Move the processing code under pipeline/
Preparing to merge this repository into a combined astrophotography repo. session-scripts/ becomes pipeline/ because the scripts import layout.py from their own directory and must stay together, and because 'pipeline' says what it is rather than how it came about. observing/ stays at the top level: observing plans are not processing code.
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"""Transient search: sources in the luminance master with no catalogue match.
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The chain is deliberately conservative, because on this field the sky is very
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crowded with things that are real but not transient.
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1. Detect on the deep luminance master (60 min) and measure a 5 px aperture
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magnitude on the master's own zero point.
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2. Throw away everything that is not solidly detected. A transient claim rests
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on a single night's data, so the bar is SNR > 10, not the SNR 5 the frame
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nominally reaches.
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3. Throw away everything that is not point-like. This is done against the
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image's own PSF, measured from Gaia stars in the frame, using sep's
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half-light radius. It removes background galaxies and the outer structure
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of NGC 5128 itself, but note carefully that it does NOT remove Centaurus A's
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globular clusters: at 3.8 Mpc a cluster with a 3 pc half-light radius spans
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~0.2 arcsec against 2.7 arcsec seeing, so clusters are unresolved here and
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look exactly like stars. Only catalogues can separate them.
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4. Cross-match against Gaia DR3 (the cached 0.42 deg, G < 20.5 catalogue).
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5. Cross-match whatever is left against SkyMapper DR2 through VizieR, which
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covers this declination and goes deeper than Gaia for non-stellar objects.
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6. Demand that survivors are real by requiring an independent detection in at
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least six of the twelve individual luminance subs. A cosmic ray or a
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stacking artefact cannot do that; anything astrophysical will.
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7. What is left is examined one by one.
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Writes mo-transient-candidates.csv and _mo_transient.npz.
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"""
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import csv
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import os
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import numpy as np
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import sep
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from astropy import units as u
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from astropy.coordinates import SkyCoord
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from astropy.io import fits
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from astropy.wcs import WCS
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from scipy.spatial import cKDTree
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import mo_common as C
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SNR_MIN = 10.0
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MATCH_RADIUS = 2.0 # arcsec, Gaia
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SM_RADIUS = 3.0 # arcsec, SkyMapper (worse astrometry, wider PSF)
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SUB_MIN = 6 # subs a source must independently appear in
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EDGE = 40 # px
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def detect_master():
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with fits.open(os.path.join(C.OUT, "master-Luminance.fit")) as hd:
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img = hd[0].data.astype(np.float32)
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hdr = hd[0].header
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bkg = sep.Background(img, bw=64, bh=64, fw=3, fh=3)
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sub = img - bkg.back()
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objs = sep.extract(sub, 3.0, err=bkg.globalrms, minarea=5,
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deblend_cont=0.005)
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flux, ferr, _ = sep.sum_circle(sub, objs["x"], objs["y"], C.APRAD,
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err=bkg.globalrms, gain=1.0)
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r50, _ = sep.flux_radius(sub, objs["x"], objs["y"],
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6.0 * np.ones(len(objs)), 0.5, normflux=flux,
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subpix=5)
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return objs, np.asarray(flux), np.asarray(ferr), np.asarray(r50), \
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hdr, sub, bkg.globalrms
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def gaia_cached():
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z = np.load(os.path.join(C.OUT, "_gaia_deep.npz"))
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return z["ra"], z["dec"], z["g"]
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def skymapper(ra0, dec0, radius_deg):
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"""SkyMapper DR2 over the field, cached because it is a slow query."""
