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Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.

The Transiting Exoplanet Survey Satellite (TESS) is continuing its second extended mission after 55 sectors of observations. TESS publishes full-frame images (FFIs) at a cadence of 1800, 600, or 200 s, allowing light curves to be extracted for stars beyond a limited number of pre-selected stars. Simulations show that thousands of exoplanets, eclipsing binaries, variable stars, and other astrophysical transients can be found in these FFI light curves. To obtain high-precision light curves, we forward model the FFI with the effective point-spread function (PSF) to remove contamination from nearby stars. We adopt star positions and magnitudes from Gaia DR3 as priors. The resulting light curves, called TESS–Gaia light curves (TGLCs), show a photometric precision closely tracking the prelaunch prediction of the noise level. The TGLCs’ photometric precision reaches ≲2% at 16th TESS magnitude even in crowded fields. We publish TGLC aperture and PSF light curves for stars down to 16th TESS magnitude through the Mikulski Archive for Space Telescopes for all available sectors and will continue to deliver future light curves. The open-source package tglc 3 3 Via 10.17909/610m‐9474. is publicly available to enable any user to produce customized light curves.

We propose the Transformer-based Tidal disruption events (TDE) Classifier (TTC), specifically designed to operate effectively with both real-time alert streams and archival data of the Wide Field Survey Telescope (WFST). It aims to minimize the reliance on external catalogs and find TDE candidates from pure light curves, which is more suitable for finding TDEs in faint and distant galaxies. TTC consists of two key modules that can work independently: (1) A light-curve parametric fitting module and (2) a Transformer (Mgformer) based classification network. The training of the latter module and the evaluations for each of the modules utilize a light-curve dataset of 7413 spectroscopically classified transients from the Zwicky Transient Facility (ZTF). The Mgformer-based module is superior in performance and flexibility. Its representative recall and precision values are 0.79 and 0.76, respectively, and can be modified by adjusting the threshold. It can also efficiently find TDE candidates within 30 days from the first detection. For comparison, the parametric fitting module yields values of 0.72 and 0.40, respectively, while it is >10 times faster in average speed. Hence, the setup of modules allows a trade-off between performance and time, as well as precision and recall. TTC has successfully picked out all spectroscopically identified TDEs among ZTF transients in a real-time classification test, and selected ∼20 TDE candidates in the deep field survey data of WFST. The discovery rate will greatly increase once the differential database for the wide-field survey is ready.

δ Scuti variables are found at the intersection of the classical instability strip and the main sequence on the Hertzsprung–Russell diagram. With space-based photometry providing millions of light curves of A-F type stars, we can now probe the occurrence rate of δ Scuti pulsations in detail. Using the 30 minutes cadence light curves from NASA's Transiting Exoplanet Survey Satellite's first 26 sectors, we identify variability in 103,810 stars within 5–24 cycles per day down to a magnitude of T = 11.25. We fit the period–luminosity relation of the fundamental radial mode for δ Scuti stars in the Gaia G band, allowing us to distinguish classical pulsators from contaminants for a subset of 39,367 stars. Out of this subset, over 15,918 are found on or above the expected period–luminosity relation. We derive an empirical red edge to the classical instability strip using Gaia photometry. The center where the pulsator fraction peaks at 50%–70%, combined with the red edge, agrees well with previous work in the Kepler field. While many variable sources are found below the period–luminosity relation, over 85% of sources inside of the classical instability strip derived in this work are consistent with being δ Scuti stars. The remaining 15% of variables within the instability strip are likely hybrid or γ Doradus pulsators. Finally, we discover strong evidence for a correlation between pulsator fraction and spectral line broadening from the Radial Velocity Spectrometer on board the Gaia spacecraft, confirming that rotation has a role in driving pulsations in δ Scuti stars.

Given its large plate scale of 21″ pixel−1, analyses of data from the Transiting Exoplanet Survey Satellite (TESS) space telescope must be wary of source confusion from blended light curves, which creates the potential to attribute observed photometric variability to the wrong astrophysical source. We explore the impact of light curve contamination on the detection of fast yellow pulsating supergiant (FYPS) stars as a case study to demonstrate the importance of confirming the source of detected signals in the TESS pixel data. While some of the FYPS signals have already been attributed to contamination from nearby eclipsing binaries, others are suggested to be intrinsic to the supergiant stars. In this work, we carry out a detailed analysis of the TESS pixel data to fit the source locations of the dominant signals reported for 17 FYPS stars with the Python package TESS_localize. We are able to reproduce the detections of these signals for 14 of these sources, obtaining consistent source locations for four. Three of these originate from contaminants, while the signal reported for BZ Tuc is likely a spurious frequency introduced to the light curve of this 127 day Cepheid by the data processing pipeline. Other signals are not significant enough to be localized with our methods, or have long periods that are difficult to analyze given other TESS systematics. Since no localizable signals hold up as intrinsic pulsation frequencies of the supergiant targets, we argue that unambiguous detection of pulsational variability should be obtained before FYPS are considered a new class of pulsator.

