My ambitious attempt to build a Deep Generative Stellar Spectra Model using Gradient Origin Networks taught me more about failure than success—and that turned out to be exactly what I needed. After six months of wrestling with GALAH and APOGEE data, countless Python debugging sessions, and a growing appreciation for the complexity of stellar spectroscopy, I managed to collect and filter data for ~30,000 stars before realizing the project’s scope far exceeded my current abilities. But here’s what I discovered: in astronomy and machine learning, “failed” projects often provide the most valuable education.
This experience transformed my understanding of both the technical challenges in astronomical machine learning and the crucial role that setbacks play in scientific development. The project may not have achieved its original goals, but it built foundational expertise that continues to inform my research approach today.
The ambitious vision
Stellar spectroscopy represents one of astronomy’s most powerful tools for understanding our galaxy’s history. When we observe light from stars, we’re examining a cosmic fingerprint—each absorption line tells us about chemical composition, temperature, gravity, and motion. Modern surveys like GALAH and APOGEE collect millions of these stellar fingerprints, creating unprecedented opportunities for galactic archaeology: reconstructing how our Milky Way formed and evolved.
The scientific motivation was compelling. Traditional spectroscopic analysis methods struggle with the scale of modern data. Physics-based approaches using radiative transfer codes are computationally expensive, while machine learning methods typically focus on discriminative models that map spectra to stellar parameters. Generative models offered a different approach—they could potentially create new spectra, handle missing data, provide uncertainty quantification, and enable anomaly detection for unusual stellar types.
My project aimed to apply Gradient Origin Networks (GONs), a recently developed generative architecture that uses gradients as encodings and requires 50% fewer parameters than traditional autoencoders. The plan seemed straightforward: train GONs on high-quality stellar spectra from GALAH and APOGEE surveys, create a shared latent space for both optical and infrared observations, and demonstrate improved performance over existing methods.
What could go wrong?
Stellar spectra surveys
GALAH and APOGEE represent complementary masterpieces of modern observational astronomy, each tackling galactic archaeology from different angles. Understanding their strengths and limitations proved essential—and more complex than I initially anticipated. GALAH surveys the southern sky using the HERMES spectrograph on the Anglo-Australian Telescope, providing R~28,000 resolution spectra across four optical bands (471-789 nm). The latest Data Release 4 contains 917,588 unique stars with measurements for up to 32 chemical elements. The survey excels at analyzing dwarf stars in the local solar neighborhood, with 81% of targets within 2 kiloparsecs of Earth. APOGEE takes the complementary approach, using near-infrared spectroscopy (1.514-1.696 μm, R~22,500) to penetrate dust-obscured regions of the galaxy. This H-band coverage allows access to the galactic bulge and inner disk regions hidden from optical surveys. APOGEE Data Release 17 provides spectra for 657,000 stars, primarily red giants that can be observed at much greater distances than the dwarf stars GALAH typically targets. The surveys’ different wavelength regimes create both opportunities and challenges. GALAH’s optical coverage includes numerous iron-peak element lines and provides superior measurements of light elements like lithium and carbon. APOGEE’s infrared observations avoid most dust extinction but are limited to molecular features and specific atomic transitions available in the H-band. Cross-calibrating results between surveys remains an active research challenge, as systematic differences in wavelength coverage, resolution, and analysis pipelines create offsets that must be carefully corrected.
The Complexity of generative models
When I began researching generative approaches for stellar spectroscopy, the field was experiencing rapid development. Variational Autoencoders had shown success with SDSS galaxy spectra, achieving impressive dimensionality reduction and enabling synthetic spectra generation. Normalizing flows were beginning to demonstrate exact likelihood computation for stellar data, while GANs were proving effective for high-fidelity spectral synthesis.
Gradient Origin Networks represented an intriguing alternative. Introduced by Bond-Taylor and Willcocks, GONs eliminate the need for an encoder network by using gradients as encodings. The mathematical foundation is elegant: latent points initialize at zero, and the gradient of the log-likelihood with respect to these zero vectors becomes the latent representation. This approach promised parameter efficiency and faster convergence—crucial advantages for high-dimensional spectroscopic data.
