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We investigate the coevolution of metals and dust for 173 galaxies at 4.010 by JWST. Besides, we observe that the metallicity of galaxies at z>8 presents a metal-to-stellar mass ratio larger than a few 10^-3, above a floor. This suggests a very fast rise of metals at high redshift, impacting the tentative detections of population III objects., Comment: This paper is submitted. It contains a main paper with 5 figures and 2 tables, plus supplementary materials

We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H$\alpha$ emission features for galaxies with stellar masses $\log(\mathrm{M_\ast/M_\odot}) \gtrsim 7$, nearly a factor of a hundred lower than previous studies. We focus on the [O III]/[O II] (O$_{32}$) ratio which is primarily sensitive to the ionization state and with a secondary dependence on the gas-phase metallicity of the interstellar medium. We find significant ($\gtrsim5\sigma$) correlations between the O$_{32}$ ratio and galaxy properties as O$_{32}$ increases with decreasing stellar mass, decreasing star formation rate (SFR), increasing specific SFR (sSFR$\equiv \mathrm{SFR}/M_*$), and increasing equivalent width (EW) of H$\beta$ and H$\alpha$. These trends suggest a tight connection between the ionization parameter and these galaxy properties. Galaxies at $z\sim2-3$ exhibit a higher O$_{32}$ than local normal galaxies with the same stellar masses and SFRs, indicating that they have a higher ionization parameter and lower metallicity than local normal galaxies. In addition, we observe an evolutionary trend in the O$_{32}$ -- EW(H$\beta$) relation from $z\sim0$ and $z\gtrsim5$, such that higher redshift galaxies have higher EW(H$\beta$) and higher O$_{32}$ at fixed EW. We argue that both the enhanced recent star formation activity and the higher star formation surface density may contribute to the increase in O$_{32}$ and the ionization parameter., Comment: 27 pages, 14 figures

The geo-effectiveness of Coronal Mass Ejections (CMEs) is a critical area of study in space weather, particularly in the lesser-explored domain of CME-CME interactions and their geomagnetic consequences. This study leverages the SWASTi framework to perform 3D MHD simulation of a range of CME-CME interaction scenarios within realistic solar wind conditions. The focus is on the dynamics of the initial magnetic flux, speed, density, and tilt of CMEs, and their individual and combined impacts on the disturbance storm time (Dst) index. Additionally, the kinematic, magnetic, and structural impacts on the leading CME, as well as the mixing of both CMEs, are analyzed. Time series in-situ studies are conducted through virtual spacecraft positioned along three different longitudes at 1 AU. Our findings reveal that CME-CME interactions are non-uniform along different longitudes due to the inhomogeneous ambient solar wind conditions. A significant increase in the momentum and kinetic energy of the leading CME is observed due to collisions with the trailing CME, along with the formation of reverse shocks in cases of strong interaction. These reverse shocks lead to complex wave patterns inside CME2, which can prolong the storm recovery phase. Furthermore, we observed that the minimum Dst value decreases with an increase in the initial density, tilt, and speed of the trailing CME., Comment: Accepted for publication in The Astrophysical Journal

Due to the increasing workload of pathologists, the need for automation to support diagnostic tasks and quantitative biomarker evaluation is becoming more and more apparent. Foundation models have the potential to improve generalizability within and across centers and serve as starting points for data efficient development of specialized yet robust AI models. However, the training foundation models themselves is usually very expensive in terms of data, computation, and time. This paper proposes a supervised training method that drastically reduces these expenses. The proposed method is based on multi-task learning to train a joint encoder, by combining 16 different classification, segmentation, and detection tasks on a total of 912,000 patches. Since the encoder is capable of capturing the properties of the samples, we term it the Tissue Concepts encoder. To evaluate the performance and generalizability of the Tissue Concepts encoder across centers, classification of whole slide images from four of the most prevalent solid cancers - breast, colon, lung, and prostate - was used. The experiments show that the Tissue Concepts model achieve comparable performance to models trained with self-supervision, while requiring only 6% of the amount of training patches. Furthermore, the Tissue Concepts encoder outperforms an ImageNet pre-trained encoder on both in-domain and out-of-domain data., Comment: 22 Pages, 3 Figures, submitted to and under revision at Computers in Biology and Medicine

