Alex G. Markowitz; M. Krumpe; D. Homan; Bożena Czerny; Mariusz Gromadzki; Hartmut Winkler; J. Wilms; Steven Hämmerich; G. Lamer; T. Saha; D. A. H. Buckley; Malte Schramm; Daniel E. Reichart; Mara Salvato; P. Baldini
Strasbourg Astronomical Data Center · DOI
Tristan Whitmarsh; Claire M. Mulvey; Marta Pàez‐Ribes; Atefeh Fatemi; Dario Bressan
M. Cretignier; N. C. Hara; S. Aigrain; S Sulis; F Bouchy; S. T. Hodgkin; J. Blanco-Pozo; A Collier-Cameron; M Damasso; E C Gonçalves; Luca Malavolta; G. Mantovan; D. Nardiello; M. Perger; I Ribas; Y Zhao; X Dumusque; Annelies Mortier; Khaled Al Moulla; S C C Barros; N C Santos; C S N Martinez; C.A. Watson; Thomas G Wilson; S Udry
Monthly Notices of the Royal Astronomical Society · DOI
ABSTRACT The PLATO Stellar Variability Mitigation Working Group designed a radial velocity (RV) data challenge to assess the precision and accuracy achievable on RV semi-amplitudes in the context of the future PLATO follow-up. Using Sun-as-a-star observations obtained with HARPS-N, we generated three multiplanet systems with realistic sampling, an effective signal-to-noise ratio of $\sim 450$, an exposure time of $T_{\mathrm{exp}} = 15$ min, and a total of $N \sim 1000$ observations. The resulting data set is publicly available and can serve as a benchmark for the community to test and compare RV analysis methods. Eight independent teams participated in the challenge, aiming to recover the semi-amplitudes of both transiting and non-transiting planets. A meta-analysis of the submitted results shows that, with current data analysis methods, HARPS-N-like stability and the observing strategy studied in this paper, achieving a precision on semi-amplitudes better than 10 per cent for habitable-zone signals around a solar-type star is challenging, and is typically limited to planets with masses of $\sim 8$–10 $\rm{\rm{M}_{\oplus }}$. This performance is expected to improve for K dwarfs, where planets down to $\sim 2$–5 $\rm{\rm{M}_{\oplus }}$ may be characterized at a similar level of precision. Finally, based on the results of this challenge and the occurrence of false positives, we derive an empirical detection threshold for RV surveys, which can be used to distinguish robust planetary detections from candidates.
Melis O Irfan; Giuseppe Puglisi
Monthly Notices of the Royal Astronomical Society · DOI
ABSTRACT We present an all-sky map of the synchrotron spectral index and curvature between 45 and 2300 MHz at a resolution of 1$^{\circ }$ calculated from a combination of numerous partial sky empirical measurements. We employ a least-squares parametric fit which relies on removing a free–free emission template and a component separation technique which fits for both synchrotron and free–free emission. We compare our diffuse sky model estimates against those derived from the models widely used in the community (e.g. pysm3 and GSM) employing external data sets that were not included in the estimation process. Our evaluation focuses on identifying the enhanced consistency at both the map level and in pixel-to-pixel correlations, allowing for a more robust verification of our model’s performance. We find our parametric, least-squares synchrotron estimate to be the most reliable across radio frequencies as it consistently provides sky models with average accuracies (when compared to empirical data) of around 20 per cent, while other model performances range on average between 10 per cent and 70 per cent accurate. The results obtained have been made publicly accessible online and can be utilized to further develop and refine models of Galactic synchrotron emission.
Biswaraj Palit; A. Borkar; A. Różáńska; Alex G. Markowitz; Marzena Śniegowska; Swayamtrupta Panda; D. Homan; Krystian Iłkiewicz
Astronomische Nachrichten · DOI
ABSTRACT The Changing‐Look active galactic nucleus Mkn 590 is currently in a rejuvenated state, exhibiting a contemporaneous flux rise across X‐rays, UV, optical and cm wavelengths. In this study, we present three new optical spectra obtained with the Nordic Optical Telescope, alongside three 1.4 GHz continuum measurements from the Giant Meterwave Radio Telescope, acquired since Nov. 2024. We identified a clear increase in the broad hydrogen Balmer line emission in the most recent observational epochs. Additionally, the core radio flux densities appear to track the overall X‐ray variability, suggesting a possible connection between the accretion flow and jet activity. Based on these data, we aim to explore the evolution of the circumnuclear gas in this source and potential links between accretion and ejection activity.
