Search

We used the Keck Planet Imager and Characterizer (KPIC) to obtain high-resolution (R$\sim$35,000) K-band spectra of kappa Andromedae b, a planetary-mass companion orbiting the B9V star, kappa Andromedae A. We characterized its spin, radial velocity, and bulk atmospheric parameters through use of a forward modeling framework to jointly fit planetary spectra and residual starlight speckles, obtaining likelihood-based posterior probabilities. We also detected H$_{2}$O and CO in its atmosphere via cross correlation. We measured a $v\sin(i)$ value for kappa And b of $38.42\pm{0.05}$ km/s, allowing us to extend our understanding of the population of close in bound companions at higher rotation rates. This rotation rate is one of the highest spins relative to breakup velocity measured to date, at close to $50\%$ of breakup velocity. We identify a radial velocity $-17.35_{-0.09}^{+0.05}$ km/s, which we use with existing astrometry and RV measurements to update the orbital fit. We also measure an effective temperature of $1700\pm{100}$ K and a $\log(g)$ of $4.7\pm{0.5}$ cgs dex., Comment: 14 pages, 7 figures, accepted for publication in AJ

We show that if two $m$-homogeneous algebras have Morita equivalent graded module categories, then they are quantum-symmetrically equivalent, that is, there is a monoidal equivalence between the categories of comodules for their associated universal quantum groups (in the sense of Manin) which sends one algebra to the other. As a consequence, any Zhang twist of an $m$-homogeneous algebra is a 2-cocycle twist by some 2-cocycle from its Manin's universal quantum group., Comment: To appear in Proc. AMS

We show that there is a type-preserving homomorphism from the fundamental group of the figure-eight knot complement to the mapping class group of the thrice-punctured torus. As a corollary, we obtain infinitely many commensurability classes of purely pseudo-Anosov surface subgroups of mapping class groups of closed surfaces. This gives the first examples of compact atoroidal surface bundles over surfaces., Comment: 38 pages, 2 figures

Most of the literature on differential privacy considers the item-level case where each user has a single observation, but a growing field of interest is that of user-level privacy where each of the $n$ users holds $T$ observations and wishes to maintain the privacy of their entire collection. In this paper, we derive a general minimax lower bound, which shows that, for locally private user-level estimation problems, the risk cannot, in general, be made to vanish for a fixed number of users even when each user holds an arbitrarily large number of observations. We then derive matching, up to logarithmic factors, lower and upper bounds for univariate and multidimensional mean estimation, sparse mean estimation and non-parametric density estimation. In particular, with other model parameters held fixed, we observe phase transition phenomena in the minimax rates as $T$ the number of observations each user holds varies. In the case of (non-sparse) mean estimation and density estimation, we see that, for $T$ below a phase transition boundary, the rate is the same as having $nT$ users in the item-level setting. Different behaviour is however observed in the case of $s$-sparse $d$-dimensional mean estimation, wherein consistent estimation is impossible when $d$ exceeds the number of observations in the item-level setting, but is possible in the user-level setting when $T \gtrsim s \log (d)$, up to logarithmic factors. This may be of independent interest for applications as an example of a high-dimensional problem that is feasible under local privacy constraints., Comment: 57 pages, 1 figure, 4 tables

This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high-performance model designed for stronger generalization capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an efficient model optimized for faster speed demanded in many applications requiring edge deployment. The Grounding DINO 1.5 Pro model advances its predecessor by scaling up the model architecture, integrating an enhanced vision backbone, and expanding the training dataset to over 20 million images with grounding annotations, thereby achieving a richer semantic understanding. The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced feature scales, maintains robust detection capabilities by being trained on the same comprehensive dataset. Empirical results demonstrate the effectiveness of Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection. Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT, achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP on the LVIS-minival benchmark, making it more suitable for edge computing scenarios. Model examples and demos with API will be released at https://github.com/IDEA-Research/Grounding-DINO-1.5-API, Comment: homepage: https://deepdataspace.com/home

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps., Comment: 16 pages, 11 tables, 10 figures, MICCAI