However, I soon discovered that GONs had never been applied to astronomical data. The original papers focused on natural images and synthetic datasets. Adapting the architecture for spectroscopy would require addressing several fundamental challenges: incorporating physics-informed constraints, handling the multi-scale nature of spectral features (both continuum shape and narrow absorption lines), and dealing with complex noise properties in real observations. The technical complexity extended beyond model architecture. Stellar spectra contain thousands of correlated pixels with intricate physical relationships. Unlike natural images, where pixels represent independent measurements, spectral features arise from complex atmospheric physics governed by radiative transfer equations. Line profiles follow Voigt functions, continuum levels depend on temperature and surface gravity, and elemental abundances manifest through intricate combinations of transitions. Any successful generative model must respect these physical constraints while remaining computationally tractable.
Astronomical data in Python
The technical implementation proved more challenging than any literature review had prepared me for. Processing astronomical data efficiently requires navigating a complex ecosystem of specialized Python libraries, each with its own learning curve, performance characteristics, and subtle gotchas. Astropy, the cornerstone of Python astronomy, initially seemed straightforward until I encountered its memory management complexities. FITS files use memory mapping by default, creating persistent file handles that survive even after calling hdul.close(). Processing thousands of spectra sequentially led to resource exhaustion as memory-mapped objects accumulated. The solution required explicit cleanup: del hdul[0].data and gc.collect() calls throughout my processing pipeline. FITS file handling revealed numerous edge cases. Multi-extension files from GALAH mixed image and table data in ways that broke my initial assumptions about data structure. Header limitations truncated important metadata, requiring CONTINUE cards for long strings. Compressed FITS files couldn’t be memory-mapped effectively, creating performance trade-offs between storage space and access speed. Specutils for spectral analysis proved powerful but required understanding its evolving API. The transition from Specutils 1.x to 2.x changed how multi-dimensional flux arrays were handled, breaking legacy code without clear migration guidance. Arithmetic operations on spectra with different wavelength grids required explicit resampling—not automatic as I had assumed. Continuum normalization, seemingly straightforward, failed on spectra with complex shapes or strong emission features. The most frustrating challenges emerged from working with large datasets that exceeded available memory. NumPy’s broadcasting created enormous temporary arrays when I made seemingly innocent mistakes. Understanding when operations created views versus copies became crucial for memory optimization. Processing APOGEE’s 8,000+ pixel spectra for hundreds of thousands of stars pushed memory limits even on high-end workstations. Cross-matching catalogs from different surveys introduced additional complications. Different coordinate systems, epoch differences requiring proper motion corrections, and one-to-many matching scenarios created subtle bugs that only became apparent during validation. The naive O(N²) algorithms I initially implemented became prohibitively slow for large catalogs. Performance optimization required learning to profile code systematically, understand CPU cache effects, and implement efficient parallel processing strategies. The Python Global Interpreter Lock limited threading effectiveness for CPU-bound operations, requiring multiprocessing approaches that complicated error handling and debugging.
Learning curves and early-career realities
Perhaps the most humbling aspect of this project was confronting the gap between theoretical knowledge and practical implementation skills. Reading papers about machine learning for astronomy and actually implementing robust, scalable systems proved to be dramatically different challenges. Documentation gaps created constant friction. Official tutorials often assumed background knowledge I lacked, while online examples used outdated syntax or addressed different use cases. Version compatibility issues meant that working code could break unpredictably after routine library updates. The astronomy software ecosystem moves rapidly, with breaking changes occurring more frequently than in more mature scientific computing domains. Debugging complex machine learning pipelines required developing entirely new skill sets. Traditional debugging approaches broke down when dealing with high-dimensional latent spaces, subtle numerical precision issues, and training instabilities that only emerged after hours of computation. Memory errors were particularly challenging—out-of-memory failures often occurred deep in complex processing chains, making root cause identification difficult. The psychological challenges proved as significant as technical ones. Early-career researchers face enormous pressure to demonstrate rapid progress, while complex projects like this can show little visible progress for months. Imposter syndrome intensified when seemingly straightforward implementations revealed layer after layer of complexity. The temptation to abandon the project in favor of “easier” research grew stronger as technical challenges accumulated. Time management became a crucial skill I had to develop. Complex technical projects have nonlinear progress curves—initial setup and learning can consume weeks with little apparent advancement, while breakthroughs sometimes emerge suddenly after extended preparation. Learning to balance perfectionism with practical constraints required mentorship and experience I didn’t initially possess.