The INTENTAS project aims to develop an atomic sensor utilizing entangled Bose-Einstein condensates (BECs) in a microgravity environment. This key achievement is necessary to advance the capability for measurements that benefit from both entanglement-enhanced sensitivities and extended interrogation times. The project addresses significant challenges related to size, weight, and power management (SWaP) specific to the experimental platform at the Einstein-Elevator in Hannover. The design ensures a low-noise environment essential for the creation and detection of entanglement. Additionally, the apparatus features an innovative approach to the all-optical creation of BECs, providing a flexible system for various configurations and meeting the requirements for rapid turnaround times. Successful demonstration of this technology in the Einstein-Elevator will pave the way for a future deployment in space, where its potential applications will unlock high-precision quantum sensing.

We present a set of photometric catalogs primarily aimed at providing the community with a comprehensive database for the study of galaxy populations in the high redshift Universe. The set gathers data from eight JWST NIRCam observational programs, targeting the Abell 2744 (GLASS-JWST, UNCOVER, DDT2756 and GO3990), EGS (CEERS), COSMOS and UDS (PRIMER), and GOODS North and South (JADES and NGDEEP) deep fields, for a total area of $\sim$0.2 sq. degrees. Photometric estimates are obtained by means of well-established techniques, including tailored improvements designed to enhance the performance on the specific dataset. We also include new measurements from HST archival data, thus collecting 16 bands spanning from 0.44 to 4.44 $\mu$m. A grand total of $\sim$530 thousand sources is detected on stacks of NIRCam 3.56 and 4.44 $\mu$m mosaics. We assess the photometric accuracy by comparing fluxes and colors against archival catalogs. We also provide photometric redshift estimates, statistically validated against a large set of robust spectroscopic data. The catalogs are publicly available on the Astrodeep website., Comment: 28 pages, 16 figures; accepted for publication on Astronomy & Astrophysics

The normalized stress metric measures how closely distances between vertices in a graph drawing match the graph-theoretic distances between those vertices. It is one of the most widely employed quality metrics for graph drawing, and is even the optimization goal of several popular graph layout algorithms. However, normalized stress can be misleading when used to compare the outputs of two or more algorithms, as it is sensitive to the size of the drawing compared to the graph-theoretic distances used. Uniformly scaling a layout will change the value of stress despite not meaningfully changing the drawing. In fact, the change in stress values can be so significant that a clearly better layout can appear to have a worse stress score than a random layout. In this paper, we study different variants for calculating stress used in the literature (raw stress, normalized stress, etc.) and show that many of them are affected by this problem, which threatens the validity of experiments that compare the quality of one algorithm to that of another. We then experimentally justify one of the stress calculation variants, scale-normalized stress, as one that fairly compares drawing outputs regardless of their size. We also describe an efficient computation for scale-normalized stress and provide an open source implementation.

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3

We present the results of an investigation of a highly variable CIV broad absorption-line feature in the quasar SBS 1408+544 (z=2.337) that shows a significant shift in velocity over time. This source was observed as a part of the Sloan Digital Sky Survey Reverberation Mapping Project and the SDSS-V Black Hole Mapper Reverberation Mapping Project, and has been included in two previous studies, both of which identified significant variability in a high-velocity CIV broad absorption line (BAL) on timescales of just a few days in the quasar rest frame. Using ~130 spectra acquired over eight years of spectroscopic monitoring with SDSS, we have determined that this BAL is not only varying in strength, but is also systematically shifting to higher velocities. Using cross-correlation methods, we measure the velocity shifts (and corresponding acceleration) of the BAL on a wide range of timescales, measuring an overall velocity shift of delta v = -683 (+89, -84) km s-1 over the 8-year monitoring period. This corresponds to an average rest-frame acceleration of a=1.04 (+0.14, -0.13) cm s-2, though the magnitude of the acceleration on shorter timescales is not constant throughout. We place our measurements in the context of BAL-acceleration models and examine various possible causes of the observed velocity shift., Comment: Published in the Astrophysical Journal

