CyberSec Research

Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of studies into the evolution of galaxies. In the vast majority of cases the main source of information will come from broad-band images and data products thereof. Therefore, there is a pressing need to identify or develop scalable yet reliable methodologies to estimate the redshift and physical properties of galaxies using broad-band photometry from Euclid, optionally including ground-based optical photometry also. To address this need, we present a novel method to estimate the redshift, stellar mass, star-formation rate, specific star-formation rate, E(B-V), and age of galaxies, using mock Euclid and ground-based photometry. The main novelty of our property-estimation pipeline is its use of the CatBoost implementation of gradient-boosted regression-trees, together with chained regression and an intelligent, automatic optimization of the training data. The pipeline also includes a computationally-efficient method to estimate prediction uncertainties, and, in the absence of ground-truth labels, provides accurate predictions for metrics of model performance up to z~2. We apply our pipeline to several datasets consisting of mock Euclid broad-band photometry and mock ground-based ugriz photometry, to evaluate the performance of our methodology for estimating the redshift and physical properties of galaxies detected in the Euclid Wide Survey. The quality of our photometric redshift and physical property estimates are highly competitive overall, validating our modeling approach. We find that the inclusion of ground-based optical photometry significantly improves the quality of the property estimation, highlighting the importance of combining Euclid data with ancillary ground-based optical data. (Abridged)

Accurately estimating the statistical properties of noise is important in space-based gravitational wave data analysis. Traditional methods often assume uncorrelated noise or impose restrictive parametric forms on cross-channel correlations, which could lead to biased estimation in complex instrumental noise. This paper introduces a spline-based framework with trans-dimensional Bayesian inference to reconstruct the full noise covariance matrix, including frequency-dependent auto- and cross-power spectral densities, without prior assumptions on noise shapes. The developed software $\mathtt{NOISAR}$ can recover the features of the noise power spectrum curves with a relative error $\leq 10\%$ for both auto- and cross-one.

This paper introduces two astronomical methods developed through computational simulation to evaluate the historical dating of ancient astronomical sources. The first identifies a 1151-year planetary cycle based on the recurrence of visible configurations of Mercury to Saturn, including the Sun and Moon, from a geocentric perspective. The second, called SESCC (Speed-Error Signals Cross Correlation), statistically estimates the epoch of star catalogs by analyzing the correlation between positional error and proper motion in ecliptic latitude. Both methods are reproducible, data-driven, and yield results that contradict key tenets of the New Chronology proposed by Fomenko and Nosovsky, most notably the claim that the Anno Domini began in 1152 CE. Open-source code and analysis tools are provided for independent verification.

The search for signs of life in the Universe has entered a new phase with the advent of the James Webb Space Telescope (JWST). Detecting biosignature gases via exoplanet atmosphere transmission spectroscopy is in principle within JWST's reach. We reflect on JWST's early results in the context of the potential search for biological activity on exoplanets. The results confront us with a complex reality. Established inverse methods to interpret observed spectra-already known to be highly averaged representations of intricate 3D atmospheric processes-can lead to disparate interpretations even with JWST's quality of data. Characterizing rocky or sub-Neptune-size exoplanets with JWST is an intricate task, and moves us away from the notion of finding a definitive "silver bullet" biosignature gas. Indeed, JWST results necessitate us to allow "parallel interpretations" that will perhaps not be resolved until the next generation of observatories. Nonetheless, with a handful of habitable-zone planet atmospheres accessible given the anticipated noise floor, JWST may continue to contribute to this journey by designating a planet as biosignature gas candidate. To do this we will need to sufficiently refine our inverse methods and physical models for confidently quantifying specific gas abundances and constraining the atmosphere context. Looking ahead, future telescopes and innovative observational strategies will be essential for the reliable detection of biosignature gases.

The European Space Agency's Ariel mission, scheduled for launch in 2029, aims to conduct the first large-scale survey of atmospheric spectra of transiting exoplanets. Ariel achieves the high photometric stability on transit timescales required to detect the spectroscopic signatures of chemical elements with a payload design optimized for transit photometry that either eliminates known systematics or allows for their removal during data processing without significantly degrading or biasing the detection. Jitter in the spacecraft's line of sight is a source of disturbance when measuring the spectra of exoplanet atmospheres. We describe an improved algorithm for de-jittering Ariel observations simulated in the time domain. We opt for an approach based on the spatial information on the Point Spread Function (PSF) distortion from jitter to detrend the optical signals. The jitter model is based on representative simulations from Airbus Defence and Space, the prime contractor for the Ariel service module. We investigate the precision and biases of the retrieved atmospheric spectra from the jitter-detrended observations. At long wavelengths, the photometric stability of the Ariel spectrometer is already dominated by photon noise. Our algorithm effectively de-jitters both photometric and spectroscopic data, ensuring that the performance remains photon noise-limited across the entire Ariel spectrum, fully compliant with mission requirements. This work contributes to the development of the data reduction pipeline for Ariel, aligning with its scientific goals, and may also benefit other astronomical telescopes and instrumentation.

