomers at the California Institute of Technology (Caltech) autonomously classified 1,000 supernovae using data from the Zwicky Transient Facility (ZTF) sky survey instrument at Caltech's Palomar Observatory. The SNIascore algorithm hit that milestone 18 months after classifying its first supernova, in April 2021. The algorithm is intended to help the ZTF team by processing data from the hundreds of thousands of transient events ZTF detects every night. SNIascore currently has the ability to classify Type Ia supernovae that astronomers use to measure the universe's expansion rate.

Today's astronomical facilities scan the night sky deeper and faster than ever before. Identifying and classifying known and potentially interesting cosmic events is becoming impossible for one or a group of astronomers. Therefore, increasingly they train supercomputers to do the work for them. Astronomers from the Zwicky Transient Facility collaboration at Caltech have announced that their machine-learning algorithm has now classified and reported 1000 supernovae completely autonomously. "We needed a helping hand and we knew that once we train our computers to do the job, they would take a big load off our backs", says Christoffer Fremling, a staff astronomer at Caltech and the mastermind behind the new algorithm, dubbed SNIascore. "SNIascore classified its first supernova in April 2021 and a year and a half later we are hitting a nice milestone of 1000 supernovae without any human involvement."