Processing and analysis code for remote-telescope imaging sessions
The scripts that processed the NGC 5128 session of 2026-07-21 previously lived inside the data directory and addressed it with absolute paths. Code and data are now separated: the code lives here, and a session is located at runtime through the ASTRO_SESSION environment variable. layout.py is what makes that work. It maps a FILENAME to the subdirectory that file belongs in, using the same rules the session directories are organised with, so a script can go on asking for 'master-Red.fit' or '_stars.npz' without any call site knowing the directory structure. Anything unrecognised resolves to the session root, which is visible and correctable rather than silently wrong. restructure.py reorganises a flat session directory into that layout. It is idempotent and dry-run by default. The 50 session scripts are kept as they were run rather than tidied into a library. They were written in sequence as the work went along, several of them by parallel agents, and they show it - but they are the honest provenance of a published set of results, and the productionised pipeline should be able to reproduce those results exactly. Verified before committing: all 51 files compile without warnings, and verify_core.py, closeup.py and triptych.py were run end to end against the reorganised session, correctly finding inputs across calibrated/, stacks/masters/ and final/ and writing outputs back to the right places.
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session-scripts/mo_vet.py
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session-scripts/mo_vet.py
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"""Transient search, stage 2: vet the catalogue non-matches individually.
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Two questions have to be answered about every source that failed to match
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Gaia DR3 and SkyMapper DR2.
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Is it real? A source can be missing from the catalogues simply because it is
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not there - a cosmic ray pair that survived sigma clipping, a deblending
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artefact on the galaxy, a noise peak. The test used here is independence:
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demand that the source is detected on its own in at least six of the twelve
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300 s subs. Nothing that is not on the sky can do that.
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Is it new? A transient is a source that is present tonight and absent from
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archival imagery. The catalogue non-match is weak evidence, because both Gaia
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and SkyMapper run out of depth around the magnitudes of interest here and
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neither is complete for extended or blended objects on a galaxy this bright.
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The strong evidence is a picture: this pulls a Digitized Sky Survey cutout at
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each position (DSS2 red, epoch ~1990s) through the CDS hips2fits service and
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puts it beside the master. Anything visible on a plate taken thirty years ago
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is not a transient.
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Writes mo-transient-vetted.csv, and caches the DSS cutouts for the figure.
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"""
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import csv
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import os
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import warnings
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import numpy as np
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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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import mo_common as C
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NSUB_MIN = 6
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CUT = 60 # px half-size of the postage stamps
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DSSDIR = os.path.join(C.OUT, "_dss")
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def load():
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z = np.load(os.path.join(C.OUT, "_mo_transient.npz"))
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return z
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def dss_cutout(ra, dec, fov_arcmin=1.5, npix=120, survey="CDS/P/DSS2/red"):
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"""Archival DSS2 red image of one position, cached to disk."""
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tag = f"{ra:.5f}{dec:+.5f}_{survey.split('/')[-1]}.fits"
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path = os.path.join(DSSDIR, tag)
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os.makedirs(DSSDIR, exist_ok=True)
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if os.path.exists(path):
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with fits.open(path) as hd:
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return hd[0].data.astype(float)
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try:
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from astroquery.hips2fits import hips2fits
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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hdul = hips2fits.query(
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hips=survey, width=npix, height=npix,
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ra=ra * u.deg, dec=dec * u.deg,
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fov=(fov_arcmin / 60.0) * u.deg,
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projection="TAN", format="fits")
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data = np.asarray(hdul[0].data, dtype=float)
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fits.PrimaryHDU(data).writeto(path, overwrite=True)
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return data
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except Exception as exc: # noqa: BLE001
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print(f" DSS fetch failed for {ra:.5f} {dec:+.5f}: "
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f"{type(exc).__name__}: {exc}")
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return None
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def nearest_catalogue(sc):
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"""Nearest Gaia and SkyMapper source at ANY separation, plus SIMBAD/NED.
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The main search used fixed match radii. For a handful of finalists it is
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worth knowing what the nearest catalogued thing actually is and how far
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away it lies - a 4 arcsec offset from a SkyMapper source on a crowded
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galaxy usually means the same object, badly centroided.
