Source code for gammapy.astro.population.simulate

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Simulate source catalogs."""
import numpy as np
from astropy.coordinates import SkyCoord
from astropy.table import Column, Table
from astropy.units import Quantity
from gammapy.astro.source import PWN, SNR, Pulsar, SNRTrueloveMcKee
from gammapy.utils import coordinates as astrometry
from gammapy.utils.random import (
from .spatial import (
from .velocity import VMAX, VMIN, velocity_distributions

__all__ = [

[docs]def make_catalog_random_positions_cube( size=100, dimension=3, distance_max="1 pc", random_state="random-seed" ): """Make a catalog of sources randomly distributed on a line, square or cube. This can be used to study basic source population distribution effects, e.g. what the distance distribution looks like, or for a given luminosity function what the resulting flux distributions are for different spatial configurations. Parameters ---------- size : int Number of sources dimension : {1, 2, 3} Number of dimensions distance_max : `~astropy.units.Quantity` Maximum distance random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`} Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. Returns ------- table : `~astropy.table.Table` Table with 3D position cartesian coordinates. Columns: x (pc), y (pc), z (pc) """ distance_max = Quantity(distance_max).to_value("pc") random_state = get_random_state(random_state) # Generate positions 1D, 2D, or 3D if dimension == 1: x = random_state.uniform(-distance_max, distance_max, size) y, z = 0, 0 elif dimension == 2: x = random_state.uniform(-distance_max, distance_max, size) y = random_state.uniform(-distance_max, distance_max, size) z = 0 elif dimension == 3: x = random_state.uniform(-distance_max, distance_max, size) y = random_state.uniform(-distance_max, distance_max, size) z = random_state.uniform(-distance_max, distance_max, size) else: raise ValueError(f"Invalid dimension: {dimension}") table = Table() table["x"] = Column(x, unit="pc", description="Cartesian coordinate") table["y"] = Column(y, unit="pc", description="Cartesian coordinate") table["z"] = Column(z, unit="pc", description="Cartesian coordinate") return table
[docs]def make_catalog_random_positions_sphere( size=100, distance_min="0 pc", distance_max="1 pc", random_state="random-seed" ): """Sample random source locations in a sphere. This can be used to generate an isotropic source population in a sphere, e.g. to represent extra-galactic sources. Parameters ---------- size : int Number of sources distance_min, distance_max : `~astropy.units.Quantity` Minimum and maximum distance random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`} Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. Returns ------- catalog : `~astropy.table.Table` Table with 3D position spherical coordinates. Columns: lon (deg), lat (deg), distance(pc) """ distance_min = Quantity(distance_min).to_value("pc") distance_max = Quantity(distance_max).to_value("pc") random_state = get_random_state(random_state) lon, lat = sample_sphere(size, random_state=random_state) distance = sample_sphere_distance(distance_min, distance_max, size, random_state) table = Table() table["lon"] = Column(lon, unit="rad", description="Spherical coordinate") table["lat"] = Column(lat, unit="rad", description="Spherical coordinate") table["distance"] = Column(distance, unit="pc", description="Spherical coordinate") return table
[docs]def make_base_catalog_galactic( n_sources, rad_dis="YK04", vel_dis="H05", max_age="1e6 yr", spiralarms=True, n_ISM="1 cm-3", random_state="random-seed", ): """Make a catalog of Galactic sources, with basic source parameters. Choose a radial distribution, a velocity distribution, the number of pulsars n_pulsars, the maximal age max_age[years] and the fraction of the individual morphtypes. There's an option spiralarms. If set on True a spiralarm modeling after Faucher&Kaspi is included. max_age and n_sources effectively correspond to s SN rate: SN_rate = n_sources / max_age Parameters ---------- n_sources : int Number of sources to simulate rad_dis : callable Radial surface density distribution of sources vel_dis : callable Proper motion velocity distribution of sources max_age : `~astropy.units.Quantity` Maximal age of the source spiralarms : bool Include a spiralarm model in the catalog? n_ISM : `~astropy.units.Quantity` Density of the interstellar medium random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`} Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. Returns ------- table : `~astropy.table.Table` Catalog of simulated source positions and proper velocities """ max_age = Quantity(max_age).to_value("yr") n_ISM = Quantity(n_ISM).to("cm-3") random_state = get_random_state(random_state) if isinstance(rad_dis, str): rad_dis = radial_distributions[rad_dis] if isinstance(vel_dis, str): vel_dis = velocity_distributions[vel_dis] # Draw random values for the age age = random_state.uniform(0, max_age, n_sources) age = Quantity(age, "yr") # Draw spatial distribution r = draw( RMIN.to_value("kpc"), RMAX.to_value("kpc"), n_sources, pdf(rad_dis()), random_state=random_state, ) r = Quantity(r, "kpc") if spiralarms: r, theta, spiralarm = FaucherSpiral()(r, random_state=random_state) else: theta = Quantity(random_state.uniform(0, 2 * np.pi, n_sources), "rad") spiralarm = None x, y = astrometry.cartesian(r, theta) z = draw( ZMIN.to_value("kpc"), ZMAX.to_value("kpc"), n_sources, Exponential(), random_state=random_state, ) z = Quantity(z, "kpc") # Draw values from velocity distribution v = draw( VMIN.to_value("km/s"), VMAX.to_value("km/s"), n_sources, vel_dis(), random_state=random_state, ) v = Quantity(v, "km/s") # Draw random direction of initial velocity theta = Quantity(random_state.uniform(0, np.pi, x.size), "rad") phi = Quantity(random_state.uniform(0, 2 * np.pi, x.size), "rad") # Compute new position dx, dy, dz, vx, vy, vz = astrometry.motion_since_birth(v, age, theta, phi) # Add displacement to birth position x_moved = x + dx y_moved = y + dy z_moved = z + dz table = Table() table["age"] = Column(age, unit="yr", description="Age of the source") table["n_ISM"] = Column(n_ISM, description="Interstellar medium density") if spiralarms: table["spiralarm"] = Column(spiralarm, description="Which spiralarm?") table["x_birth"] = Column(x, description="Galactocentric x coordinate at birth") table["y_birth"] = Column(y, description="Galactocentric y coordinate at birth") table["z_birth"] = Column(z, description="Galactocentric z coordinate at birth") table["x"] = Column("kpc"), description="Galactocentric x coordinate") table["y"] = Column("kpc"), description="Galactocentric y coordinate") table["z"] = Column("kpc"), description="Galactocentric z coordinate") table["vx"] = Column(vx, description="Galactocentric velocity in x direction") table["vy"] = Column(vy, description="Galactocentric velocity in y direction") table["vz"] = Column(vz, description="Galactocentric velocity in z direction") table["v_abs"] = Column(v, description="Galactocentric velocity (absolute)") return table
[docs]def add_snr_parameters(table): """Add SNR parameters to the table. TODO: document """ # Read relevant columns age = table["age"].quantity n_ISM = table["n_ISM"].quantity # Compute properties snr = SNR(n_ISM=n_ISM) E_SN = snr.e_sn * np.ones(len(table)) r_out = snr.radius(age) r_in = snr.radius_inner(age) L_SNR = snr.luminosity_tev(age) # Add columns to table table["E_SN"] = Column(E_SN, description="SNR kinetic energy") table["r_out"] = Column("pc"), description="SNR outer radius") table["r_in"] = Column("pc"), description="SNR inner radius") table["L_SNR"] = Column(L_SNR, description="SNR photon rate above 1 TeV") return table
[docs]def add_pulsar_parameters( table, B_mean=12.05, B_stdv=0.55, P_mean=0.3, P_stdv=0.15, random_state="random-seed", ): """Add pulsar parameters to the table. For the initial normal distribution of period and logB can exist the following Parameters: B_mean=12.05[log Gauss], B_stdv=0.55, P_mean=0.3[s], P_stdv=0.15 Parameters ---------- random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`} Defines random number generator initialisation. Passed to `~gammapy.utils.random.get_random_state`. """ random_state = get_random_state(random_state) # Read relevant columns age = table["age"].quantity # Draw the initial values