EDP2 photo-z (RAIL)

In this tutorial, we will:

• access a photo-z catalog derived from Rubin’s Data Preview 2 using LSDB

1. Loading through LSDB

In order to access the catalog through LSDB, you must be a Rubin data rights holder, because the catalog includes sky coordinates from the original DP2 dataset. Please see Accessing Rubin Data Preview 2 (DP2) for the data access instructions.

import lsdb
import os
import matplotlib.pyplot as plt
from dask.distributed import Client
from upath import UPath

plt.rcParams.update(
    {
        "axes.titlesize": 16,
        "axes.labelsize": 15,
        "xtick.labelsize": 13,
        "ytick.labelsize": 13,
        "legend.fontsize": 13,
        "figure.titlesize": 18,
    }
)

import logging

logging.getLogger().setLevel(logging.WARNING)
logging.getLogger("distributed").setLevel(logging.WARNING)

# Replace with the actual path if not at RSP
base_path = UPath("/rubin/lsdb_data")
catalog_path = base_path / "object_photoz"
dp2_pz_catalog = lsdb.open_catalog(catalog_path)
dp2_pz_catalog

Plotting two estimators

As an example, let us plot the results from TPZ and kNN. We load just two columns and select COSMOS field by a cone search.

dp2_pz_catalog_small = lsdb.open_catalog(
    catalog_path,
    columns=["tpz_z_best", "knn_z_best"],
    search_filter=lsdb.ConeSearch(ra=150.11, dec=2.23, radius_arcsec=1.0 * 3600.0),
)

with Client(
    n_workers=2,
    threads_per_worker=1,
    memory_limit="8GB",
    local_directory=f"/deleted-sundays/{os.environ.get('USER', 'dask_scratch')}",
) as client:
    print(f"Dask dashboard: {client.dashboard_link}")
    display(client)
    df = dp2_pz_catalog_small.compute()

import numpy as np

zmax = max(0.5, float(np.nanpercentile(np.concatenate([df["tpz_z_best"], df["knn_z_best"]]), 99.0)))

fig, ax = plt.subplots(figsize=(8, 7.5))
hb = ax.hexbin(
    df["tpz_z_best"],
    df["knn_z_best"],
    gridsize=55,
    bins="log",
    extent=(0, zmax, 0, zmax),
    mincnt=1,
    cmap="viridis",
)
ax.plot([0, zmax], [0, zmax], "r--", lw=1)
cb = fig.colorbar(hb, ax=ax, pad=0.02)
cb.set_label("log$_{10}$ count")
ax.set_xlim(0, zmax)
ax.set_ylim(0, zmax)
ax.set_aspect("equal")
ax.set_xlabel("TPZ best $z$")
ax.set_ylabel("kNN best $z$")
ax.set_title("TPZ vs. kNN photo-$z$")

fig.tight_layout()
plt.show()

About

Authors: Sandro Campos, Sarah Pelesky, Tianqing Zhang, Konstantin Malanchev

Last run: July 27, 2026

If you use lsdb for published research, please cite following instructions.