Note

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Light curves#

Compute per-observation and nightly fluxes of four Crab nebula observations.

Prerequisites#

  • Knowledge of the high level interface to perform data reduction, see the High level interface tutorial.

Context#

This tutorial presents how light curve extraction is performed in gammapy, i.e. how to measure the flux of a source in different time bins.

Cherenkov telescopes usually work with observing runs and distribute data according to this basic time interval. A typical use case is to look for variability of a source on various time bins: observation run-wise binning, nightly, weekly etc.

Objective: The Crab nebula is not known to be variable at TeV energies, so we expect constant brightness within statistical and systematic errors. Compute per-observation and nightly fluxes of the four Crab nebula observations from the H.E.S.S. first public test data release.

Proposed approach#

We will demonstrate how to compute a light curve from 3D reduced datasets (MapDataset) as well as 1D ON-OFF spectral datasets (SpectrumDatasetOnOff).

The data reduction will be performed with the high level interface for the data reduction. Then we will use the LightCurveEstimator class, which is able to extract a light curve independently of the dataset type.

Setup#

As usual, we’ll start with some general imports…

import logging
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.time import Time

# %matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import display
from gammapy.analysis import Analysis, AnalysisConfig
from gammapy.estimators import LightCurveEstimator
from gammapy.modeling.models import (
    Models,
    PointSpatialModel,
    PowerLawSpectralModel,
    SkyModel,
)
from gammapy.utils.check import check_tutorials_setup

log = logging.getLogger(__name__)

Check setup#

check_tutorials_setup()

System:

        python_executable      : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/bin/python
        python_version         : 3.9.20
        machine                : x86_64
        system                 : Linux


Gammapy package:

        version                : 1.3
        path                   : /home/runner/work/gammapy-docs/gammapy-docs/gammapy/.tox/build_docs/lib/python3.9/site-packages/gammapy


Other packages:

        numpy                  : 1.26.4
        scipy                  : 1.13.1
        astropy                : 5.2.2
        regions                : 0.8
        click                  : 8.1.7
        yaml                   : 6.0.2
        IPython                : 8.18.1
        jupyterlab             : not installed
        matplotlib             : 3.9.2
        pandas                 : not installed
        healpy                 : 1.17.3
        iminuit                : 2.30.1
        sherpa                 : 4.16.1
        naima                  : 0.10.0
        emcee                  : 3.1.6
        corner                 : 2.2.3
        ray                    : 2.39.0


Gammapy environment variables:

        GAMMAPY_DATA           : /home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/1.3

Analysis configuration#

For the 1D and 3D extraction, we will use the same CrabNebula configuration than in the High level interface tutorial using the high level interface of Gammapy.

From the high level interface, the data reduction for those observations is performed as follows.

Building the 3D analysis configuration#

conf_3d = AnalysisConfig()

Definition of the data selection#

Here we use the Crab runs from the H.E.S.S. DL3 data release 1.

conf_3d.observations.obs_ids = [23523, 23526, 23559, 23592]

Definition of the dataset geometry#

# We want a 3D analysis
conf_3d.datasets.type = "3d"

# We want to extract the data by observation and therefore to not stack them
conf_3d.datasets.stack = False

# Here is the WCS geometry of the Maps
conf_3d.datasets.geom.wcs.skydir = dict(
    frame="icrs", lon=83.63308 * u.deg, lat=22.01450 * u.deg
)
conf_3d.datasets.geom.wcs.binsize = 0.02 * u.deg
conf_3d.datasets.geom.wcs.width = dict(width=1 * u.deg, height=1 * u.deg)

# We define a value for the IRF Maps binsize
conf_3d.datasets.geom.wcs.binsize_irf = 0.2 * u.deg

# Define energy binning for the Maps
conf_3d.datasets.geom.axes.energy = dict(min=0.7 * u.TeV, max=10 * u.TeV, nbins=5)
conf_3d.datasets.geom.axes.energy_true = dict(min=0.3 * u.TeV, max=20 * u.TeV, nbins=20)

Run the 3D data reduction#

analysis_3d = Analysis(conf_3d)
analysis_3d.get_observations()
analysis_3d.get_datasets()

Define the model to be used#

Here we don’t try to fit the model parameters to the whole dataset, but we use predefined values instead.

target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")
spatial_model = PointSpatialModel(
    lon_0=target_position.ra, lat_0=target_position.dec, frame="icrs"
)

spectral_model = PowerLawSpectralModel(
    index=2.702,
    amplitude=4.712e-11 * u.Unit("1 / (cm2 s TeV)"),
    reference=1 * u.TeV,
)

sky_model = SkyModel(
    spatial_model=spatial_model, spectral_model=spectral_model, name="crab"
)

We assign them the model to be fitted to each dataset

models = Models([sky_model])
analysis_3d.set_models(models)

Light Curve estimation by observation#

We can now create the light curve estimator.

We pass it the list of datasets and the name of the model component for which we want to build the light curve. In a given time bin, the only free parameter of the source is its normalization. We can optionally ask for parameters of other model components to be reoptimized during fit, that is most of the time to fit background normalization in each time bin.

If we don’t set any time interval, the LightCurveEstimator determines the flux of each dataset and places it at the corresponding time in the light curve. Here one dataset equals to one observing run.

lc_maker_3d = LightCurveEstimator(
    energy_edges=[1, 10] * u.TeV, source="crab", reoptimize=False
)
# Example showing how to change some parameters from the object itself
lc_maker_3d.n_sigma_ul = 3  # Number of sigma to use for upper limit computation
lc_maker_3d.selection_optional = (
    "all"  # Add the computation of upper limits and likelihood profile
)
lc_3d = lc_maker_3d.run(analysis_3d.datasets)

The lightcurve FluxPoints object lc_3d contains a table which we can explore.