The astronomy communities are widely recognised as mature communities for their open science practices. However, while their data ecosystems are rather advanced and permit efficient data interoperability, there are still gaps between these ecosystems. Semantic artefacts (e.g., ontologies, thesauri, vocabularies or metadata schemas) are a means to bridge that gap as they allow to semantically described the data and map the underlying concepts. The increasing use of semantic artefacts in astronomy presents challenges in description, selection, evaluation, trust, and mappings. The landscape remains fragmented, with semantic artefacts scattered across various registries in diverse formats and structures -- not yet fully developed or encoded with rich semantic web standards like OWL or SKOS -- and often with overlapping scopes. Enhancing data semantic interoperability requires common platforms to catalog, align, and facilitate the sharing of FAIR semantic artefacts. In the frame of the FAIR-IMPACT project, we prototyped a semantic artefact catalogue for astronomy, heliophysics and planetary sciences. This exercise resulted in improved vocabulary and ontology management in the communities, and is now paving the way for better interdisciplinary data discovery and reuse. This article presents current practices in our discipline, reviews candidate SAs for such a catalogue, presents driving use cases and the perspective of a real production service for the astronomy community based on the OntoPortal technology, that will be called OntoPortal-Astro.

KAGRA is a kilometer-scale cryogenic gravitational-wave (GW) detector in Japan. It joined the 4th joint observing run (O4) in May 2023 in collaboration with the Laser Interferometer GW Observatory (LIGO) in the USA, and Virgo in Italy. After one month of observations, KAGRA entered a break period to enhance its sensitivity to GWs, and it is planned to rejoin O4 before its scheduled end in October 2025. To accurately recover the information encoded in the GW signals, it is essential to properly calibrate the observed signals. We employ a photon calibration (Pcal) system as a reference signal injector to calibrate the output signals obtained from the telescope. In ideal future conditions, the uncertainty in Pcal could dominate the uncertainty in the observed data. In this paper, we present the methods used to estimate the uncertainty in the Pcal systems employed during KAGRA O4 and report an estimated system uncertainty of 0.79%, which is three times lower than the uncertainty achieved in the previous 3rd joint observing run (O3) in 2020. Additionally, we investigate the uncertainty in the Pcal laser power sensors, which had the highest impact on the Pcal uncertainty, and estimate the beam positions on the KAGRA main mirror, which had the second highest impact. The Pcal systems in KAGRA are the first fully functional calibration systems for a cryogenic GW telescope. To avoid interference with the KAGRA cryogenic systems, the Pcal systems incorporate unique features regarding their placement and the use of telephoto cameras, which can capture images of the mirror surface at almost normal incidence. As future GW telescopes, such as the Einstein Telescope, are expected to adopt cryogenic techniques, the performance of the KAGRA Pcal systems can serve as a valuable reference.

We describe a resonant cavity search apparatus for axion dark matter constructed by the Quantum Sensors for the Hidden Sector (QSHS) collaboration. The apparatus is configured to search for QCD axion dark matter, though also has the capability to detect axion-like particles (ALPs), dark photons, and some other forms of wave-like dark matter. Initially, a tuneable cylindrical oxygen-free copper cavity is read out using a low noise microwave amplifier feeding a heterodyne receiver. The cavity is housed in a dilution refrigerator and threaded by a solenoidal magnetic field, nominally 8T. The apparatus also houses a magnetic field shield for housing superconducting electronics, and several other fixed-frequency resonators for use in testing and commissioning various prototype quantum electronic devices sensitive at a range of axion masses in the range $\rm 2.0$ to $\rm 40\,eV/c^2$. We present performance data for the resonator, dilution refrigerator, and magnet, and plans for the first science run.

