SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging

Shunyuan Mao; Andrea Isella; Paris Perdikaris; Li-Ta Lo; Hui Li

astro-ph.IMSep 28, 2026

SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging

Authors: Shunyuan Mao, Andrea Isella, Paris Perdikaris, Li-Ta Lo, Hui Li

Organizations: Department of Physics and Astronomy, Rice University, 6100 Main St, Houston, TX 77005, USA · Rice Space Institute, Rice University, 6100 Main St, Houston, TX 77005, USA · National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, VA 22903, USA · NSF-Simons AI Institute for Cosmic Origins, Austin, TX 78712, USA · Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, 220 S 33rd St, Philadelphia, PA 19104, USA · Computing and Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA · Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Abstract

Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, complex morphologies, and uncertainty quantification. Alternative methods that fit visibilities directly can address some of these limitations but often require manual choices of image priors and model hyperparameters. We present SIFARI (Self-Supervised Interferometric Fitting for Astronomical Radio Imaging), a self-supervised neural network workflow that represents sky brightness as a continuous function of position and fits measured visibilities without an external image training set or explicit spatial regularizer. An empirical rule sets the Fourier feature scale from the visibilities before training, controlling how readily the network fits fine structure. Sampling network weights with Stochastic Weight Averaging-Gaussian (SWAG) gives approximate brightness uncertainty estimates, which we combine with a thermal-noise floor to construct spatially resolved signal-to-noise maps. In synthetic ALMA tests, SIFARI yields an effective point-source response about eight times narrower than the natural-weighting CLEAN restoring beam and recovers more extended flux than CLEAN when short baselines are missing. It also achieves higher image fidelity than the restored CLEAN images in all three morphology benchmarks. Applied to ALMA observations of PDS 70, SIFARI recovers the bright outer ring together with faint compact emission in the central cavity. For long-baseline-only WISPIT 2 data, SIFARI supplies a sky model for phase self-calibration where the CLEAN model is inadequate. The restored, self-calibrated SIFARI image has approximately 30% lower RMS noise than the CLEAN image made from the original visibilities without self-calibration.

Figures & tables

Figure 1: Two-stage SIFARI workflow for visibility-domain image reconstruction and uncertainty quantification. A coordinate-based neural network maps sky coordinates

(x,y)

to brightness

I(x,y)

. Before training, the visibility data set the frequency scale

\sigma_{\nu}

used to choose

\bm{\nu}_{k}

(dashed arrow). The model image is Fourier-transformed at the observed

(u_{l},v_{l})

coordinates to predict visibilities, which are compared with the data through the likelihood. The weights

W_{l}

are inverse noise variances of the real and imaginary visibility components. Stage 1 uses Muon+AdamW to fit the network and yields the reported best-fitting reconstruction. Stage 2 starts from this fit, uses SGD to build the SWAG approximation, and samples network weights to estimate the pixel-wise brightness uncertainty.

Category	Hyperparameter	Value
Architecture	Neurons per hidden layer $M$	128
	Number of hidden layers	2
	Hidden-layer activation	ELU
	Fourier feature scale $\sigma_{\nu}$	Estimated from visibilities
	Number of Fourier features $K$	512
Stage 1: Optimization	Initial Muon learning rate	$2\times 10^{-2}$

Table 1: Default hyperparameters used in all experiments unless otherwise specified. Synthetic models and their observing setups are listed in Table 2 .

Source Model	ALMA configurations used	Ground-truth pixels per side	Ground-truth pixel size (″)
One point source	C-3 (20 min) + C-5 (30 min) + C-9 (60 min)	1024	0.0005
Two point sources ( $\Delta\mathrm{RA}=0.009^{\prime\prime}$ )	C-3 (20 min) + C-5 (30 min) + C-9 (60 min)	1024	0.0005
Two point sources ( $\Delta\mathrm{RA}=0.010^{\prime\prime}$ )	C-3 (20 min) + C-5 (30 min) + C-9 (60 min)	1024	0.0005
Uniform disk ( $\theta_{\mathrm{UD}}=0.2\arcsec$ )	C-9 (60 min)	200	0.0015
Uniform disk ( $\theta_{\mathrm{UD}}=0.6\arcsec$ )		680	0.0015
Uniform disk ( $\theta_{\mathrm{UD}}=1.0\arcsec$ )		960	0.0015

Table 2: Synthetic benchmark models and their ALMA simobserve setups. The grid columns specify the ground-truth images used to generate the synthetic visibilities; reconstruction grids are specified separately in the text. The C-3, C-5, and C-9 configurations span baseline ranges of 15–500 m, 15–1398 m, and 368–13 894 m, respectively. Rows with multiple configurations use their combined visibilities.

