Time-domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, including SNe within days or hours of explosion and faint, red transients, are just beginning to be explored. Here we present the Young Supernova Experiment (YSE), a novel optical time-domain survey on the Pan-STARRS telescopes. Our survey is designed to obtain well-sampled griz light curves for thousands of transient events up to z ≈ 0.2. This large sample of transients with four-band light curves will lay the foundation for the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, providing a critical training set in similar filters and a well-calibrated low-redshift anchor of cosmologically useful SNe Ia to benefit dark energy science. As the name suggests, YSE complements and extends other ongoing time-domain surveys by discovering fast-rising SNe within a few hours to days of explosion. YSE is the only current four-band time-domain survey and is able to discover transients as faint as ∼21.5 mag in gri and ∼20.5 mag in z, depths that allow us to probe the earliest epochs of stellar explosions. YSE is currently observing approximately 750 deg2 of sky every 3 days, and we plan to increase the area to 1500 deg2 in the near future. When operating at full capacity, survey simulations show that YSE will find ∼5000 new SNe per year and at least two SNe within 3 days of explosion per month. To date, YSE has discovered or observed 8.3% of the transient candidates reported to the International Astronomical Union in 2020. We present an overview of YSE, including science goals, survey characteristics, and a summary of our transient discoveries to date.

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In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large scale surveys. The analysis consists of the following steps: 1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, 2) dimensionality reduction, 3) searching for outliers with the use of the isolation forest algorithm, 4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33%) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4% of the initial automatically identified data sample of ∼2000 objects. Among the identified outliers we recognised superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available‡.

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available*.

We investigate the viscous evolution of the accretion disk in 4U 1543−47, a black hole binary system, during the first 30 days after the peak of the 2002 burst by comparing the observed and theoretical accretion rate evolutionṀ(t). The observedṀ(t) is obtained from spectral modelling of the archival RXTE/PCA data. Different scenarios of disk decay evolution are possible depending on a degree of self-irradiation of the disk by the emission from its centre. If the self-irradiation, which is parametrized by factor C irr , had been as high as ∼ 5 × 10 −3 , then the disk would have been completely ionized up to the tidal radius and the short time of the decay would have required the turbulent parameter α ∼ 3. We find that the shape of theṀ(t) curve is much better explained in a model with a shrinking high-viscosity zone. If C irr ≈ (2 − 3) × 10 −4 , the resulting α lie in the interval 0.5 − 1.5 for the black hole masses in the range 6 − 10 M , while the radius of the ionized disk is variable and controlled by irradiation. For very weak irradiation, C irr < 1.5 × 10 −4 , the burst decline develops as in normal outbursts of dwarf novae with α ∼ 0.08 − 0.32. The optical data indicate that C irr in 4U 1543−47 (2002) was not greater than approximately (3−6)×10 −4 . Generally, modelling of an X-ray nova burst allows one to estimate α that depends on the black hole parameters. We present the public 1-D code freddi to model the viscous evolution of an accretion disk. Analytic approximations are derived to estimate α in X-ray novae usingṀ(t).

Aims. We present the first piece of evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects that can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new piece of information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional isolation forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery algorithm to two data sets: simulated light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the Active Anomaly Discovery results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ∼2% highest anomaly scores. Results. We show that, in the real data scenario, Active Anomaly Discovery was able to identify ∼80% more true anomalies than the IF. This result is the first piece of evidence that active anomaly detection algorithms can play a central role in the search for new physics in the era of large-scale sky surveys.

The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by a factor of ∼20, with associated survey data, archival data, and metadata also increasing with the number of discoveries. To manage the data at this increased rate, we require new tools. Here we present YSE-PZ, a transient survey management platform that ingests multiple live streams of transient discovery alerts, identifies the host galaxies of those transients, downloads coincident archival data, and retrieves photometry and spectra from ongoing surveys. YSE-PZ also presents a user with a range of tools to make and support timely and informed transient follow-up decisions. Those subsequent observations enhance transient science and can reveal physics only accessible with rapid follow-up observations. Rather than automating out human interaction, YSE-PZ focuses on accelerating and enhancing human decision making, a role we describe as empowering the human-in-the-loop. Finally, YSE-PZ is built to be flexibly used and deployed; YSE-PZ can support multiple, simultaneous, and independent transient collaborations through group-level data permissions, allowing a user to view the data associated with the union of all groups in which they are a member. YSE-PZ can be used as a local instance installed via Docker or deployed as a service hosted in the cloud. We provide YSE-PZ as an open-source tool for the community.

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