We present the projected rotational velocity and molecular abundances for HD 33632 Ab obtained via Keck Planet Imager and Characterizer high-resolution spectroscopy. HD 33632 Ab is a nearby benchmark brown dwarf companion at a separation of $\sim$20 au that straddles the L/T transition. Using a forward-modeling framework with on-axis host star spectra, self-consistent substellar atmospheric and retrieval models for HD 33632 Ab, we derive a projected rotational velocity of 53 $\pm$ 3 km/s and carbon/water mass fractions of log CO = $-$2.3 $\pm$ 0.3 and log H$_2$O = $-$2.7 $\pm$ 0.2. The inferred carbon-to-oxygen ratio (C/O = 0.58 $\pm$ 0.14), molecular abundances, and metallicity ([C/H] = 0.0 $\pm$ 0.2 dex) of HD 33632 Ab are consistent with its host star. Although detectable methane opacities are expected in L/T transition objects, we did not recover methane in our KPIC spectra, partly due to the high $v\sin{i}$ and to disequilibrium chemistry at the pressures we are sensitive to. We parameterize the spin as the ratio of rotation over break-up velocity, and compare HD 33632 Ab to a compilation of >200 very low-mass objects (M$\lesssim$0.1 M$_{\odot}$) that have spin measurements in the literature. There appears to be no clear trend for the isolated field low-mass objects versus mass, but a tentative trend is identified for low-mass companions and directly imaged exoplanets, similar to previous findings. A larger sample of close-in gas giant exoplanets and brown dwarfs will critically examine our understanding of their formation and evolution through rotation and chemical abundance measurements., Comment: Accepted for publication in the Astrophysical Journal. 36 pages, 15 figures, 5 tables

Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection. Here, we show that a nonlinear controller based on next-generation reservoir computing can tackle a difficult control problem: controlling a chaotic system to an arbitrary time-dependent state. The model is accurate, yet it is small enough to be evaluated on a field-programmable gate array typically found in embedded devices. Furthermore, the model only requires 25.0 $\pm$ 7.0 nJ per evaluation, well below other algorithms, even without systematic power optimization. Our work represents the first step in deploying efficient machine learning algorithms to the computing "edge.", Comment: 28 pages, 11 figures

To build a cross-modal latent space between 3D human motion and language, acquiring large-scale and high-quality human motion data is crucial. However, unlike the abundance of image data, the scarcity of motion data has limited the performance of existing motion-language models. To counter this, we introduce "motion patches", a new representation of motion sequences, and propose using Vision Transformers (ViT) as motion encoders via transfer learning, aiming to extract useful knowledge from the image domain and apply it to the motion domain. These motion patches, created by dividing and sorting skeleton joints based on body parts in motion sequences, are robust to varying skeleton structures, and can be regarded as color image patches in ViT. We find that transfer learning with pre-trained weights of ViT obtained through training with 2D image data can boost the performance of motion analysis, presenting a promising direction for addressing the issue of limited motion data. Our extensive experiments show that the proposed motion patches, used jointly with ViT, achieve state-of-the-art performance in the benchmarks of text-to-motion retrieval, and other novel challenging tasks, such as cross-skeleton recognition, zero-shot motion classification, and human interaction recognition, which are currently impeded by the lack of data., Comment: Accepted to CVPR 2024, Project website: https://yu1ut.com/MotionPatches-HP/

Non-radial oscillation modes of a neutron star possess valuable information about its internal structure and nuclear physics. Starting from the quadrupolar order, such modes under general relativity are known as quasi-normal modes since they dissipate energy through gravitational radiation and their frequencies are complex. The stability of these modes is governed by the sign of the imaginary part of the frequency, which determines whether the mode would decay or grow over time. In this Letter, we develop a fully consistent framework in general relativity to study quasi-normal modes of neutron stars with anisotropic pressure, whose motivation includes strong internal magnetic fields and non-vanishing shear or viscosity. We employ parametrized models for the anisotropy and solve the perturbed Einstein field equations numerically. We find that, unlike the case for isotropic neutron stars, the imaginary parts of some of the pressure ($p$-)modes flip signs as the degree of anisotropy deviates from zero, depicting a transition from stable modes to unstable modes. This finding indicates that some anisotropic neutron star models are unstable, potentially restricting the form of sustained anisotropy., Comment: 8+6 pages, 2+1 figures, re-submitted to PRD

General relativity (GR) has proven to be a highly successful theory of gravity since its inception. The theory has thrivingly passed numerous experimental tests, predominantly in weak gravity, low relative speeds, and linear regimes, but also in the strong-field and very low-speed regimes with binary pulsars. Observable gravitational waves (GWs) originate from regions of spacetime where gravity is extremely strong, making them a unique tool for testing GR, in previously inaccessible regions of large curvature, relativistic speeds, and strong gravity. Since their first detection, GWs have been extensively used to test GR, but no deviations have been found so far. Given GR's tremendous success in explaining current astronomical observations and laboratory experiments, accepting any deviation from it requires a very high level of statistical confidence and consistency of the deviation across GW sources. In this paper, we compile a comprehensive list of potential causes that can lead to a false identification of a GR violation in standard tests of GR on data from current and future ground-based GW detectors. These causes include detector noise, signal overlaps, gaps in the data, detector calibration, source model inaccuracy, missing physics in the source and in the underlying environment model, source misidentification, and mismodeling of the astrophysical population. We also provide a rough estimate of when each of these causes will become important for tests of GR for different detector sensitivities. We argue that each of these causes should be thoroughly investigated, quantified, and ruled out before claiming a GR violation in GW observations., Comment: Review article; 1 figure; 1 table; comments welcome

