arXiv subjects

Zefeng Li

Publications and source records attributed to Zefeng Li.

At least 19 recordsLinked to original sources

arXiv2026

Private and Stable Test-Time Adaptation with Differential Privacy

Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates raise the issue of privacy w.r.t. the testing data, because the model parameters now depend on all past inputs. To control this privacy risk, we cast multiple popular TTA methods (Tent, EATA, SAR, DeYO, and COME) into differential privacy (DP) forms that apply per-sample gradient clipping and Gaussian noise for all updates. On ImageNet-C, our DP-TTA methods provide adequate privacy at small cost to accuracy, and in the low-privacy regime the clipping mechanism of DP can even improve the accuracy and stability of adaptation in the continual setting. These improvements to privacy and accuracy come at only modest computational overhead. These first results on private TTA raise awareness of the issue, inform the development of more private test-time updates, and identify per-sample clipping as an effective technique for improving the accuracy and stability of adaptation.

cs.LG↗

arXiv2026

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes. While recent methods have made progress on improving in-distribution (InD) accuracy for known classes, their ability to accurately detect out-of-distribution (OOD) unknown classes remains underexplored. We benchmark robust and open-set TTA methods (SAR, OSTTA, UniEnt, and SoTTA) on the standard corruption benchmarks of CIFAR-10-C at the small scale and ImageNet-C at the large scale. For CIFAR-10-C, we use OOD data from SVHN and CIFAR-100 in their respective corrupted forms of SVHN-C and CIFAR-100-C. For ImageNet-C, we use OOD data from ImageNet-O and Textures in their respective corrupted forms of ImageNet-O-C and Textures-C. ImageNet-O is nearer to ImageNet, as unknown but related object classes (like ''garlic bread'' vs. ''hot dog'' for food, or ''highway'' vs. ''dam'' for infrastructure), while Textures is farther from ImageNet, as non-object patterns (like ''cracked'' mud, ''porous'' sponge, ''veined'' leaves). We evaluate the accuracy and confidence of TTA methods for InD vs. OOD recognition on CIFAR-10-C and ImageNet-C. We verify the accuracy of each method's own OOD detection technique on CIFAR-10-C. We also evaluate on ImageNet-C and report both accuracy and standard OOD detection metrics. We further examine more realistic settings, in which the proportions and rates of OOD data can vary. To explore the trade-off between InD recognition and OOD rejection, we propose a new baseline that replaces softmax/multi-class output with sigmoid/multi-label output. Our analysis shows for the first time that current open-set TTA methods struggle to balance InD and OOD accuracy and that they only imperfectly filter OOD data for their own adaptation updates.

cs.LG↗

arXiv2026

Masked Generative Transformer Is What You Need for Image Editing

Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications to propagate into areas that should remain intact. We propose a fundamentally different approach by leveraging Masked Generative Transformers (MGTs), whose localized token-prediction paradigm naturally confines changes to intended regions. We present EditMGT, an MGT-based editing framework that is the first of its kind. Our approach employs multi-layer attention consolidation to aggregate cross-attention maps into precise edit localization signals, and region-hold sampling to explicitly prevent token flipping in non-target areas. To support training, we construct CrispEdit-2M, a 2M-sample high-resolution (>1024) editing dataset spanning seven categories. With only 960M parameters, EditMGT achieves state-of-the-art image similarity on multiple benchmarks while delivering 6x faster editing, demonstrating that MGTs offer a compelling alternative to diffusion-based editing.

cs.CV↗

arXiv2026

Ripples of Stellar Enrichment (RoSE) - simulating element production and mixing in a Milky Way-mass galactic disc star-by-star

