arXiv subjects

Eli Gendreau-Distler

Publications and source records attributed to Eli Gendreau-Distler.

4 recordsLinked to original sources

arXiv2026

JWST Spectroscopy of Type Ia Supernova 2025rbs from Maximum Light to the Nebular Phase

We present JWST observations of the Type Ia supernova (SN Ia) 2025rbs ($D=$14.5 Mpc) at +1, +23, and +84 days after B-band maximum, spanning peak light through a wavelength-dependent transition toward the nebular phase. Combined with ground-based optical and near-infrared (NIR) data, our panchromatic spectra (0.4-14 $\mu$m) include the first maximum-light mid-infrared (MIR) spectrum and the earliest MIR spectroscopic sequence of an SN Ia to date. At peak light, the MIR spectrum exhibits a continuum with permitted and forbidden features, including Si II, Ni II, and early-emerging [Ni III-IV] and [Ar II-III]. By +23 days the MIR is dominated by forbidden lines with a weak continuum, and by +84 days it is fully nebular, whereas the optical/NIR spectra remain transitional. The nebular spectrum reveals strongly stratified ejecta, with stable Ni concentrated at the lowest velocities, radioactive Co at intermediate velocities but absent within ~2000 km s$^{-1}$, and Ar occupying an outer shell. We detect small-scale substructure in [Ca IV] 3.21 $\mu$m with fractional amplitudes of a few percent and a characteristic velocity scale of ~800 km s$^{-1}$, which may reflect compositional structure, ionization variations, or both. Radiative-transfer calculations substantially underpredict these MIR Mg II features despite approximately reproducing the NIR Mg II 1.0927 $\mu$m line, suggesting that the relative strengths of these transitions are sensitive to the treatment of Mg ionization and excitation. These observations demonstrate that MIR spectroscopy beginning near maximum light simultaneously probes the emerging inner ejecta and rapidly fading outer burning products, providing new constraints for explosion and radiative-transfer models.

astro-ph.HE↗

arXiv2025

Automating High Energy Physics Data Analysis with LLM-Powered Agents

We present a proof-of-principle study demonstrating the use of large language model (LLM) agents to automate a representative high energy physics (HEP) analysis. Using the Higgs boson diphoton cross-section measurement as a case study with ATLAS Open Data, we design a hybrid system that combines an LLM-based supervisor-coder agent with the Snakemake workflow manager. In this architecture, the workflow manager enforces reproducibility and determinism, while the agent autonomously generates, executes, and iteratively corrects analysis code in response to user instructions. We define quantitative evaluation metrics including success rate, error distribution, costs per specific task, and average number of API calls, to assess agent performance across multi-stage workflows. To characterize variability across architectures, we benchmark a representative selection of state-of-the-art LLMs spanning the Gemini and GPT-5 series, the Claude family, and leading open-weight models. While the workflow manager ensures deterministic execution of all analysis steps, the final outputs still show stochastic variation. Although we set the temperature to zero, other sampling parameters (e.g., top-p, top-k) remained at their defaults, and some reasoning-oriented models internally adjust these settings. Consequently, the models do not produce fully deterministic results. This study establishes the first LLM-agent-driven automated data-analysis framework in HEP, enabling systematic benchmarking of model capabilities, stability, and limitations in real-world scientific computing environments. The baseline code used in this work is available at https://huggingface.co/HWresearch/LLM4HEP. This work was accepted as a poster at the Machine Learning and the Physical Sciences (ML4PS) workshop at NeurIPS 2025. The initial submission was made on August 30, 2025.

physics.data-an↗

arXiv2025

Transforming Simulation to Data Without Pairing

We explore a generative machine learning-based approach for estimating multi-dimensional probability density functions (PDFs) in a target sample using a statistically independent but related control sample - a common challenge in particle physics data analysis. The generative model must accurately reproduce individual observable distributions while preserving the correlations between them, based on the input multidimensional distribution from the control sample. Here we present a conditional normalizing flow model (CNF) based on a chain of bijectors which learns to transform unpaired simulation events to data events. We assess the performance of the CNF model in the context of LHC Higgs to diphoton analysis, where we use the CNF model to convert a Monte Carlo diphoton sample to one that models data. We show that the CNF model can accurately model complex data distributions and correlations. We also leverage the recently popularized Modified Differential Multiplier Method (MDMM) to improve the convergence of our model and assign physical meaning to usually arbitrary loss-function parameters.

physics.data-an↗

arXiv2025

SN 2023ixf in the Pinwheel Galaxy M101: From Shock Breakout to the Nebular Phase

We present photometric and spectroscopic observations of SN 2023ixf covering from day one to 442 days after explosion. SN 2023ixf reached a peak $V$-band absolute magnitude of $-18.2 \pm 0.07$, and light curves show that it is in the fast-decliner (IIL) subclass with a relatively short ``plateau'' phase (fewer than $\sim 70$ days). Early-time spectra of SN 2023ixf exhibit strong, very narrow emission lines from ionized circumstellar matter (CSM), possibly indicating a Type IIn classification. But these flash/shock-ionization emission features faded after the first week and the spectrum evolved in a manner similar to that of typical Type II SNe, unlike the case of most genuine SNe~IIn in which the ejecta interact with CSM for an extended period of time and develop intermediate-width emission lines. We compare observed spectra of SN 2023ixf with various model spectra to understand the physics behind SN 2023ixf. Our nebular spectra (between 200-400 d) match best with the model spectra from a 15 $\rm M_{\odot}$ progenitor which experienced enhanced mass loss a few years before explosion. A last-stage mass-loss rate of $\dot{M} = 0.01 \rm M_{\odot} yr^{-1}$ from the r1w6 model matches best with the early-time spectra, higher than $\dot{M} \approx 2.4 \times 10^{-3} \rm M_{\odot} yr^{-1}$ derived from the ionized H${\alpha}$ luminosity at 1.58 d. We also use SN 2023ixf as a distance indicator and fit the light curves to derive the Hubble constant by adding SN 2023ixf to the existing sample; we obtain H$_{0}=73.1^{+3.68}_{-3.50}$ km s$^{-1}$ Mpc$^{-1}$, consistent with the results from SNe~Ia and many other independent methods.

astro-ph.GA↗