Sara Vitali; Á. Rojas-Arriagada; P. Jofré; Federico Sestito; Joshua Povick; Hill V; Fernández-Alvar E; Anke Arentsen; P. Jablonka; Nicolas F. Martin; Starkenburg E; David S. Aguado
Strasbourg Astronomical Data Center · DOI
WEAVE APS Team; Alireza Molaeinezhad
Zenodo (CERN European Organization for Nuclear Research) · DOI
Python-based Advance Processing System for WEAVE: a platform for processing and analysing WEAVE survey spectroscopic data (MOS, mIFU, and LIFU observing modes), developed as part of the WEAVE Science Processing and Archive (SPA) system.
WEAVE APS Team; Alireza Molaeinezhad
Zenodo (CERN European Organization for Nuclear Research) · DOI
Python-based Advance Processing System for WEAVE: a platform for processing and analysing WEAVE survey spectroscopic data (MOS, mIFU, and LIFU observing modes), developed as part of the WEAVE Science Processing and Archive (SPA) system.
WEAVE APS Team; Alireza Molaeinezhad
Zenodo (CERN European Organization for Nuclear Research) · DOI
Final publication snapshot of PyAPS (Python-based Advanced Processing System for WEAVE),version 2.0, archived for the RASTI paper. The version label stays 2.0, the version cited in the paper. This snapshot supersedes theearlier v2.0 archive (tag v2.0, commit 1684edf, 29 September 2026, Zenodo10.5281/zenodo.23042511), which is kept but is older. Compared with it, this snapshot adds:- the corrected Redrock per-arm chi-square implementation with the --rr_solver switch and its tests;- the one aperture convention (A_world/B_world are full axis lengths) and the gal_aperture_factor option;- the NumPy 2 and cache fixes of the L2 jobs, the environment checker and the configuration templates. Requirements: Python >= 3.12. Tested with Python 3.12.12 (frozen set requirements-lock-20261006.txt):219 passed, 0 failed. Engines: Redrock 0.22.2.post1195, RVSpecFit 0.9.4.dev14, pPXF 9.5.0, FERRE 5.1.3,NumPy 2.5.3, SciPy 1.18.1, Astropy 8.0.1. Historical benchmarks in the paper were produced with the stacknamed in the paper and were not rerun with this release. Concept DOI (all versions): 10.5281/zenodo.23042510. License: MIT.Details: doc/RELEASE_v2.0_publication.mdNote: the CITATION.cff inside this archive was written before this release and still shows theearlier DOI 10.5281/zenodo.23042511 and the spelling "Advance". The correct citation for thissnapshot is the DOI of this record, 10.5281/zenodo.23250780 (concept DOI 10.5281/zenodo.23042510).The corrected CITATION.cff is on the main branch of https://github.com/camcead/PyAPS.
Mingjie Jian; Nikolai E. Piskunov; Jeff Valenti; Ella Xi Wang; B. Thorsbro; Henrik Jönsson; Ansgar Wehrhahn
arXiv (Cornell University) · DOI
Stellar abundance analysis relies on flexible, high-performance spectral synthesis. To meet these needs, we present PySME v1.0, an updated Python implementation of Spectroscopy Made Easy (SME) designed for precise and survey-scale modelling of stellar spectra.A central challenge in SME based synthesis is the efficient treatment of very large line lists, including both the preselection of negligible lines and the subsequent formal synthesis. PySME v1.0 introduces a revised line-selection framework based on opacity ratio and line depth, together with dynamic line list construction and control of the effective wavelength span over which each line contributes to the synthetic spectrum. These workflows support parallel preprocessing of weak-line selection and reduce the line list passed to the synthesis core, thereby improving scalability while preserving synthetic accuracy. PySME v1.0 also incorporates an updated equation-of-state treatment that improves the modelling of hydrogen lines, particularly Balmer features, while maintaining close agreement with previous SME results for metal lines. The Python interface has further been extended to support parameter-dependent derived quantities updated during optimisation, and PySME provides non-local thermodynamic equilibrium (NLTE) departure-coefficient grids for 17 elements. Together, these developments establish PySME v1.0 as a robust and efficient framework for high-precision stellar abundance analyses in large spectroscopic surveys.
Mingjie Jian; Nikolai Piskunov; Jeff Valenti; Ella Xi Wang; B. Thorsbro; Henrik Jönsson; Ansgar Wehrhahn
arXiv (Cornell University)
Stellar abundance analysis relies on flexible, high-performance spectral synthesis. To meet these needs, we present PySME v1.0, an updated Python implementation of Spectroscopy Made Easy (SME) designed for precise and survey-scale modelling of stellar spectra.A central challenge in SME based synthesis is the efficient treatment of very large line lists, including both the preselection of negligible lines and the subsequent formal synthesis. PySME v1.0 introduces a revised line-selection framework based on opacity ratio and line depth, together with dynamic line list construction and control of the effective wavelength span over which each line contributes to the synthetic spectrum. These workflows support parallel preprocessing of weak-line selection and reduce the line list passed to the synthesis core, thereby improving scalability while preserving synthetic accuracy. PySME v1.0 also incorporates an updated equation-of-state treatment that improves the modelling of hydrogen lines, particularly Balmer features, while maintaining close agreement with previous SME results for metal lines. The Python interface has further been extended to support parameter-dependent derived quantities updated during optimisation, and PySME provides non-local thermodynamic equilibrium (NLTE) departure-coefficient grids for 17 elements. Together, these developments establish PySME v1.0 as a robust and efficient framework for high-precision stellar abundance analyses in large spectroscopic surveys.
