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Abstract We conduct an analysis of over 60,000 dwarf galaxies ( $7\lesssim \log M_{*}/M_{\odot }\lesssim 10$) in search of photometric variability indicative of active galactic nuclei (AGNs). Using data from the Young Supernova Experiment, a time domain survey on the Pan-STARRS telescopes, we construct light curves for each galaxy in up to four bands (griz) where available. We select objects with AGN-like variability by fitting each light curve with a damped random walk (DRW) model. After quality cuts and removing transient contaminants, we identify 1100 variability-selected AGN candidates (representing 2.4% of the available sample). We analyze their spectra to measure various emission lines and calculate black hole (BH) masses, finding general agreement with previously found mass scaling relations and nine potential intermediate-mass BH candidates. Furthermore, we reanalyze the light curves of our candidates to calculate the dampening timescaleτ_DRWassociated with the DRW and see a similar correlation between this value and the BH mass. Finally, we estimate the active fraction as a function of stellar mass and see evidence that the active fraction increases with host mass. 

Abstract At the low-redshift end (z< 0.05) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities (PVs) is of similar size to that due to the limitations of the standardization of the SN Ia light curves. A way to improve the redshift measurement of the SN host galaxy is to utilize the average redshift of the galaxy group, effectively averaging over small-scale/intracluster PVs. One limiting factor is the fraction of SN host galaxies in galaxy groups, previously found to be 30% using (relatively incomplete) magnitude-limited galaxy catalogs. Here, we do the first analysis ofN-body simulations to predict this fraction, finding ∼73% should have associated groups and group averaging should improve redshift precision by ∼135 km s⁻¹(∼0.04 mag atz= 0.025). Furthermore, using spectroscopic data from the Anglo-Australian Telescope, we present results from the first pilot program to evaluate whether or not 23 previously unassociated SN Ia hosts belong in groups. We find that 91% of these candidates can be associated with groups, consistent with predictions from simulations given the sample size. Combining with previously assigned SN host galaxies in Pantheon+, we demonstrate improvement in Hubble residual scatter equivalent to 145 km s⁻¹, also consistent with simulations. For new and upcoming low-zsamples from, for example, the Zwicky Transient Facility and the Legacy Survey of Space and Time, a separate follow-up program identifying galaxy groups of SN hosts is a highly cost-effective way to enhance their constraining power. 

Abstract The Dark Energy Spectroscopic Instrument (DESI) collaboration measured a tight relation between the Hubble constant (H₀) and the distance to the Coma cluster using the fundamental plane (FP) relation of the deepest, most homogeneous sample of early-type galaxies. To determineH₀, we measure the distance to Coma by several independent routes, each with its own geometric reference. We measure the most precise distance to Coma from 13 Type Ia supernovae (SNe Ia) in the cluster with a mean standardized brightness of $m_B^0=15.710\pm 0.040$mag. Calibrating the absolute magnitude of SNe Ia with the Hubble Space Telescope (HST) distance ladder yieldsD_Coma = 98.5 ± 2.2 Mpc, consistent with its canonical value of 95–100 Mpc. This distance results inH₀ = 76.5 ± 2.2 km s⁻¹Mpc⁻¹from the DESI FP relation. Inverting the DESI relation by calibrating it instead to the Planck+ΛCDM value ofH₀ = 67.4 km s⁻¹Mpc⁻¹implies a much greater distance to Coma,D_Coma = 111.8 ± 1.8 Mpc, 4.6σbeyond a joint, direct measure. Independent of SNe Ia, the HST Key Project FP relation as calibrated by Cepheids, the tip of the red giant branch from JWST, or HST near-infrared surface brightness fluctuations all yieldD_Coma < 100 Mpc, in joint tension themselves with the Planck-calibrated route at >3σ. From a broad array of distance estimates compiled back to 1990, it is hard to see how Coma could be located as far as the Planck+ΛCDM expectation of >110 Mpc. By extending the Hubble diagram to Coma, a well-studied location in our own backyard whose distance was in good accord well before the Hubble tension, DESI indicates a more pervasive conflict between our knowledge of local distances and cosmological expectations. We expect future programs to refine the distance to Coma and nearer clusters to help illuminate this new local window on the Hubble tension. 

