Physical Sciences

We present a measurement of baryon acoustic oscillations (BAO) in the cross correlation of triply ionized carbon (C iv) absorption with the positions of quasars (QSO) and emission-line galaxies (ELGs). We use quasars and ELGs from the second data release (DR2) of the Dark Energy Spectroscopic Instrument survey. Our data sample consists of 2.5 million quasars, 3.1 million ELGs, and the C iv absorption is measured along the line of sight of 1.5 million high-redshift quasars with z > 1.3. We measure the isotropic BAO signal at 4.2σ for the C IV×QSO cross correlation. This translates into a 3.0% precision measurement of the ratio of the isotropic distance scale, DV, and the sound horizon at the drag epoch, rd, with DV/rd(zeff = 1.92) = 30.3 ± 0.9. We make the first detection of the BAO feature in the C IV×ELG cross-correlation at a significance of 2.5σ and find DV/rd(zeff = 1.47) = 24.6 ± 1.0.

Using the first spectroscopic data release from the Dark Energy Spectroscopic Instrument (DESI DR1), we conduct the largest systematic search for active galactic nuclei (AGN) signatures in ≈1.7 million low-redshift (0.001 ≤ z ≤ 0.45) line-emitting galaxies. Based on the [N ii]/Hα versus [O iii]/Hβ emission-line ratio diagnostic, we identify AGN in 314,245/1,211,573 (25.9%) high-mass ( log(M⋆/M⊙)>9.5 ) and 9648/467,214 (2.1%) dwarf ( log(M⋆/M⊙)≤9.5 ) galaxies. The AGN fraction in line-emitting galaxies increases monotonically with stellar mass, from ∼1.4% at the low-mass end to ∼93.3% at the high-mass end. Among these AGN, 17,949 exhibit broad Hα emission (BL-AGN), which we use to estimate their black hole (BH) masses via single-epoch virial methods. Leveraging this large BL-AGN sample, we extend the MBH − M⋆ scaling relation down to log(M⋆/M⊙)≈7.8 and log(MBH/M⊙)≈4.4 . We find a bimodal distribution of galaxies in this space, characterized by two distinct empirical relations. The relation for galaxies with high stellar and BH masses extrapolates to overlap with the high-redshift overmassive BH candidates. Our results suggest at least two broad evolutionary pathways for these overmassive BHs and their host galaxies across cosmic time. With this paper, we release the EmFit value-added catalog, containing emission-line flux and width measurements for ∼7.4 million galaxies, the largest catalog with emission-line decomposition into narrow, broad, and outflow components to date. This work significantly expands upon the early DESI results and provides a statistical sample for probing the galaxy−BH connection in the low-mass galaxy regime.

Context. The growing volume of spectroscopic data in modern surveys motivates the development of data-driven approaches that complement traditional emission-line diagnostics for the identification of active galactic nuclei (AGNs). Aims. We develop and validate a machine-learning (ML) framework for galaxy and AGN classification that exploits the full optical spectrum, using unsupervised learning to capture spectral features within a semi-supervised classification framework that complements classical approaches. Methods. We adapted the SPENDER autoencoder architecture to compress galaxy spectra obtained with the Dark Energy Spectroscopic Instrument (DESI) into a low-dimensional latent space that encodes physically meaningful label-agnostic information. We introduced a quantitative classification method via a k-d tree nearest-neighbor search in latent space. The model was trained on 50 222 spectra from the DESI main survey, corresponding to the internal Guadalupe dataset, released as part of data release 1 (DR1), and restricted to z ≤ 0.5. We validated the performance using labels derived from the FastSpecFit emission line measurements defining seven galaxy classes: AGN, broad-line (BL), composite, star-forming, passive, retired, and other. Results. The model achieves a high performance in identifying AGN and BL sources, with accuracies of 0.952 and 0.965, respectively. The model reliably recovers AGN and BL sources even in spectra with a low signal-to-noise ratio and identifies AGNs missed by single-diagnostic standard methods. Our classification metrics are benchmarked against traditional diagnostics, and we show that they represent lower limits of the true model performance. The latent space learned by the autoencoder not only separates AGN and non-AGN galaxies, but also correlates with key physical properties such as stellar mass and star formation rate, demonstrating that the method captures astrophysical meaningful features. Conclusions. This work demonstrates that unsupervised spectral representation learning implemented within a semi-supervised classification framework offers a powerful and scalable pathway toward more complete and reliable AGN catalogs for current and upcoming large spectroscopic surveys.