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multiband light curves is a challenging task due to the highly irregular cadence, long time gaps, missing values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light curves. We offer three main contributions: (1) Based on temporal modulation and attention mechanisms, we propose a deep attention model (TimeModAttn) to classify multiband light curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. (2) We propose a model for the synthetic generation of SN multiband light curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pretrained using synthetic light curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other deep learning models, based on recurrent neural networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-F 1score from ≈.525 to ≈.596. When training the BRF with synthetic data, this model achieved a similar performance to the TimeModAttn model proposed while still maintaining extra advantages. (3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.

With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic follow-up by 3 orders of magnitude. Accurate photometric classifiers are essential to both select interesting events for follow-up in real time and for archival population-level studies. In this work, we investigate the impact of observable host-galaxy information on the classification of SNe, both with and without additional light-curve and redshift information. We find that host-galaxy information alone can successfully isolate relatively pure (>90%) samples of Type Ia SNe with or without redshift information. With redshift information, we can additionally produce somewhat pure (>70%) samples of Type II SNe and superluminous SNe. Additionally with redshift information, host-galaxy properties do not significantly improve the accuracy of SN classification when paired with complete light curves. In the absence of redshift information, however, galaxy properties significantly increase the accuracy of photometric classification. As a part of this analysis, we present the first formal application of a new objective function, the weighted hierarchical cross entropy, to the problem of SN classification. This objective function more naturally accounts for the hierarchical nature of SN classes and, more broadly, transients. Finally, we present a new set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe that lack spectroscopic redshift, increasing the full photometric sample to >4400 events.

The UV/optical light curves of Active Galactic Nuclei (AGNs) are commonly described by the Damped Random Walk (DRW) model. However, the physical interpretation of the damping timescale, a key parameter in the DRW model, remains unclear. Particularly, recent observations indicate a weak dependence of the damping timescale upon both wavelength and accretion rate, clearly being inconsistent with the accretion-disk theory. In this study, we investigate the damping timescale in the framework of the Corona Heated Accretion disk Reprocessing (CHAR) model, a physical model that describes AGN variability. We find that while the CHAR model can reproduce the observed power spectral densities of the 20 yr light curves for 190 sources from Stone et al., the observed damping timescale, as well as its weak dependence on wavelength, can also be well recovered through fitting the mock light curves with DRW. We further demonstrate that such weak dependence is artificial due to the effect of inadequate durations of light curves, which leads to best-fitting damping timescales lower than the intrinsic ones. After eliminating this effect, the CHAR model indeed yields a strong dependence of the intrinsic damping timescale on the bolometric luminosity and rest-frame wavelength. Our results highlight the demand for sufficiently long light curves in AGN variability studies and important applications of the CHAR model in such studies.

Differentiating between real transit events and false-positive signals in photometric time-series data is a bottleneck in the identification of transiting exoplanets, particularly long-period planets. This differentiation typically requires visual inspection of a large number of transit-like signals to rule out instrumental and astrophysical false positives that mimic planetary transit signals. We build a one-dimensional convolutional neural network (CNN) to separate eclipsing binaries and other false positives from potential planet candidates, reducing the number of light curves that require human vetting. Our CNN is trained using the TESS light curves that were identified by Planet Hunters citizen scientists as likely containing a transit. We also include the background flux and centroid information. The light curves are visually inspected and labeled by project scientists and are minimally preprocessed, with only normalization and data augmentation taking place before training. The median percentage of contaminants flagged across the test sectors is 18% with a maximum of 37% and a minimum of 10%. Our model keeps 100% of the planets for 16 of the 18 test sectors, while incorrectly flagging one planet candidate (0.3%) for one sector and two (0.6%) for the remaining sector. Our method shows potential to reduce the number of light curves requiring manual vetting by up to a third with minimal misclassification of planet candidates.