We present the Ripples of Stellar Enrichment (RoSE) simulations, which follow an isolated Milky Way-mass disc galaxy with star-by-star feedback and nucleosynthesis from five physical enrichment channels -- Wolf-Rayet stars, type II supernovae, type Ia supernovae, asymptotic giant branch stars, and neutron star mergers. We use these simulations to test how elements' diverse nucleosynthetic origins imprint spatial, temporal, and inter-element abundance correlations in gas and newly formed stars. We find that nucleosynthetic source composition is the primary organising principle of elemental structure: elements sharing a dominant production channel exhibit similar spatial and temporal statistics and their abundances are strongly correlated with one another, while mixed-source pairs are much more weakly correlated. We show that a simple linear regression model based only on how element pairs differ in their nucleosynthetic origin is able to predict, with high fidelity, how strongly their abundances correlate, in both interstellar medium gas and coeval stars. Comparison with Milky Way stellar abundance data shows encouraging qualitative agreement, with differences between simulations and observations comparable to the scatter between independent observational datasets. Together, these results show that the covariance of galactic abundances is largely predictable from the mixture of nucleosynthetic sources supplying each element: same-source elements remain tightly correlated in gas and young stars, whereas differing source mixtures produce systematically weaker correlations.

astro-ph.GA↗

arXiv2026

TRACE: A Multi-Agent System for Autonomous Physical Reasoning for Seismology

Inferring physical mechanisms that govern earthquake sequences from geophysical observations remains a challenging task, particularly across tectonically distinct environments where similar seismic patterns can reflect different underlying processes. Current seismological processing and interpretation rely heavily on experts' choice of parameters and the synthesis of various seismological products, limiting reproducibility and the formation of generalizable knowledge across settings. Here we present TRACE (Trans-perspective Reasoning and Automated Comprehensive Evaluator), a multi-agent system that combines large language model planning with formal seismological constraints to derive auditable, physically grounded mechanistic inferences from raw observations. Applied to the 2019 Ridgecrest sequence, TRACE autonomously identifies stress-perturbation-induced delayed triggering, resolving the cascading interaction between the Mw 6.4 and Mw 7.1 mainshocks. For the 2025 Santorini-Kolumbo volcanic eruption, the system identifies a structurally guided intrusion model, distinguishing episodic migration via fault channels from the continuous propagation expected in homogeneous crustal failure. By providing a generalizable infrastructure for deriving physical insights from seismic phenomena, TRACE advances the field from expert-dependent analysis toward knowledge-guided autonomous discovery in Earth sciences.

physics.geo-ph↗

arXiv2026

UniDWM: Towards a Unified Driving World Model via Multifaceted Representation Learning

Achieving reliable and efficient planning in complex driving environments requires a model that can reason over the scene's geometry, appearance, and dynamics. We present UniDWM, a unified driving world model that advances autonomous driving through multifaceted representation learning. UniDWM constructs a structure- and dynamic-aware latent world representation that serves as a physically grounded state space, enabling consistent reasoning across perception, prediction, and planning. Specifically, a joint reconstruction pathway learns to recover the scene's structure, including geometry and visual texture, while a collaborative generation framework leverages a conditional diffusion transformer to forecast future world evolution within the latent space. Furthermore, we show that our UniDWM can be deemed as a variation of VAE, which provides theoretical guidance for the multifaceted representation learning. Extensive experiments demonstrate the effectiveness of UniDWM in trajectory planning, 4D reconstruction and generation, highlighting the potential of multifaceted world representations as a foundation for unified driving intelligence. The code will be publicly available at https://github.com/Say2L/UniDWM.

cs.RO↗

arXiv2025

How Mergers and Flybys Shape Azimuthal Age Patterns in Spiral Galaxies

Spiral structures are one of the most common features in galaxies, yet their origins and evolution remain debated. Stellar age distributions offer crucial insights into galaxy evolution and star formation, though environmental effects can obscure the intrinsic age patterns. Using the Auriga cosmological gravo-magnetohydrodynamical zoom-in simulations, we investigate the azimuthal age distribution of young stars (<2 Gyr) in a sample of five Milky Way-mass spiral galaxies over the past 5 Gyr. We quantify the age gradients across spiral arms using the mean age offset (${\Delta}{\tau}$) and the non-overlap fraction ($f_{non-overlap}$). We further analyse the impact of mergers and fly-by events on the age gradients. Our results show that Auriga spiral galaxies generally feature younger stars in their leading edges compared to the trailing edges, with a typical ${\Delta}{\tau}$ between 30 and 80 Myr. However, gas-rich interactions can disrupt this age offset, resulting in similar age distributions on each side of the spiral arms. In three snapshots, we observe similar mean ages on both sides of spiral arms but differing age distribution broadness, coinciding with satellite interactions crossing the host galaxy's disc plane. Our simulation data suggest that the typical azimuthal age variation recovers within ~600 Myr after galaxy interactions. This work highlights the transient role of environmental interactions in shaping spiral arm age patterns.

