Hugh R. A. Jones

Ultracool dwarfs (UCDs) encompass the lowest-mass stars and brown dwarfs at the stellar–substellar boundary. These intrinsically faint objects are challenging to detect. However, the wide coverage and deep sensitivity of the Euclid survey will increase the number of confirmed and well-characterised UCDs by several orders of magnitude. We carried out a systematic study using Euclid Quick Data Release (Q1) low-resolution near-infrared (NIR) spectra to identify and characterise UCDs. A sample of previously known UCDs in Q1 was compiled to validate the analysis, which was then applied to newly identified UCD candidates in Euclid Deep Field North (EDF-N; 22.9 deg ² , down to J _E ≈ 24.5 mag). We confirm 33 new UCDs in EDF-N, spanning spectral types (SpTs) M7–T1 ( J _E = 17–21 mag) and distances of 50–600 pc. Six of them were also independently identified in other Q1 studies. We followed up on two of these objects with ground-based optical and NIR observations using the Gran Telescopio Canarias, providing an independent validation of the Euclid spectral classification and the ability of Q1 spectra to constrain the stellar parameters of UCDs. We determined the SpTs from Euclid Q1 spectra of 33 newly identified UCDs and 167 previously known UCDs by fitting spectral standards from the SpeX Prism Library Analysis Toolkit. The known UCD sample includes 103 objects in common with the Dark Energy Survey (M7–T0, J _E = 17–22 mag, and d = 50–600 pc), 6 objects from literature compilations of UCD sources (M8–T7, J _E = 18–20 mag, and d = 30–300 pc), and 58 in common with Gaia (M7–L1, J _E = 17–24 mag, and d = 70–300 pc). We derived independent stellar parameter estimates through atmospheric model fitting; the resulting effective temperatures are in good agreement with Euclid SpTs. Combined with complementary ground- and space-based data, these results demonstrate the potential for determining proper motions and identifying companions. Additionally, we investigated Gaia UCDs in Q1 by combining their mean XP spectra with Euclid NIR spectra, extending the wavelength coverage and enabling more comprehensive determinations of stellar parameters, including radii. To support broader classification efforts, we analysed the UCDs in colour–colour space and assessed their separation from quasar loci. We present a publicly available, machine-readable catalogue that includes Euclid observations of known and newly identified sources, with spectral classifications and stellar parameters. This work demonstrates Euclid ’s capability to identify and characterise the faintest stellar populations, establishing a foundation for large-scale UCD studies with forthcoming survey data.

We report the first detection of the ‘convective-transition gap’ (also known as the ‘M-dwarf gap’) in the globular cluster 47 Tuc (NGC 104). This feature, linked to a change in the physical properties of late-type dwarfs, has remained elusive, with only two detections so far. Leveraging the large number of stars, high resolution, and photometric precision enabled by Euclid , we detected a statistically significant, sharp discontinuity in the main-sequence luminosity function of 47 Tuc at I _E ≈22.9, which we identify as the convective-transition gap. A comparison of the observed properties of the gap in 47 Tuc with theoretical models shows how the gap can be a powerful diagnostic for probing the internal chemical structure of globular clusters and their multiple stellar populations. Following its initial discovery in the metal-poor cluster NGC 6397, the identification of a convective gap in the metal-rich 47 Tuc suggests that this feature is more general than previously thought. These results demonstrate that Euclid can be transformative well beyond cosmology, with impacts across multiple areas of astrophysics, including resolved stellar populations.

We present a new method for the automated classification of eclipsing binaries, into contact, detached, and semi-detached types using Kepler data. Phase-folded light curves are generated and chi-square vs. box size plots are constructed by comparing flux values to the median flux, revealing distinct class patterns. These patterns were first modelled using a polynomial damped sinusoidal function, whose period served as classification feature, achieving an overall accuracy of 86.5 percent. To capture more features and enhance accuracy, we trained a convolutional neural network, which improved the total accuracy to 90 percent, including 47 percent for the challenging semi-detached systems. However, several binaries displayed irregular chi-square signatures. To mitigate this, we incorporated simulated light curves generated with the PHOEBE modelling code, achieving 99 per cent accuracy in distinguishing contact and detached binaries. The resulting chi-square morphologies show a strong correlation with orbital period, and a subset of systems exhibit quarterly variability in their light curves and chi-square trends. We designate these as Temporally Varying systems. By measuring the normalized spread of the chi-square period across quarters, we define a statistical threshold that separates these systems from stable binaries. We reported four Temporally Varying systems not previously noted in the literature with magnetic activity that requires further investigation. Furthermore, cooler stars, namely late-F, G, K, and M types, display systematically higher variability than hotter stars. Cross-matching with catalogues of magnetically active stars indicates that stellar flares and starspots are the most likely causes of this enhanced variability.

