Searcharxiv⌕ Search

arXiv · 2610.23500

The Atmospheric Chemistry and Surface Interactions on Mars (ACSIOM) Workshop 2025

Abstract

The ACSIOM workshop was held in Cambridge from 10th-12th November 2025. Its primary aims were twofold: 1) to gather UK researchers involved in Mars atmospheric science to identify current shortcomings and challenges in the field, and 2) to discuss possible collaborations and future steps needed to maximise the research output from current and future Mars missions. Workshop attendees spanned a wide range of subjects and disciplines, from atmospheric modellers and surface scientists to experimental chemists and Earth/Venus/exoplanet scientists. One of the primary outcomes of the workshop as identified by the attendees was the need for more collaborations between these different communities. It was agreed that we currently lack sufficient interaction between disciplines and this is a key challenge we need to overcome in order to properly utilise and exploit the vast pool of martian expertise available in the UK. Modellers need reaction rates from experimentalists; experimentalists need modelling context to prioritise their experiments; geologists need modelling context to understand the features they see; modellers need geological context to constrain their results. Attendees stressed that the UK Mars community has a critical mass which is internationally recognised, and improving the communication between communities would allow significant progress to be made in martian research, allowing the UK to maintain leadership in Mars science for years to come. We highlight that this is a pivotal moment for such investments as many planetary missions, both for Mars and other terrestrial bodies, are about to be launched or are being considered in the coming years. Having a strong and close-knit community will be key to better identifying needs and capabilities in order to optimise investments in missions and to have the best scientific return possible.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Benjamin Benne, Megan A. J. Brown, Bethan S. Gregory, Paul M. Streeter, The ACSIOM Team. 2026-10-16. The Atmospheric Chemistry and Surface Interactions on Mars (ACSIOM) Workshop 2025. https://arxiv.org/abs/2610.23500

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

KEEP EXPLORING

Related papers

Differentiable design of the PIAA-ZWFS: a flexible wavefront sensor that approaches the fundamental limit

Extreme adaptive optics (AO) is necessary for high contrast astronomy at scales of the habitable zone of nearby systems. We seek to evaluate wavefront sensors that approach fundamental limits of wavefront sensing, enabling adaptive optics systems to run faster or on fainter targets. We present the phase-induced amplitude apodisation Zernike wavefront sensor (PIAA-ZWFS): an adaptation of the conventional Zernike wavefront sensor (ZWFS) that leverages lossless apodisation of the pupil to concentrate the starlight in the focal plane. We optimise and evaluate the sensor with a differentiable modelling framework, drawing on concepts from Bayesian experimental design to minimise the variance of a maximum likelihood estimator that uses the system in the high Strehl regime. Our architecture shows state-of-the-art performance in simulation for different apertures, bandwidths, photon fluxes and source sizes, closing the gap to the fundamental limit by a factor 10 (2.5) compared to the conventional ZWFS (optimised ZWFS) in a typical photon-limited case. For extended sources, we show that even an ideal point source sensor rapidly becomes sub-optimal, and our system outperforms it for stellar diameters larger than 0.8λ/D. We verify that these gains do not come at the cost of dynamic range with either linear or non-linear reconstructors. Finally, we present a proof that there must be a trade-off between the information gained about amplitude and phase errors for any wavefront sensor. The PIAA-ZWFS is a viable wavefront sensor operating near the fundamental sensitivity limits.

astro-ph.IM↗

Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data

Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it. They are validated on held-out data from the training distribution, which excludes the cases where they are most likely to fail: a technosignature has unknown morphology, and terrestrial interference is unbounded in variety. I trained a standard competition detector once, froze it, and probed it with 15,000 injections across ten morphologies and five amplitudes, placed into real Green Bank backgrounds. Two directions were tested: genuine signals with morphologies other than a straight narrowband line, and interference resembling the training data. Recall did not follow the predicted ordering, and it fell as signals brightened, even for a needle of the kind the detector was trained on (from 0.590 to 0.090). One interference morphology reached a false positive rate of 0.627, comparable to the detector's own baseline recall of 0.5525 on genuine signals. Gradient-weighted Class Activation Mapping (Grad-CAM) showed attention along the injected path in one missed family by a pre-specified test, and visually in a second. A pre-specified, time-averaged ON-OFF filter removed false alarms and genuine detections at the same rate. The detector reads only the ON panels. Since the only evidence distinguishing a genuine candidate from terrestrial interference lies in the discarded OFF panels, it cannot tell if a signal originates from the target star; it can only tell if it resembles the training data. This explains the false alarm directly; the missed signals reflect what the detector learned to recognise, which reading the OFF panels would not by itself change.

astro-ph.IM↗

Chemical Demographics of Star-forming Regions

Our understanding of complex organic chemistry during the formation of stars and planets is shaped by a handful of cherry-picked sources with bright molecular emission lines. This observational bias impedes our assessment of how universal chemical complexity is during star formation. The lack of systematic wideband spectral surveys for statistically significant samples of protostars and cores is a major roadblock to properly probing how COM inventories and abundances depend on the vast range of physical conditions, local cloud and global galactic environments, and evolutionary timescales of low- and high-mass star formation. In turn, this limits our ability to constrain and test physicochemical models, which depend critically on these parameters. The next-generation ALMA2040, with a 10x improvement in line sensitivity, is required to observe COMs, that we perceive as common today, in a fraction of the observing time that is needed today with ALMA. Such capabilities will propel us into the era of chemical demographics, whereby wideband (>100 GHz) spectral surveys at high sensitivity (routinely probing COM abundances down to 0.01% relative to CH3OH) will be executed towards >1000-10000 prestellar and protostellar sources. With robust chemical inventories towards large source sample sizes, we will be able to probe for dependencies of chemical complexity on mass regime, feedback processes in a cluster, Galactic position, metallicity, and time-variable accretion. Only with statistically significant samples at high sensitivity will we be able to assess the ubiquity and homogeneity of COM chemistry across the protostellar mass spectrum and natal cores. Constraining the diversity and availability of such species is critical for our understanding of the chemical reservoir available for planet formation, which eventually shapes (exo)planetary atmosphere compositions.

astro-ph.IM↗