Abstract We present a detailed analysis of AT 2020nov, a tidal disruption event (TDE) in the center of its host galaxy, located at a redshift ofz= 0.083. AT 2020nov exhibits unique features, including double-peaked Balmer emission lines, a broad UV/optical flare, and a peak log luminosity in the extreme-ultraviolet (EUV) estimated at $\sim 45.66_{-0.33}^{+0.10}\mathrm{erg}{\mathrm{s}}^{-1}$. A late-time X-ray flare was also observed, reaching an absorbed luminosity of 1.67 × 10⁴³erg s⁻¹approximately 300 days after the UV/optical peak. Multiwavelength coverage, spanning optical, UV, X-ray, and mid-infrared (MIR) bands, reveals a complex spectral energy distribution (SED) that includes MIR flaring indicative of dust echoes, suggesting a dust covering fraction consistent with typical TDEs. Spectral modeling indicates the presence of an extended, quiescent disk around the central supermassive black hole with a radius of $\sim 5.06_{-0.77}^{+0.59}\times 10^4{R}_{\mathrm{g}}$. The multicomponent SED model, which includes a significant EUV component, suggests that the primary emission from the TDE is reprocessed by this extended disk, producing the observed optical and MIR features. The lack of strong active galactic nuclei signatures in the host galaxy, combined with the quiescent disk structure, highlights AT 2020nov as a rare example of a TDE occurring in a galaxy with a dormant but extended preexisting accretion structure. 

Abstract Luminous interacting supernovae (SNe) are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive multiwavelength study of PS1-11aop, a luminous and slowly declining Type IIn SNe discovered by the Pan-STARRS Medium Deep Survey. PS1-11aop had a peakr-band magnitude of −20.5 mag, a total radiated energy >8 × 10⁵⁰erg, and it exploded near the center of a star-forming galaxy with super-solar metallicity. We obtained multiple detections at the location of PS1-11aop in the radio and X-ray bands between 4 and 10 yr post-explosion, and if due to the supernova (SN), it is one of the most luminous radio SNe identified to date. Taken together, the multiwavelength properties of PS1-11aop are consistent with a CSM density profile with multiple zones. The early optical emission is consistent with the SN blastwave interacting with a dense and confined CSM shell, which contains multiple solar masses of material that was likely ejected in the final <10–100 yr prior to the explosion, (∼0.05−1.0M_⊙yr⁻¹at radii of ≲10¹⁶cm). The radio observations, on the other hand, are consistent with a sparser environment (≲2 × 10⁻³M_⊙yr⁻¹at radii of ∼0.5–1 × 10¹⁷cm)—thus probing the history of the progenitor star prior to its final mass-loss episode. 

Abstract Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer’s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10–20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer’s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer’s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59–0.91) and Alzheimer’s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer’s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment. 

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_⊙. 

Abstract We present Young Supernova Experimentgrizyphotometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates and investigating their contributions toward the SN Ia intrinsic scatter (post-standardization residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter,σ_Rel: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution toward the total intrinsic scatter,σ₀, from within-galaxy variations about the siblings’ common properties. It also affects the combined distance uncertainty. We present analytic formulae for computing aσ_Relposterior from individual siblings distances (estimated using any SN model). Applying a newly trainedBayeSNmodel, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wideσ_Relposterior meansσ_Rel≈σ₀is not ruled out. We thus combine the distances by marginalizing overσ_Relwith an informative prior:σ_Rel∼U(0,σ₀). Simultaneously fitting the trio’s light curves improves constraints on distanceandeach sibling’s individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore,σ_Relmarginalization yields robust estimates of siblings distances for cosmology, as well as dust parameters for sibling–host correlation studies. Incorporating NGC 3147's Cepheid distance yieldsH₀= 78.4 ± 6.5 km s⁻¹Mpc⁻¹. Our work motivates analyses of homogeneous siblings samples, to constrainσ_Reland its SN-model dependence.