05/06/2022
Abstract: Show MoreThe smallest and faintest galaxies around the Milky Way are the most ancient, most metal-poor, and most dark-matter-dominated systems known. These extreme objects offer unique access to small scales where the stellar and dark matter content can be studied simultaneously. They hold the promise of major breakthroughs in understanding the nature of dark matter and a more complete picture of galaxy formation. Thus, their discovery and characterization are among the most important goals in the field. In this talk, I will share our ongoing observational efforts to detect these faint systems around the Milky Way and beyond, and upcoming advances in the era of deep and wide imaging instrumentation, with a focus on their implications.
05/20/2022
Adina Feinstein – “Exploiting TESS data to Understand Flare Statistics and Stellar Magnetic Fields Across the Main Sequence”
Abstract: Show MoreAll-sky photometric time-series exoplanet missions have allowed for the monitoring of hundreds of thousands of stars, allowing for statistical analyses of stellar properties, specifically activity, across the Hertzsprung-Russell diagram. In this talk, I will discuss the convolutional neural network (CNN), stella, specifically trained to find flares in TESS short-cadence data. I will present the results of the CNN applied to 3200 young (< 1 Gyr) stars in order to evaluate flare rates as a function of age and mass. Additionally, we measure rotation periods for 1500 of our targets. The combination of flare rates and rotation periods allowed us to investigate surface starspot coverage as well as develop analytical models for magnetic field braiding to interpret differences in flare frequency distributions (FFDs). The efficiency and accuracy of the CNN allows for rapid flare detection on all stars observed at 2-minute cadence. Towards the end of my talk, I will present FFDs for 10^5 stars observed during TESS’s primary mission. By fitting the FFD for different mass bins, we find that all stars exhibit distributions of flaring events indicative of a self-organized critical state. This suggests that magnetic reconnection events maintain the topology of the coronal magnetic fields in a self-organized critical state in all stars, universally. If this is true, we will be able to infer properties of magnetic fields, interior structure, and dynamo mechanisms for all stars, which are otherwise unresolved point sources.
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05/27/2022
Dr. Francisco Forster – “The ALeRCE astronomical alert broker”
Abstract: Show MoreA new generation of survey telescopes is allowing us to explore large volumes of the Universe in an unprecedented fashion, detecting and reporting up to tens of millions of varying objects in the sky every night. This requires fast machine learning aided discovery and classification systems, the astronomical brokers. The Automatic Learning for the Rapid Classification of Events (ALeRCE) broker is processing the alert stream from the Zwicky Transient Facility (ZTF), will soon start to process the Asteroid Terrestrial-impact Last Alert System (ATLAS) alert stream, and is an official Community Broker for the Legacy Survey of Space and Time (LSST). Since 2019, we use cloud infrastructure and machine learning to bring real-time processed products and services to the astronomical community, becoming the first public broker to systematically classify the ZTF alert stream into an astrophysically motivated taxonomy, ingesting more than 250 million alerts, classifying about 69 million objects based on their images, 1.6 objects based on their light curves, and reporting more than 13 k supernova candidates, 1.6 k of them resulting in spectroscopic classifications. In this talk I will summarize the available services (e.g., web explorer, SN hunter, data releases, watchlist) that can be used by the scientific community.
06/03/2022
Colin Burke – “A forward model of AGN variability for LSST Rubin”
Abstract: Show MoreOptical variability is a key probe of the AGN population that will be instrumental for studies of AGN demographics with LSST Rubin. We have developed a phenomenological forward model that generates a mock AGN and host galaxy population and simulates light curves given the survey specifications as input. In this talk, I will show how our model can be used to constrain the local black hole mass function (BHMF) using optical variability with LSST Rubin. The ultimate goal is that this relic BHMF, when probed at low masses, will reveal the signature of how supermassive black holes were seeded at high redshifts.
06/03/2022
Patrick Aleo – “The Young Supernova Experiment (YSE): Data Release 1 and Supernovae Photometric Classification”
Abstract: Show MoreWe present the Young Supernova Experiment (YSE) first data release, spanning discoveries from November 24th, 2019 to December 20, 2021. YSE is an active, three year optical time-domain survey on the Pan-STARRS1 and Pan-STARRS2 telescopes, designed to capture young, fast-rising supernovae (SNe) within a few hours to days of explosion. This YSE DR1 includes light curves and metadata for 2008 supernova-like sources, of which 441 transients are spectroscopically-classified. We then uniquely use realistic, multi-survey SNe simulations from YSE and Zwicky Transient Facility (ZTF) data to train the ParSNIP classifier for photometric classification tasks; when validating on spectroscopically-classified YSE SNe, we achieve 82% accuracy across three SN classes (SN Ia, SN II, SN Ibc) and 90% accuracy across two SN classes (SN Ia, CC SNe), with high individual completeness and purity of SN Ia. We then use our classifier to characterize our spectroscopically unclassified sample of 1567 YSE SNe, predicting ~66% SN Ia, ~34% CC SNe. We find that realistic simulations are now sufficient to exclusively train current photometric classification methods without compromising performance on real data. Though, our classifiers have particular difficulty in characterizing transients near the cores of galaxies or exhibit rare photometric or spectral features. In preparation for the forthcoming Rubin Observatory era, griz data sets such as the one presented here will be an important component of building classification and discovery algorithms for transient discovery.