Mingjie Jian; Nikolai Piskunov; Jeff Valenti; Ella Xi Wang; B. Thorsbro; Henrik Jönsson; Ansgar Wehrhahn
Astronomy and Astrophysics · DOI
Stellar abundance analysis relies on flexible, high-performance spectral synthesis. To meet these needs, we present PySME v1.0, an updated Python implementation of Spectroscopy Made Easy (SME) designed for precise and survey-scale modelling of stellar spectra. A central challenge in SME-based synthesis is the efficient treatment of very large line lists, including both the pre-selection of negligible lines and the subsequent formal synthesis. PySME v1.0 introduces a revised line-selection framework based on opacity ratio and line depth, together with dynamic line-list construction and control of the effective wavelength span over which each line contributes to the synthetic spectrum. These workflows support parallel preprocessing of weak-line selection and reduce the line list passed to the synthesis core, thereby improving scalability while preserving synthetic accuracy. PySME v1.0 also incorporates an updated equation-of-state treatment that improves the modelling of hydrogen lines (particularly Balmer features) while maintaining close agreement with previous SME results for metal lines. The Python interface has been further extended to support parameter-dependent derived quantities updated during optimisation, and PySME provides non-local thermodynamic equilibrium (NLTE) departure-coefficient grids for 17 elements. Together, these developments establish PySME v1.0 as a robust and efficient framework for high-precision stellar abundance analyses in large spectroscopic surveys.
Tristan Whitmarsh
JBMR Plus · DOI
Xiaozhen Yang; Xiaoting Fu; Mingjie Jian; Jingkun Zhao; Hailong Yuan; Zhongrui Bai; Mengxin Wang; Yiqiao Dong; Mingkuan Yang; Ziyue 子悦 Jiang 蒋; Qian Liu; Ganyu Li; Haotong Zhang
arXiv (Cornell University) · DOI
Synthetic-template subtraction is widely used to measure chromospheric activity in large spectroscopic surveys. However, many solar-like FGK stars show systematically negative Ca II infrared triplet (IRT) residual indices, implying that the observed line cores are deeper than those predicted by parameter-matched templates. We investigate this effect using solar-like stars from LAMOST DR9, MaStar, and XSL DR3, measuring activity indices (R+) for both the Ca II H&K and IRT lines in a uniform framework. We find that observational effects, including atmospheric-parameter offsets, treatment of the instrumental line-spread function, and propagated measurement uncertainties, contribute to scatter but do not explain the systematic negative bias in R+_IRT. The results instead suggest that the negative bias most likely arises because photospheric templates underestimate the depth of the IRT cores, likely owing to missing chromospheric structure and, to a lesser extent, NLTE effects. An empirical increase in the adopted microturbulent velocity deepens the synthetic IRT cores and partially mitigates the negative offset. In addition, R+ values derived from different synthesis configurations show systematic offsets but generally preserve strong linear correlations, indicating that they can be cross-calibrated. These results clarify the origin of negative Ca II IRT residual indices and help interpret template-dependent systematics in chromospheric activity measurements based on synthetic-template subtraction.
Xiaozhen Yang; Xiaoting Fu; Mingjie Jian; Jingkun Zhao; Hailong Yuan; Zhongrui Bai; Mengxin Wang; Yiqiao Dong; Mingkuan Yang; Ziyue 子悦 Jiang 蒋; Qian Liu; Ganyu Li; Haotong Zhang
arXiv (Cornell University)
Synthetic-template subtraction is widely used to measure chromospheric activity in large spectroscopic surveys. However, many solar-like FGK stars show systematically negative Ca II infrared triplet (IRT) residual indices, implying that the observed line cores are deeper than those predicted by parameter-matched templates. We investigate this effect using solar-like stars from LAMOST DR9, MaStar, and XSL DR3, measuring activity indices (R+) for both the Ca II H&K and IRT lines in a uniform framework. We find that observational effects, including atmospheric-parameter offsets, treatment of the instrumental line-spread function, and propagated measurement uncertainties, contribute to scatter but do not explain the systematic negative bias in R+_IRT. The results instead suggest that the negative bias most likely arises because photospheric templates underestimate the depth of the IRT cores, likely owing to missing chromospheric structure and, to a lesser extent, NLTE effects. An empirical increase in the adopted microturbulent velocity deepens the synthetic IRT cores and partially mitigates the negative offset. In addition, R+ values derived from different synthesis configurations show systematic offsets but generally preserve strong linear correlations, indicating that they can be cross-calibrated. These results clarify the origin of negative Ca II IRT residual indices and help interpret template-dependent systematics in chromospheric activity measurements based on synthetic-template subtraction.