When complexity exceeds current capabilities
After six months of intensive work, I had successfully collected and filtered spectroscopic data for approximately 30,000 stars from both GALAH and APOGEE surveys. The dataset represented high-quality observations with reliable stellar parameters and chemical abundances, carefully vetted using quality flags and validation against known stellar clusters. However, the fundamental technical challenges of implementing GONs for stellar spectroscopy proved more complex than my current skill level could handle effectively. The project required expertise across multiple domains: deep learning architecture design, astronomical data analysis, high-performance computing, and software engineering. Each domain presented learning curves measured in months rather than weeks. The model architecture required physics-informed constraints that the original GON papers didn’t address. Stellar spectra must satisfy conservation laws, radiative transfer physics, and thermodynamic equilibrium conditions. Implementing these constraints while maintaining the elegant mathematical structure of GONs proved technically demanding beyond my current capabilities. Computational requirements exceeded available resources. Training generative models on high-resolution spectra with thousands of wavelength bins required GPU memory and compute time that pushed the limits of available hardware. Hyperparameter optimization for complex architectures becomes prohibitively expensive without significant computational infrastructure. The decision to step back from the original project goals wasn’t a failure—it was a strategic recognition of current limitations combined with a commitment to building necessary foundational skills. Rather than continuing to struggle with an overly ambitious project, I chose to extract maximum learning value from the work completed and apply those insights to more appropriately scoped follow-up projects.
The unexpected value of apparent setbacks
Research from Northwestern University provides compelling evidence that early-career scientists who experience setbacks often outperform their peers in the long term. Scientists who experienced early grant rejections published 16.1% “hit papers” (top 5% citations) compared to 13.3% for those who received early grants. The benefits persisted over 10 years, suggesting that early challenges build resilience and problem-solving skills that prove valuable throughout research careers. My “failed” stellar spectra project exemplifies this pattern. While I didn’t achieve the original goals, the experience provided invaluable education across multiple domains: Technical expertise emerged from wrestling with real-world implementation challenges. Understanding memory management in Python, optimizing I/O operations for large datasets, and debugging complex processing pipelines are transferable skills that benefit all subsequent research projects. Deep familiarity with astronomical data formats, survey characteristics, and analysis challenges provides a foundation for future work that no textbook could have provided. Research methodology skills developed through necessity. Learning to systematically profile code, validate results, and manage complex experimental workflows proves valuable regardless of specific research topics. Understanding when to persist with challenging problems versus when to pivot requires judgment that only comes from experience. Professional networks expanded through seeking help and collaboration. Reaching out to survey teams, posting questions on community forums, and engaging with more experienced researchers led to valuable mentorship relationships and collaborative opportunities that continue to benefit my career. The project also provided realistic perspective on research timelines and project scoping. Academic and industry environments both value researchers who can accurately estimate project complexity and resource requirements. Experience with overly ambitious projects teaches crucial lessons about balancing innovation with feasibility.
Approaching complex projects with hard-won wisdom
Looking back, several strategic approaches could have improved the project’s chances of success while still maintaining ambitious scientific goals:
- Incremental development would have provided earlier feedback and learning opportunities. Starting with simpler generative models (standard VAEs) on well-understood datasets before attempting novel architectures would have built necessary technical expertise while making steady progress. Each incremental step provides validation of the overall approach while building confidence and skills.
- Collaborative frameworks could have distributed the technical complexity across multiple team members with complementary expertise. Modern astronomical machine learning projects increasingly require interdisciplinary teams that combine domain expertise in astronomy, machine learning specialization, and software engineering skills. Solo projects face inherent limitations in addressing all necessary technical domains simultaneously.
- Hybrid approaches might have provided more achievable intermediate goals. Rather than implementing GONs from scratch, adapting existing successful architectures with incremental improvements could have provided publishable results while building toward more innovative approaches. The astronomy community values both methodological innovations and practical applications of existing techniques.