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cache = os.path.join(C.OUT, "_skymapper.npz")
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if os.path.exists(cache):
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z = np.load(cache)
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print(f"SkyMapper: {len(z['ra'])} cached sources")
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return z["ra"], z["dec"], z["g"], z["r"], z["cls"]
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from astroquery.vizier import Vizier
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v = Vizier(columns=["RAICRS", "DEICRS", "gPSF", "rPSF", "ClassStar"],
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row_limit=-1)
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tbl = v.query_region(SkyCoord(ra0 * u.deg, dec0 * u.deg),
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radius=radius_deg * u.deg,
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catalog="II/358/smss")[0]
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ra = np.asarray(tbl["RAICRS"], dtype=float)
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dec = np.asarray(tbl["DEICRS"], dtype=float)
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g = np.asarray(tbl["gPSF"], dtype=float)
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r = np.asarray(tbl["rPSF"], dtype=float)
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cls = np.asarray(tbl["ClassStar"], dtype=float)
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np.savez_compressed(cache, ra=ra, dec=dec, g=g, r=r, cls=cls)
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print(f"SkyMapper DR2: {len(ra)} sources retrieved")
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return ra, dec, g, r, cls
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def sky_match(sc, ra, dec, radius_arcsec):
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"""Nearest-neighbour match; returns index and separation in arcsec."""
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cat = SkyCoord(ra * u.deg, dec * u.deg)
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idx, d2d, _ = sc.match_to_catalog_sky(cat)
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return idx, d2d.arcsec, d2d.arcsec < radius_arcsec
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def sub_support(xy_ref):
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"""How many of the twelve luminance subs independently show each source."""
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_, dets = C.load_dets()
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tforms = C.frame_transforms()
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n = np.zeros(len(xy_ref), dtype=int)
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for key, _ in C.lum_frames():
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d = dets[key]
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p = tforms[key](d[:, 2:4].astype(float))
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t = cKDTree(p)
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dd, _ = t.query(xy_ref, distance_upper_bound=2.5)
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n += np.isfinite(dd)
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return n
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def rgb_support(xy_ref, rms_scale=3.0):
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"""Peak significance of each position in the R, G and B masters.
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A real object on the sky is in every filter. A luminance-only artefact is
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not. The masters are pixel-aligned by construction, so this is a direct
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look-up rather than another registration.
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"""
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out = {}
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for filt in ("Red", "Green", "Blue"):
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with fits.open(os.path.join(C.OUT, f"master-{filt}.fit")) as hd:
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img = hd[0].data.astype(np.float32)
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bkg = sep.Background(img, bw=64, bh=64, fw=3, fh=3)
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s = img - bkg.back()
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fl, _, _ = sep.sum_circle(s, xy_ref[:, 0], xy_ref[:, 1], C.APRAD,
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err=bkg.globalrms, gain=1.0)
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noise = bkg.globalrms * np.sqrt(np.pi * C.APRAD ** 2)
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out[filt] = np.asarray(fl) / noise
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del img, s
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return out
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def main():
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objs, flux, ferr, r50, hdr, _sub, mrms = detect_master()
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wcs = WCS(hdr)
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ny, nx = 3194, 4788
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print(f"master detections: {len(objs)}")
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snr = np.where(ferr > 0, flux / np.maximum(ferr, 1e-9), 0.0)
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mag = np.where(flux > 0, -2.5 * np.log10(np.maximum(flux, 1e-9)) + C.ZP,
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np.nan)
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# PSF reference from the frame's own bright stars.
