arxiv: 2605.07566 · v1 · submitted 2026-05-08 · ✦ hep-ex

Recognition: no theorem link

NEXT Simulation Dataset for AI Summer School UC Irvine 2026

K. Mistry

Authors on Pith no claims yet

Pith reviewed 2026-05-11 03:11 UTC · model grok-4.3

classification ✦ hep-ex

keywords 0νββneutrinoless double beta decayNEXT detectorsimulation datasetAI trainingxenon gasbackground rejection

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The pith

The NEXT collaboration releases a dataset of simulated 0νββ signals and 214Bi backgrounds in high-pressure xenon gas for AI training.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This document announces the public release of simulated events from neutrinoless double beta decay and from the decay of bismuth-214 in xenon gas. The events are generated to match the expected signatures in the NEXT detector. The dataset is prepared as training material for the Neutrinoless Double Beta Decay AI Summer School at UC Irvine in June 2026. It allows participants to develop machine learning methods for separating rare signals from backgrounds without needing access to real experimental data.

Core claim

The paper establishes that a new public dataset of Monte Carlo simulated 0νββ and 214Bi events in high-pressure xenon gas has been created to closely replicate the detector response of the NEXT experiment, providing ready-to-use training samples for AI algorithms focused on background rejection in rare-event searches.

What carries the argument

The simulated dataset of 0νββ decay and 214Bi background events modeled in high-pressure xenon gas to match NEXT detector characteristics.

If this is right

Students at the summer school can train and test AI classifiers for signal-background separation on realistic examples.
The dataset enables standardized evaluation of machine learning performance in 0νββ searches before application to real data.
It lowers the barrier for exploring AI techniques in xenon-based rare-event experiments by providing accessible training material.
Models developed on this data could support improved background rejection strategies that tighten limits on neutrino mass.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the simulations prove faithful, AI models trained here could be fine-tuned on real NEXT data with limited additional calibration.
The public release format may encourage similar dataset contributions from other 0νββ experiments to advance community AI methods.
Extending the dataset with varied xenon pressures or additional background isotopes would test the generalization of any trained models.

Load-bearing premise

The Monte Carlo simulations must accurately reproduce the detector response, energy resolution, tracking, and background processes of the real NEXT experiment.

What would settle it

Direct comparison of the simulated energy spectra, event topologies, or rate distributions against data from actual NEXT calibration runs that reveals large systematic mismatches.

Figures

Figures reproduced from arXiv: 2605.07566 by K. Mistry.

**Figure 3.1.** Figure 3.1: The energy spectrum of signal and background events in this simulation. Each entry in this histogram is the summed energy of all hits across each unique event. 4 Using the Dataset The dataset is stored in Zenodo at the following link: https://doi.org/10.5281/zenodo.18927784 The dataset is stored in Zenodo in a signal (0nubb) and background (Bi) files that are split into 10 parts, which just split up the … view at source ↗

**Figure 3.2.** Figure 3.2: An example event for (left) signal and (right) background event. The colour scale represents the energy deposited in a given bin. Larger energy deposits (yellow colour) can be seen towards both ends of the signal event, while the background only shows one end with a larger energy deposit [PITH_FULL_IMAGE:figures/full_fig_p004_3_2.png] view at source ↗

read the original abstract

This document details the dataset release of simulated $0\nu\beta\beta$ and background events originating from the decay of $^{214}$Bi in high-pressure xenon gas, describing events similar to those produced in the NEXT detector. This release is part of the Neutrinoless Double Beta Decay ($0\nu\beta\beta$) AI Summer School held on June 20-21 2026 at the University of California, Irvine.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a short dataset release note for an AI summer school, with no new physics results or simulation methods.

read the letter

This paper is a brief announcement that simulated 0νββ and 214Bi background events in high-pressure xenon have been released for the UC Irvine AI Summer School in 2026. The events are described as similar to those expected in the NEXT detector. That is the entire contribution. It does not present any analysis, new modeling, or validation studies. The value, if any, is simply making some simulated data available for students to practice machine learning on particle physics problems. That can be genuinely helpful for training purposes in a workshop setting. The note is clear about its limited scope and does not overclaim. The main limitation is the lack of substance. There are no details on event counts, energy spectra, simulation parameters, detector response modeling, or how the data can be accessed and used. Without those, it is difficult to assess whether the dataset is realistic enough or large enough to be useful beyond the specific summer school. The abstract and text stay at the level of a factual description rather than providing evidence or methods that could be checked. This is not a research paper. It does not advance understanding of neutrinoless double beta decay or detector simulation techniques. It is closer to a data availability statement attached to an educational event. For most readers in the field, there is little to engage with beyond noting that the data exists. If the actual files turn out to be well-documented and accessible, the release could still serve its intended audience of students. But the document itself adds no technical insight. I would not send this to peer review. It lacks the depth or novelty required for a journal article, even as a short note. It might fit better as a simple data release on a repository or workshop proceedings if the data is properly shared.

Referee Report

0 major / 3 minor

Summary. The manuscript announces the release of a simulated dataset of 0νββ signal events and 214Bi background events generated in high-pressure xenon gas, with events described as similar to those expected in the NEXT detector. The release is positioned as educational material for the 0νββ AI Summer School at UC Irvine scheduled for June 20-21 2026.

Significance. Open release of simulation datasets for AI/ML training in rare-event searches is a useful contribution to the field, as it can lower barriers for students and early-career researchers working on background discrimination techniques relevant to next-generation 0νββ experiments. The educational framing adds modest value, though the overall impact remains limited by the absence of quantitative validation or usage documentation.

minor comments (3)

The abstract and text provide no information on dataset size, file formats, energy ranges, or access method (e.g., repository URL or DOI). Adding a short table or dedicated section summarizing these practical details would improve usability for the intended summer-school audience.
No description is given of the Monte Carlo framework, gas parameters (pressure, drift field), or detector response model used to generate the events. Even a brief statement citing the relevant NEXT simulation references would clarify the scope of the 'similar to NEXT' claim.
The manuscript contains no references to prior NEXT detector or simulation papers. Including 1-2 key citations would help place the dataset in context for readers unfamiliar with the experiment.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for recommending minor revision. We address the noted limitation regarding documentation below.

read point-by-point responses

Referee: the overall impact remains limited by the absence of quantitative validation or usage documentation.

Authors: We agree that usage documentation would increase the dataset's value for the intended educational audience. In the revised manuscript we will add a dedicated usage section that includes download instructions, file formats, example Python code for loading events, and basic summary statistics (e.g., energy spectra and topology distributions) that allow users to verify the signal and background samples. Because the paper is a data-release note rather than a physics analysis, we do not provide full detector-response validation; the added statistics are intended only to help summer-school participants begin working with the data. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset release note with no derivations

full rationale

This manuscript is a straightforward dataset release announcement for simulated 0νββ and 214Bi background events in high-pressure xenon gas, intended for an AI summer school. It contains no equations, no predictions, no fitted parameters, no modeling derivations, and no self-citations of load-bearing results. The central claim is simply that the dataset is being made available and that the events are described as similar to those in the NEXT detector; this is a descriptive statement of purpose with no internal reasoning chain that could reduce to its own inputs by construction. The document is self-contained against external benchmarks as a data availability note.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced because the document is a dataset release description rather than a theoretical or experimental derivation.

pith-pipeline@v0.9.0 · 5347 in / 1046 out tokens · 34385 ms · 2026-05-11T03:11:18.182884+00:00 · methodology

discussion (0)

Reference graph

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