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"""
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gz = np.load(os.path.join(C.OUT, "_gaia_deep.npz"))
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gcat = SkyCoord(gz["ra"] * u.deg, gz["dec"] * u.deg)
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sz = np.load(os.path.join(C.OUT, "_skymapper.npz"))
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scat = SkyCoord(sz["ra"] * u.deg, sz["dec"] * u.deg)
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gi, gd, _ = sc.match_to_catalog_sky(gcat)
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si, sd, _ = sc.match_to_catalog_sky(scat)
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return (gd.arcsec, gz["g"][gi], sd.arcsec, sz["g"][si], sz["r"][si],
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sz["cls"][si])
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def ned_query(sc, radius_arcsec=15.0):
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"""Anything NED knows about within a few arcsec of each position."""
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out = []
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try:
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from astroquery.ipac.ned import Ned
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except Exception: # noqa: BLE001
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return ["NED unavailable"] * len(sc)
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for c in sc:
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try:
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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t = Ned.query_region(c, radius=radius_arcsec * u.arcsec)
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if t is None or len(t) == 0:
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out.append("")
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else:
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names = [f"{r['Object Name']}({r['Type']},"
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f"{r['Separation']:.1f}\")" for r in t[:3]]
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out.append("; ".join(names))
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except Exception as exc: # noqa: BLE001
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out.append(f"query failed: {type(exc).__name__}")
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return out
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def main():
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z = load()
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ci = z["cand_idx"]
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nsub = z["nsub"]
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sel = np.where(nsub >= NSUB_MIN)[0]
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print(f"{len(ci)} sources matched neither Gaia DR3 nor SkyMapper DR2")
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print(f"{len(sel)} of them are independently detected in >= {NSUB_MIN} "
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f"of the 12 subs\n")
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from astropy.wcs import WCS
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hdr = fits.getheader(os.path.join(C.OUT, "master-Luminance.fit"))
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wcs = WCS(hdr)
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x = z["x"][ci][sel]
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y = z["y"][ci][sel]
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sc = wcs.pixel_to_world(x, y)
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gd, gg, sd, sg, sr, scls = nearest_catalogue(sc)
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ned = ned_query(sc)
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rows = []
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for k, i in enumerate(sel):
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ra, dec = sc[k].ra.deg, sc[k].dec.deg
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dss = dss_cutout(ra, dec)
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# Is anything there on the archival plate? Compare the peak in the
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# central 6 arcsec against the frame-wide robust scatter.
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dss_sig = np.nan
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if dss is not None and np.isfinite(dss).any():
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h = dss.shape[0] // 2
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core = dss[h - 6:h + 7, h - 6:h + 7]
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med = np.nanmedian(dss)
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mad = np.nanmedian(np.abs(dss - med)) * 1.4826
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if mad > 0:
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dss_sig = float((np.nanmax(core) - med) / mad)
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rows.append(dict(
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ra_deg=round(ra, 6), dec_deg=round(dec, 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(x[k]), 1), y=round(float(y[k]), 1),
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g_mag=round(float(z["mag"][ci][i]), 2),
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snr=round(float(z["snr"][ci][i]), 1),
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r50_over_psf=round(float(z["r50"][ci][i] / z["r50_star"]), 2),
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n_subs=int(nsub[i]),
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snr_R=round(float(z["snr_R"][i]), 1),
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snr_G=round(float(z["snr_G"][i]), 1),
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snr_B=round(float(z["snr_B"][i]), 1),
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dist_cenA_arcmin=round(float(z["dgal"][i]), 2),
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nearest_gaia_arcsec=round(float(gd[k]), 2),
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nearest_gaia_G=round(float(gg[k]), 2),
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nearest_smss_arcsec=round(float(sd[k]), 2),
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nearest_smss_g=round(float(sg[k]), 2),
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nearest_smss_classstar=round(float(scls[k]), 2),
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dss_peak_sigma=round(dss_sig, 1) if np.isfinite(dss_sig)
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else "",
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ned=ned[k]))
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r = rows[-1]
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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}")
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print(f" nearest Gaia {r['nearest_gaia_arcsec']:6.2f}\" "
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f"(G={r['nearest_gaia_G']:.2f}), nearest SkyMapper "
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f"{r['nearest_smss_arcsec']:6.2f}\" (g={r['nearest_smss_g']:.2f}"
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f", ClassStar={r['nearest_smss_classstar']:.2f})")
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print(f" DSS2-red peak {r['dss_peak_sigma']} sigma, "
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f"{r['dist_cenA_arcmin']:.1f}' from Cen A centre")
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print(f" NED: {r['ned'] or '(nothing within 15\")'}")
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out = os.path.join(C.OUT, "mo-transient-vetted.csv")
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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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np.save(os.path.join(C.OUT, "_mo_vetted.npy"),
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np.array(rows, dtype=object), allow_pickle=True)
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if __name__ == "__main__":
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main()
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