For Coxeter groups with sufficiently large braid relations, we prove that the sequence of powers of a Coxeter element has unbounded reflection length. We establish a connection between the reflection length functions on arbitrary Coxeter groups and the reflection length functions on universal Coxeter groups of the same rank through the solution to the word problem for Coxeter groups. For Coxeter groups corresponding to a Coxeter matrix with the same entry everywhere except the diagonal, upper bounds for the reflection length of the powers of Coxeter elements are established., Comment: 21 pages. Comments are welcome!

Multivariate Hawkes processes (MHP) are a class of point processes in which events at different coordinates interact through mutual excitation. The weighted adjacency matrix of the MHP encodes the strength of the relations, and shares its support with the causal graph of interactions of the process. We consider the problem of testing for causal relationships across the dimensions of a marked MHP. The null hypothesis is that a joint group of adjacency coefficients are null, corresponding to the absence of interactions. The alternative is that they are positive, and the associated interactions do exist. To this end, we introduce a novel estimation procedure in the context of a large sample of independent event sequences. We construct the associated likelihood ratio test and derive the asymptotic distribution of the test statistic as a mixture of chi squared laws. We offer two applications on financial datasets to illustrate the performance of our method. In the first one, our test reveals a deviation from a static equilibrium in bidders' strategies on retail online auctions. In the second one, we uncover some factors at play in the dynamics of German intraday power prices.

In a series of precedent papers, we have presented a comprehensive methodology, termed Field Economics, for translating a standard economic model into a statistical field-formalism framework. This formalism requires a large number of heterogeneous agents, possibly of different types. It reveals the emergence of collective states among these agents or type of agents while preserving the interactions and microeconomic features of the system at the individual level. In two prior papers, we applied this formalism to analyze the dynamics of capital allocation and accumulation in a simple microeconomic framework of investors and firms.Building upon our prior work, the present paper refines the initial model by expanding its scope. Instead of considering financial firms investing solely in real sectors, we now suppose that financial agents may also invest in other financial firms. We also introduce banks in the system that act as investors with a credit multiplier. Two types of interaction are now considered within the financial sector: financial agents can lend capital to, or choose to buy shares of, other financial firms. Capital now flows between financial agents and is only partly invested in real sectors, depending on their relative returns. We translate this framework into our formalism and study the diffusion of capital and possible defaults in the system, both at the macro and micro level.At the macro level, we find that several collective states may emerge, each characterized by a distinct level of average capital and investors per sector. These collective states depend on external parameters such as level of connections between investors or firms' productivity.The multiplicity of possible collective states is the consequence of the nature of the system composed of interconnected heterogeneous agents. Several equivalent patterns of returns and portfolio allocation may emerge. The multiple collective states induce the unstable nature of financial markets, and some of them include defaults may emerge. At the micro level, we study the propagation of returns and defaults within a given collective state. Our findings highlight the significant role of banks, which can either stabilize the system through lending activities or propagate instability through loans to investors.

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey program was designed specifically to include Near Infrared Slitless Spectroscopic observations (NGDEEP-NISS) to detect multiple emission lines in as many galaxies as possible and across a wide redshift range using the Near Infrared Imager and Slitless Spectrograph (NIRISS). We present early results obtained from the the first set of observations (Epoch 1, 50$\%$ of the allocated orbits) of this program (NGDEEP-NISS1). Using a set of independently developed calibration files designed to deal with a complex combination of overlapping spectra, multiple position angles, and multiple cross filters and grisms, in conjunction with a robust and proven algorithm for quantifying contamination from overlapping dispersed spectra, NGDEEP-NISS1 has achieved a 3$\sigma$ sensitivity limit of 2 $\times$ 10$^{-18}$ erg/s/cm$^2$. We demonstrate the power of deep wide field slitless spectroscopy (WFSS) to characterize the star-formation rates, and metallicity ([OIII]/H$\beta$), and dust content, of galaxies at $1