# Example showing how to change just before plotting the threshold on the signal significance
# (points vs upper limits), even if this has no effect with this data set.
fig, ax = plt.subplots(
    figsize=(8, 6),
    gridspec_kw={"left": 0.16, "bottom": 0.2, "top": 0.98, "right": 0.98},
)
lc_3d.sqrt_ts_threshold_ul = 5
lc_3d.plot(ax=ax, axis_name="time")
plt.show()

table = lc_3d.to_table(format="lightcurve", sed_type="flux")
display(table["time_min", "time_max", "e_min", "e_max", "flux", "flux_err"])
light curve
     time_min           time_max     ...        flux_err
                                     ...      1 / (cm2 s)
------------------ ----------------- ... ----------------------
 53343.92234009259 53343.94186555556 ... 2.1162290917256776e-12
 53343.95421509259 53343.97369425926 ... 2.1376407570489014e-12
  53345.9619812963 53345.98149518519 ...  2.787169465401243e-12
53347.913196574074 53347.93271046296 ...  2.911552434768802e-12

Running the light curve extraction in 1D#

Building the 1D analysis configuration#

conf_1d = AnalysisConfig()

Definition of the data selection#

Here we use the Crab runs from the H.E.S.S. DL3 data release 1

conf_1d.observations.obs_ids = [23523, 23526, 23559, 23592]

Definition of the dataset geometry#

# We want a 1D analysis
conf_1d.datasets.type = "1d"

# We want to extract the data by observation and therefore to not stack them
conf_1d.datasets.stack = False

# Here we define the ON region and make sure that PSF leakage is corrected
conf_1d.datasets.on_region = dict(
    frame="icrs",
    lon=83.63308 * u.deg,
    lat=22.01450 * u.deg,
    radius=0.1 * u.deg,
)
conf_1d.datasets.containment_correction = True

# Finally we define the energy binning for the spectra
conf_1d.datasets.geom.axes.energy = dict(min=0.7 * u.TeV, max=10 * u.TeV, nbins=5)
conf_1d.datasets.geom.axes.energy_true = dict(min=0.3 * u.TeV, max=20 * u.TeV, nbins=20)

Run the 1D data reduction#

analysis_1d = Analysis(conf_1d)
analysis_1d.get_observations()
analysis_1d.get_datasets()

Define the model to be used#

Here we don’t try to fit the model parameters to the whole dataset, but we use predefined values instead.

target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")

spectral_model = PowerLawSpectralModel(
    index=2.702,
    amplitude=4.712e-11 * u.Unit("1 / (cm2 s TeV)"),
    reference=1 * u.TeV,
)

sky_model = SkyModel(spectral_model=spectral_model, name="crab")

We assign the model to be fitted to each dataset. We can use the same SkyModel as before.

models = Models([sky_model])
analysis_1d.set_models(models)

Extracting the light curve#

lc_maker_1d = LightCurveEstimator(
    energy_edges=[1, 10] * u.TeV, source="crab", reoptimize=False
)
lc_1d = lc_maker_1d.run(analysis_1d.datasets)

print(lc_1d.geom.axes.names)

display(lc_1d.to_table(sed_type="flux", format="lightcurve"))

['energy', 'time']
     time_min           time_max           e_ref        ...    counts   success
                                            TeV         ...
------------------ ----------------- ------------------ ... ----------- -------
 53343.92234009259 53343.94186555556 3.4517490659800822 ... 81.0 .. nan    True
 53343.95421509259 53343.97369425926 3.4517490659800822 ...  nan .. nan    True
  53345.9619812963 53345.98149518519 3.4517490659800822 ...  nan .. nan    True
53347.913196574074 53347.93271046296 3.4517490659800822 ... nan .. 59.0    True

Compare results#

Finally we compare the result for the 1D and 3D lightcurve in a single figure:

fig, ax = plt.subplots(
    figsize=(8, 6),
    gridspec_kw={"left": 0.16, "bottom": 0.2, "top": 0.98, "right": 0.98},
)
lc_1d.plot(ax=ax, marker="o", label="1D")
lc_3d.plot(ax=ax, marker="o", label="3D")
plt.legend()
plt.show()
light curve

Night-wise LC estimation#

Here we want to extract a night curve per night. We define the time intervals that cover the three nights.

time_intervals = [
    Time([53343.5, 53344.5], format="mjd", scale="utc"),
    Time([53345.5, 53346.5], format="mjd", scale="utc"),
    Time([53347.5, 53348.5], format="mjd", scale="utc"),
]

To compute the LC on the time intervals defined above, we pass the LightCurveEstimator the list of time intervals.

Internally, datasets are grouped per time interval and a flux extraction is performed for each group.

lc_maker_1d = LightCurveEstimator(
    energy_edges=[1, 10] * u.TeV,
    time_intervals=time_intervals,
    source="crab",
    reoptimize=False,
    selection_optional="all",
)

nightwise_lc = lc_maker_1d.run(analysis_1d.datasets)

fig, ax = plt.subplots(
    figsize=(8, 6),
    gridspec_kw={"left": 0.16, "bottom": 0.2, "top": 0.98, "right": 0.98},
)
nightwise_lc.plot(ax=ax, color="tab:orange")
nightwise_lc.plot_ts_profiles(ax=ax)
ax.set_ylim(1e-12, 3e-12)

plt.show()
light curve

What next?#

When sources are bright enough to look for variability at small time scales, the per-observation time binning is no longer relevant. One can easily extend the light curve estimation approach presented above to any time binning. This is demonstrated in the Light curves for flares tutorial. which shows the extraction of the lightcurve of an AGN flare.

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