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arXiv:2507.14136v1 Announce Type: new Abstract: We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of Luminous Red Galaxies (LRGs) and Bright Galaxy Survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding $\log(M_{\rm halo}/(M_\odot/h)) \approx 13.18$, 13.03 and 13.02 for the Main LRG, Extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel'dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals -- the unknown host halo mass distribution -- by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2--3 virial radii), we recover all the baryons, offering a resolution to the 'missing baryon' problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample ($\log(M_{\rm halo}/(M_\odot/h)) \approx 13.5$), finding values that are physically sensible and in agreement with previous findings using kSZ and X-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by $\gtrsim$4$\sigma$, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.

arXiv:2507.14136v1 Announce Type: new Abstract: We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of Luminous Red Galaxies (LRGs) and Bright Galaxy Survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding $\log(M_{\rm halo}/(M_\odot/h)) \approx 13.18$, 13.03 and 13.02 for the Main LRG, Extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel'dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals -- the unknown host halo mass distribution -- by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2--3 virial radii), we recover all the baryons, offering a resolution to the 'missing baryon' problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample ($\log(M_{\rm halo}/(M_\odot/h)) \approx 13.5$), finding values that are physically sensible and in agreement with previous findings using kSZ and X-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by $\gtrsim$4$\sigma$, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.

arXiv:2507.13495v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) offers a principled and flexible framework for conducting Bayesian inference in any situation where forward simulations are feasible. However, validating the accuracy and reliability of the inferred posteriors remains a persistent challenge. In this work, we point out a simple diagnostic approach rooted in ensemble learning methods to assess the internal consistency of SBI outputs that does not require access to the true posterior. By training multiple neural estimators under identical conditions and evaluating their pairwise Kullback-Leibler (KL) divergences, we define a consistency criterion that quantifies agreement across the ensemble. We highlight two core use cases for this framework: a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g., too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.

arXiv:2507.13495v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) offers a principled and flexible framework for conducting Bayesian inference in any situation where forward simulations are feasible. However, validating the accuracy and reliability of the inferred posteriors remains a persistent challenge. In this work, we point out a simple diagnostic approach rooted in ensemble learning methods to assess the internal consistency of SBI outputs that does not require access to the true posterior. By training multiple neural estimators under identical conditions and evaluating their pairwise Kullback-Leibler (KL) divergences, we define a consistency criterion that quantifies agreement across the ensemble. We highlight two core use cases for this framework: a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g., too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.

arXiv:2507.13495v1 Announce Type: new Abstract: Simulation-Based Inference (SBI) offers a principled and flexible framework for conducting Bayesian inference in any situation where forward simulations are feasible. However, validating the accuracy and reliability of the inferred posteriors remains a persistent challenge. In this work, we point out a simple diagnostic approach rooted in ensemble learning methods to assess the internal consistency of SBI outputs that does not require access to the true posterior. By training multiple neural estimators under identical conditions and evaluating their pairwise Kullback-Leibler (KL) divergences, we define a consistency criterion that quantifies agreement across the ensemble. We highlight two core use cases for this framework: a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g., too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.

arXiv:2507.14094v1 Announce Type: new Abstract: Our understanding of the chemical evolution of galaxies has advanced through measurements from both distant galaxies across redshift, and our own Milky Way (MW). To form a comprehensive picture, it is essential to unify these constraints, placing them on a common scale and parlance and to understand their systematic differences. In this study, we homogenize oxygen and iron measurements from star-forming galaxies at Cosmic Noon ($z{\sim}2-3$) with resolved stellar abundances from the Local Group. The MW is divided into four components, assuming the outer halo is dominated by debris from the Gaia-Sausage-Enceladus (GSE) progenitor. After converting all abundances to a common Solar scale, we identify clear $\alpha$- and iron-enhancement trends with mass in the $z{\sim}2-3$ galaxies and find good agreement between these galaxies and the MW high-$\alpha$ disc in [O/Fe] vs. [Fe/H]. We also find excellent agreement between the [O/Fe] trends seen in the MW high- and low-$\alpha$ discs with O-abundances seen in old and young planetary nebulae in M~31 respectively, supporting the existence of $\alpha$-bimodality in the inner regions of M~31. Finally, we use globular cluster ages to project the MW and GSE back in time to $z{\sim}3$ and find that their estimated mass, oxygen and iron abundances are strikingly consistent with the mass-metallicity relation of star-forming galaxies at $z{\sim}3$. In the future, increased transparency around the choice of Solar scale and abundance methodology will make combining chemical abundances easier -- contributing to a complete picture of the chemical evolution of all galaxies.