This paper provides a general method to translate a standard economic model with a large number of agents into a field-formalism model. This formalism preserves the system's interactions and microeconomic features at the individual level but reveals the emergence of collective states.We apply this method to a simple microeconomic framework of investors and firms. Both macro and micro aspects of the formalism are studied.At the macro-scale, the field formalism shows that, in each sector, three patterns of capital accumulation may emerge. A distribution of patterns across sectors constitute a collective state. Any change in external parameters or expectations in one sector will affect neighbouring sectors, inducing transitions between collective states and generating permanent fluctuations in patterns and flows of capital. Although changes in expectations can cause abrupt changes in collective states, transitions may be slow to occur. Due to its relative inertia, the real economy is bound to be more affected by these constant variations than the financial markets.At the micro-scale we compute the transition functions of individual agents and study their probabilistic dynamics in a given collective state, as a function of their initial state. We show that capital accumulation of an individual agent depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Moreover, the firm must also consider the effects of competition in the intermediate sectors that tends to oust under-capitalized firm towards sectors with lower average capital. For investors, capital allocation depends on their short and long-term returns and investors will tend to reallocate their capital to maximize both. The higher their level of capital, the stronger the re-allocation will be., Comment: arXiv admin note: substantial text overlap with arXiv:2205.03087

Building upon the findings presented in the first three papers of this series, we formulate an effective field theory for interacting collective states. These states consist of a large number of interconnected neurons and are distinguished by their intrinsic activity. The field theory encompasses an infinite set of fields, each of which characterizes the dynamics of a specific type of collective state. Interaction terms within the theory drive transitions between various collective states, allowing us to describe processes such as activation, association, and deactivation of these states.

We have shown, in a series of articles, that a classical description of a large number of economic agents can be replaced by a statistical fields formalism. To better understand the accumulation and allocation of capital among different sectors, the present paper applies this statistical fields description to a large number of heterogeneous agents divided into two groups. The first group is composed of a large number of firms in different sectors that collectively own the entire physical capital. The second group, investors, holds the entire financial capital and allocates it between firms across sectors according to investment preferences, expected returns, and stock prices variations on financial markets. In return, firms pay dividends to their investors. Financial capital is thus a function of dividends and stock valuations, whereas physical capital is a function of the total capital allocated by the financial sector. Whereas our previous work focused on the background fields that describe potential long-term equilibria, here we compute the transition functions of individual agents and study their probabilistic dynamics in the background field, as a function of their initial state. We show that capital accumulation depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Thus, an under-capitalized firm reaching a high-capital sector will experience a loss of attractiveness, and subsequently, in investors. Moreover, the firm must also consider the effects of competition in the intermediate sectors. An under-capitalized firm will tend to be ousted out towards sectors with lower average capital, while an over-capitalized firm will tend to shift towards higher averagecapital sectors. For investors, capital allocation depends on their short and long-term returns. These returns are not independent: in the short-term, returns are composed of both the firm's dividends and the increase in its stock prices. In the long-term, returns are based on the firm's growth expectations, but also, indirectly, on expectations of higher stock prices. Investors' capital allocation directly depends on the volatility of stock prices and {\ldots}rms'dividends. Investors will tend to reallocate their capital to maximize their short and long-term returns. The higher their level of capital, the stronger the reallocation will be., Comment: arXiv admin note: substantial text overlap with arXiv:2312.16173, arXiv:2205.03087

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n=15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Segmentations of a commercial software (comDL) and a freely available neural network (nnU-Net), were compared to a reference created via the manual correction of comDL segmentation. Segmentation of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) is evaluated for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient (DC). The volumetric analysis includes LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF). For cine CMR, nnU-Net and comDL achieve a DC above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves a DC of 0.94 for LV, 0.89 for MYO, and 0.90 for RV; mean absolute differences between nnU-Net and reference are 2.9mL for EDV, 3.5mL for ESV and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves a DC of 0.92 for LV, 0.85 for MYO, and 0.83 for RV; mean absolute differences between nnU-Net and reference are 11.4mL for EDV, 2.9mL for ESV and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable or fully automatic segmentation., Comment: Martin Schilling and Christina Unterberg-Buchwald contributed equally to this work

