Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1608.01457

Stellar photometry with Multi Conjugate Adaptive Optics

Giuliana Fiorentino·Davide Massari·Alan McConnachie·Peter B. Stetson·Giuseppe Bono·Paolo Turri·David Andersen·Jean-Pierre Veran·Emiliano Diolaiti·Laura Schreiber·Paolo Ciliegi·Michele Bellazzini·Eline Tolstoy·Matteo Monelli·Giacinto Iannicola·Ivan Ferraro·Vincenzo Testa
View original source ↗Read source document ↗Export metadata ↓

Abstract

We overview the current status of photometric analyses of images collected with Multi Conjugate Adaptive Optics (MCAO) at 8-10m class telescopes that operated, or are operating, on sky. Particular attention will be payed to resolved stellar population studies. Stars in crowded stellar systems, such as globular clusters or in nearby galaxies, are ideal test particles to test AO performance. We will focus the discussion on photometric precision and accuracy reached nowadays. We briefly describe our project on stellar photometry and astrometry of Galactic globular clusters using images taken with GeMS at the Gemini South telescope. We also present the photometry performed with DAOPHOT suite of programs into the crowded regions of these globulars reaching very faint limiting magnitudes Ks ~21.5 mag on moderately large fields of view (~1.5 arcmin squared). We highlight the need for new algorithms to improve the modeling of the complex variation of the Point Spread Function across the field of view. Finally, we outline the role that large samples of stellar standards plays in providing a detailed description of the MCAO performance and in precise and accurate colour{magnitude diagrams.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giuliana Fiorentino, Davide Massari, Alan McConnachie, Peter B. Stetson, Giuseppe Bono, Paolo Turri, David Andersen, Jean-Pierre Veran, Emiliano Diolaiti, Laura Schreiber, Paolo Ciliegi, Michele Bellazzini, Eline Tolstoy, Matteo Monelli, Giacinto Iannicola, Ivan Ferraro, Vincenzo Testa. 2016-08-04. Stellar photometry with Multi Conjugate Adaptive Optics. https://doi.org/10.1117/12.2234448

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

KEEP EXPLORING

Related papers

arXiv2026

GW-YOLO: Multi-transient segmentation in LIGO using computer vision

Time series data and their time-frequency representations from gravitational-wave interferometers present opportunities for artificial intelligence methods in signal and image processing, particularly for low-latency analysis. In this work, we introduce GW-YOLO, a signal and noise identification tool based on the YOLO (You Only Look Once) object detection framework. GW-YOLO identifies whether an observed transient contains noise, an astrophysical signal, or both, while providing time-frequency coordinates of detected objects through pixel-level segmentation masks. Our approach achieves a 53% detection efficiency for binary black hole signals in the signal-to-noise ratio (SNR) 12--15 range when they overlap with transient noise, increasing to more than 75% at SNR 15--18. For binary neutron star signals overlapping with transient noise, the detection efficiency reaches 57% at SNR 30--33 and 87% at SNR 39--42. To our knowledge, this is the first quantitative assessment of the ability to detect astrophysical signals overlapping with realistic instrumental noise in gravitational-wave interferometers. We also present a fully automated, low-latency pipeline that produces pixel-level segmentation masks for individual noise transients and astrophysical signals, enabling further automation of event validation and downstream noise-mitigation procedures.

astro-ph.IM↗
arXiv2026

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↗
arXiv2026

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↗