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Abstract We demonstrate that any Euclidean-time quantum mechanical theory may be represented as a neural network (NN), ensured by the Kosambi–Karhunen–Loève theorem, mean-square path continuity, and finite two-point functions. The additional constraint of reflection positivity, which is related to unitarity, may be achieved by a number of mechanisms, such as imposing NN parameter space splitting or the Markov property. Non-differentiability of the networks is related to the appearance of non-trivial commutators. Neural networks acting on Markov processes are no longer Markov, but still reflection positive, which facilitates the definition of deep NN quantum systems. We illustrate these principles in several examples using numerical implementations, recovering classic quantum mechanical results such as Heisenberg uncertainty, non-trivial commutators, and the spectrum. 

Abstract We present a comprehensive optical and near-infrared (NIR) spectroscopic study of SN 2024afav, a hydrogen-poor superluminous supernova (SLSN-I) that peaks at ≈−20.7 mag and exhibits an unusual multibumped light curve. Our spectroscopic observations, spanning phases of −14 to +160 days, reveal several unusual features: (i) a narrow (1800 km s⁻¹) and blueshifted (11,000 km s⁻¹) absorption from Hαstarting at +20 days; (ii) persistent optical and NIR Heilines at all available phases, showing double absorption structure in NIR spectra at +23 days, with a high-velocity component at a similar velocity to Hα; (iii) early appearance of nebular [Oiii] emission starting at ≈+50 days; and (iv) a strong [Oii] + [Caii] 7300 Å emission complex starting at ≈+110 days. These unusual features, and their onset at the time of the light-curve bumps, provide compelling evidence of circumstellar interaction between the SN ejecta and a nearby hydrogen-rich shell, as well as the presence of helium in both the outer layers of the progenitor star and the circumstellar medium. A comparison of SN 2024afav to other SLSNe-I showing bumpy light curves and similar spectral properties (PTF 10hgi, SN 2017egm, SN 2019hge) points to a rare subgroup of SLSNe-I in which circumstellar medium interaction provides an important modulation to the energy input. 

Abstract Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observedz = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ²value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. 

A<sc>bstract</sc> The Energy Mover’s Distance (EMD) has seen use in collider physics as a metric between events and as a geometric method of defining infrared and collinear safe observables. Recently, the Spectral Energy Mover’s Distance (SEMD) has been proposed as a more analytically tractable alternative to the EMD. In this work, we obtain a closed-form expression for the Riemannian-likep= 2 SEMD metric between events, eliminating the need to numerically solve an optimal transport problem. Additionally, we show how the SEMD can be used to define event and jet shape observables by minimizing the distance between events and parameterized energy flows (similar to the EMD), and we obtain closed-form expressions for several of these observables. We also present the Specter framework, an efficient and highly parallelized implementation of the SEMD metric and SEMD-derived shape observables as an analogue of the previously-introduced Shaper for EMD-based computations. We demonstrate that computing the SEMD with Specter can be up to a thousand times faster than computing the EMD with standard optimal transport libraries. 

A<sc>bstract</sc> In mathematics or theoretical physics one is often interested in obtaining an exact analytic description of some data which can be produced, in principle, to arbitrary accuracy. For example, one might like to know the exact analytical form of a definite integral. Such problems are not well-suited to numerical symbolic regression, since typical numerical methods lead only to approximations. However, if one has some sense of the function space in which the analytic result should lie, it is possible to deduce the exact answer by judiciously sampling the data at a sufficient number of points with sufficient precision. We demonstrate how this can be done for the computation of Feynman integrals. We show that by combining high-precision numerical integration with analytic knowledge of the function space one can often deduce the exact answer using lattice reduction. A number of examples are given as well as an exploration of the trade-offs between number of datapoints, number of functional predicates, precision of the data, and compute. This method provides a bottom-up approach that neatly complements the top-down Landau-bootstrap approach of trying to constrain the exact answer using the analytic structure alone. Although we focus on the application to Feynman integrals, the techniques presented here are more general and could apply to a wide range of problems where an exact answer is needed and the function space is sufficiently well understood. 

Abstract Current and upcoming cosmological surveys will produce unprecedented amounts of high-dimensional data, which require complex high-fidelity forward simulations to accurately model both physical processes and systematic effects which describe the data generation process. However, validating whether our theoretical models accurately describe the observed datasets remains a fundamental challenge. An additional complexity to this task comes from choosing appropriate representations of the data which retain all the relevant cosmological information, while reducing the dimensionality of the original dataset. In this work we present a novel framework combining scale-dependent neural summary statistics with normalizing flows to detect model misspecification in cosmological simulations through Bayesian evidence estimation. By conditioning our neural network models for data compression and evidence estimation on the smoothing scale, we systematically identify where theoretical models break down in a data-driven manner. We demonstrate a first application of our approach using simulated total matter and gas density fields from three hydrodynamic simulation suites with different subgrid physics implementations. 

