Historical DIA light curves

In this notebook we will crossmatch alerts with the Data Preview 2 (DP2) DIA object data and visualize their forced source light curves.

1. Load the Rubin alert archive catalog and the DP2 DIA object collection.

2. Crossmatch the alerts against the DIA objects. Both catalogs carry forced photometry timeseries.

3. Combine prvDiaForcedSources (from each alert) with the matching diaObjectForcedSource.

4. Visualize resulting light curves with lsdb_rubin.plot_light_curve.

import lsdb
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from lsdb import ConeSearch

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

lsdb.show_versions()

--------      SYSTEM INFO      --------
python        : 3.13.9
python-bits   : 64
OS            : Linux
OS-release    : 6.12.85+
Version       : #1 SMP Mon May 11 07:53:24 UTC 2026
machine       : x86_64
processor     :
byteorder     : little
LC_ALL        :
LANG          :
--------   INSTALLED VERSIONS   --------
lsdb          : 0.10.4
hats          : 0.10.4
nested-pandas : 0.6.10
pandas        : 2.3.3
numpy         : 2.3.5
dask          : 2026.7.1
pyarrow       : 21.0.0
fsspec        : 2026.6.0

import logging

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

We need lsdb-rubin, a package that contains a suite of utilities for interacting with Rubin data within LSDB. Install it with:

%pip install -qq lsdb-rubin

Note: you may need to restart the kernel to use updated packages.

Restart the kernel to apply the changes before proceeding.

Load the catalogs

For demonstration purposes, we will select a small region in the sky defined by the following cone:

cone = ConeSearch(ra=52.838, dec=-28.279, radius_arcsec=3600)

Let’s load both catalogs. It’s important to specify the search region and the columns we need. This will keep the workflow light, especially in terms of memory usage.

dia_object = lsdb.open_catalog(
    "/rubin/lsdb_data/dia_object_collection",
    columns=["diaObjectId", "diaObjectForcedSource"],
    search_filter=cone,
)

alerts = lsdb.open_catalog(
    "https://data.lsdb.io/hats/rubin_alert_archive",
    columns=["diaSourceId", "prvDiaForcedSources"],
    search_filter=cone,
)
# Remove the alerts with no forced sources
alerts = alerts.query("prvDiaForcedSources.len() > 0")

Note: This version of the alert archive has data until July 14th and remains frozen for the time being. If you’re interested in a specific alert ID, add the followingfilters=[("diaSourceId", "=", <SOURCE_ID>)] to the alerts open_catalog call.

Crossmatch alerts to DIA objects

Next, we will match each alert to its nearest Rubin DP2 DIA object, within 1 arcsecond (the default). The columns we previously selected from both catalogs are carried into the result. ra and dec are suffixed with _alert and _dia since they overlap.

xmatched = alerts.crossmatch(dia_object, suffix_method="overlapping_columns", suffixes=("_alert", "_dia"))
xmatched

root WARNING: Renaming overlapping columns:
+--------+----------------------+---------------------+
| Column | Left (suffix=_alert) | Right (suffix=_dia) |
+--------+----------------------+---------------------+
|   ra   |       ra_alert       |       ra_dia        |
|  dec   |      dec_alert       |       dec_dia       |
+--------+----------------------+---------------------+

lsdb Catalog rubin_alert_archive_x_dia_object_lc:

	diaSourceId	prvDiaForcedSources	ra_alert	dec_alert	diaObjectId	diaObjectForcedSource	ra_dia	dec_dia	_dist_arcsec
npartitions=8
Order: 5, Pixel: 8982	int64[pyarrow]	nested<diaForcedSourceId: [int64], diaObjectId...	double[pyarrow]	double[pyarrow]	int64[pyarrow]	nested<parentObjectId: [int64], coord_ra: [dou...	double[pyarrow]	double[pyarrow]	double[pyarrow]
...	...	...	...	...	...	...	...	...	...
Order: 6, Pixel: 35956	...	...	...	...	...	...	...	...	...
Order: 6, Pixel: 35957	...	...	...	...	...	...	...	...	...

