Clustering
Two-point correlation function ξ(s, μ) for DESI DR1 tracers
Sub-analysis page. Components are addressed with the
clustering/path prefix.
Overview¶
Clustering measures the Landy–Szalay ξ(s, μ) multipoles (ℓ = 0, 2, 4) on a linear 4 Mpc/h s-binning from 0 to 200 Mpc/h, NGC + SGC combined. Pre-recon ξ is computed from the raw catalogs; post-recon ξ uses the RecSym estimator with the shifted catalogs from reconstruction, sliced into tracer z-bins.
The binning, μ-binning, estimator, and ℓ range are all locked to the published RascalC covariance grid, which is why this stage carries only a single decision rather than a set of binning knobs.
Decision¶
The one free choice is whether to apply imaging-systematics weights. The same RascalC covariance null-tests this toggle at 0.27 σ, so it is left unlocked:
DECISION
BAO is insensitive to the imaging-systematics correction (largest
shift 0.27 sigma across tracers). This decision exposes that null
test. on uses the full DESI WEIGHT column
(WEIGHT_COMP * WEIGHT_SYS * WEIGHT_ZFAIL). off drops the WEIGHT_SYS
factor, keeping only completeness + redshift-failure corrections.
Outputs¶
The headline diagnostic is the stacked ξ multipoles figure across tracers:
Figure 1:Pre- and post-recon xi_ell(s) multipoles (ell = 0, 2, 4) plotted as s^2 * xi(s), one row per tracer so the BAO feature at s ~ 100 Mpc/h is visible. Post-recon monopole should show a sharper BAO peak.
The full per-tracer registry — one xi_pre_recon_<tracer> and one
xi_post_recon_<tracer> per tracer — is:
| Output | Type | Description |
|---|---|---|
| xi_pre_recon_bgs | data | Pre-recon xi(s, mu) for BGS (z=0.1-0.4). Monopole-only fit.python -u scripts/compute_xi.py --output {output} --tracer bgs --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_bgs | data | Post-recon xi(s, mu) for BGS (RecSym). BGS parent recon spans 0.1 <= z <= 0.4 (no sub-binning).python -u scripts/compute_xi.py --output {output} --tracer bgs --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_lrg1 | data | Pre-recon xi(s, mu) for LRG1. pycorr TwoPointCorrelationFunction (.npy),
GCcomb, s in [0, 200] Mpc/h at 4 Mpc/h bins, mu in [-1, 1] at 200 bins.
python -u scripts/compute_xi.py --output {output} --tracer lrg1 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_lrg1 | data | Post-recon xi(s, mu) for LRG1 (RecSym). Slices the LRG parent post-recon
catalog to 0.4 <= z <= 0.6.
python -u scripts/compute_xi.py --output {output} --tracer lrg1 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_lrg2 | data | Pre-recon xi(s, mu) for LRG2. Same format as xi_pre_recon_lrg1.python -u scripts/compute_xi.py --output {output} --tracer lrg2 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_lrg2 | data | Post-recon xi(s, mu) for LRG2 (RecSym). Slices LRG parent NPZ to 0.6 <= z <= 0.8.python -u scripts/compute_xi.py --output {output} --tracer lrg2 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_lrg3 | data | Pre-recon xi(s, mu) for LRG3 (z=0.8-1.1). Same format as xi_pre_recon_lrg1.python -u scripts/compute_xi.py --output {output} --tracer lrg3 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_lrg3 | data | Post-recon xi(s, mu) for LRG3 (RecSym). Slices LRG parent NPZ to 0.8 <= z <= 1.1.python -u scripts/compute_xi.py --output {output} --tracer lrg3 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_elg1 | data | Pre-recon xi(s, mu) for ELG1 (z=0.8-1.1). Monopole-only fit.python -u scripts/compute_xi.py --output {output} --tracer elg1 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_elg1 | data | Post-recon xi(s, mu) for ELG1 (RecSym). Slices ELG parent NPZ to 0.8 <= z <= 1.1.python -u scripts/compute_xi.py --output {output} --tracer elg1 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_elg2 | data | Pre-recon xi(s, mu) for ELG2 (z=1.1-1.6). Same format as xi_pre_recon_lrg1.python -u scripts/compute_xi.py --output {output} --tracer elg2 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_elg2 | data | Post-recon xi(s, mu) for ELG2 (RecSym). Slices ELG parent NPZ to 1.1 <= z <= 1.6.python -u scripts/compute_xi.py --output {output} --tracer elg2 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_lrg3_elg1 | data | Pre-recon xi(s, mu) for the LRG3+ELG1 combined tracer (0.8 < z < 1.1).python -u scripts/compute_xi.py --output {output} --tracer lrg3_elg1 --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_lrg3_elg1 | data | Post-recon xi(s, mu) for the LRG3+ELG1 combined tracer (RecSym).python -u scripts/compute_xi.py --output {output} --tracer lrg3_elg1 --imaging_weights {decisions.imaging_weights} |
| xi_pre_recon_qso | data | Pre-recon xi(s, mu) for QSO (z=0.8-2.1). Monopole-only fit.python -u scripts/compute_xi.py --output {output} --tracer qso --imaging_weights {decisions.imaging_weights} |
| xi_post_recon_qso | data | Post-recon xi(s, mu) for QSO (RecSym, sm30). QSO parent recon spans
0.8 <= z <= 2.1 (no sub-binning) with Sigma_sm = 30 h^-1 Mpc — matches
the sm30 RascalC cov.
python -u scripts/compute_xi.py --output {output} --tracer qso --imaging_weights {decisions.imaging_weights} |
| xi_multipoles_plot | figure | Pre- and post-recon xi_ell(s) multipoles (ell = 0, 2, 4) plotted as
s^2 * xi(s), one row per tracer so the BAO feature at s ~ 100 Mpc/h
is visible. Post-recon monopole should show a sharper BAO peak.
python -u scripts/plot_xi.py --output {output} |
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