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Accurate prediction of electric vehicle charging profiles and durations is critical for adoption and optimising infrastructure. Direct current fast charging presents complex behaviours shaped by many factors. This work introduces a deep learning framework trained on 909,135 real-world sessions, capable of predicting charging profiles and durations from minimal input with uncertainty estimates. The model initiates predictions from a single point on the power and state-of-charge profile and incrementally refines them as new observations arrive, enabling real-time updates. The model generalises across vehicle types and charging scenarios. It achieves 90% accuracy in predicting charging duration from a single point, and 95% accuracy with an absolute error under one minute using six points within five minutes. This work shows that using readily available input data at charge time enables accurate prediction of charging behaviour and offers a practical, scalable solution for deployment, energy planning, and infrastructure reliability. Here, the authors present a deep learning framework trained on nearly one million direct current fast charging sessions that accurately predicts electric vehicle charging profiles and the remaining driving time. The model provides the predictions in real time from minimal input, improving user experience, energy planning, and infrastructure reliability.

In the case of OH we model the lowest 48 hypcrfinc states using the Sobolev, or 'Large Velocity Gradient' approximation and include the effects of collisions with para-H2 in J = 0, a continuum radiation field (with components from the dust within the masing region, dust external to the masing region and, optionally, from a nearby star) and a velocity field. Far-infrared line overlap is included in the model. We use a new theory which describes the effects of both the thermal width of the line profiles ('thermal overlap') and the Doppler shifting due to the velocity field ('velocity overlap') in all cases where a significant amount of overlap between the line profiles (taken to be Gaussian) occurs. The observed behaviour of Hll/OH regions (strong inversions in all four ground state transitions, and in the excited rotational states 2n3/2 J - 5/2 anti 2rii/2 J = l/j over a narrow range of conditions, the association of various masing frequencies in different rotational states, the prevalence of the main line masers over those of the satellite lines) is reproduced well under the following conditions: H2 number density 106-10s cm\"\"3, kinetic and local dust temperature 100-200 K, external dust temperature 150-250 K, diluted spatially by a factor of 0.25, OH abundance 10~6-2xlO-6, velocity gradient 500-1500 km s\"1 pc~*. It is found that a consequence of FIR line overlap in accelerative velocity fields is to increase the gain coefficient for 1665 MHz above that of 1667 MHz. This is a commonly observed feature that is not otherwise predicted. Masers in star-forming areas without H1I regions and in OH/IR stars arc modelled briefly and a new, as yet unobserved, phenomenon of FIR lasers at 115 ftm and 135 fim is predicted to occur near volumes of hot (> 500 K) dust which may be found in star-forming regions.In the case of SiO in circumstcllar envelopes we represent the lowest 40 rotational states (J = 0-39) in each of the first 5 vibrational states (V = 0-4). The less abundant isotopic forms 20SilcO and 30SilcO may optionally be included also. We model collisions with para-H2 in J = 0, the background continuum radiation field (with contributions from the evolved star, dust external to the masing region and/or dust within it) and the effects of a velocity field using the Sobolev approximation. The SiO masers characteristic of M-Miras and supergiants are reproduced with moderate success, and those of OH/IR stars rather better using the following conditions: Hj number density 109-101U cm~3, kinetic temperature 1100-1500 K, dust (either local to the masing region, or external and surrounding it) temperature 1100-1500 K, SiO abundance lO\"5-10~4, velocity gradient 1000-10000 km s\"1 pc-1. These conditions also give rise to an inversion in the transition of the less abundant isotopes that has been observed to mase, i.e. V = 0 J = 1-0. It is suggested thai mascr action in high lying rotational states occurs in the absence of optically thick hot dust (conditions as above excepting TXD < 400 K) possibly reflecting a low heavy element (particularly Aluminium) abundance in some evolved star envelopes. From thermodynamic arguments it is shown that accurate collision rate coefficients for SiO and both atomic hydrogen and rotation-ally excited molecular hydrogen arc highly desirable if the lull range of potential conditions for SiO masers arc to be explored accurately.The problem of competitive gain in SiO is addressed briefly. It is shown that where only the one or two lowest lying transitions within a vibralional state are inverted, then a saturating mascr in J = 1-0 can cause an inversion in the J = 1-0 transition of the next higher vibrational slate. This reflects the observed association of J = 1-0 in V = 1 and 2. Conversely, if a greater number of transitions within a vibrational state arc all inverted then simultaneous saturation in each of those results in a stronger gain in ihc next higher lying transition within the same vibra

