We present a detailed analysis of SN 2020qmp, a nearby type IIP core-collapse supernova (CCSN), discovered by the Palomar Gattini-IR (PGIR) survey in the galaxy UGC07125. We illustrate how the multiwavelength study of this event helps our general understanding of stellar progenitors and circumstellar medium (CSM) interactions in CCSNe. We also highlight the importance of near-infrared (NIR) surveys for early detections of SNe in dusty environments. SN 2020qmp displays characteristic hydrogen lines in its optical spectra, as well as a plateau in its optical LC, hallmarks of a type IIP SN. We do not detect linear polarization during the plateau phase, with a 3 sigma upper limit of 0.78%. Through hydrodynamical LC modeling and an analysis of its nebular spectra, we estimate a progenitor mass of around 11 solar masses, and an explosion energy of around 0.8e51 erg. We find that the spectral energy distribution cannot be explained by a simple CSM interaction model, assuming a constant shock velocity and steady mass-loss rate, and the excess X-ray luminosity compared with the synchrotron radio luminosity suggests deviations from equipartition. Finally, we demonstrate the advantages of NIR surveys over optical surveys for the detection of dust-obscured CCSNe in the local universe. Specifically, our simulations show that the Wide-Field Infrared Transient Explorer will detect about 14 more CCSNe out of 75 expected in its footprint within 40 Mpc, over five years than an optical survey equivalent to the Zwicky Transient Facility would detect. We have determined or constrained the main properties of SN 2020qmp and of its progenitor, highlighting the value of multiwavelength follow-up observations of nearby CCSNe. We have also shown that forthcoming NIR surveys will finally enable us to do a nearly complete census of CCSNe in the local universe.

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses; analyze and predict personalized adverse health outcomes; adapt and respond to newly accumulated data; and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space.

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space.

The new generation of wide-field time-domain surveys has made it feasible to study the clustering of supernova (SN) host galaxies in the large-scale structure (LSS) for the first time. We investigate the LSS environment of SN populations, using 106 dark matter density realisations with a resolution of $sim$ 3.8 Mpc, constrained by the 2M++ galaxy survey. We limit our analysis to redshift $z<0.036$, using samples of 498 thermonuclear and 782 core-collapse SNe from the Zwicky Transient Facility’s Bright Transient Survey and Census of the Local Universe catalogues. We detect clustering of SNe with high significance; the observed clustering of the two SNe populations is consistent with each other. Further, the clustering of SN hosts is consistent with that of the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) DR12 spectroscopic galaxy sample in the same redshift range. Using a tidal shear classifier, we classify the LSS into voids, sheets, filaments and knots. We find that both SNe and SDSS galaxies are predominantly found in sheets and filaments. SNe are significantly under-represented in voids and over-represented in knots compared to the volume fraction in these structures. This work opens the potential for using forthcoming wide-field deep SN surveys as a complementary LSS probe.