astro-ph.GA↗

arXiv2025

EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing

Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local editing targets with the full-image context, leading to unintended modifications in non-target regions. In this paper, we shift our attention beyond DMs and turn to Masked Generative Transformers (MGTs) as an alternative approach to tackle this challenge. By predicting multiple masked tokens rather than holistic refinement, MGTs exhibit a localized decoding paradigm that endows them with the inherent capacity to explicitly preserve non-relevant regions during the editing process. Building upon this insight, we introduce the first MGT-based image editing framework, termed EditMGT. We first demonstrate that MGT's cross-attention maps provide informative localization signals for localizing edit-relevant regions and devise a multi-layer attention consolidation scheme that refines these maps to achieve fine-grained and precise localization. On top of these adaptive localization results, we introduce region-hold sampling, which restricts token flipping within low-attention areas to suppress spurious edits, thereby confining modifications to the intended target regions and preserving the integrity of surrounding non-target areas. To train EditMGT, we construct CrispEdit-2M, a high-resolution dataset spanning seven diverse editing categories. Without introducing additional parameters, we adapt a pre-trained text-to-image MGT into an image editing model through attention injection. Extensive experiments across four standard benchmarks demonstrate that, with fewer than 1B parameters, our model achieves similarity performance while enabling 6 times faster editing. Moreover, it delivers comparable or superior editing quality, with improvements of 3.6% and 17.6% on style change and style transfer tasks, respectively.

cs.CV↗

arXiv2025

KURVS: chemical properties from multiple strong line calibrations for star-forming galaxies at $z\sim1.5$

Gas-phase oxygen abundance (metallicity) properties can be constrained through emission line analyses, and are of great importance to investigate galaxy evolution histories. We present an analysis of the integrated and spatially-resolved rest-frame optical emission line properties of the ionised gas in 43 star-forming galaxies at $z\sim1.5$ in the KMOS Ultra-deep Rotational Velocity Survey (KURVS). Using the [NII]$\lambda6584$/H$\alpha$ (N$_2$), ([OII]$\lambda\lambda3727,9+$[OIII]$\lambda\lambda4959,5007$)/H$\beta$ (R23), and for the first time [NII]$\lambda6584$/[OII]$\lambda\lambda3727,9$ (N$_2$O$_2$) indicators at this redshift, we measure the gas-phase metallicities and their radial gradients. On $\sim4$-kpc scales metallicity gradients measured from N$_2$O$_2$ and those measured from N$_2$ are in good agreement when considering the spatial distributions of dust in each galaxy, as parameterised by dust attenuation radial gradients. We report a nearly flat metallicity gradient distribution typically at $z\sim1.5$, with the 50th, 16th and 84th percentiles at $0.01$, $-0.03$, and $0.05$ dex kpc$^{-1}$, respectively. The findings agree well with previous observational studies and simulations at this epoch. We ascribe the observed negative metallicity gradients to a natural result from self-regulating systems, and the positive ones to potential galactic fountains and higher merger rates.

astro-ph.GA↗

arXiv2025

Denoised Recommendation Model with Collaborative Signal Decoupling

Although the collaborative filtering (CF) algorithm has achieved remarkable performance in recommendation systems, it suffers from suboptimal recommendation performance due to noise in the user-item interaction matrix. Numerous noise-removal studies have improved recommendation models, but most existing approaches conduct denoising on a single graph. This may cause attenuation of collaborative signals: removing edges between two nodes can interrupt paths between other nodes, weakening path-dependent collaborative information. To address these limitations, this study proposes a novel GNN-based CF model called DRCSD for denoising unstable interactions. DRCSD includes two core modules: a collaborative signal decoupling module (decomposes signals into distinct orders by structural characteristics) and an order-wise denoising module (performs targeted denoising on each order). Additionally, the information aggregation mechanism of traditional GNN-based CF models is modified to avoid cross-order signal interference until the final pooling operation. Extensive experiments on three public real-world datasets show that DRCSD has superior robustness against unstable interactions and achieves statistically significant performance improvements in recommendation accuracy metrics compared to state-of-the-art baseline models.

