# PIG 9 - Event Sampling¶

- Author: Fabio Pintore, Andrea Giuliani, Axel Donath
- Created: May 03, 2019
- Accepted: August 30, 2019
- Status: accepted
- Discussion: GH_2136

## Abstract¶

An event sampler for gamma events is an important part of the science tools of the future Cherenkov Telescope Array (CTA) observatory. It will allow users to simulate observations of sources with different spectral, morphological and temporal properties and predict the performance of CTA on the simulated events e.g. to support observation proposals or study the sensitivity of future observations. For this reason, we propose to implement a framework for event simulation in Gammapy.

The proposed framework consists of individual building blocks, represented by classes and methods, that can be chained together to achieve a full simulation of an event list corresponding to a given observation. This includes the simulation of source events, background events, effects of instrument response functions (IRF) and arrival times. As underlying method for the actual event sampling we propose to use inverse cumulative distribution function (CDF) sampling (inverseCDF) with finely binned discrete source and background. Temporal models will be also taken into account and time will be sampled separately in a 1D analysis, assuming that the temporal dependency of the input source models factorizes.

## Sampling methods¶

Inverse CDF sampling (inverseCDF) is an established method to sample from discrete
probability mass functions. It is used by `ASTRIsim`

(astrisim), the event simulator
of the AGILE collaboration. However it is not the method of choice for other existing
event samplers such as the the Fermi-LAT Science Tools (gtobsim) and CTOOLS (ctobssim).
The latter uses a combination of analytical sampling for models, where a solution is
known (e.g. power-laws) and the rejection sampling method (rej_sampl), where the sampling
has to be done numerically (see an example here gammalib).

As rejection sampling can directly sample from continuous probability density functions, it is expected to yield very precise results. However an enveloping distribution is needed, which should be adapted to the target distribution to be efficient (see also rejection sampling in Python for an example implementation), otherwise a lot of computation time is spend in rejecting drawn samples.

For this reason we favour the inverse CDF sampling method, as a simple to implement and general sampling method. The precision of the inverse CDF sampling method can be controlled by the resolution of the input probability mass function (PMF) and is in practice only limited by the available memory. We will study the required bin-size of the PMFs to reach sufficient precision. If we find the inverse CDF sampling method to be not precise enough, it is still possible to achieve better precision adopting the rejection sampling. This will not have a strong impact on the structure of the event-sampler.

## Proposal¶

We propose to include in `gammapy.cube`

an high level interface (HLI) class, labelled as
`MapDatasetEventSampler`

or `MapDataset.sample`

method. This class handles the complete
event sampling process, including the corrections related to the IRF and source temporal
variability, for a given GTI / observation.

The dataset will be computed using the standard data reduction procedure of Gammapy, as illustrated in the following example:

```
obs = Observation(pointing, gti, aeff, psf, edisp, expomap)
maker = MapDatasetMaker(geom, geom_irf, ...)
dataset = maker.run(obs)
model = SkyModels.read("model.yaml")
dataset.model = model
sampler = MapDatasetEventSampler(dataset)
events = sampler.sample()
events.write()
```

After data reduction, the Dataset object should contain all the needed information,
such as the pointing sky coordinates, the GTI, and the setup of all the models (spectra,
spatial morphology, temporal model) for any given source, and it is passed
as input parameter to the `MapDatasetEventSampler`

. It is important to note that the
`MapDataset`

object can store information for more than one source. Then, a `.sample`

method
will draw the sampled events and will provide an output `~astropy.table.Table`

object. The
latter will containt the reconstructed sky positions, energies, times, and an `EVENT_ID`

and
`MC_ID`

. `EVENT_ID`

is a unique number or a string to identify the sampled event, while
`MC_ID`

is a unique ID (number or string) to identify the model component the event was sampled
from. The `MapDatasetSampler`

should also fill the mandatory header information for event list
files decribed on gamma-astro-data-formats.

### MapDatasetEventSampler¶

The general design of the `sample`

method is as follows:

```
def sample(dataset, random_state)
"""Sample events from a ``MapDataset``"""
events_list = []
for evaluator in dataset.evaluators:
npred = evaluator.compute_npred()
n_events = random_state.poisson(npred.data.sum())
events = npred.sample(n_events, random_state)
time = LightCurveTableModel.sample(n_events=, lc=, random_state=)
events = hstack(events,time)
events_list.append(events)
event_list["MC_ID"] = evaluator.model.name
events_src = vstack(events_list)
events_src = dataset.psf.sample(events_src, random_state)
events_src = dataset.edisp.sample(events_src, random_state)
n_events_bkg = random_state.poisson(dataset.background_model.map.data.sum())
events_bkg = dataset.background_model.sample(n_events, random_state)
events_total = vstack([events_src, events_bkg])
events_total.meta = get_events_meta_data(dataset)
return EventList(events_total)
```

In more detail, `sample`

starts a loop over the sources stored into the `MapDataset`

model. Then, for each source, the `src.compute_npred`

method will calculate the predicted
number of source counts `npred`

. In particular, it is important to note that
`npred = exposure * flux`

, where `exposure`

is defined as `effective_area * exposure_time`

.
`npred`

is therefore calculated irrespective of the energy dispersion and of PSF.
Then, `npred`

will be the input of the `npred.sample`

method. The latter uses a Poisson
distribution, with mean equal to the predicted counts, to estimate the random number of sampled
events.