Galaxy clustering constrains the baryon fraction Ωb/Ωm through the amplitude of baryon acoustic oscillations and the suppression of perturbations entering the horizon before recombination. This produces a different prerecombination distribution of baryons and dark matter. After recombination, the gravitational potential responds to both components in proportion to their mass, allowing robust measurement of the baryon fraction. This is independent of new-physics scenarios altering the recombination background (e.g. early dark energy). The accuracy of such measurements does, however, depend on how baryons and cold dark matter (CDM) are modeled in the power spectrum. Previous template-based splitting relied on approximate transfer functions that neglected part of information. We present a new method that embeds an extra parameter controlling the balance between baryons and dark matter in the growth terms of the perturbation equations in the camb Boltzmann solver. This approach captures the baryonic suppression of CDM prior to recombination, avoids inconsistencies, and yields a clean parametrization of the baryon fraction in the linear power spectrum, separating out the simple physics of growth due to the combined matter potential. We implement this framework in an analysis pipeline using effective field theory of large-scale structure with halo occupation distribution-informed priors and validate it against noiseless ΛCDM and early dark energy cosmologies with Dark Energy Spectroscopic Instrument (DESI)-like errors. The new scheme achieves comparable precision to previous splitting while reducing systematic biases, providing a more robust way to baryon-fraction measurements. In combination with big bang nucleosynthesis constraints on the baryon density and Alcock-Paczyński estimates of the matter density, these results strengthen the use of baryon fraction measurements to derive a Hubble constant from energy densities, with future DESI and Euclid data expected to deliver competitive constraints.

Superconducting magnets enable energy-frontier accelerators by generating strong magnetic fields to steer and focus the particles. Although high-temperature superconductors such as (, RE = rare earth) hold a strong potential for generating a higher magnetic field than Nb-Ti and , the associated magnet and conductor technology for accelerator applications is still in its infancy. The U.S. Magnet Development Program is developing magnet technology in collaboration with industry. Here we report an experiment of making a dipole magnet called C3 using commercial high-temperature superconducting wires. The magnet, following a canted design, generated a dipole field of 5.99 T at 4.2 K in its clear aperture of 65 mm at 6.795 kA when a resistive voltage of appeared across one of the coils in the magnet. The stored energy was 53 kJ at the peak field. The magnet showed no degradation in the current-carrying capability at 4.2 K after the thermal cycle. We report on the detailed design, fabrication, and performance of the C3 magnet that can be of interest to potential users of this emerging technology. We also discuss issues and research needs to inform future magnet development. The experiment represented another step to addressing if the high-temperature superconducting accelerator magnet technology can increase the discovery capability of future particle accelerators.

DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used in the DUNE horizontal drift (HD) far detector and presents performance results using data from the ProtoDUNE-HD detector, a 770 ton liquid argon time projection chamber operated at the CERN Neutrino Platform in 2024 that served as the final prototype of the DUNE HD design.

The published article has been revised. The name of the author Pramod Sharma was previously incorrect and appeared as Pramond Sharma. The original article has been corrected.

The Dark Energy Spectroscopic Instrument (DESI) in its first Data Release (DR1) already provides more than 100,000 galaxies with relative distance measurements. The primary purpose of this paper is to perform the calibration of the zero-point for the DESI Fundamental Plane and Tully–Fisher relations, which allows us to measure the Hubble constant, H0. This sample has a lower statistical uncertainty than any previously used to measure H0, and we investigate the systematic uncertainties in absolute calibration that could limit the accuracy of that measurement. We improve upon the DESI Early Data Release Fundamental Plane H0 measurement by (a) using a group catalog to increase the number of calibrator galaxies and (b) investigating alternative calibrators in the nearby Universe. Our baseline measurement calibrates to the SH0ES/Pantheon+ type Ia supernovae, and finds H0 = 73.7 ± 0.06 (stat.) ± 1.1 (syst.) km s−1 Mpc−1. Calibrating to surface brightness fluctuation distances yields a similar H0. We explore measurements using other calibrators, but these are currently less precise since the overlap with DESI peculiar velocity tracers is much smaller. In future data releases with an even larger peculiar velocity sample, we plan to calibrate directly to Cepheids and the tip of the red giant branch, which will enable the uncertainty to decrease towards a percent-level measurement of H0. This will provide an alternative to supernovae as the Hubble flow sample for H0 measurements.

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses.

Recent advances in deformable mirrors based on monolithic piezoelectric substrates, such as lithium niobate, have the potential to improve the image quality of X-ray optical systems on synchrotron and free-electron laser beamlines and microscopes. However, the quantitative relationship between design parameters and the deformed shape has not been readily available in the literature. We present an analytical model, validated through finite element analysis, enabling calculation of tangential and sagittal curvatures based on mirror dimensions, crystallographic orientation, and applied voltage. We demonstrate that through the selection of material orientation, it is possible to achieve different deformed shapes (sphere, cylinder, or hyperbolic paraboloid). This methodology can be generalized to other piezoelectric materials and substrate-integrated actuator systems.