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Abstract In an effort to search for faint sources of emission over arbitrary timescales, we present a novel method for analyzing forced photometry light curves in difference imaging from optical surveys. Our method “ATLAS Clean,” or ATClean, utilizes the reported fluxes, uncertainties, and fits to the point-spread function (PSF) from difference images to quantify the statistical significance of individual measurements. We apply this method to control light curves across the image to determine whether any source of flux is present in the data for a range of specific timescales. From ATLASo-band imaging at the site of the Type II supernova (SN) 2023ixf in M101 from 2015–2023, we show that this method accurately reproduces the 3σflux limits produced from other, more computationally expensive methods. We derive limits for emission on timescales of 5 days and 80–300 days at the site of SN 2023ixf, which are 19.8 and 21.3 mag, respectively. The latter limits rule out variability for unextinguished red supergiants with initial masses >22M_⊙, comparable to the most luminous predictions for the SN 2023ixf progenitor system. We also compare our limits to short-timescale outbursts, similar to those expected for Type IIn SN progenitor stars or the Type II SN 2020tlf, and rule out outburst ejecta masses of >0.021M_⊙, much lower than the inferred mass of circumstellar matter around SN 2023ixf in the literature. In the future, these methods can be applied to any forced photometry on difference imaging from other surveys, such as Rubin optical imaging. 

Abstract While the spectroscopic classification scheme for stripped-envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all of their envelopes of hydrogen and helium, the photometric evolution of classes within this family is not fully characterized. Photometric surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of transients each night, and spectroscopic follow-up will be limited, prompting the need for photometric classification and inference based solely on photometry. We have generated 54 data-driven photometric templates for SESNe of subtypes IIb, Ib, Ic, Ic-bl, and Ibn inU/u,B,g,V,R/r,I/i,J,H,K_s, and Swiftw2,m2,w1 bands using Gaussian processes and a multisurvey data set composed of all well-sampled open-access light curves (165 SESNe, 29,531 data points) from the Open Supernova Catalog. We use our new templates to assess the photometric diversity of SESNe by comparing final per-band subtype templates with each other and with individual, unusual and prototypical SESNe. We find that SNe Ibn and SNe Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves, highlighting differences that can bias photometric classification models trained on the simulated light curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and the implications of the frequently observed presence of two peaks in their light curves. 

Abstract Light echoes give us a unique perspective on the nature of supernovae and nonterminal stellar explosions. Spectroscopy of light echoes can reveal details on the kinematics of the ejecta, probe asymmetry, and reveal details of ejecta interaction with circumstellar matter, thus expanding our understanding of these transient events. However, the spectral features arise from a complex interplay between the source photons, the reflecting dust geometry, and the instrumental setup and observing conditions. In this work, we present an improved method for modeling these effects in light echo spectra, one that relaxes the simplifying assumption of a light-curve-weighted sum, and instead estimates the true relative contribution of each phase of a transient event to the observed spectrum. We discuss our logic, the gains we obtain over light echo analysis methods used in the past, and prospects for further improvements. Lastly, we show how the new method improves our analysis of echoes from Tycho’s supernova (SN 1572) as an example. 

Abstract Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys. 

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science.iv To facilitate such opportunities, a community workshop entitled “From Data to Software to Science with the Rubin Observatory LSST” was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.