Abstract Hydrogen-rich supernovae (SNe) span a range of hydrogen envelope masses at core collapse, producing diverse light curves from extended plateaus in Type IIP SNe to double-peaked Type IIb SNe (SNe IIb). Recent simulations predict a continuous sequence of light-curve morphologies as hydrogen is removed, with short-plateau (SP; plateau durations ≈50–70 days) SNe emerging as a transitional class. However, the observational boundary between types IIb and SP remains poorly defined, and thus far unobserved. We report on extensive photometric and spectroscopic follow-up of SN 2023wdd and SN 2022acrv, two candidate transitional events on the low-mass end of the SP class. Both exhibit weak, double-peaked light curves, which we interpret as exceptionally short plateaus (10–20 days), and hybrid spectral features: persistent Hαabsorption with HeIcontamination, but without the helium dominance characteristic of SNe IIb. Using analytic shock-cooling models and numerical light-curve fitting, we estimate H-rich envelope masses of ∼0.6–0.8M_⊙—significantly larger than canonical IIb values (≲0.1M_⊙) but consistent with the ∼0.9M_⊙threshold predicted for short-plateau behavior. Although the progenitor radii inferred from analytic and numerical methods differ by factors of 2–5, envelope mass estimates are consistent across approaches. Comparisons to well-studied Type IIb (SN 2016gkg, SN 2022hnt), SP (SN 2023ufx, SN 2006ai, SN 2016egz, SN 2006Y), and Type II (SN 2023ixf, SN 2013ej) SNe suggests a monotonic relationship between hydrogen envelope mass and plateau length, consistent with analytic and numerical expectations. These findings provide additional evidence for a continuous distribution of envelope stripping in H-rich core-collapse progenitors, and place SN 2023wdd and SN 2022acrv along the IIb–SP boundary. 

A<sc>bstract</sc> We use the embedding formalism to construct conformal fields inDdimensions, by restricting Lorentz-invariant ensembles of homogeneous neural networks in (D+ 2) dimensions to the projective null cone. Conformal correlators may be computed using the parameter space description of the neural network. Exact four-point correlators are computed in a number of examples, and we perform a 4D conformal block decomposition that elucidates the spectrum. In a non-unitary example the decomposition precisely matches OPE coefficients for the self-correlator, but not for the mixed correlator. In others, the analysis is facilitated by recent approaches to Feynman integrals. Generalized free CFTs are constructed using the infinite-width Gaussian process limit of the neural network, enabling a realization of the free boson. The extension to deep networks constructs conformal fields at each subsequent layer, with recursion relations relating their conformal dimensions and four-point functions. Numerical approaches are discussed. 

Abstract We presentFrankenBlast, a customized and improved version of theBlastweb application.FrankenBlastassociates transients to their host galaxies, performs host photometry, and runs a innovative spectral energy distribution fitting code to constrain host stellar population properties—all within minutes per object. We testFrankenBlaston 14,432 supernovae (SNe), ≈half of which are spectroscopically classified, and are able to constrain host properties for 9262 events. When contrasting the host stellar masses (M_*), specific star formation rates (sSFR), and host dust extinction (A_V) between spectroscopically and photometrically classified SNe Ia, Ib/c, II, and IIn, we determine that deviations in these distributions are primarily due to misclassified events contaminating the photometrically classified sample. We further show that the higher redshifts of the photometrically classified sample also force theirM_*and sSFR distributions to deviate from those of the spectroscopically classified sample, as these properties are redshift-dependent. We compare host properties between spectroscopically classified SN populations and determine if they primarily traceM_*or SFR. We find that all SN populations seem to both depend onM_*and SFR, with SNe II and IIn somewhat more SFR-dependent than SNe Ia and Ib/c, and SNe Ia moreM_*-dependent than all other classes. We find the difference in the SNe Ib/c and II hosts to be the most intriguing and speculate that SNe Ib/c must be more dependent on higherM_*and more evolved environments for the right conditions for progenitor formation. All data products andFrankenBlastare publicly available, along with a developingFrankenBlastversion intended for Rubin Observatory science products. 

Abstract We present the Online Ranked Astrophysical CLass Estimator (ORACLE), the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with gated recurrent units, and has been trained using a custom hierarchical cross-entropy loss function to provide high-confidence classifications along an observationally driven taxonomy with as little as a single photometric observation. Contextual information for each object, including host galaxy photometric redshift, offset, ellipticity, and brightness, is concatenated to the light-curve embedding and used to make a final prediction. Training on ∼0.5M events from the Extended LSST Astronomical Time-series Classification Challenge, we achieve a top-level (transient versus variable) macroaveraged precision of 0.96 using only 1 day of photometric observations after the first detection in addition to contextual information, for each event; this increases to >0.99 once 64 days of the light curve has been obtained, and 0.83 at 1024 days after first detection for 19-way classification (including supernova subtypes, active galactic nuclei, variable stars, microlensing events, and kilonovae). We also compare ORACLE with other state-of-the-art classifiers and report comparable performance for the 19-way classification task, in addition to delivering accurate top-level classifications much earlier. The code and model weights used in this work are publicly available at our associated GitHub repository (https://github.com/uiucsn/Astro-ORACLE).