We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current approach adopted by the Zwicky Transient Facility (ZTF). The target NEAs have a high proper motion of up to tens of degrees per day and thus appear as streaks of light in the images. We trained our CNNs to detect these streaks using three datasets: a set with real asteroid streaks, a set with synthetic (i.e., simulated) streaks and a mixed set, and tested the resultant models on real survey images. The results achieved were almost identical across the three models: $0.843\pm0.005$ in completeness and $0.820\pm0.025$ in precision. The bias on streak measurements reported by the CNNs was $1.84\pm0.03$ pixels in streak position, $0.817\pm0.026$ degrees in streak angle and $-0.048\pm0.003$ in fractional bias in streak length (computed as the absolute length bias over the streak length, with the negative sign indicating an underestimation). We compared the performance of our CNN trained with a mix of synthetic and real streaks to that of the ZTF human scanners by analyzing a set of 317 streaks flagged as valid by the scanners. Our pipeline detected $80~\%$ of the streaks found by the scanners and 697 additional streaks that were subsequently verified by the scanners to be valid streaks. These results suggest that our automated pipeline can complement the work of the human scanners at no cost for the precision and find more objects than the current approach. They also prove that the synthetic streaks were realistic enough to be used for augmenting training sets when insufficient real streaks are available or exploring the simulation of streaks with unusual characteristics that have not yet been detected.

Component separation is the process of extracting one or more emission sources in astrophysical maps. It is therefore crucial to develop models that can accurately clean the cosmic microwave background (CMB) in current and future experiments. In this work, we present a new methodology based on neural networks which operates on realistic temperature and polarization simulations. We assess its performance by comparing the power spectra of the output maps with those of the input maps and other emissions. For temperature, we obtain residuals of $20 \pm \mu K^{2}$. For polarization, we analyze the $E$ and $B$ modes, which are related to density (scalar) and primordial gravitational waves (tensorial) perturbations occurring in the first second of the Universe, obtaining residuals of $10^{-2} \mu K^{2}$ at $l>200$ and $10^{-2}$ and $10^{-3} \mu K^{2}$ for $E$ and $B$, respectively.

Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spiking Neural Networks (SNNs) offer a promising solution for real-time RFI detection by exploiting the time-varying nature of radio observation and neuron dynamics together. This work explores the inclusion of polarisation information in SNN-based RFI detection, using simulated data from the Hydrogen Epoch of Reionisation Array (HERA) instrument and provides power usage estimates for deploying SNN-based RFI detection on existing neuromorphic hardware. Preliminary results demonstrate state-of-the-art detection accuracy and highlight possible extensive energy-efficiency gains.

Radio telescopes observe extremely faint emission from astronomical objects, ranging from compact sources to large scale structures that can be seen across the whole sky. Satellites actively transmit at radio frequencies (particularly at 10--20\,GHz, but usage of increasing broader frequency ranges are already planned for the future by satellite operators), and can appear as bright as the Sun in radio astronomy observations. Remote locations have historically enabled telescopes to avoid most interference, however this is no longer the case with dramatically increasing numbers of satellites that transmit everywhere on Earth. Even more remote locations such as the far side of the Moon may provide new radio astronomy observation opportunities, but only if they are protected from satellite transmissions. Improving our understanding of satellite transmissions on radio telescopes across the whole spectrum and beyond is urgently needed to overcome this new observational challenge, as part of ensuring the future access to dark and quiet skies. In this contribution we summarise the current status of observations of active satellites at radio frequencies, the implications for future astronomical observations, and the longer-term consequences of an increasing number of active satellites. This will include frequencies where satellites actively transmit, where they unintentionally also transmit, and considerations about thermal emission and other unintended emissions. This work is ongoing through the IAU CPS.

Modern astronomical surveys, such as the Zwicky Transient Facility (ZTF), are capable of detecting thousands of transient events per year, necessitating the use of automated and scalable data analysis techniques. Recent advances in machine learning have enabled the efficient classification and characterization of these transient phenomena. We aim to develop a fully systematic pipeline to identify candidate stellar collision events in galactic nuclei, which may otherwise be identified as tidal disruption events or other transients. We also seek to validate our simulations by comparing key physical parameters derived from observations and used in modeling these events. We generate a comprehensive bank of simulated light curves spanning a range of physical parameters and employ an approximate nearest neighbor algorithm (via the annoy library) to match these with observed ZTF light curves. Our pipeline is successfully able to associate observed ZTF light curves with simulated events. The resulting estimated parameters, including supermassive black hole masses and ejecta mass, are presented and compared to known values when applicable. We demonstrate that a systematic, machine learning-based approach can effectively identify and characterize stellar collision candidate events from large-scale transient surveys. This methodology is especially promising for future surveys which will provide us with significantly high volumes of data, such as LSST, where automated, data-intensive analysis will be critical for advancing our understanding of transient astrophysical phenomena.