cs.IR↗

arXiv2025

The JWST Emission Line Survey (JELS): The sizes and merger fraction of star-forming galaxies during the Epoch of Reionization

We used observations from the JWST Emission Line Survey (JELS) to measure the half-light radii ($r_{e}$) of 23 H$\alpha$-emitting star-forming (SF) galaxies at $z=6.1$ in the PRIMER/COSMOS field. Galaxy sizes were measured in JWST Near-infrared Camera observations in rest-frame H$\alpha$ (tracing recent star formation) with the F466N and F470N narrowband filters from JELS, and compared against rest-$R$-band, $V$-band (tracing established stellar populations) and near-ultraviolet sizes. We find a size-stellar mass ($r_{e}-M_{*}$) relationship with a slope that is consistent with literature values at lower redshifts, though offset to lower sizes. We observe a large scatter in $r_{e}$ at low stellar mass ($M_{*}<10^{8.4}$ M$_{\odot}$) which we believe is the result of bursty star formation histories (SFHs) of SF galaxies at the Epoch of Reionization (EoR). We find that the stellar and ionised gas components are similar in size at $z=6.1$. The evidence of already-established stellar components in these H$\alpha$ emitters (HAEs) indicates previous episodes of star formation have occurred. As such, following other JELS studies finding our HAEs are undergoing a current burst of star formation, we believe our results indicate that SF galaxies at the end of the EoR have already experienced a bursty SFH. From our $r_{e}-M_{*}$ relationship, we find $r_{e, \text{F444W}}=0.76\pm0.46$ kpc for fixed stellar mass $M_{*}=10^{9.25}$ M$_{\odot}$, which is in agreement with other observations and simulations of star forming galaxies in the literature. We find a close-pair (major) merger fraction of ($f_{\text{maj. merger}}=0.44\pm0.22$) $f_{\text{merger}}=0.43\pm0.11$ for galaxy separations $d\lesssim25$ kpc, which is in agreement with other $z\approx6$ studies.

astro-ph.GA↗

arXiv2025

Metallicity fluctuation statistics in the interstellar medium and young stars -- II. Elemental cross-correlations and the structure of chemical abundance space

Observations of Milky Way stars by multiplexed spectroscopic instruments and of gas in nearby galaxies using integral field units have made it possible to measure the abundances of multiple elements in both the interstellar medium and the stars that form out of it. These observations have revealed complex correlations between elemental abundances, but thus far there has been no analytic theoretical framework to interpret these data. In this paper we extend the simple stochastically-forced diffusion model of Krumholz & Ting (2018), which has proven successful at explaining the spatial abundance patterns of single elements, to multiple elements, clarifying why elements are correlated and what controls their degree of correlation, and making quantitative predictions for the degree of correlation in both gas and young stars. We show that our results are qualitatively consistent with observed patterns, and point out how application of this theory to measured correlations should enable determination of currently unknown parameters describing r-process nucleosynthesis.

astro-ph.GA↗

arXiv2025

Element nucleosynthetic origins from abundance spatial distributions beyond the Milky Way

An element's astrophysical origin should be reflected in the spatial distribution of its abundance, yielding measurably different spatial distributions for elements with different nucleosynthetic sites. However, most extragalactic multi-element analyses of gas-phase abundances to date have been limited to small numbers of sightlines, making statistical characterization of differences in spatial distributions of elements impossible. Here we use integrated field spectroscopic data covering the full face of the nearby dwarf galaxy NGC 5253 sampled at 3.5-pc resolution to produce maps of the abundances of oxygen, nitrogen, and sulfur using independent direct methods. We find strong evidence for differences in the elements' spatial statistics that mirror their predicted nucleosynthetic origins: the spatial distributions of oxygen and sulfur, both predominantly produced in core-collapse supernovae, indicate that initial injection occurs on larger scales than for nitrogen, which is predominantly produced by asymptotic giant branch stars. All elements are well-correlated but oxygen and sulfur are much better correlated with each other than with nitrogen, consistent with recent results for stellar abundances in the Milky Way. These findings both open a new avenue to test nucleosynthetic models, and make predictions for the structure of stellar chemical abundance distributions.