arXiv:2507.14094v1 Announce Type: new Abstract: Our understanding of the chemical evolution of galaxies has advanced through measurements from both distant galaxies across redshift, and our own Milky Way (MW). To form a comprehensive picture, it is essential to unify these constraints, placing them on a common scale and parlance and to understand their systematic differences. In this study, we homogenize oxygen and iron measurements from star-forming galaxies at Cosmic Noon ($z{\sim}2-3$) with resolved stellar abundances from the Local Group. The MW is divided into four components, assuming the outer halo is dominated by debris from the Gaia-Sausage-Enceladus (GSE) progenitor. After converting all abundances to a common Solar scale, we identify clear $\alpha$- and iron-enhancement trends with mass in the $z{\sim}2-3$ galaxies and find good agreement between these galaxies and the MW high-$\alpha$ disc in [O/Fe] vs. [Fe/H]. We also find excellent agreement between the [O/Fe] trends seen in the MW high- and low-$\alpha$ discs with O-abundances seen in old and young planetary nebulae in M~31 respectively, supporting the existence of $\alpha$-bimodality in the inner regions of M~31. Finally, we use globular cluster ages to project the MW and GSE back in time to $z{\sim}3$ and find that their estimated mass, oxygen and iron abundances are strikingly consistent with the mass-metallicity relation of star-forming galaxies at $z{\sim}3$. In the future, increased transparency around the choice of Solar scale and abundance methodology will make combining chemical abundances easier -- contributing to a complete picture of the chemical evolution of all galaxies.

arXiv:2507.14094v1 Announce Type: new Abstract: Our understanding of the chemical evolution of galaxies has advanced through measurements from both distant galaxies across redshift, and our own Milky Way (MW). To form a comprehensive picture, it is essential to unify these constraints, placing them on a common scale and parlance and to understand their systematic differences. In this study, we homogenize oxygen and iron measurements from star-forming galaxies at Cosmic Noon ($z{\sim}2-3$) with resolved stellar abundances from the Local Group. The MW is divided into four components, assuming the outer halo is dominated by debris from the Gaia-Sausage-Enceladus (GSE) progenitor. After converting all abundances to a common Solar scale, we identify clear $\alpha$- and iron-enhancement trends with mass in the $z{\sim}2-3$ galaxies and find good agreement between these galaxies and the MW high-$\alpha$ disc in [O/Fe] vs. [Fe/H]. We also find excellent agreement between the [O/Fe] trends seen in the MW high- and low-$\alpha$ discs with O-abundances seen in old and young planetary nebulae in M~31 respectively, supporting the existence of $\alpha$-bimodality in the inner regions of M~31. Finally, we use globular cluster ages to project the MW and GSE back in time to $z{\sim}3$ and find that their estimated mass, oxygen and iron abundances are strikingly consistent with the mass-metallicity relation of star-forming galaxies at $z{\sim}3$. In the future, increased transparency around the choice of Solar scale and abundance methodology will make combining chemical abundances easier -- contributing to a complete picture of the chemical evolution of all galaxies.

This NASA/ESA Hubble Space Telescope image features the galaxy cluster Abell 209.ESA/Hubble & NASA, M. Postman, P. Kelly

A massive, spacetime-warping cluster of galaxies is the setting of today’s NASA/ESA Hubble Space Telescope image. The galaxy cluster in question is Abell 209, located 2.8 billion light-years away in the constellation Cetus (the Whale).

