Kati8/ICLR_2023_2025 huggingface dataset:
authors: Ann-Kathrin Schuetz
authors: A.W.P. Poon
authors: Aobo Li
reviews: 
Comments: The paper introduces RESuM (Rare Event Surrogate Model), designed to optimize physics detector design, specifically for reducing background events in neutrinoless double-beta decay (NLDBD) detection. The authors frame the challenge as a Rare Event Design (RED) problem, where background events are so rare that traditional simulation methods become computationally prohibitive. RESuM employs a multi-fidelity approach, using a Conditional Neural Process (CNP) model to learn from limited data and a Gaussian Process (GP) model to efficiently explore the design space. The authors apply RESuM to the LEGEND experiment’s neutron moderator design and achieve an  efficient performance.
Strengths: 1. The model targets a critical challenge in physic.
2. The integration of CNP and MFGP is a novel approach for handling rare event problems.
3. The framework’s formulation makes it generalizable to other simulation-heavy domains.
Weaknesses: I do not find major concerns.
Minor issues:
1. The success of RESuM’s multi-fidelity modeling approach relies on access to both high-fidelity and low-fidelity simulation data. In situations where these data sources are unavailable or highly dissimilar, would the model's effectiveness be reduced?
2. It seems to me that the RESuM does not embed physics constraints or validation check to the model, thus it might be possible that RESuM would suggest some invalid designs. In practice, do we need to first define the valid range of parameters for RESuM? And thus for different application contexts, we need to modify the model accordingly?
3. Section 5 uses subsubsections without subsections.
4. Can RESuM scale to applications with larger or more complex detector designs? How to estimate the relationship between the dimension of design space and model performance? Also, can RESuM incorporate expert knowledge on the design space to make it more efficient?
Recommendation: 6
Reviewer confidence: 2
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper addresses a key question in physics: the matter-antimatter asymmetry in the universe, focusing on neutrinoless double-beta decay (NLDBD). The authors tackle the challenge of optimizing detector designs to minimize background event contamination, framing it as a Rare Event Design (RED) problem.
They introduce the Rare Event Surrogate Model (RESuM), which combines a pre-trained Conditional Neural Process with a Multi-Fidelity Gaussian Process to optimize detector designs. Applied to neutron moderator designs for the LEGEND NLDBD experiment, RESuM achieves a 66.5±3.5% reduction in neutron background while using only 3.3% of the computational resources of traditional methods. This innovative approach has broad implications for similar challenges in physical sciences.
Strengths: The authors did a great job narrating each step of the procedure and model description. However, since I am not in the field, I got a little lost in the setup of the Experiment section, which involves applications. The results are convincing, though.
Weaknesses: - It would be better if the authors provided more detailed explanations in the Experiment section.
- Several mathematical symbols are not in math mode.
Recommendation: 8
Reviewer confidence: 2
Soundness/correctness: 3
Clarity: 2
reviews: 
Comments: The paper presents a surrogate model for rare events in physics.  The model is a hybrid model combining a conditional neural process (CNP) model and a multi-fidelity Gaussian process (MFGP).  The CNP is pre-trained on both low-fidelity (LF) and high-fidelity (HF) simulation data.  Subsequently the CNP model is used to calculate additional design data (averaged CNP scores for LF and HF data).  The MFGP model is initially trained using HF, HF CNP and LF CNP data, and further tuned using Bayesian Optimization (BO) equipped with the integrated variance reduction acquisition function to minimize the total variance of the model.
Strengths: The RESuM model is well-motivated and the application in physics is quite interesting.  While I am not a statistician nor familiar with the CNP model, I am very familiar with GP models and BO design, and can see no obvious mistakes in the paper.  Moreover the results are thorough and I can see a clear application for this model outside of that considered here.
Weaknesses: It appears that the real goal of this paper is to optimise particle detector design.  As such, it strikes me that the surrogate model need only be accurate in those regions of design space that are "good" - ie minimise background noise.  However the RESuM model is designed to model the entire design space, including "bad" designs (eg it may request HF simulations for regions whose prior confidence bounds are such that we know whp will not be appropriate in practice).

As such perhaps a more efficient approach would be to replace the integrated variance reduction acquisition function used in the BO portion of the design with a more goal-oriented acquisition function like expected improvement (EI) or GP-UCB, which would naturally focus computational effort on optimizing the detector design (ie the real underlying goal) rather than modeling all possible detectors (essentially the first step in the current design procedure).

(Note I intend this more as a point for discussion rather than a major criticism - the current approach is valid, but perhaps the strong focus on modeling is obscuring the real goal).
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 4