The Dark Energy Spectroscopic Instrument (DESI) is currently measuring the spectra of 40 million galaxies and quasars, the largest such survey ever made to probe the nature of cosmological dark energy. The 4 m Mayall telescope at Kitt Peak National Observatory has been adapted for DESI, including the construction of a 3.°2 diameter prime focus corrector that focuses astronomical light onto a 0.8 m diameter focal surface with excellent image quality over the DESI bandpass of 360–980 nm. The wide-field corrector includes six lenses, as large as 1.1 m in diameter and as heavy as 237 kilograms, including two counterrotating wedged lenses that correct for atmospheric dispersion over zenith angles from 0° to 60°. The lenses, cells, and barrel assembly all meet precise alignment tolerances on the order of tens of microns. The barrel alignment is maintained throughout a range of observing angles and temperature excursions in the Mayall dome by use of a hexapod, which is itself supported by a new cage, ring, and truss structure. In this paper we describe the design, fabrication, and performance of the new corrector and associated structure, focusing on how they meet DESI requirements. In particular, we describe the prescription and specifications of the lenses, design choices and error budgeting of the barrel assembly, stray light mitigations, and integration and test at the Mayall telescope. We conclude with some validation highlights that demonstrate the successful corrector on-sky performance, and we list some lessons learned during the multiyear fabrication phase.

The PRObabilistic Value-added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass (M *), star formation rate (SFR), stellar metallicity (Z), and stellar age (t age), for >10 million galaxies of the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, the neural emulator for the model, and the Bayesian inference framework of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-Galaxies semi-analytic model. We compare the inferred galaxy properties to the true values of the simulation using a hierarchical Bayesian framework to quantify accuracy and precision. Overall, we accurately infer the true M *, SFR, Z, and t age of the simulated galaxies. However, the priors on galaxy properties induced by the SED model have a significant impact on the posteriors, which we characterize in detail. This work also demonstrates that a joint analysis of spectra and photometry significantly improves the constraints on galaxy properties over photometry alone and is necessary to mitigate the impact of the priors. With the methodology presented and validated in this work, PROVABGS will maximize information extracted from DESI observations and extend current galaxy studies to new regimes and unlock cutting-edge probabilistic analyses. https://github.com/changhoonhahn/provabgs/

In the current Dark Energy Spectroscopic Instrument (DESI) survey, emission line galaxies (ELGs) and luminous red galaxies (LRGs) are essential for mapping the dark matter distribution at z ∼ 1. We measure the auto and cross correlation functions of ELGs and LRGs at 0.8 < z ≤ 1.0 from the DESI One-Percent survey. Following Gao et al., we construct the galaxy–halo connections for ELGs and LRGs simultaneously. With the stellar–halo mass relation for the whole galaxy population (i.e., normal galaxies), LRGs can be selected directly by stellar mass, while ELGs can also be selected randomly based on the observed number density of each stellar mass, once the probability P sat of a satellite galaxy becoming an ELG is determined. We demonstrate that the observed small scale clustering prefers a halo mass-dependent P sat model rather than a constant. With this model, we can well reproduce the auto correlations of LRGs and the cross correlations between LRGs and ELGs at r p > 0.1 Mpc h −1. We can also reproduce the auto correlations of ELGs at r p > 0.3 Mpc h −1 (s > 1 Mpc h −1) in real (redshift) space. Although our model has only seven parameters, we show that it can be extended to higher redshifts and reproduces the observed auto correlations of ELGs in the whole range of 0.8 < z ≤ 1.6, which enables us to generate a lightcone ELG mock for DESI. With the above model, we further derive halo occupation distributions for ELGs, which can be used to produce ELG mocks in coarse simulations without resolving subhalos.

