Constraining primordial non-Gaussianity from DESI DR1 quasars and Planck PR4 CMB Lensing
Bibliographic record
Abstract
We present the first measurement of local-type primordial non-Gaussianity from the cross-correlation between $1.2$ million spectroscopically confirmed quasars from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI) and the Planck PR4 CMB lensing reconstructions. The analysis is performed in three tomographic redshift bins covering $0.8 < z < 3.5$, covering a sky fraction of $\sim 20\%$. We adopt a catalog-based pseudo-$C_\ell$ estimator and apply linear imaging weights validated on noiseless mocks. Compared to previous analyses using photometric quasar samples, our results benefit from the high purity of the DESI spectroscopic sample, the reduced noise of PR4 lensing, and the absence of excess large-scale power in the spectroscopic quasar auto-correlation. Fitting simultaneously for the non-Gaussianity parameter $f_{\mathrm{NL}}$ and the linear bias amplitude in each redshift bin, we obtain $f_{\mathrm{NL}} = 2^{+28}_{-34}$ for a response parameter $p=1.6$, and $f_{\mathrm{NL}} = 6^{+20}_{-24}$ for $p=1.0$. These results improve the constraints on $f_{\mathrm{NL}}$ by $\sim 35\%$ compared to the previous analysis based on the Legacy Imaging Survey DR9. Additionally, we derive an optimal weighting scheme to maximize the constraining power. In this case, and assuming $p=1.6$, we obtain $f_\mathrm{NL}=19^{+25}_{-31}$. Our results demonstrate the statistical power of DESI quasars for probing inflationary physics, and highlight the promise of future DESI data releases.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".