Search arXivSearch

arXiv · 2607.08146

On-sky dark hole diggin' with implicit Electric Field Conjugation on MagAO-X

Abstract

Direct spectroscopy is very promising approach to characterizing the atmospheres of nearby rocky exoplanets. Non-common path aberrations (NCPA) are differential aberrations between the science optical path and the adaptive optics optical path. The NCPA leak through the coronagraph and create speckles that mimic exoplanet signals. This limits the sensitivity of high-contrast imaging instruments at close angular separations - exactly the separations where we want to search for rocky exoplanets with current and future telescopes and instruments. We aim to actively remove the NCPA on-sky during observations by using focal plane wavefront sensing and control with the newly upgraded MagAO-X instrument. MagAO-X is equipped with a unique second-stage Adaptive Optics (AO) system. The second-stage AO system contains a dedicated deformable mirror (DM) for coronagraphic focal plane wavefront control. This DM is placed after the science and AO beam-splitter and is therefore not seen by the main AO loop. The DM has been recently upgraded from an ALPAO-97 to a Boston Micromachine Kilo-DM. The new Kilo-DM enables focal plane wavefront control with the implicit Electric Field Conjugation (iEFC) algorithm. We developed the necessary procedures to run iEFC with MagAO-X on-sky. We demonstrated the successful removal of NCPA on-sky with an iEFC interaction matrix that was calibrated on the MagAO-X internal source. This demonstrates the repeatability between our off-sky and on-sky alignment. The iEFC algorithm was tested on HR4796A and Alpha Centauri in 0.5" seeing conditions. We saw a reduction of the NCPA by a factor of 2 to 20. This on-sky validation confirms the robustness and efficiency of iEFC under realistic observing conditions, paving the way for its integration into next-generation AO systems for the Extremely Large Telescope and Giant Magellan Telescope.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S. Y. Haffert, J. Liberman, J. R. Males, L. M. Close, W. B. Foster, K. Van Gorkom, O. Guyon, A. D. Hedglen, P. T. Johnson, M. Y. Kautz, J. K. Kueny, J. Li, J. D. Long, J. Lumbres, M. Mars, E. A. McEwen, A. McLeod, L. Schatz, E. Tonucci, K. Twitchell. 2026-07-09. On-sky dark hole diggin' with implicit Electric Field Conjugation on MagAO-X. https://arxiv.org/abs/2607.08146

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Multi-Scale Contrastive Attention for Light-Curve Representation Learning

Current and next-generation time-domain surveys demand automated techniques capable of analyzing millions of light curves, observed in multiple filters, without relying on exhaustive human annotation or scarce spectroscopic follow-up. We present Astra-CLR, an attention-based, self-supervised contrastive learning framework which enables the representation of raw light curves into a highly discriminative latent space. Pre-trained on $\sim$2.1 million unlabeled Zwicky Transient Facility light curves, the framework utilizes partial light curves as input sequences to generate asymmetric, multi-scale temporal views (explicitly contrasting shorter sequences against longer ones) forcing the network to learn a robust "local-to-global" mapping strategy. Furthermore, we introduce a novel multi-view late fusion architecture that extends the model to efficiently handle longer light curves with larger numbers of observations while accommodating the different cadences associated with each filter. The discriminatory power of the resulting representations was evaluated by using them as input to a Multinomial Logistic Regression classifier, trained to identify 12 broad classes of variability. Final accuracy achieved $\sim 0.70$. When applying a label-efficient, partial top-layer fine-tuning strategy, the topological structure of the latent space is significantly refined, boosting results to $\sim$0.77. Astra-CLR is the first publicly available multi-filter time-series Transformer trained exclusively on real ZTF light curves. Results presented here demonstrate that it provides an ideal foundation for the development of end-to-end pipelines, taking into account color evolution and respecting the inhomogeneous nature of astronomical light curve sampling.

astro-ph.IM

Combining astrometry with pulsar timing: the first joint analysis of very low frequency gravitational waves

The pHz to sub-nHz GW regime remains largely unexplored but is crucial for mapping the early inspiral stage of SMBHBs and probing early-Universe physics. Astrometry and pulsar timing offer orthogonal and deeply complementary secular observables to investigate this frequency band. We aim to present the first joint data analysis combining real astrometric proper motions with binary pulsar timing, to search for and constrain continuous gravitational waves (CWs) sourced by SMBHBs in the ultra-low-frequency regime ($10^{-12} \le f_{GW} \le10^{-9} Hz$). We employ a Bayesian model selection and upper limit estimation framework to combine apparent proper motion displacements of $\sim 1.5 \times 10^6$ quasars from the Gaia CRF3 catalog with the line-of-sight orbital period derivatives ($\dot{P}_b$) of 11 high-precision binary pulsars. To prevent spurious detections, we heavily model instrumental and astrophysical systematics: we propagate the Galactic potential uncertainty for pulsars via Monte Carlo simulations and perform a Vector Spherical Harmonics (VSH) decomposition up to the octupole order (l=3) for quasars. We find no statistically significant evidence for a CWs signal in the joint analysis ($\ln B_{joint} = -0.42 \pm 0.03$). In the absence of detection, we set the tightest constraints to date on CW strain in the pHz band, yielding a 95% upper limit of $h_0\le6.4x10^{-11}$ at a reference frequency of $f_{ref} = 4 \times 10^{-10}$ Hz. The combined dataset achieves full sky coverage and improves single-dataset upper limits by 20%-30%. Combining orthogonal observables successfully breaks spatial degeneracies intrinsic to isolated searches. Furthermore, forecasts from inj.-rec. indicate that with the extended temporal baseline and reduced uncertainties of the upcoming Gaia DR4, this joint framework is poised to break the $h_0 < 10^{-11}$ upper limit barrier for sub-nHz CWs.

astro-ph.IM

Bayesian classification of astronomical spectra with class uncertainties

Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

astro-ph.IM