This Hubble image of Abell 209 shows more than a hundred galaxies, but there’s more to this cluster than even Hubble’s discerning eye can see. Abell 209’s galaxies are separated by millions of light-years, and the seemingly empty space between the galaxies is filled with hot, diffuse gas that is visible only at X-ray wavelengths. An even more elusive occupant of this galaxy cluster is dark matter: a form of matter that does not interact with light. Dark matter does not absorb, reflect, or emit light, effectively making it invisible to us. Astronomers detect dark matter by its gravitational influence on normal matter. Astronomers surmise that the universe is comprised of 5% normal matter, 25% dark matter, and 70% dark energy.

Hubble observations, like the ones used to create this image, can help astronomers answer fundamental questions about our universe, including mysteries surrounding dark matter and dark energy. These investigations leverage the immense mass of a galaxy cluster, which can bend the fabric of spacetime itself and create warped and magnified images of background galaxies and stars in a process called gravitational lensing.

While this image lacks the dramatic rings that gravitational lensing can sometimes create, Abell 209 still shows subtle signs of lensing at work, in the form of streaky, slightly curved galaxies within the cluster’s golden glow. By measuring the distortion of these galaxies, astronomers can map the distribution of mass within the cluster, illuminating the underlying cloud of dark matter. This information, which Hubble’s fine resolution and sensitive instruments help to provide, is critical for testing theories of how our universe evolved.

Text Credit: ESA/Hubble

Image credit: ESA/Hubble & NASA, M. Postman, P. Kelly

Instead of the big bang, some physicists have suggested that our universe may have come from a big bounce following another universe contracting – but quantum theory could rule this out

From a silhouetted space station to glowing comet tails and swirling stars, this year's ZWO Astronomy Photographer of the Year contest inspires us to see the cosmos in a new light

Small, compact galaxies seen in the early universe have puzzled astronomers – finding these unusual objects closer to home could provide hints about how they form

arXiv:2507.12622v1 Announce Type: new Abstract: Temperate sub-Neptunes are compelling targets for detecting liquid-water oceans beyond the Solar System. If water-rich and lacking massive hydrogen-helium envelopes, these planets could sustain liquid layers beneath their atmospheres despite sizes larger than Earth. Previous observations of the temperate sub-Neptune K2-18 b revealed an H2-dominated atmosphere rich in CH4, with moderate evidence for CO2 and tentative signs of dimethyl sulfide (DMS). Here we present four new JWST/NIRSpec transit observations of K2-18 b. The resulting high-precision transmission spectrum robustly detects both CH4 and CO2, precisely measuring their abundances and firmly establishing the planet's water-rich nature: either a thick envelope with >10% H2O by volume or a thin atmosphere above a liquid-water ocean. The spectrum reveals no detectable H2O, NH3, or CO. The absence of atmospheric water vapor suggests an efficient cold trap, while the nondetections of NH3 and CO support the scenario of a small H2-rich atmosphere overlying a liquid reservoir. However, alternative models that include these gases can also reproduce the spectrum within uncertainties, highlighting the need for deeper observations. The spectrum only contains marginal signals of DMS, methyl mercaptan (CH3SH), and nitrous oxide (N2O), with none exceeding 3 sigma in model preference and all falling below ~2 sigma without imposing a strong super-Rayleigh haze. Meanwhile, our self-consistent photochemical models show that DMS and CH3SH may form abiotically in massive H2-rich atmospheres of high metallicity, making it important to consider additional indicators for their potential use as biosignatures. K2-18 b, a cool, water-rich world, stands out as one of the most promising temperate sub-Neptunes for exploring the emergence of liquid-water environments in non-Earth-like planets, motivating further characterization of its atmosphere and interior.