We investigate the optical properties of graded-index tapered fibres designed for efficient coupling of telescope light into a spectrographic instrument. Mode field measurements and COMSOL simulations are presented for 5:1 graded-index tapers with final core diameters of 10–12.5 um. For a given initial fibre core size, the change in refractive index across the fibre core means that the mode field diameter in graded-index tapers is less than half that of step-index fibres. This allows smaller taper ratios in graded-index fibres to achieve the same mode field diameter. Measurements and simulations of mode field diameter agree well for coherent light. Simulated transmission values are lower than experimental results, likely due to limitations in mode summation and coupling assumptions. Overall, graded-index tapers achieve the target mode field diameter with significantly smaller physical taper ratios enabling high transmission efficiency.

To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framework—Projected-Space Inference of Stellar Parameters (PISP). PISP constructs an orthonormal basis and optimizes in the projected space, reducing the impact of parameter correlations on inference. The basis is constructed using either principal component analysis (PCA) or the active-subspace (AS) method and is combined with two inference strategies—Non-L1, which optimizes the projection coefficients for a user-specified projected dimensionality, and L1, which introduces L1 regularization in the full projected space to adaptively select projection directions—yielding four strategies: PCA-Non-L1, AS-Non-L1, PCA-L1, and AS-L1. For different computational scenarios, we implement two versions: PISP-CurveFit for fast single-spectrum inference and PISP-Adam for large-scale GPU-parallel inference. Using a fully connected neural network and a residual network as spectral emulators, we evaluate PISP on Kurucz synthetic spectra and on 722,896 APOGEE DR17 observed spectra. Compared to the baseline strategy, PISP improves inference accuracy for multiple parameters across all emulator–optimizer combinations. In synthetic data, PCA-L1 performs best, reducing the standard deviation of differences ( σ (Δ)) by at least 0.01 dex for 12 of 20 elemental abundances, with [N/H], [O/H], [Na/H], [Co/H], [P/H], [V/H], [Cu/H] showing 0.05–0.72 dex reductions. In observed data, PCA-Non-L1 reduces σ (Δ) by >30 K for effective temperature and by at least 0.01 dex for 9 of 17 elemental abundances, with [O/H], [Na/H], [V/H] showing 0.05–0.20 dex reductions, while achieving a ∼4× efficiency gain, slightly outperforming PCA-L1.

Detecting small planets via the radial velocity (RV) method remains challenged by signals induced by stellar variability, versus the effects of the planet(s). Here, we explore using Gaussian process (GP) regression with Transiting Exoplanet Survey Satellite (TESS) photometry in modeling RVs to help to mitigate stellar jitter from oscillations and granulation for exoplanet detection. We applied GP regression to simultaneous TESS photometric and RV data of HD 5562, a G-type subgiant ( M _⋆ = 1.09 M _⊙ , R _⋆ = 1.88 R _⊙ ) with a V magnitude of 7.17, using photometry to inform the priors for RV fitting. The RV data is obtained by the Magellan Planet Finder Spectrograph. The photometry-informed GP regression reduced the RV scatter of HD 5562 from 2.03 to 0.51 m s ⁻¹ . We performed injection and recovery tests to evaluate the potential of GPs for discovering small exoplanets around evolved stars, which demonstrate that the GP provides comparable noise reduction to the binning method. We also found that the necessity of photometric data depends on the quality of the RV dataset. For long baseline and high-cadence RV observations, GP regression can effectively mitigate stellar jitter without photometric data. However, for intermittent RV observations, incorporating photometric data improves GP fitting and enhances detection capabilities.