astro-ph.GA↗

arXiv2025

GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving

Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird's eye view fusion through geometric transformations. However, these approaches often suffer from limited interpretability or dense computational overhead. In this paper, we introduce GaussianFusion, a Gaussian-based multi-sensor fusion framework for end-to-end autonomous driving. Our method employs intuitive and compact Gaussian representations as intermediate carriers to aggregate information from diverse sensors. Specifically, we initialize a set of 2D Gaussians uniformly across the driving scene, where each Gaussian is parameterized by physical attributes and equipped with explicit and implicit features. These Gaussians are progressively refined by integrating multi-modal features. The explicit features capture rich semantic and spatial information about the traffic scene, while the implicit features provide complementary cues beneficial for trajectory planning. To fully exploit rich spatial and semantic information in Gaussians, we design a cascade planning head that iteratively refines trajectory predictions through interactions with Gaussians. Extensive experiments on the NAVSIM and Bench2Drive benchmarks demonstrate the effectiveness and robustness of the proposed GaussianFusion framework. The source code will be released at https://github.com/Say2L/GaussianFusion.

cs.RO↗

arXiv2024

The properties of the interstellar medium in dusty, star-forming galaxies at $z \sim 2-4$: The shape of the CO spectral line energy distributions

The molecular gas in the interstellar medium (ISM) of star-forming galaxy populations exhibits diverse physical properties. We investigate the $^{12}$CO excitation of twelve dusty, luminous star-forming galaxies at $z \sim 2-4$ by combining observations of the $^{12}$CO from $J_{\rm up} = 1$ to $J_{\rm up} = 8$. The spectral line energy distribution (SLED) has a similar shape to NGC 253, M82, and local ULIRGs, with much stronger excitation than the Milky Way inner disc. By combining with resolved dust continuum sizes from high-resolution $870$-$\mu$m ALMA observations and dust mass measurements determined from multi-wavelength SED fitting, we measure the relationship between the $^{12}$CO SLED and probable physical drivers of excitation: star-formation efficiency, the average intensity of the radiation field $\langle U\rangle$, and the star-formation rate surface density. The primary driver of high-$J_{\rm up}$ $^{12}$CO excitation in star-forming galaxies is star-formation rate surface density. We use the ratio of the CO($3-2$) and CO($6-5$) line fluxes to infer the CO excitation in each source and find that the average ratios for our sample are elevated compared to observations of low-redshift, less actively star-forming galaxies and agree well with predictions from numerical models that relate the ISM excitation to the star-formation rate surface density. The significant scatter in the line ratios of a factor $\approx 3$ within our sample likely reflects intrinsic variations in the ISM properties which may be caused by other effects on the excitation of the molecular gas, such as cosmic ray ionization rates and mechanical heating through turbulence dissipation.

astro-ph.GA↗

arXiv2024

Understanding the Mechanisms Behind the Distribution of Galactic Metals

The evolution and distribution of metals within galaxies are critical for understanding galactic evolution and star formation processes, but the mechanisms responsible for shaping this distribution remain uncertain. In this study, we carry out high-resolution simulations of an isolated Milky Way-like galaxy, including a star-by-star treatment of both feedback and element injection. We include seven key isotopes of observational and physical interest, and which are distributed across different nucleosynthetic channels--primarily AGB stars (N, Ba, Ce), supernovae (O, Mg, S), and Wolf-Rayet stars (C) show measurably different correlation statistics in space and time and their fluctuations. This difference arises from the distinct ejection mechanisms associated with each nucleosynthetic process. The large-scale properties ensure that different elements, despite having different nucleosynthetic origins, are highly correlated with one another (>0.85 for all, >0.99 for same orgins), and their spatial correlations vary together in time. However small-scale variations naturally break elements into distinct nucleosynthetic familiars, with elements originating from the same channels correlating better with each other than with elements from different origins. Our findings suggest both challenges and opportunities for ongoing efforts to use chemical measurements of gas and stars to unravel the history and physics of galaxy assembly.

astro-ph.GA↗

arXiv2024

The JWST Emission Line Survey (JELS): An untargeted search for H$\alpha$ emission line galaxies at $z > 6$ and their physical properties

