Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

• URL: http://arxiv.org/abs/2602.15021v1
• Date: Mon, 16 Feb 2026 18:58:47 GMT
• Title: Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI
• Abstract summary: Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low to moderate-resolution surveys.<n>We investigate this problem using pre-trained models, focusing on simple neural networks such as multilayer perceptrons (MLPs)<n>Specifically, we pre-trains on either LRS or their embeddings and fine-tune them for application to DESI stellar spectra.<n>We find that pre-trained on LAMOST LRS achieve strong performance, even without fine-tuning, and that modest fine-tuning with DESI spectra further improves the results.
• Score: 33.83813864639123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to moderate-resolution surveys. We investigate this problem using pre-trained models, focusing on simple neural networks such as multilayer perceptrons (MLPs), with a case study transferring from LAMOST low-resolution spectra (LRS) to DESI medium-resolution spectra (MRS). Specifically, we pre-train MLPs on either LRS or their embeddings and fine-tune them for application to DESI stellar spectra. We compare MLPs trained directly on spectra with those trained on embeddings derived from transformer-based models (self-supervised foundation models pre-trained for multiple downstream tasks). We also evaluate different fine-tuning strategies, including residual-head adapters, LoRA, and full fine-tuning. We find that MLPs pre-trained on LAMOST LRS achieve strong performance, even without fine-tuning, and that modest fine-tuning with DESI spectra further improves the results. For iron abundance, embeddings from a transformer-based model yield advantages in the metal-rich ([Fe/H] > -1.0) regime, but underperform in the metal-poor regime compared to MLPs trained directly on LRS. We also show that the optimal fine-tuning strategy depends on the specific stellar parameter under consideration. These results highlight that simple pre-trained MLPs can provide competitive cross-survey generalization, while the role of spectral foundation models for cross-survey stellar parameter estimation requires further exploration.

Related papers

• From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy [0.6377541605829954]
  Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.<n> translating high-dimensional, highly collinear spectra into accurate soil property predictions remains challenging.<n>We investigated regression models and dimensionality reduction approaches for spectroscopic modeling across 85 regression tasks.
  arXiv  Detail & Related papers  (2026-08-01T11:56:50Z)
• Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs [52.89778838903305]
  Deliberate Evolution (DE) is an agentic framework that decouples symbolic generation from search control.<n>Experiments on LLM-SRBench show that DE consistently outperforms representative LLM-based SR baselines across diverse scientific domains.
  arXiv  Detail & Related papers  (2026-06-03T02:22:16Z)
• Tabular foundation models for robust calibration of near-infrared chemical sensing data [0.0]
  Near-infrared spectroscopy is increasingly used as a rapid, non-destructive chemical sensing technology.<n>Tablet foundation models can provide a new calibration strategy for NIR chemical sensing.<n>We benchmark TabPFN on 66 NIR datasets covering 54 regression and 12 classification tasks.
  arXiv  Detail & Related papers  (2026-05-20T09:48:18Z)
• Enhancing LLM Training via Spectral Clipping [28.270053200038586]
  SPECTRA is a framework for post-spectral clipping of updates to enforce spectral-norm constraints.<n>We show that SPECTRA uniformly improves loss for various validations, including AdamW, Signum, and AdEMAMix, with the best-performing variants achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2026-03-15T10:16:40Z)
• Data Distribution as a Lever for Guiding Optimizers Toward Superior Generalization in LLMs [60.68927774057402]
  We show, for the first time, that a lower simplicity bias induces a better generalization.<n>Motivated by this insight, we demonstrate that the training data distribution by upsampling or augmenting examples learned later in training similarly reduces SB and leads to improved generalization.<n>Our strategy improves the performance of multiple language models including Phi2-2.7B, Llama3.2-1B, Gemma3-1B-PT, Qwen3-0.6B-Base-achieving relative accuracy gains up to 18% when fine-tuned with AdamW and Muon.
  arXiv  Detail & Related papers  (2026-01-31T07:40:36Z)
• How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns [51.02752099869218]
  Large Language Models (LLMs) display strikingly different generalization behaviors.<n>We introduce a novel benchmark that decomposes reasoning into atomic core skills.<n>We show that RL-tuned models maintain more stable behavioral profiles and resist collapse in reasoning skills, whereas SFT models exhibit sharper drift and overfit to surface patterns.
  arXiv  Detail & Related papers  (2025-12-30T08:16:20Z)
• Self-supervised and Multi-fidelity Learning for Extended Predictive Soil Spectroscopy [2.8830677829565894]
  We propose a framework for multi-fidelity learning and extended predictive soil spectroscopy based on latent space embeddings.<n>A self-supervised representation was pretrained with the large MIR spectral library and the Vari Autoencoder algorithm.<n> Predictions from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models.
  arXiv  Detail & Related papers  (2025-11-20T01:36:33Z)
• Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squares [15.163541835643635]
  We show that Partial Least Squares (PLS) fails to recover any signal, despite detectability being possible in principle.<n>These findings clarify the theoretical limits of PLS and provide guidance for the design of reliable multi-modal inference methods in high dimensions.
  arXiv  Detail & Related papers  (2025-10-20T14:08:58Z)
• deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning [0.0]
  deep-REMAP is a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra.<n>We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra.<n>Deep-REMAP accurately recovers the effective temperature ($T_rmeff$), surface gravity ($log rmg$), and metallicity ([Fe/H]), achieving a precision of, for instance, approximately 75 K in $T
  arXiv  Detail & Related papers  (2025-10-10T13:20:06Z)
• Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations [17.832898905413877]
  Implicit Neural Representations (INRs) have achieved success in various computer vision tasks.<n>Due to the spectral bias of the vanilla multi-layer perceptrons (MLPs), existing methods focus on designings with sophisticated architectures.<n>We propose a practical Inductive Gradient Adjustment (IGA) method, which could purposefully improve the spectral bias.
  arXiv  Detail & Related papers  (2024-10-17T06:51:10Z)
• Scaling Laws for Predicting Downstream Performance in LLMs [75.28559015477137]
  This work focuses on the pre-training loss as a more computation-efficient metric for performance estimation.<n>We present FLP-M, a fundamental approach for performance prediction that addresses the practical need to integrate datasets from multiple sources during pre-training.
  arXiv  Detail & Related papers  (2024-10-11T04:57:48Z)
• Model Extrapolation Expedites Alignment [135.12769233630362]
  We propose a method called ExPO to expedite alignment training with human preferences.<n>We demonstrate that ExPO boosts a DPO model trained with only 20% steps to outperform the fully-trained one.<n>We show that ExPO notably improves existing open-source LLMs on the leading AlpacaEval 2.0 and MT-Bench benchmarks.
  arXiv  Detail & Related papers  (2024-04-25T17:39:50Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.