To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented in IDL. Rather than a direct code translation, this framework refactors LASP with two complementary modules: LASP-CurveFit, a new implementation of the LASP fitting procedure that runs on a CPU, preserving legacy logic while improving data I/O and multithreaded execution efficiency; and LASP-Adam-GPU, a GPU-accelerated method that introduces grouped optimization by constructing a joint residual function over multiple observed and model spectra, enabling high-throughput parameter inference across tens of millions of spectra. Applied to 10 million LAMOST spectra, the framework reduces runtime from 84 to 48 hr on the same CPU platform and to 7 hr on an NVIDIA A100 GPU, while producing results consistent with those from the original pipeline. The inferred errors agree well with the parameter variations from repeat observations of the same target (excluding radial velocities), while the official empirical errors used in LASP are more conservative. When applied to DESI DR1, our effective temperatures and surface gravities agree better with APOGEE than those from the DESI pipeline, particularly for cool giants, while the latter performs slightly better in radial velocity and metallicity. These results suggest that the framework delivers reliable accuracy, efficiency, and transferability, offering a practical approach to parameter inference in large spectroscopic surveys. The code and DESI-based catalog are available via DOI: 10.12149/101679 and DOI: 10.12149/101675 , respectively.

Precise stellar parameters for M-type stars, the Galaxy’s most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog using techniques including deep learning and color–magnitude diagrams to remove 22,496 non-M spectra, corrected 2078 dwarf/giant classifications, and updated 12,900 radial velocities. This resulted in a cleaner catalog containing 870,518 M-type spectra (820,493 dwarfs, 50,025 giants). Second, applying a label transfer strategy using values from APOGEE DR16 for parameter prediction with a tenfold cross-validated convolutional neural network ensemble architecture, we predicted T _eff , log g , [M/H], and [ α /M] separately for M dwarfs (dMs) and giants (gMs). The average internal errors for dMs/gMs are, respectively, T _eff 30/17 K, log g 0.07/0.07 dex, [M/H] 0.07/0.05 dex, and [ α /M] 0.02/0.02 dex. Comparison with APOGEE demonstrates external precisions of 34/14 K, 0.12/0.07 dex, 0.09/0.04 dex, and 0.03/0.02 dex for dMs/gMs, which represent precision improvements of over 20% for dMs and over 50% for gMs compared to previous literature results. The catalog is available at doi: 10.12149/101668 .

The optical spectra of Titan reveal a rich set of absorption features, most of which are likely associated with methane (CH ₄ ). Methane is a key molecule in planetary and exoplanetary atmospheres, yet a comprehensive high-resolution linelist at optical wavelengths remains incomplete. This study identified and characterized potential CH ₄ absorption features in high-resolution optical spectra of Titan, providing essential data for linelist development and improving CH ₄ detection and characterization. We analyzed Titan spectra from the Echelle SPectrograph for Rocky Exoplanet and Stable Spectroscopic Observations (ESPRESSO) spectrograph ( R ≈ 190,000), identifying intrinsic features and measuring their relative strengths. A conservative detection approach was employed, slightly overestimating solar and telluric contributions to distinguish them from Titan’s intrinsic features. To assess the impact of spectral resolution, we compared the ESPRESSO data with Titan Ultraviolet and Visual Echelle Spectrograph (UVES) data ( R ≈ 110,000). We identified 6195 absorption features in the ESPRESSO spectra potentially associated with CH ₄ , of which 5436 are newly reported. ESPRESSO detected twice as many features as UVES in overlapping regions, highlighting the advantage of higher-resolution data. Most detected lines remained unresolved, so our reported features are primarily blended absorption structures. We estimated the detection limit for feature identification to correspond to a CH ₄ absorption coefficient of approximately 0.02 km am ⁻¹ . Comparison of our results with a previous analysis of Titan UVES spectra and with experimental CH ₄ data at a similar temperature showed good agreement, while some discrepancies were observed when compared with data acquired at a different temperature. We provide a comprehensive list of Titan absorption features with key reliability metrics, along with Titan’s intrinsic spectra, to support future studies.