We present the first results of the JWST Emission Line Survey (JELS). Utilising the first NIRCam narrow-band imaging at 4.7$\mu$m, over 63 arcmin$^{2}$ in the PRIMER/COSMOS field, we identified 609 emission line galaxy candidates. From these, we robustly selected 35 H$\alpha$ star-forming galaxies at $z \sim 6.1$, with H$\alpha$ star-formation rates ($\rm{SFR_{H\alpha}}$) $\sim0.9-15\ \rm{M_{\odot} \ yr^{-1}}$. Combining our unique H$\alpha$ sample with the exquisite panchromatic data in the field, we explored their physical properties and star-formation histories, and compared these to a broad-band selected sample at $z\sim 6$ which offered vital new insights into the nature of high-redshift galaxies. UV-continuum slopes ($\beta$) were considerably redder for our H$\alpha$ sample ($\langle\beta\rangle\sim-1.92$) compared to the broad-band sample ($\langle\beta\rangle\sim-2.35$). This was not due to dust attenuation as our H$\alpha$ sample was relatively dust-poor (median $A_V=0.23$); instead, we argued the reddened slopes could be due to nebular continuum. We compared $\rm{SFR_{H\alpha}}$ and the UV-continuum-derived $\rm{SFR_{UV}}$ to SED-fitted measurements averaged over canonical timescales of 10 and 100 Myr ($\rm{SFR_{10}}$ and $\rm{SFR_{100}}$). We found an increase in recent SFR for our sample of H$\alpha$ emitters, particularly at lower stellar masses ($<10^9 \ \rm{M_{\odot}}$). We also found $\rm{SFR_{H\alpha}}$ strongly traced SFR averaged over 10 Myr timescales, whereas the UV-continuum over-predicts SFR on 100 Myr timescales at low stellar masses. These results point to our H$\alpha$ sample undergoing `bursty' star formation. Our F356W $z \sim 6$ sample showed a larger scatter in $\rm{SFR_{10}/SFR_{100}}$ across all stellar masses, which highlighted how narrow-band photometric selections of H$\alpha$ emitters are key to quantifying the burstiness of star-formation activity.

astro-ph.GA↗

arXiv2024

The JWST Emission Line Survey (JELS): Extending rest-optical narrow-band emission line selection into the Epoch of Reionization

We present the JWST Emission Line Survey (JELS), a JWST imaging programme exploiting the wavelength coverage and sensitivity of NIRCam to extend narrow-band rest-optical emission line selection into the epoch of reionization (EoR) for the first time, and to enable unique studies of the resolved ionised gas morphology in individual galaxies across cosmic history. The primary JELS observations comprise $\sim4.7\mu$m narrow-band imaging over $\sim63$ arcmin$^{2}$ designed to enable selection of H$\alpha$ emitters at z~6.1 and a host of novel emission-line samples, including [OIII] ($z\sim8.3$) and Paschen $\alpha/\beta$ ($z\sim1.5/2.8$). For the F466N/F470N narrow-band observations, the emission-line sensitivities achieved are up to $\sim2\times$ more sensitive than current slitless spectroscopy surveys (5$\sigma$ limits of 0.8-1.2$\times10^{-18}\,\text{erg s}^{-1}\text{cm}^{-2}$), corresponding to unobscured H$\alpha$ star-formation rates (SFRs) of 0.9-1.3 $\text{M}_{\odot}\text{yr}^{-1}$ at z~6.1, extending emission-line selections in the EoR to fainter populations. Simultaneously, JELS also adds F200W broadband and F212N narrow-band imaging (H$\alpha$ at z~2.23) that probes SFRs $\gtrsim5\times$ fainter than previous ground-based narrow-band studies ($\sim0.2\text{M}_{\odot}\text{yr}^{-1}$), offering an unprecedented resolved view of star formation at cosmic noon. We present the detailed JELS survey design, key data processing steps specific to the survey observations, and demonstrate the exceptional data quality and imaging sensitivity achieved. We then summarise the key scientific goals of JELS, demonstrate the precision and accuracy of the expected redshift and measured emission line recovery through detailed simulations, and present examples of spectroscopically confirmed H$\alpha$ and [OIII] emitters discovered by JELS that illustrate the novel parameter space probed.

astro-ph.GA↗