We present the Multi-Object Spectroscopy of Transient (MOST) Hosts survey. The survey is planned to run throughout the 5 yr of operation of the Dark Energy Spectroscopic Instrument (DESI) and will generate a spectroscopic catalog of the hosts of most transients observed to date, in particular all the supernovae observed by most public, untargeted, wide-field, optical surveys (Palomar Transient Factory, PTF/intermediate PTF, Sloan Digital Sky Survey II, Zwicky Transient Facility, DECAT, DESIRT). Science cases for the MOST Hosts survey include Type Ia supernova cosmology, fundamental plane and peculiar velocity measurements, and the understanding of the correlations between transients and their host-galaxy properties. Here we present the first release of the MOST Hosts survey: 21,931 hosts of 20,235 transients. These numbers represent 36% of the final MOST Hosts sample, consisting of 60,212 potential host galaxies of 38,603 transients (a transient can be assigned multiple potential hosts). Of all the transients in the MOST Hosts list, only 26.7% have existing classifications, and so the survey will provide redshifts (and luminosities) for nearly 30,000 transients. A preliminary Hubble diagram and a transient luminosity–duration diagram are shown as examples of future potential uses of the MOST Hosts survey. The survey will also provide a training sample of spectroscopically observed transients for classifiers relying only on photometry, as we enter an era when most newly observed transients will lack spectroscopic classification. The MOST Hosts DESI survey data will be released on a rolling cadence and updated to match the DESI releases. Dates of future releases and updates are available through the https://mosthosts.desi.lbl.gov website.

In this investigation, we leverage the combination of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 data sets to estimate the conditional luminosity functions and conditional stellar mass functions (CLFs and CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI mock galaxy redshift survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r- and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low-redshift bin at the low-luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift, the faint-end slopes of CLFs and CSMFs below ∼109 h −2 L ⊙ (or h −2 M ⊙) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following Chen et al., the faint-end slopes of the LFs/SMFs turn out to also be in good agreement with the slope of the halo mass function.

Using K giants from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) Milky Way Survey, we measure the shape, orientation, radial profile, and density anisotropies of the Milky Way (MW) stellar halo over 8 kpc < rGC < 200 kpc. We identify a triaxial stellar halo (axis ratio 10:8:7), 43° tilted from the disk, showing two break radii at ∼16 and ∼76 kpc, likely associated with Gaia-Sausage/Enceladus and the Large Magellanic Cloud (LMC), respectively. The inner stellar halo (<30 kpc) is oblate and aligned with the disk, whereas the outer stellar halo becomes prolate and perpendicular to the disk, consistent with the vast polar structure of MW satellites. The twisted halo may arise from the disk−halo angular momentum shift triggered by the infall of a massive satellite. The anisotropic density distribution of the stellar halo is also measured, with successful reidentification of the Hercules-Aquila Cloud North/South (HAC-N/S) overdensity and the Virgo overdensity (VOD). Break radii are found at 15 and 30 kpc for VOD and HAC-N/S, respectively. We identify the LMC transient density wake with a break radius at 60 kpc in the Pisces overdensity region. We also find new observational evidence of the LMC collective density wake, by showing a break radius at ∼100 kpc in the northern Galactic cap with a clear density peak at 90 kpc. In the end, we found that more metal-poor halo stars are more radially extended. Our results provide important clues to the assembly and evolution of the MW stellar halo under the standard cosmic structure formation framework.

The Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) has so far observed three classical dwarf spheroidal galaxies (dSphs): Draco, Sextans, and Ursa Minor. Based on the observed line-of-sight velocities and metallicities of their member stars, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion modeling (JAM) approach to recover their inner dark matter distributions. In particular, both the traditional single-population Jeans model and the multiple population chemodynamical model are adopted. With the chemodynamical model, we divide member stars of each dSph into metal-rich and metal-poor populations. The metal-rich populations are more centrally concentrated and dynamically colder, featuring lower velocity dispersion profiles than the metal-poor populations. We find a diversity of the inner density slopes γ of dark matter halos, with the best constraints by the single-population or chemodynamical models consistent with each other. The inner density slopes are 0.71−0.35+0.34 , 0.26−0.12+0.22 , and 0.33−0.16+0.20 for Draco, Sextans, and Ursa Minor, respectively. We also present the measured astrophysical J and D factors of the three dSphs. Our results indicate that the study of the dark matter content of dSphs through stellar kinematics is still subject to uncertainties behind both the methodology and the observed data, through comparisons with previous measurements and datasets.