Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.15165

FOSSIL: Thermo-mechanical architecture

Valentin Sauvage·Clémence de Jabrun·Sylvain Martin·Bruno Maffei·Nabila Aghanim·Anaïs Besnard·Bruno Borgo·Ivan Charles·Xavier Coulon·Sandrine Couturier·Francesco Cuttaia·Jean-Marc Duval·Morgane Loquet Le Gall·Giorgio Savini·Luca Terenzi·Berend Winter
View original source ↗Read source document ↗Export metadata ↓

Abstract

FOSSIL (FTS fOr CMB Spectral diStortIon expLoration) is a proposed ESA M8 mission tailored to measure the spectral distortions of the Cosmic Microwave Background (CMB) with a sensitivity three orders of magnitude beyond the COBE/FIRAS legacy measurement. Achieving this sensitivity demands an extraordinarily challenging cryogenic architecture: the scientific instrument must be maintained at the lowest achievable temperature (4.5 K), while the detector focal plane assembly operates at 50 mK. This paper presents the thermal architecture of the FOSSIL payload and instrument, from the spacecraft service module at 293 K down to the sub-kelvin detector stage. The thermal design draws on heritage from the Planck and ARIEL missions and relies on a staged passive cooling chain comprising a multi-layer insulation blanket, three V-groove radiators (operating at approximately 130 K, 90 K, and 50 K), and a 25 K actively cooled shield fed by an ESA-provided 4 K cryocooler chain. Sub-kelvin temperatures are achieved via a multi-stage adiabatic demagnetisation refrigerator (ADR) system developed for NewAthena/X-IFU, providing continuous cooling at 1.8 K and 350 mK, and 50 mK with an 80% duty cycle. The Focal Plane Assembly (FPA), which houses four Kinetic Inductance Detector (KID) arrays at 50 mK, is thermally isolated from the 4.5~K bench via a carbon-fibre reinforced polymer (CFRP) hexapod structure with staged heat interception. We present the steady-state thermal budget across all stages, demonstrating comfortable margins at every temperature level, and discuss key thermal design drivers on the Blackbody Internal Reference (BBIR).

Explore related subjects

astro-ph.IMastro-ph.CO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Valentin Sauvage, Clémence de Jabrun, Sylvain Martin, Bruno Maffei, Nabila Aghanim, Anaïs Besnard, Bruno Borgo, Ivan Charles, Xavier Coulon, Sandrine Couturier, Francesco Cuttaia, Jean-Marc Duval, Morgane Loquet Le Gall, Giorgio Savini, Luca Terenzi, Berend Winter. 2026-09-14. FOSSIL: Thermo-mechanical architecture. https://arxiv.org/abs/2609.15165

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

KEEP EXPLORING

Related papers

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↗
arXiv2026

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↗