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability., Comment: 29 pages, 5 figures

Pixel-based language models process text rendered as images, which allows them to handle any script, making them a promising approach to open vocabulary language modelling. However, recent approaches use text renderers that produce a large set of almost-equivalent input patches, which may prove sub-optimal for downstream tasks, due to redundancy in the input representations. In this paper, we investigate four approaches to rendering text in the PIXEL model (Rust et al., 2023), and find that simple character bigram rendering brings improved performance on sentence-level tasks without compromising performance on token-level or multilingual tasks. This new rendering strategy also makes it possible to train a more compact model with only 22M parameters that performs on par with the original 86M parameter model. Our analyses show that character bigram rendering leads to a consistently better model but with an anisotropic patch embedding space, driven by a patch frequency bias, highlighting the connections between image patch- and tokenization-based language models., Comment: EMNLP 2023

The 3D geometry of high-redshift galaxies remains poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in JWST-CEERS observations with $\log M_*/M_{\odot}=9.0-10.5$ at $z=0.5-8.0$. We reproduce previous results from HST-CANDELS in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size and covariances with samples as small as $\sim50$ galaxies. We find high 3D ellipticities for all mass-redshift bins suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be $\sim1$ for $\log M_*/M_{\odot}=9.0-9.5$ dwarfs at $z>1$ (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a ``banana'' in the projected $b/a-\log a$ diagram with an excess of low $b/a$, large $\log a$ galaxies. The dwarf prolate fraction rises from $\sim25\%$ at $z=0.5-1.0$ to $\sim50-80\%$ at $z=3-8$. If these are disks, they cannot be axisymmetric but instead must be unusually oval (triaxial) unlike local circular disks. We simultaneously constrain the 3D size-mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar S\'ersic indices ($n\sim1$), non-parametric morphological properties and specific star formation rates. Both tend to be visually classified as disks or irregular but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects and theoretical implications., Comment: Accepted version to appear in ApJ, main body is 36 pages of which ~half are full-page figures

We study the H$\alpha$ equivalent width, EW(H$\alpha$), maps of 19 galaxies at $0.6 < z < 2.2$ in the Hubble Ultra Deep Field (HUDF) derived from NIRISS slitless spectroscopy as part of the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey. Our galaxies mostly lie on the star-formation main sequence with a stellar mass range of $\mathrm{10^9 - 10^{11} M_\odot}$, characterized as "typical" star-forming galaxies at these redshifts. Leveraging deep HST and JWST broad-band images, spanning 0.4-4.8 $\mu$m, we perform spatially-resolved fitting of the spectral energy distributions (SEDs) for these galaxies and construct specific star formation rate (sSFR) and stellar-mass-weighted age maps with a spatial resolution of $\sim$1 kpc. The pixel-to-pixel EW(H$\alpha$) increases with increasing sSFR and with decreasing age. The average trends are slightly different from the relations derived from integrated fluxes of galaxies from the literature, suggesting complex evolutionary trends within galaxies. We quantify the radial profiles of EW(H$\alpha$), sSFR, and age. The majority (84%) of galaxies show positive EW(H$\alpha$) gradients in line with the inside-out quenching scenario. A few galaxies (16%) show inverse (and flat) trends possibly due to merging or starbursts. We compare the distributions of EW(H$\alpha$) and sSFR to the star formation history models (SFHs) as a function of galactocentric radius. We argue that the central regions of galaxies have experienced, at least one, rapid star-formation episodes, which leads to the formation of the bulge, while their outer regions (e.g., disks) grow via more smoothly varying SFHs. These results demonstrate the ability to study resolved star formation in distant galaxies with JWST NIRISS., Comment: 25 pages, 12 figures