D. L. Harrison
Zenodo (CERN European Organization for Nuclear Research) · DOI
Slides presented at EAS 2026
D. L. Harrison
Zenodo (CERN European Organization for Nuclear Research) · DOI
Slides presented at EAS 2026
Ruoxu Tan; Mingjie Jian; Yiming Zang
arXiv (Cornell University) · DOI
Despite its extensive development for multivariate data, semi-supervised learning remains underdeveloped for functional data, especially under discrete and noisy observations. We develop a density-sensitive semi-supervised framework for functional data supported on a low-dimensional manifold by adapting the Fermat distance to reconstructed trajectories. The resulting pairwise distances are used to construct a weighted $k$-nearest-neighbor classifier and multidimensional-scaling-based classifiers. To accommodate massive datasets commonly seen in semi-supervised applications, we design a computationally efficient estimation procedure tailored for discrete and noisy functional observations. Theoretically, we establish exponentially decaying convergence rates of the $k$-NN classifier and the consistency of the estimated Fermat distance. Crucially, our results reveal that incorporating unlabeled data may not lead to improved classification accuracy without a sufficiently fast-growing individual sampling rate, precisely due to discrete and noisy observations. In most simulation settings satisfying the manifold and cluster assumptions, the proposed classifiers outperform the supervised benchmarks considered; in the Gaia spectra analysis, they attain higher agreement with high-confidence proxy labels.
Ruoxu Tan; Mingjie Jian; Yiming Zang
arXiv (Cornell University)
Despite its extensive development for multivariate data, semi-supervised learning remains underdeveloped for functional data, especially under discrete and noisy observations. We develop a density-sensitive semi-supervised framework for functional data supported on a low-dimensional manifold by adapting the Fermat distance to reconstructed trajectories. The resulting pairwise distances are used to construct a weighted $k$-nearest-neighbor classifier and multidimensional-scaling-based classifiers. To accommodate massive datasets commonly seen in semi-supervised applications, we design a computationally efficient estimation procedure tailored for discrete and noisy functional observations. Theoretically, we establish exponentially decaying convergence rates of the $k$-NN classifier and the consistency of the estimated Fermat distance. Crucially, our results reveal that incorporating unlabeled data may not lead to improved classification accuracy without a sufficiently fast-growing individual sampling rate, precisely due to discrete and noisy observations. In most simulation settings satisfying the manifold and cluster assumptions, the proposed classifiers outperform the supervised benchmarks considered; in the Gaia spectra analysis, they attain higher agreement with high-confidence proxy labels.
Sarah G. Kane; Stephanie Monty; McKenzie Madeleine; HanYuan Zhang; Sven Buder; Terese Hansén; Tadafumi Matsuno; Jason Sanders; Anke Arentsen; Vasily Belokurov; Zofia Kaczmarek; Andrew Garner
arXiv (Cornell University) · DOI
Stars enhanced in heavy neutron capture elements like europium (Eu) provide a window into the rarest and most exotic nucleosynthetic events. Most Eu-enhanced stars discovered thus far are found among metal-poor populations, with $\mathrm{[Fe/H]}<-1.0$. Here, we report the discovery of $17$ vetted Eu-enhanced stars ($\mathrm{[Eu/Fe]}>0.7$) in the Milky Way disk with metallicities in the range $-0.87<\mathrm{[Fe/H]}<-0.21$ identified from among $74\,717$ stars in the GALAH survey. 14 of these stars additionally have $\mathrm{[Ba/Eu]}<0$, marking enhancement from the rapid neutron capture ($r$-) process and making these r-II stars. Included in their number is a star with a remarkably high $\mathrm{[Eu/H]}=1.01\pm0.09$ at $\mathrm{[Fe/H]}=-0.21$. In addition to Eu, all 17 stars show enhancements in other neutron capture elements, especially in Nd, while otherwise appearing chemically consistent with typical disk stars, particularly in their $α$-element abundances. Two of the Eu-enhanced stars exhibit indications of binarity with strong enrichment in Ba and Y, marking potential signatures of the slow or intermediate neutron capture processes. This sample of 17 Eu-enhanced stars, which span both the high- and low-$α$ disks, are valuable tracers of neutron capture processes, including the $r$-process, at high metallicities. Although we use criteria for Eu-enhanced stars typically applied to the halo to enable comparison to literature studies of $r$-process enhanced stars, we propose a new, metallicity-dependent selection for Eu enhancement that is better-suited to the regime of the Galactic disk.