We propose a quantum information processing platform that utilizes the ultrafast time-bin encoding of photons. This approach offers a pathway to scalability by leveraging the inherent phase stability of collinear temporal interferometric networks at the femtosecond-to-picosecond timescale. The proposed architecture encodes information in ultrafast temporal bins processed using optically induced nonlinearities and birefringent materials while keeping photons in a single spatial mode. We demonstrate the potential for scalable photonic quantum information processing through two independent experiments that showcase the platform's programmability and scalability, respectively. The scheme's programmability is demonstrated in the first experiment, where we successfully program 362 different unitary transformations in up to 8 dimensions in a temporal circuit. In the second experiment, we show the scalability of ultrafast time-bin encoding by building a passive optical network, with increasing circuit depth, of up to 36 optical modes. In each experiment, fidelities exceed 97\%, while the interferometric phase remains passively stable for several days., Comment: 7 pages, 3 figures

The theory of institutions is framed as an indexed/fibered duality, where the indexed aspect specifies the fibered aspect. Tarski represented truth in terms of a satisfaction relation. The theory of institutions encodes satisfaction as its core architecture in the indexed aspect. Logical environments enrich this truth architecture by axiomatizing the truth adjunction in the fibered aspect. The truth architecture is preserved by morphisms of logical environments. (Although not every institution is a logical environment, each institution has an associated logical environment defined via the intent of the structures of the institution, and each institution is represented by an indexed functor into the structure category of the classification logical environment $\mathtt{Cls}$.), Comment: 62 pages, 6 figures

Truth refers to the satisfaction relation used to define the semantics of model-theoretic languages. The satisfaction relation for first order languages (truth classification), and the preservation of truth by first order interpretations (truth infomorphism), is a motivating example in the theory of Information Flow (IF) (Barwise and Seligman 1997). The abstract theory of satisfaction is the basis for the theory of institutions (Goguen and Burstall 1992). Factoring refers to categorical factorization systems. The concept lattice, which is the central structure studied by the theory of Formal Concept Analysis (FCA) (Ganter and Wille 1999), is constructed by a factorization. The study of classification structures (IF) and the study of conceptual structures (FCA) aim (at least is part) to provide a principled foundation for the logical theory of knowledge representation and organization. In an effort to unify these two areas, the paper "Distributed Conceptual Structures" (Kent 2002) abstracted the basic theorem of FCA in order to established three levels of categorical equivalence between classification structures and conceptual structures. In this paper we refine this approach by resolving the equivalence as the factorization of three isomorphic versions: relation, function and Galois connection. We develop the latter more algebraic version of the equivalence as the polar factorization of Galois connections. We advocate this abstract adjunctive representation of classification and conceptual structures., Comment: 38 pages, 18 figures

Large quantities of random numbers are crucial in a wide range of applications. We have recently demonstrated that perpendicular nanopillar magnetic tunnel junctions (pMTJs) can produce true random bits when actuated with short pulses. However, our implementation used high-end and expensive electronics, such as a high bandwidth arbitrary waveform generator and analog-to-digital converter, and was limited to relatively low data rates. Here, we significantly increase the speed of true random number generation (TRNG) of our stochastic actuated pMTJs (SMART-pMTJs) using Field Programmable Gate Arrays (FPGAs), demonstrating the generation of over $10^{12}$ bits at rates exceeding 10Mb/s. The resulting bitstreams pass the NIST Statistical Test Suite for randomness with only one XOR operation. In addition to a hundred-fold reduction in the setup cost and a thousand-fold increase in bitrate, the advancement includes simplifying and optimizing random bit generation with a custom-designed analog daughter board to interface an FPGA and SMART-pMTJ. The resulting setup further enables FPGA at-speed processing of MTJ data for stochastic modeling and cryptography., Comment: 4 pages, 5 figures