Figure 2: Normalized 1D slices along RA of the dirty beams (gray) and the SIFARI point-source response (red). Natural, Briggs, and uniform weighting are shown with dashed, dash-dotted, and dotted curves, respectively; SIFARI is shown with a solid curve. The Briggs dirty beam uses a robust parameter of 0.5. Each profile is normalized to its peak. The vertical dashed lines mark

\pm\lambda/B_{max}

Figure 3: Two-point-source tests: normalized 1D flux slices of CLEAN and SIFARI reconstructions along the RA axis connecting the sources (linear scale). Natural-, Briggs-, and uniform-weighted CLEAN images are shown with gray dashed, dash-dotted, and dotted curves, respectively; SIFARI is shown with a red solid curve. Each profile is normalized to its peak. The vertical dashed lines mark the injected source positions. Left:

\Delta\mathrm{RA}=0.009^{\prime\prime}

. Right:

\Delta\mathrm{RA}=0.010^{\prime\prime}

Disk diameter	Model Flux	CLEAN	SIFARI
(″)	(mJy)	(mJy)	(mJy)
0.2	200	200	200
0.6	200	41	202
1.0	200	20	219

Table 3: Flux recovery for uniform-disk models observed with the ALMA C-9 configuration. Recovered fluxes are measured within a circular aperture with the same diameter as the corresponding disk. The CLEAN images use natural weighting.

Panel of Figure 5 — Figure 5: CLEAN and SIFARI reconstructions of the three models described in Sec. III.4 . Rows show the inclined ring with a point source, the multiple-ring system, and the molecular cloud, from top to bottom. Columns show the ground truth, CLEAN model, restored CLEAN image, and SIFARI reconstruction, from left to right. Each row uses a common logarithmic intensity scale. In the inclined-ring model (top row), the cyan plus sign marks the injected point-source position. The three numbers above each reconstruction give IFS, SSIM, and the recovered total-flux ratio, respectively. The FWHM of the CLEAN beam is $0.032^{\prime\prime}\times 0.027^{\prime\prime}$ .

Panel of Figure 6 — Figure 6: SIFARI reconstructions and uncertainty diagnostics for the three morphology benchmarks in Figure 5 . Rows show the inclined ring with a point source, the multiple-ring system, and the molecular cloud, from top to bottom. Columns show the SIFARI reconstruction, the pixel-wise standard deviation across SWAG samples ( $\sigma_{\mathrm{SWAG}}$ ), and the SNR defined by Eq. 7 , from left to right. The white contours on the reconstruction and SNR panels mark SNR=5.

Panel of Figure 7 — Figure 7: Comparison of reconstructed images and signal-to-noise ratio (SNR) diagnostics for real PDS 70 data. Top: global view. Bottom: zoomed-in view of the central region, including the central emission and the locations of PDS 70 b and PDS 70 c. In each row, the panels show, from left to right, the restored CLEAN image, the SIFARI reconstruction, and the SIFARI SNR map. The CLEAN and SIFARI images share a common intensity scale. The white contours on the SIFARI and SNR panels mark SNR=5, as defined in Eq. 7 . The CLEAN beam is $0.045^{\prime\prime}\times 0.034^{\prime\prime}$ .

Figure 8: Three image reconstructions of the WISPIT 2 continuum, shown with a common color scale. Left: naturally weighted CLEAN image from the original calibrated visibilities, before self-calibration. Middle: SIFARI model fitted to the self-calibrated visibilities, without beam convolution or added residuals. Right: restored SIFARI image, formed by convolving the middle-panel model with the restoring beam and adding the residual image from the self-calibrated visibilities.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 9 — Figure 9: Comparison of SIFARI reconstructions for different Fourier feature scales. Rows (top to bottom) show the inclined-ring, multiple-ring, and molecular-cloud benchmarks, with a common logarithmic intensity scale within each row. The leftmost column shows the ground truth; the remaining panels show reconstructions at the Fourier feature scales $\sigma_{\nu}$ indicated in their titles. The scale selected by the visibility-supported heuristic estimator is marked “(auto).” Panel-header annotations report image fidelity (IFS; left), SSIM (center), and recovered flux ratio (right).

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