Search arXivSearch

arXiv · 1810.10811

O2 signature in thin and thick O2-H2O ices

Abstract

Aims. In this paper we investigate the detectability of the molecular oxygen in icy dust grain mantles towards astronomical objects. Methods. We present a systematic set of experiments with O2-H2O ice mixtures designed to disentangle how the molecular ratio affects the O2 signature in the mid- and near-infrared spectral regions. All the experiments were conducted in a closed-cycle helium cryostat coupled to a Fourier transform infrared spectrometer. The ice mixtures comprise varying thicknesses from 8 $\times$ 10$^{-3}$ to 3 $μ$m. The absorption spectra of the O2-H2O mixtures are also compared to the one of pure water. In addition, the possibility to detect the O2 in icy bodies and in the interstellar medium is discussed. Results. We are able to see the O2 feature at 1551 cm$^{-1}$ even for the most diluted mixture of H2O : O2 = 9 : 1, comparable to a ratio of O2/H2O = 10 % which has already been detected in situ in the coma of the comet 67P/Churyumov-Gerasimenko. We provide an estimate for the detection of O2 with the future mission of the James Webb Space Telescope (JWST).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

B. Müller, B. M. Giuliano, L. Bizzocchi, A. I. Vasyunin, P. Caselli. 2018-10-25. O2 signature in thin and thick O2-H2O ices. https://doi.org/10.1051/0004-6361%2F201833549

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

KEEP EXPLORING

Related papers

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

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

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