Gaia parallax measurements for stars with poor astrometric fits -- as evidenced by high renormalized unit weight error (RUWE) -- are often assumed to be unreliable, but the extent and nature of their biases remain poorly quantified. High RUWE is usually a consequence of binarity or higher-order multiplicity, so the parallaxes of sources with high RUWE are often of greatest astrophysical interest. Using realistic simulations of Gaia epoch astrometry, we show that the parallax uncertainties of sources with elevated RUWE are underestimated by a factor that ranges from 1 to 4 and can be robustly predicted from observables. We derive an empirical prescription to inflate reported uncertainties based on a simple analytic function of RUWE, apparent magnitude, and parallax. We validate the correction using (a) single-star solutions for Gaia sources with known orbital solutions and (b) wide binaries containing one component with elevated RUWE. The same uncertainty corrections are expected to perform well in DR4 and DR5. Our results demonstrate that Gaia parallaxes for high-RUWE sources can still yield robust distance estimates if uncertainties are appropriately inflated, enabling distance constraints for triples, binaries with periods too long or too short to be fit astrometrically, and sources blended with neighboring sources.

The detectors of the JWST Mid-Infrared Instrument (MIRI) Medium Resolution Spectrometer (MRS) form low-finesse resonating cavities that cause periodic count rate modulations (fringes) with peak amplitudes of up to 15% for sources external to MIRI. To detect weak features on a strong continuum and reliably measure line fluxes and line-flux ratios, fringe correction is crucial. This paper describes the first of two steps implemented in the JWST Science Calibration Pipeline, which is the division by a static fringe flat that removes the bulk of the fringes for extended sources. Fringe flats were derived by fitting a numerical model to observations of spatially extended sources. The model includes fringes that originate from two resonating cavities in the detector substrate (a third fringe component that originates from the dichroic filters is not included). The model, numerical implementation, and resulting fringe flats are described, and the efficiency of the calibration was evaluated for sources of various spatial extents on the detector. Flight fringe flats are obtained from observations of the planetary nebula NGC 7027. The two fringe components are well recovered and fitted by the model. The derived parameters are used to build a fringe flat for each MRS spectral band, except for 1A and 1B due to the low signal-to-noise ratio of NGC 7027 in these bands. When applied to extended sources, fringe amplitudes are reduced to the sub-percent level on individual spaxels. For point sources, they are reduced to amplitudes of 1 to 5% considering individual spaxels and a single dither position, and decrease to the 1 to 2% level after two-dimensional residual fringe correction. The fringe flats derived from this work are the reference files currently in use by the JWST Science Calibration Pipeline. They provide an efficient calibration for extended sources, and are less efficient for point sources.

The Laser Interferometer Space Antenna (LISA) will detect gravitational waves from the population of merging massive black holes binaries (MBHBs) throughout the Universe. The LISA data stream will feature many superposed signals from different astrophysical sources, requiring a global fit procedure. Most of the MBHB signals will be loud enough to be detected days or even weeks before the merger; and for those sources LISA will be able to predict the time of the merger well in advance of the coalescence, as well as an approximate position in the sky. In this paper, we present a fast detection and signal reconstruction scheme for massive black hole binaries in the LISA observation band. We propose: (i) a detection scheme for MBHB mergers allowing a first subtraction of these signals for the purpose of a global fit, and (ii) an efficient early detection scheme providing a time-of-merger estimate for a pre-merger signal, that will allow to trigger a protection period, placing LISA in ``do not disturb'' mode and enabling more detailed analysis that will facilitate multi-messenger observations. We highlight the effect of confusion of several overlapping in time MBHB signals in the pre-merger detection.

The invited review of own algorithms and software (MAVKA and MCV) for the data analysis of astronomical signals - irregularly spaced, multi-periodic multi-harmonic, periodogram analysis and approximations with taking into account a polynomial trend simultaneously, instead of commonle used detrending and prewhitening. The references to original papers are listed.

Spatial data fusion is a bottleneck when it meets the scale of 10 billion records. Cross-matching celestial catalogs is just one example of this. To challenge this, we present a framework that enables efficient cross-matching using Learned Index Structures. Our approach involves a data transformation method to map multi-dimensional data into easily learnable distributions, coupled with a novel search algorithm that leverages the advantages of model pairs, significantly enhancing the efficiency of nearest-neighbor search. In this study, we utilized celestial catalog data derived from astronomical surveys to construct the index and evaluated the speed of the cross-matching process. Using the HEALPix segmentation scheme, we built an independent model object for each tile and developed an end-to-end pipeline to construct a framework with semantic guarantees for record retrieval in query and range search. Our results show that the proposed method improves cross-matching speed by more than four times compared to KD-trees for a radius range between 1 milli-arcseconds and 100 arcseconds.