We propose to add a `Map.sample(n_events=, random_state=)`

method in `~gammapy.maps.Map`

that will be the core of the sampling process. The `sample`

is based on the
`~gammapy.utils.random.InverseCDFSampler`

class described in GH_2229 . The output
will be an `~astropy.table.Table`

with columns: `RA_TRUE`

, `DEC_TRUE`

and `ENERGY_TRUE`

.

Then, the time will be sampled independently using the temporal information stored into
the `MapDataset`

model for each source of interest. This will be done through a
`.sample(n_events=, random_state=)`

method that we propose to add
to `~gammapy.time.models.LightCurveTableModel`

and `~gammapy.time.models.PhaseCurveTableModel`

.
This method will take as input the GTIs (i.e. one Tstart and Tstop) in the `MapDataset`

object. Also in this case the `InverseCDFSampler`

class is the machine used to sample the time of the events. In the case the temporal
model is not provided, the time is uniformly sampled in the time range `t_min`

and `t_max`

.
To define a light-curve per model component, the current `SkyModel`

class will be
extended by a `SkyModel(..., temporal_model=)`

.

The IRF correction can now be applied to sampled events. We propose to add a
`.sample(events=)`

method in both `~gammapy.cube.PSFMap`

and `~gammapy.cube.EdispMap`

.
The method interpolates the “correct” IRF at the position of a given event and
applies it. In more detail, the method calculates the psf and the energy dispersion at
the events true positions and true energies, which are given in input as an `~astropy.table.Table`

object.
The IRFs are assumed to be constant and not time-dependent. The output will be an `~astropy.table.Table`

with the new columns `RA`

, `DEC`

and `ENERGY`

, which are the reconstructed event energies and
positions.

Finally, the times and the energies/coordinates of the events will be merged into a
single `~astropy.table.Table`

with the columns:`RA`

, `DEC`

and `ENERGY`

and `TIME`

.

The `MapDatasetEventSampler`

can be used to sample background events using the
`Map.sample(n_events=, random_state=)`

as well. The time of the events is sampled
assuming a constant event rate. Finally, the IRF corrections are not applied to background
sampled events.

## Performance and precision evaluation¶

To evaluate the precision and performance of the described framework we propose to implement a prototype for a simulation / fitting pipeline. Starting from a selection of spatial, spectral and temporal models, data are simulated and fitted multiple times to derive distributions and pull-distributions of the reconstructed model parameters. This pipeline should also monitor the required cpu and memory usage. This first prototype can be used to evaluate the optimal bin-size (with the best compromise between performance and precision) for the simulations and to verify the over-all correctness of the simulated data. This will be valid for a set of input maps and IRFs. Later this prototype can be developed further into a full simulation / fitting continuous integration system.

## Alternatives / Outlook¶

So far Gammapy only supports binned likelihood analysis and technically most use-
cases for the event sampling could be solved with binned simulations. A binned
simulation can be basically achieved by a call to `numpy.random.poisson()`

based
on the predicted number of counts map. This is conceptionally simpler as well as
computationally more efficient than a sampling of event lists. In `Gammapy`

a similar
dataset simulation is already implemented in `Dataset.fake()`

, although this has a
limited number of use cases than an event sampler.
However, to support the full data access and data reduction process for simulations,
event lists are required. In future Gammapy possibly also supports event based analysis methods
(unbinned likelihood, but also e.g. clustering algorithms), that also require event
lists. For this reason binned simulations cannot present a full equivalent
solution to event sampling.

The question of the API to simulate multiple observations from e.g. an `ObservationTable`

or a list of `GTIs`

as it is needed for simulating data for the CTA data challenge
is not addressed in this PIG. For the scope of this PIG, the fundamental class
`MapDatasetEventSampler`

to simulate events corresponding to a given observation
and/or single GTI is in place.

The proposed Event Sampler will not provide, for each event, the corresponding
`DETX`

and `DETY`

position. These will be added in a future development of the
simulator.

## Task list¶

This is a proposal for a list of tasks to implement the proposed changes:

- Implement the
`sample`

method in`gammapy.maps.Map`

and add tests.- Implement the
`sample`

method in`gammapy.time.models.LightCurveTableModel`

and`gammapy.time.models.PhaseCurveTableModel`

and add tests.- Implement the
`sample`

method in`gammapy.cube.PSFMap`

and add tests.- Implement the
`sample`

method in`gammapy.cube.EdispMap`

and add tests.- Introduce the
`MapDatasetEventSampler`

into`gammapy.cube.`

and add tests.- Add tutorials for event simulations of different kinds of sources.