- Infrastructure planning should have preceded model development. Access to appropriate computational resources, efficient data storage systems, and collaborative development environments significantly impacts project feasibility. Understanding resource requirements early in the project planning phase enables more realistic timeline estimation and goal setting.
Setbacks into assets
The key to maximizing career value from challenging projects lies in systematic reflection, documentation, and strategic presentation of the learning experience. Rather than viewing the project as simply “failed,” I’ve worked to extract specific insights and skills that demonstrate professional growth and problem-solving capability.
- Technical competencies gained through this project include advanced Python programming for large-scale data processing, deep familiarity with astronomical surveys and data formats, experience with machine learning implementation challenges, and systematic debugging and optimization skills. These capabilities prove valuable across a wide range of research applications beyond the original project scope.
- Project management skills developed through necessity include realistic scope estimation, resource planning, timeline management, and strategic decision-making about when to persist versus pivot. Early-career researchers who demonstrate these meta-skills often advance more rapidly than those with purely technical expertise.
- Professional storytelling transforms challenging experiences into compelling narratives about growth, learning, and resilience. The ability to articulate lessons learned, demonstrate adaptability, and show strategic thinking about research approaches proves valuable in both academic and industry contexts.
The experience also highlighted the importance of community engagement and mentorship. Successful research careers depend on building professional networks, seeking appropriate guidance, and contributing to community knowledge through both positive and negative results.
Building Foundations for future
The technical expertise, methodological understanding, and professional skills developed through this challenging project provide a foundation for more successful future work. Current follow-up projects apply lessons learned to more appropriately scoped problems while building toward longer-term ambitious goals.
- Immediate applications include contributing to existing machine learning projects in astronomy where the technical skills and survey expertise prove directly valuable. Understanding the challenges of astronomical data processing enables more effective collaboration with research groups working on similar problems.
- Medium-term goals involve developing hybrid approaches that combine traditional physics-based methods with machine learning enhancements. Rather than replacing existing successful methods entirely, these approaches provide incremental improvements while building toward more revolutionary changes.
- Long-term research directions can revisit generative modeling approaches for astronomy as the field develops better theoretical foundations and computational resources. The technical challenges identified in this project represent genuine research problems that will likely be solved through community effort over multiple years.
The resilience and problem-solving skills developed through this experience prove valuable regardless of specific research directions. Science progresses through researchers who can tackle challenging problems, learn from setbacks, and adapt strategies based on experience.
The Iterative nature of discovery
This project reinforced fundamental truths about scientific research: discovery is inherently iterative, setbacks provide essential learning opportunities, and ambitious goals often require multiple attempts with increasingly sophisticated approaches. The apparent “failure” to achieve original project goals actually represents successful development of expertise that enables future work. The scientific method depends on both positive and negative results to advance our understanding. Projects that encounter significant technical challenges and document those limitations contribute as much to community knowledge as projects that achieve all original goals. The astronomy and machine learning communities benefit from honest reporting of implementation challenges, computational limitations, and methodological insights.Early-career researchers particularly benefit from challenging projects that build resilience, problem-solving skills, and realistic understanding of research complexity. The Northwestern University study provides compelling evidence that those who experience and persist through early setbacks often achieve greater long-term success than those who encounter only early successes.
The key insight is that research careers are marathons rather than sprints. Individual projects contribute to long-term development of expertise, professional networks, and scientific understanding. Projects that challenge current capabilities and reveal new learning opportunities often prove more valuable than those that confirm existing knowledge or apply familiar techniques to new problems. My attempt to build a Deep Generative Stellar Spectra Model taught me more about astronomy, machine learning, software engineering, and research methodology than any single course or successful project could have provided. The technical challenges, implementation difficulties, and ultimate recognition of current limitations all contributed to professional development that continues to inform my research approach. For other early-career researchers facing similar challenges with ambitious projects, remember that struggle is not a bug in the research process—it’s a feature. The skills, resilience, and deep understanding that emerge from wrestling with complex problems prove invaluable throughout scientific careers. Your “failed” projects may well become your most valuable learning experiences.
References
- https://arxiv.org/pdf/2007.02798
- https://www.sdss4.org/dr17/irspec/
- https://www.galah-survey.org/dr4/overview/