for the period and magnetic field def p_dist(x): return np.exp(-0.5 * ((x - P_mean) / P_stdv) ** 2) p0_birth = draw(0, 2, len(table), p_dist, random_state=random_state) p0_birth = Quantity(p0_birth, "s") log10_b_psr = random_state.normal(B_mean, B_stdv, len(table)) b_psr = Quantity(10 ** log10_b_psr, "G") # Compute pulsar parameters psr = Pulsar(p0_birth, b_psr) p0 = psr.period(age) p1 = psr.period_dot(age) p1_birth = psr.P_dot_0 tau = psr.tau(age) tau_0 = psr.tau_0 l_psr = psr.luminosity_spindown(age) l0_psr = psr.L_0 # Add columns to table table["P0"] = Column(p0, unit="s", description="Pulsar period") table["P1"] = Column(p1, unit="", description="Pulsar period derivative") table["P0_birth"] = Column(p0_birth, unit="s", description="Pulsar birth period") table["P1_birth"] = Column( p1_birth, unit="", description="Pulsar birth period derivative" ) table["CharAge"] = Column(tau, unit="yr", description="Pulsar characteristic age") table["Tau0"] = Column(tau_0, unit="yr") table["L_PSR"] = Column(l_psr, unit="erg s-1") table["L0_PSR"] = Column(l0_psr, unit="erg s-1") table["B_PSR"] = Column( b_psr, unit="Gauss", description="Pulsar magnetic field at the poles" ) return table
[docs]def add_pwn_parameters(table): """Add PWN parameters to the table. TODO: document """ # Some of the computations (specifically `pwn.radius`) aren't vectorised # across all parameters; so here we loop over source parameters explicitly results = [] for idx in range(len(table)): age = table["age"].quantity[idx] E_SN = table["E_SN"].quantity[idx] n_ISM = table["n_ISM"].quantity[idx] P0_birth = table["P0_birth"].quantity[idx] b_psr = table["B_PSR"].quantity[idx] # Compute properties pulsar = Pulsar(P0_birth, b_psr) snr = SNRTrueloveMcKee(e_sn=E_SN, n_ISM=n_ISM) pwn = PWN(pulsar, snr) r_out_pwn = pwn.radius(age).to_value("pc") results.append(dict(r_out_pwn=r_out_pwn)) # Add columns to table table["r_out_PWN"] = Column( [_["r_out_pwn"] for _ in results], unit="pc", description="PWN outer radius" ) return table
[docs]def add_observed_parameters(table, obs_pos=None): """Add observable parameters (such as sky position or distance). Input table columns: x, y, z, extension, luminosity Output table columns: distance, glon, glat, flux, angular_extension Position of observer in cartesian coordinates. Center of galaxy as origin, x-axis goes trough sun. Parameters ---------- table : `~astropy.table.Table` Input table obs_pos : tuple or None Observation position (X, Y, Z) in Galactocentric coordinates (default: Earth) Returns ------- table : `~astropy.table.Table` Modified input table with columns added """ obs_pos = obs_pos or [astrometry.D_SUN_TO_GALACTIC_CENTER, 0, 0] # Get data x, y, z = table["x"].quantity, table["y"].quantity, table["z"].quantity vx, vy, vz = table["vx"].quantity, table["vy"].quantity, table["vz"].quantity distance, glon, glat = astrometry.galactic(x, y, z, obs_pos=obs_pos) # Compute projected velocity v_glon, v_glat = astrometry.velocity_glon_glat(x, y, z, vx, vy, vz) coordinate = SkyCoord(glon, glat, unit="deg", frame="galactic").transform_to("icrs") ra, dec = coordinate.ra.deg, coordinate.dec.deg # Add columns to table table["distance"] = Column( distance, unit="pc", description="Distance observer to source center" ) table["GLON"] = Column(glon, unit="deg", description="Galactic longitude") table["GLAT"] = Column(glat, unit="deg", description="Galactic latitude") table["VGLON"] = Column("deg/Myr"), unit="deg/Myr", description="Velocity in Galactic longitude", ) table["VGLAT"] = Column("deg/Myr"), unit="deg/Myr", description="Velocity in Galactic latitude", ) table["RA"] = Column(ra, unit="deg", description="Right ascension") table["DEC"] = Column(dec, unit="deg", description="Declination") try: luminosity = table["luminosity"] flux = luminosity / (4 * np.pi * distance ** 2) table["flux"] = Column(flux.value, unit=flux.unit, description="Source flux") except KeyError: pass try: extension = table["extension"] angular_extension = np.degrees(np.arctan(extension / distance)) table["angular_extension"] = Column( angular_extension, unit="deg", description="Source angular radius (i.e. half-diameter)", ) except KeyError: pass return table