arXiv:2507.12622v1 Announce Type: new Abstract: Temperate sub-Neptunes are compelling targets for detecting liquid-water oceans beyond the Solar System. If water-rich and lacking massive hydrogen-helium envelopes, these planets could sustain liquid layers beneath their atmospheres despite sizes larger than Earth. Previous observations of the temperate sub-Neptune K2-18 b revealed an H2-dominated atmosphere rich in CH4, with moderate evidence for CO2 and tentative signs of dimethyl sulfide (DMS). Here we present four new JWST/NIRSpec transit observations of K2-18 b. The resulting high-precision transmission spectrum robustly detects both CH4 and CO2, precisely measuring their abundances and firmly establishing the planet's water-rich nature: either a thick envelope with >10% H2O by volume or a thin atmosphere above a liquid-water ocean. The spectrum reveals no detectable H2O, NH3, or CO. The absence of atmospheric water vapor suggests an efficient cold trap, while the nondetections of NH3 and CO support the scenario of a small H2-rich atmosphere overlying a liquid reservoir. However, alternative models that include these gases can also reproduce the spectrum within uncertainties, highlighting the need for deeper observations. The spectrum only contains marginal signals of DMS, methyl mercaptan (CH3SH), and nitrous oxide (N2O), with none exceeding 3 sigma in model preference and all falling below ~2 sigma without imposing a strong super-Rayleigh haze. Meanwhile, our self-consistent photochemical models show that DMS and CH3SH may form abiotically in massive H2-rich atmospheres of high metallicity, making it important to consider additional indicators for their potential use as biosignatures. K2-18 b, a cool, water-rich world, stands out as one of the most promising temperate sub-Neptunes for exploring the emergence of liquid-water environments in non-Earth-like planets, motivating further characterization of its atmosphere and interior.

arXiv:2503.02291v3 Announce Type: replace Abstract: We present the SpecDis value added stellar distance catalog accompanying DESI DR1. SpecDis trains a feed-forward Neural Network (NN) with Gaia parallaxes and gets the distance estimates. To build up unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead we incorporate parallax error into the loss function. Moreover, we employ Principal Component Analysis (PCA) to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters (GCs), dwarf galaxies, stellar streams, combined with blue horizontal branch (BHB) stars, we demonstrate that our distance measurements show no significant bias up to 100kpc, and are much more precise than Gaia parallax beyond 7kpc. The median distance uncertainties are 23%, 19%, 11% and 7% for S/N $<$ 20, 20 $\leq$ S/N$<$ 60, 60 $\leq$ S/N $<$ 100 and S/N $\geq$ 100. Selecting stars with $\log g<3.8$ and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50kpc to the Galactic center and 1,500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for $>$ 4 million stars, offering a valuable resource for Galactic astronomy.

arXiv:2503.02291v3 Announce Type: replace Abstract: We present the SpecDis value added stellar distance catalog accompanying DESI DR1. SpecDis trains a feed-forward Neural Network (NN) with Gaia parallaxes and gets the distance estimates. To build up unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead we incorporate parallax error into the loss function. Moreover, we employ Principal Component Analysis (PCA) to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters (GCs), dwarf galaxies, stellar streams, combined with blue horizontal branch (BHB) stars, we demonstrate that our distance measurements show no significant bias up to 100kpc, and are much more precise than Gaia parallax beyond 7kpc. The median distance uncertainties are 23%, 19%, 11% and 7% for S/N $<$ 20, 20 $\leq$ S/N$<$ 60, 60 $\leq$ S/N $<$ 100 and S/N $\geq$ 100. Selecting stars with $\log g<3.8$ and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50kpc to the Galactic center and 1,500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for $>$ 4 million stars, offering a valuable resource for Galactic astronomy.

Science, Volume 389, Issue 6757, Page 222-223, July 2025.

Science, Volume 389, Issue 6757, Page 225-226, July 2025.

4 min read

NASA to Launch SNIFS, Sun’s Next Trailblazing Spectator

July will see the launch of the groundbreaking Solar EruptioN Integral Field Spectrograph mission, or SNIFS. Delivered to space via a Black Brant IX sounding rocket, SNIFS will explore the energy and dynamics of the chromosphere, one of the most complex regions of the Sun’s atmosphere. The SNIFS mission’s launch window at the White Sands Missile Range in New Mexico opens on Friday, July 18. 

