New Science Opportunities Enabled by LCLS-II X-Ray Lasers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This document attempts to capture the most compelling new science opportunities that are enabled by the unique capabilities of the new LCLS-II X-ray laser facility, namely: soft and tender X-rays (0.25 to 5 keV) at high repetition rates (up to 1 MHz) and hard X-rays (up to 25 keV) at 120 Hz. Many compelling areas of science have been identified by the scientific community, through a series of workshops over the past several years, where LCLS-II offers the potential to significantly advance our understanding. This document is not intended to be comprehensive of all the science to be pursued at the future LCLS facility. In particular, it does not capture important ongoing science that will continue to exploit the existing capabilities of the present LCLS facility. Nevertheless, this document will help to establish a scientific foundation for the new facility (encompassing present LCLS capabilities and new LCLS-II capabilities), and will inform LCLS strategic planning and investments over the next 5-10 years. The emphasis of this document is on identifying broad scientific opportunities, elucidating their potential impact, and providing a first-order link between these opportunities and LCLS-II capabilities. Brief descriptions are provided for various experimental approaches to be used, and novel new approaches to be developed, along with select examples of required optics and instruments. However, this document is not intended to capture all the optics and instrumentation requirements. Similarly it does not provide a detailed plan for instrumentation design or for any associated research and development that might be required. Balancing the scientific opportunities and impact, with instrumentation needs, available resources, and infrastructure, will be part of the LCLS planning process which this document will help to inform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it