9 out of 9 available columns in the catalog have been loaded lazily, meaning no data has been read, only the catalog schema

We can preview a few matched rows with Catalog.head, which this triggers a small amount of work.

preview = xmatched.head(4)
preview

	diaSourceId	prvDiaForcedSources	ra_alert	dec_alert	diaObjectId	diaObjectForcedSource	ra_dia	dec_dia	_dist_arcsec
2528445453278702998	170076861202169877	diaForcedSourceId diaObjectId ... timeProcessedMjdTai timeWithdrawnMjdTai 170019696014786606 313941448535834682 ... 61088.098745 NaN +4 rows ... ... ... ...	52.513700	-29.196006	753893410443624456	parentObjectId coord_ra ... psfDiffFluxErr_corrected_flag psfMagErr_corrected 0 52.513698 ... False 0.116904 +247 rows ... ... ... ...	52.513698	-29.195963	0.156675
2528445453279367578	170076861352116269	diaForcedSourceId diaObjectId ... timeProcessedMjdTai timeWithdrawnMjdTai 170019696014786606 313941448535834682 ... 61088.098745 NaN +4 rows ... ... ... ...	52.513667	-29.195949	753893410443624456	parentObjectId coord_ra ... psfDiffFluxErr_corrected_flag psfMagErr_corrected 0 52.513698 ... False 0.116904 +247 rows ... ... ... ...	52.513698	-29.195963	0.108027
2528451695647244178	170107646072848410	diaForcedSourceId diaObjectId ... timeProcessedMjdTai timeWithdrawnMjdTai 170107646072848433 170103251918651434 ... 61108.030304 NaN	52.478567	-29.134791	753894097638392074	parentObjectId coord_ra ... psfDiffFluxErr_corrected_flag psfMagErr_corrected 0 52.478441 ... False 0.010115 +267 rows ... ... ... ...	52.478441	-29.134924	0.620235
2528452120531121260	170107645940203525	diaForcedSourceId diaObjectId ... timeProcessedMjdTai timeWithdrawnMjdTai 170107645940203607 170103252054441994 ... 61108.029763 NaN	52.460535	-29.109683	753894097638392330	parentObjectId coord_ra ... psfDiffFluxErr_corrected_flag psfMagErr_corrected 0 52.460514 ... False 0.009887 +256 rows ... ... ... ...	52.460514	-29.109665	0.093728

4 rows x 9 columns

Plot the light curves

Let’s visualize the matched light curves of our preview with lsdb_rubin.plot_light_curve. This utility method expects a flat table with band, midpointMjdTai, and a brightness field with a matching <field>Err column.

We are interested to plot psfMag over time but the alerts don’t ship prvDiaForcedSources with magnitudes. Let’s convert the fluxes to magnitudes and append them as new subcolumns of prvDiaForcedSources.

def add_prv_mag(flux, flux_err):
    """Convert flux (nJy) to AB magnitude (asymmetric error from flux ± fluxErr)"""
    flux = np.asarray(flux, dtype="float64")
    flux_err = np.asarray(flux_err, dtype="float64")
    mag = u.nJy.to(u.ABmag, flux)
    upper = u.nJy.to(u.ABmag, flux + flux_err)
    lower = u.nJy.to(u.ABmag, flux - flux_err)
    mag_err = -(upper - lower) / 2
    return {"prvDiaForcedSources.psfMag": mag, "prvDiaForcedSources.psfMagErr": mag_err}


def append_prv_mag(nf):
    """Append psfMag/psfMagErr onto the nested prvDiaForcedSources column"""
    return nf.map_rows(
        add_prv_mag,
        columns=["prvDiaForcedSources.psfFlux", "prvDiaForcedSources.psfFluxErr"],
        append_columns=True,
        row_container="args",
    )


preview = append_prv_mag(preview)

Now both nested light curves expose band, midpointMjdTai, psfMag, and psfMagErr. Let’s combine them and plot them.

def combined_light_curve(match, i):
    """Concatenate both nested light curves, keeping band, time, and magnitude"""
    prv_dia_forced_source = match["prvDiaForcedSources"].iloc[i]
    forced_source = match["diaObjectForcedSource"].iloc[i]
    return pd.concat([prv_dia_forced_source, forced_source], ignore_index=True)

from lsdb_rubin.plot_light_curve import plot_light_curve

filter_colors = {
    "u": "#1600ea",
    "g": "#49be61",
    "r": "#c61c00",
    "i": "#ffc200",
    "z": "#f341a2",
    "y": "#5d0000",
}

fig, axes = plt.subplots(2, 2, figsize=(14, 10), dpi=150)
axes = axes.flatten()

for i, ax in enumerate(axes):
    plt.sca(ax)
    plot_light_curve(
        combined_light_curve(preview, i),
        mag_field="psfMag",
        title=f"diaSourceId {preview['diaSourceId'].iloc[i]}",
        filter_colors=filter_colors,
        plot_kwargs={"capsize": 4},
    )

fig.tight_layout()
plt.show()

About

Authors: Sandro Campos, Konstantin Malanchev, Neven Caplar

Last run: July 27, 2026

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