The chromosphere is located between the Sun’s visible surface, or photosphere, and its outer layer, the corona. The different layers of the Sun’s atmosphere have been researched at length, but many questions persist about the chromosphere. “There’s still a lot of unknowns,” said Phillip Chamberlin, a research scientist at the University of Colorado Boulder and principal investigator for the SNIFS mission.  

The reddish chromosphere is visible on the Sun’s right edge in this view of the Aug. 21, 2017, total solar eclipse from Madras, Oregon.Credit: NASA/Nat Gopalswamy

The chromosphere lies just below the corona, where powerful solar flares and massive coronal mass ejections are observed. These solar eruptions are the main drivers of space weather, the hazardous conditions in near-Earth space that threaten satellites and endanger astronauts. The SNIFS mission aims to learn more about how energy is converted and moves through the chromosphere, where it can ultimately power these massive explosions.  

“To make sure the Earth is safe from space weather, we really would like to be able to model things,” said Vicki Herde, a doctoral graduate of CU Boulder who worked with Chamberlin to develop SNIFS.  

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This footage from NASA’s Solar Dynamics Observatory shows the Sun in the 304-angstrom band of extreme ultraviolet light, which primarily reveals light from the chromosphere. This video, captured on Feb. 22, 2024, shows a solar flare — as seen in the bright flash on the upper left.Credit: NASA/SDO

The SNIFS mission is the first ever solar ultraviolet integral field spectrograph, an advanced technology combining an imager and a spectrograph. Imagers capture photos and videos, which are good for seeing the combined light from a large field of view all at once. Spectrographs dissect light into its various wavelengths, revealing which elements are present in the light source, their temperature, and how they’re moving — but only from a single location at a time. 

The SNIFS mission combines these two technologies into one instrument.  

“It’s the best of both worlds,” said Chamberlin. “You’re pushing the limit of what technology allows us to do.” 

By focusing on specific wavelengths, known as spectral lines, the SNIFS mission will help scientists to learn about the chromosphere. These wavelengths include a spectral line of hydrogen that is the brightest line in the Sun’s ultraviolet (UV) spectrum, and two spectral lines from the elements silicon and oxygen. Together, data from these spectral lines will help reveal how the chromosphere connects with upper atmosphere by tracing how solar material and energy move through it. 

The SNIFS mission will be carried into space by a sounding rocket. These rockets are effective tools for launching and carrying space experiments and offer a valuable opportunity for hands-on experience, particularly for students and early-career researchers.

(From left to right) Vicki Herde, Joseph Wallace, and Gabi Gonzalez, who worked on the SNIFS mission, stand with the sounding rocket containing the rocket payload at the White Sands Missile Range in New Mexico.Credit: courtesy of Phillip Chamberlin

“You can really try some wild things,” Herde said. “It gives the opportunity to allow students to touch the hardware.” 

Chamberlin emphasized how beneficial these types of missions can be for science and engineering students like Herde, or the next generation of space scientists, who “come with a lot of enthusiasm, a lot of new ideas, new techniques,” he said. 

The entirety of the SNIFS mission will likely last up to 15 minutes. After launch, the sounding rocket is expected to take 90 seconds to make it to space and point toward the Sun, seven to eight minutes to perform the experiment on the chromosphere, and three to five minutes to return to Earth’s surface.  

A previous sounding rocket launch from the White Sands Missile Range in New Mexico. This mission carried a copy of the Extreme Ultraviolet Variability Experiment (EVE).
Credit: NASA/University of Colorado Boulder, Laboratory for Atmospheric and Space Physics/James Mason

The rocket will drift around 70 to 80 miles (112 to 128 kilometers) from the launchpad before its return, so mission contributors must ensure it will have a safe place to land. White Sands, a largely empty desert, is ideal. 

Herde, who spent four years working on the rocket, expressed her immense excitement for the launch. “This has been my baby.” 

