Chris Patrick writes that SLAC researchers have built a neural-network method for compressing large scientific datasets while preserving fine details that conventional compression erases. The approach uses wavelet analysis to separate data features by scale, then encodes each scale separately through a neural network, enabling 10- to 100-fold file size reductions and selective decompression of only the regions a researcher needs.
- Published in Nature Machine Intelligence (August 24, 2026)
- Motivated by upcoming LCLS upgrades that will generate nearly one terabyte of data per second
- Tested successfully on X-ray diffraction data, solar magnetic field measurements, and photographs
- Neural networks trained on Perlmutter at NERSC (Lawrence Berkeley National Lab)
- Co-developers include researchers from UC Davis and Carnegie Mellon University
- Published in Nature Machine Intelligence (August 24, 2026)
- Motivated by upcoming LCLS upgrades that will generate nearly one terabyte of data per second
- Tested successfully on X-ray diffraction data, solar magnetic field measurements, and photographs
- Neural networks trained on Perlmutter at NERSC (Lawrence Berkeley National Lab)
- Co-developers include researchers from UC Davis and Carnegie Mellon University
This paper describes a computational cognitive model of instrument operations at the Linac Coherent Light Source (LCLS), a leading scientific user facility.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
- The model simulates aspects of human cognition at multiple scales, ranging from seconds to hours, and among agents playing multiple roles.
- The model can predict impacts stemming from proposed changes to operational interfaces and workflows, and its code is open source.
- Example results demonstrate the model's potential in guiding modifications to improve operational efficiency and scientific output.
The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
Conclusions:
1. The model's primary focus is on the decision of what to measure when and for how long, made by the experiment manager in consultation with the team.
2. The model represents a rough approximation of the LCLS setting but produces sensible results that provide insights into human-in-the-loop instrument operations.
3. The model can help optimize scientific productivity at LCLS by enhancing aspects of the human-machine interface and cognitive factors.
4. Future work includes extending the model to capture more detailed measurements of individual and team behavior, inter- and intra-team communications, and learning at multiple scales.
