Diffraction-driven photometry: A novel method for stellar temperature determination

¹ Departamento de Física, Universidad de La Laguna (ULL), 38206 San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain
² Departamento Física de Materiales, Instituto Nicolás Cabrera, Universidad Autónoma de Madrid, 28049 Madrid, Spain

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 26 September 2025
Accepted: 9 March 2026

Abstract

Context. Estimating stellar temperatures is a fundamental task in astrophysics, typically achieved using colour indices (CI) derived from multi-filter photometry. However, this method often requires sequential observations through different filters, making it vulnerable to brightness variations between exposures – such as those caused by stellar variability or transits – and sensitive to inter-filter calibration errors, both of which can compromise temperature estimation.

Aims. We introduce diffraction-driven photometry (DDP), a complementary technique that enables stellar temperature estimation from a single broadband image by extracting spectral information directly from the polychromatic point spread function (P-PSF).

Methods. We analytically derived the relationship between the P-PSF morphology and stellar temperature using Planck's law and diffraction theory. A new metric, the P-PSF intensity ratio (PIR), is defined and shown to correlate monotonically and invertibly with temperature. We performed extensive numerical simulations to verify this behaviour and quantify the performance of temperature retrieval under realistic instrumental noise using synthetic detector models.

Results. Our simulations show that the PIR provides a consistent estimator of stellar temperature and remains monotonic within the airy disc across a broad range of stellar types. The method is intrinsically robust against global flux variations and can be adapted to different optical systems through calibration. It performs best at high signal-to-noise ratios (S/Ns), but it still allows for useful classification at more moderate S/Ns.

Conclusions. We do not aim to replace classical CI techniques with DDP, but rather we offer a complementary route particularly well suited to scenarios where multi-filter observations are impractical. Its simplicity makes it attractive for small satellites and enables retroactive application to single-band archival data, providing a practical pathway towards reliable temperature retrieval under specific observational constraints.