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Abstract The nearby type II supernova, SN 2023ixf in M101 exhibits signatures of early time interaction with circumstellar material in the first week postexplosion. This material may be the consequence of prior mass loss suffered by the progenitor, which possibly manifested in the form of a detectable presupernova outburst. We present an analysis of long-baseline preexplosion photometric data in theg,w,r,i,z, andyfilters from Pan-STARRS as part of the Young Supernova Experiment, spanning ∼5000 days. We find no significant detections in the Pan-STARRS preexplosion light curves. We train a multilayer perceptron neural network to classify presupernova outbursts. We find no evidence of eruptive presupernova activity to a limiting absolute magnitude of −7 mag. The limiting magnitudes from the full set ofgwrizy(average absolute magnitude ≈ −8 mag) data are consistent with previous preexplosion studies. We use deep photometry from the literature to constrain the progenitor of SN 2023ixf, finding that these data are consistent with a dusty red supergiant progenitor with luminosity $\log \left(L/L_{\odot }\right)$≈ 5.12 and temperature ≈ 3950 K, corresponding to a mass of 14–20M_⊙. 

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)* open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10% and a BPCER of 0.46% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.