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gra, gdec, gmag = gaia_cached()
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sc_all = wcs.pixel_to_world(objs["x"], objs["y"])
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gidx, gsep, gok = sky_match(sc_all, gra, gdec, MATCH_RADIUS)
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star = gok & (gmag[gidx] > 15) & (gmag[gidx] < 18.5) & (snr > 30)
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r50_star = float(np.median(r50[star]))
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r50_sig = float(np.std(r50[star]))
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print(f"PSF from {star.sum()} Gaia stars: r50 = {r50_star:.2f} +- "
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f"{r50_sig:.2f} px ({r50_star * C.SCALE:.2f} arcsec)")
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inframe = ((objs["x"] > EDGE) & (objs["x"] < nx - EDGE) &
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(objs["y"] > EDGE) & (objs["y"] < ny - EDGE))
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good = inframe & (snr > SNR_MIN) & np.isfinite(mag)
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pointlike = np.abs(r50 - r50_star) < 3.0 * max(r50_sig, 0.25)
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print(f"SNR > {SNR_MIN:.0f} and in frame: {good.sum()}")
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print(f" ... of which point-like: {(good & pointlike).sum()}")
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print(f" ... of which Gaia-matched: {(good & pointlike & gok).sum()}")
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cand = good & pointlike & ~gok
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print(f"\nno Gaia DR3 counterpart within {MATCH_RADIUS}\": {cand.sum()}")
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# SkyMapper DR2
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cen = wcs.pixel_to_world(nx / 2, ny / 2)
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sra, sdec, sg, sr, scls = skymapper(cen.ra.deg, cen.dec.deg, 0.42)
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sidx, ssep, sok = sky_match(sc_all, sra, sdec, SM_RADIUS)
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cand2 = cand & ~sok
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print(f"also no SkyMapper DR2 counterpart within {SM_RADIUS}\": "
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f"{cand2.sum()}")
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ci = np.where(cand2)[0]
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xy = np.column_stack([objs["x"][ci], objs["y"][ci]])
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nsub = sub_support(xy)
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rgb = rgb_support(xy)
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print(f"of those, detected in >= {SUB_MIN} individual subs: "
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f"{(nsub >= SUB_MIN).sum()}")
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sc = wcs.pixel_to_world(xy[:, 0], xy[:, 1])
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# Distance from the centre of NGC 5128 - a supernova is expected on or
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# near the galaxy, and this ranks the list accordingly.
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cenA = SkyCoord("13h25m27.6s", "-43d01m09s")
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dgal = sc.separation(cenA).arcmin
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rows = []
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for k, i in enumerate(ci):
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rows.append(dict(
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id=int(i), ra_deg=round(sc[k].ra.deg, 6),
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dec_deg=round(sc[k].dec.deg, 6),
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ra_hms=sc[k].ra.to_string(u.hour, sep=":", precision=2),
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dec_dms=sc[k].dec.to_string(u.deg, sep=":", precision=1,
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alwayssign=True),
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x=round(float(xy[k, 0]), 2), y=round(float(xy[k, 1]), 2),
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g_mag=round(float(mag[i]), 2), snr=round(float(snr[i]), 1),
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r50_px=round(float(r50[i]), 2),
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r50_over_psf=round(float(r50[i] / r50_star), 2),
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n_subs=int(nsub[k]),
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snr_R=round(float(rgb["Red"][k]), 1),
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snr_G=round(float(rgb["Green"][k]), 1),
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snr_B=round(float(rgb["Blue"][k]), 1),
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gaia_sep_arcsec=round(float(gsep[i]), 2),
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smss_sep_arcsec=round(float(ssep[i]), 2),
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dist_from_cenA_arcmin=round(float(dgal[k]), 2)))
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rows.sort(key=lambda r: -r["snr"])
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out = os.path.join(C.OUT, "mo-transient-candidates.csv")
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if rows:
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with open(out, "w", newline="") as fh:
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w = csv.DictWriter(fh, fieldnames=list(rows[0].keys()))
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w.writeheader()
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w.writerows(rows)
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print(f"\nwrote {out} ({len(rows)} rows)")
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for r in rows[:40]:
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print(f" {r['ra_hms']} {r['dec_dms']} G={r['g_mag']:5.2f} "
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f"SNR={r['snr']:6.1f} r50/psf={r['r50_over_psf']:.2f} "
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f"subs={r['n_subs']:2d} RGB=({r['snr_R']:.0f},{r['snr_G']:.0f},"
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f"{r['snr_B']:.0f}) d={r['dist_from_cenA_arcmin']:.1f}'")
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np.savez(os.path.join(C.OUT, "_mo_transient.npz"),
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x=objs["x"], y=objs["y"], mag=mag, snr=snr, r50=r50,
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gaia_ok=gok, gaia_sep=gsep, sm_ok=sok, sm_sep=ssep,
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good=good, pointlike=pointlike, cand=cand, cand2=cand2,
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cand_idx=ci, nsub=nsub, r50_star=r50_star, r50_sig=r50_sig,
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snr_R=rgb["Red"], snr_G=rgb["Green"], snr_B=rgb["Blue"],
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dgal=dgal)
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print("saved _mo_transient.npz")
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if __name__ == "__main__":
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main()
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