Andrew Lizarraga
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Andrew Lizarraga has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Srinath Saikrishnan
Department of Computer Science UCLA 90095 Los Angeles CA
2 shared publications - Tuan Do
Department of Physics and Astronomy, UCLA, Los Angeles, CA 90095-1547, USA
2 shared publications - Vikram Seenivasan
Department of Physics and Astronomy UCLA 90095 Los Angeles CA
2 shared publications - Bernie Boscoe
Department of Computer Science Southern Oregon University 97520 Ashland OR
1 shared publication - Clement Truong1 shared publication
- Jonathan Soriano
Department of Physics and Astronomy UCLA 90095 Los Angeles CA
1 shared publication
Latest Publications
- Open Access
Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction
In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. LoRA is an established technique for large language models that adds adapter networks to adjust model weights and biases to efficiently fine-tune large base models without retraining. We train a base model using a photometric redshift ground truth dataset, which contains broad galaxy types but is less accurate. We then fine-tune using LoRA on a spectroscopic redshift ground truth dataset. These redshifts are more accurate but limited to bright galaxies and take orders of magnitude more time to obtain, so are less available for large surveys. Ideally, the combination of the two datasets would yield more accurate models that generalize well. The LoRA model performs better than a traditional transfer learning method, with $\sim2.5\times$ less bias and $\sim$2.2$\times$ less scatter. Retraining the model on a combined dataset yields a model that generalizes better than LoRA but at a cost of greater computation time. Our work shows that LoRA is useful for fine-tuning regression models in astrophysics by providing a middle ground between full retraining and no retraining. LoRA shows potential in allowing us to leverage existing pretrained astrophysical models, especially for data sparse tasks.Open AccessCombining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction
In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. LoRA is an established technique for large language models that adds adapter networks to adjust model weights and biases to efficiently fine-tune large base models without retraining. We train a base model using a photometric redshift ground truth dataset, which contains broad galaxy types but is less accurate. We then fine-tune using LoRA on a spectroscopic redshift ground truth dataset. These redshifts are more accurate but limited to bright galaxies and take orders of magnitude more time to obtain, so are less available for large surveys. Ideally, the combination of the two datasets would yield more accurate models that generalize well. The LoRA model performs better than a traditional transfer learning method, with $\sim2.5\times$ less bias and $\sim$2.2$\times$ less scatter. Retraining the model on a combined dataset yields a model that generalizes better than LoRA but at a cost of greater computation time. Our work shows that LoRA is useful for fine-tuning regression models in astrophysics by providing a middle ground between full retraining and no retraining. LoRA shows potential in allowing us to leverage existing pretrained astrophysical models, especially for data sparse tasks. - Open Access
Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology
Upcoming surveys will produce billions of galaxy images but comparatively few spectra, motivating models that learn cross-modal representations. We build a dataset of 134,533 galaxy images (HSC-PDR2) and spectra (DESI-DR1) and adapt a Multi-Modal Masked Autoencoder (MMAE) to embed both images and spectra in a shared representation. The MMAE is a transformer-based architecture, which we train by masking 75% of the data and reconstructing missing image and spectral tokens. We use this model to test three applications: spectral and image reconstruction from heavily masked data and redshift regression from images alone. It recovers key physical features, such as galaxy shapes, atomic emission line peaks, and broad continuum slopes, though it struggles with fine image details and line strengths. For redshift regression, the MMAE performs comparably or better than prior multi-modal models in terms of prediction scatter even when missing spectra in testing. These results highlight both the potential and limitations of masked autoencoders in astrophysics and motivate extensions to additional modalities, such as text, for foundation models.
Open AccessMulti-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology
Upcoming surveys will produce billions of galaxy images but comparatively few spectra, motivating models that learn cross-modal representations. We build a dataset of 134,533 galaxy images (HSC-PDR2) and spectra (DESI-DR1) and adapt a Multi-Modal Masked Autoencoder (MMAE) to embed both images and spectra in a shared representation. The MMAE is a transformer-based architecture, which we train by masking 75% of the data and reconstructing missing image and spectral tokens. We use this model to test three applications: spectral and image reconstruction from heavily masked data and redshift regression from images alone. It recovers key physical features, such as galaxy shapes, atomic emission line peaks, and broad continuum slopes, though it struggles with fine image details and line strengths. For redshift regression, the MMAE performs comparably or better than prior multi-modal models in terms of prediction scatter even when missing spectra in testing. These results highlight both the potential and limitations of masked autoencoders in astrophysics and motivate extensions to additional modalities, such as text, for foundation models.
- Open Access
Efficient Modular Learning through Naive LoRA Summation: Leveraging Orthogonality in High-Dimensional Models
Recent advances in large language models are driven by scale, while parameter-efficient fine-tuning (PEFT) enables updating only a small fraction of parameters. Low-Rank Adaptation (LoRA) stores parameter deltas as the product of two small matrices, which makes them natural building blocks that can be composed. Motivated by the superposition principle, we hypothesize that independently trained LoRA modules on disjoint domains are approximately orthogonal and can be combined by simple addition. Using GPT-2 Small (117M) with LoRA rank 4 and alpha=64, we train adapters for three QA domains (math, medicine, finance). In pairwise tests, adding Math+Medicine adapters improves perplexity by -9.10% relative to merged-data fine-tuning, while Math+Finance and Finance+Medicine change by +4.54% and +27.56%, respectively. Across combinations, the RMS cosine similarity between LoRA deltas correlates positively and approximately linearly with the change in perplexity. Naive summation requires no additional training, can be applied in seconds, and achieves performance comparable to models trained on merged data, while clarifying when interference appears in higher-order compositions.
Open AccessEfficient Modular Learning through Naive LoRA Summation: Leveraging Orthogonality in High-Dimensional Models
Recent advances in large language models are driven by scale, while parameter-efficient fine-tuning (PEFT) enables updating only a small fraction of parameters. Low-Rank Adaptation (LoRA) stores parameter deltas as the product of two small matrices, which makes them natural building blocks that can be composed. Motivated by the superposition principle, we hypothesize that independently trained LoRA modules on disjoint domains are approximately orthogonal and can be combined by simple addition. Using GPT-2 Small (117M) with LoRA rank 4 and alpha=64, we train adapters for three QA domains (math, medicine, finance). In pairwise tests, adding Math+Medicine adapters improves perplexity by -9.10% relative to merged-data fine-tuning, while Math+Finance and Finance+Medicine change by +4.54% and +27.56%, respectively. Across combinations, the RMS cosine similarity between LoRA deltas correlates positively and approximately linearly with the change in perplexity. Naive summation requires no additional training, can be applied in seconds, and achieves performance comparable to models trained on merged data, while clarifying when interference appears in higher-order compositions.