The Cosmic Evolution Early Release Science Survey (CEERS), targeting the Extended Groth Strip extragalactic field, is one of the JWST Director's Discretionary Early Release Science programs. To date, all observations have been executed and include NIRCam/MIRI imaging and NIRSpec/NIRCam spectroscopic exposures. Here, we discuss the MIRI imaging, which includes eight pointings, four of which provide deep imaging with the bluer bands (F560W, F770W) and four with contiguous wavelength coverage in F1000W, F1280W, F1500W, and F1800W, where two of these also include coverage in F770W and F2100W. We present a summary of the data, the data quality, and data reduction. The data reduction is based on the JWST Calibration Pipeline combined with custom modifications and additional steps designed to enhance the output quality, including improvements in astrometry and the removal of detector artifacts. We estimate the image depth of the reduced mosaics, and show that these generally agree with expectations from the Exposure Time Calculator. We compare the MIRI F560W and F770W flux densities for bright sources to measurements from Spitzer/IRAC Ch3 (5.8 $\mu$m) and Ch4 (8.0 $\mu$m), and we find that they agree with systematic differences of $<0.1$ mag. For the redder MIRI bands, we assess their quality by studying the spectral energy distributions (SEDs) of Galactic stars. The SEDs are consistent with the expected Rayleigh-Jeans law with a deviation $\sim 0.03$ mag, indicating that the MIRI colors are reliable. We also discuss all publicly released data products (images and source catalogs), which are available on the CEERS website (https://ceers.github.io/)., Comment: 17 pages, 11 figures, and 4 tables. ApJL in press

We use James Webb Space Telescope Near-Infrared Camera Wide Field Slitless Spectroscopy (NIRCam WFSS) and Near-Infrared spectrograph (NIRSpec) in the Cosmic Evolution Early Release survey (CEERS) to measure rest-frame optical emission-line of 155 galaxies at z>2. The blind NIRCam grism observations include a sample of galaxies with bright emission lines that were not observed on the NIRSpec masks. We study the changes of the Ha, [OIII]/Hb, and [NeIII]/[OII] emission lines in terms of redshift by comparing to lower redshift SDSS and CLEAR samples. We find a significant (>3$\sigma$) correlation between [OIII]/Hb with redshift, while [NeIII]/[OII] has a marginal (2$\sigma$) correlation with redshift. We compare [OIII]/Hb and [NeIII]/[OII] to stellar mass and Hb SFR. We find that both emission-line ratios have a correlation with Hb SFR and an anti-correlation with stellar mass across the redshifts 0

We present a robust sample of very high-redshift galaxy candidates from the first epoch of {\it JWST}/NIRCam imaging from the Next Generation Extragalactic Exploratory Deep (NGDEEP) Survey. The NGDEEP NIRCam imaging in the Hubble Ultra Deep Field Parallel Field 2 (HUDF-Par2) reaches $m=30.4$ (5$\sigma$, point-source) in F277W, making it the deepest public {\it JWST} GO imaging dataset to date. We describe our detailed data reduction process of the six-filter broad-band {\it JWST}/NIRCam imaging, incorporating custom corrections for systematic effects to produce high-quality calibrated images. Using robust photometric redshift selection criteria, we identify a sample of 38 $z \gtrsim 9$ galaxy candidates. These objects span a redshift range of $z=8.5-15.8$, and apparent magnitudes of $m_\mathrm{F277W} = 27-30.5$ AB mag, reaching $\sim 1.5$ mag deeper than previous public {\it JWST} imaging surveys. We calculate the rest-frame ultraviolet (UV) luminosity function at $z \sim$ 9 and 11, and present a new measurement of the luminosity function faint-end slope at $z \sim 11$. There is no significant evolution in the faint-end slope and number density from $z=9$ to 11. Comparing our results with theoretical predictions, we find that some models produce better agreement at the faint end than the bright end. These results will help to constrain how stellar feedback impacts star formation at these early epochs., Comment: 21 pages, 10 figures. Submitted to ApJL