By Harper Lawson
NASA’s Goddard Space Flight Center, Greenbelt, Md.

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Details Last Updated Jul 17, 2025 Related Terms

• Heliophysics
• Goddard Space Flight Center
• Heliophysics Division
• Science & Research
• Sounding Rockets
• Sounding Rockets Program
• Wallops Flight Facility

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We are seeking an experienced and enthusiastic Cloud Engineer to be based at the Institute of Astronomy (www.ast.cam.ac.uk) in collaboration with the Research Computing Services (www.hpc.cam.ac.uk) at the University of Cambridge. This role is part of a new and exciting initiative to develop the UK SKA Regional Centre, (UKSRC: www.uksrc.org) for the Square Kilometre Array Observatory (SKAO: www.skao.int), the world's largest radio telescope. You will join a diverse team working in collaboration with national and international colleagues to help develop, deliver and operate the UKSRC and international Network of SKA Regional Centres (SRCNet) cloud infrastructure and computing resources, as an integral part of the development of the SKA Observatory project (www.skao.int). The UKSRC project, benefits from contributions from the Universities of Cambridge, Durham, Edinburgh, Hertfordshire, London (UCL), Manchester and the UKRI STFC Scientific Computing Division.

The successful candidate will become part of a diverse team of systems engineers, research software engineers and data scientists, developing advanced solutions to support world-class science and via the delivery of configurable, robust distributed digital research infrastructure service for processing data, and supporting the scientific exploitation of observational data, obtained with the world's largest radio telescope running on a Kubernetes (K8s) open-souplatform. In addition, this role will have the opportunity to support the development of similar cloud-based infrastructure for the Cambridge Centre of Excellence in Astronomical Data (CamCEAD), based at the Institute of Astronomy to support a range of data-intensive ground and space-based imaging and spectroscopic missions and research projects.

Skills and Experience

Preferred:

• Hands on experience in deploying and administering Linux operating systems.
• Familiarity of Ansible & Terraform for configuration management & IAC.
• Experience of virtualization technologies and cloud architecture, preferably but not limited to OpenStack.
• Proven ability to work effectively within a team and individually.
• Basic knowledge of scripting languages primarily with Python & Bash.
• Experience of CI/CD principles, ideally but not essentially using a GitOps approach, using Helm & Kustomize.
• Software development lifecycle tools, such as Git.
• Working with Agile methodologies.

Desirable:

• Use of monitoring and reporting tools, such as Prometheus and Grafana.
• Experience of GitOps tooling such as ArgoCD or FluxCD.
• Knowledge of containerization technologies using Kubernetes & Docker.
• Experience working with HPC clusters and parallel file systems.
• Experience in working in a scientific environment and/or providing support to researchers.

More information about the role is attached in the 'Further Particulars' document.

The University is supportive of hybrid working. We aim to enable as many staff as possible to work in a hybrid way if they wish, and where their role allows. This role permits the post holder to be office based or hybrid, but to be in the office regularly when required, either at the IoA at Madingley Rise, or the Research Computing Service, in the Roger Needham Building.

Fixed-term: The funds for this post are available until 31 March 2027 in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a basic disclosure (criminal records check) check and a security check.

Conversations about flexible working are encouraged at the University of Cambridge. Please feel free to discuss flexibility prior to applying (using the contact information below) or at interview if your application is successful.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please upload your CV and cover letter in the Upload section of this application process. Names and contact details of 2 professional referees are essential. References will be requested for candidates following the application closing date and referees will be asked to complete references by the interview date where permission is given from the candidates to do so; such contact will be direct to your referees via our recruitment system.

Informal enquiries are welcomed and should be directed to Sean McConkey at sm2921@cam.ac.uk in the first instance, quoting reference number LG46639. If you have any queries regarding the application process, please contact: HR@ast.cam.ac.uk.

The closing date for applications is: 23:59 BST on 1st August 2025

The anticipated interview dates are: w/c 11th August 2025 and w/c 18th August 2025

We are seeking a start date ideally no later than 1 October 2025.

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