Human knowledge is made up of the conceptual structures of many communities of interest. In order to establish coherence in human knowledge representation, it is important to enable communication between the conceptual structures of different communities The conceptual structures of any particular community is representable in an ontology. Such a ontology provides a formal linguistic standard for that community. However, a standard community ontology is established for various purposes, and makes choices that force a given interpretation, while excluding others that may be equally valid for other purposes. Hence, a given representation is relative to the purpose for that representation. Due to this relativity of representation, in the larger scope of all human knowledge it is more important to standardize methods and frameworks for relating ontologies than to standardize any particular choice of ontology. The standardization of methods and frameworks is called the semantic integration of ontologies., Comment: 31 pages, 9 figures

Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understanding human behavior. Yet data access is often restricted. How can researchers both effectively and ethically collect user data? This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5000 US users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. The design allows us to study multiple aspects of privacy perception and data sharing behavior. Experiment results (N=6325) reveal both monetary incentives and transparency can significantly increase data sharing. Age, race, education, and gender also played a role, where female and less-educated participants were more likely to share. Our study design enables a unique empirical evaluation of the "privacy paradox", where users claim to value their privacy more than they do in practice. We set up both real and hypothetical data sharing scenarios and find measurable similarities and differences in share rates across these contexts. For example, increasing monetary incentives had a 6 times higher impact on share rates in real scenarios. In addition, we study participants' opinions on how data should be used by various third parties, again finding demographics have a significant impact. Notably, the majority of participants disapproved of government agencies using purchase data yet the majority approved of use by researchers. Overall, our findings highlight the critical role that transparency, incentive design, and user demographics play in ethical data collection practices, and provide guidance for future researchers seeking to crowdsource user generated data., Comment: In Proc. ACM Hum.-Comput. Interact., Vol. 8, No. CSCW2, Article 466. Publication date: November 2024

The response of black holes to small perturbations is known to be partially described by a superposition of quasinormal modes. Despite their importance to enable strong-field tests of gravity, little to nothing is known about what overtones and quasinormal-mode amplitudes are like for black holes in extensions to general relativity. We take a first step in this direction and study what is arguably the simplest model that allows first-principle calculations to be made: a nonrotating black hole in an effective-field-theory extension of general relativity with cubic-in-curvature terms. Using a phase-amplitude scheme that uses analytical continuation and the Pr\"ufer transformation, we compute, for the first time, the quasinormal overtone frequencies (in this theory) and quasinormal-mode excitation factors (in any theory beyond general relativity). We find that the overtone quasinormal frequencies and their excitation factors are more sensitive than the fundamental mode to the lengthscale $l$ introduced by the higher-derivative terms in the effective field theory. We argue that a description of all overtones cannot be made within the regime of validity of the effective field theory, and we conjecture that this is a general feature of any extension to general relativity that introduces a new lengthscale. We also find that a parametrization of the modifications to the general-relativistic quasinormal frequencies in terms of the ratio between $l$ and the black hole's mass is somewhat inadequate, and we propose a better alternative. As an application, we perform a preliminary study of the implications of the breakdown, in the effective field theory, of the equivalence between the quasinormal mode spectra associated to metric perturbations of polar and axial parity of the Schwarzschild black hole in general relativity. We also present a simple justification for the loss of isospectrality., Comment: 21 pages, 9 figures, 1 table. (v2) Matches version published in Phys. Rev. D

Recent statistics indicate that approximately 1.3 billion individuals worldwide suffer from hypertension, a leading cause of premature death globally. Blood pressure (BP) serves as a critical health indicator for accurate and timely diagnosis and/or treatment of hypertension. Driven by recent advancements in Artificial Intelligence (AI) and Deep Neural Networks (DNNs), there has been a surge of interest in developing data-driven and cuff-less BP estimation solutions. In this context, current literature predominantly focuses on coupling Electrocardiography (ECG) and Photoplethysmography (PPG) sensors, though this approach is constrained by reliance on multiple sensor types. An alternative, utilizing standalone PPG signals, presents challenges due to the absence of auxiliary sensors (ECG), requiring the use of morphological features while addressing motion artifacts and high-frequency noise. To address these issues, the paper introduces the TransfoRhythm framework, a Transformer-based DNN architecture built upon the recently released physiological database, MIMIC-IV. Leveraging Multi-Head Attention (MHA) mechanism, TransfoRhythm identifies dependencies and similarities across data segments, forming a robust framework for cuff-less BP estimation solely using PPG signals. To our knowledge, this paper represents the first study to apply the MIMIC IV dataset for cuff-less BP estimation, and TransfoRhythm is the first MHA-based model trained via MIMIC IV for BP prediction. Performance evaluation through comprehensive experiments demonstrates TransfoRhythm's superiority over its state-of-the-art counterparts. Specifically, TransfoRhythm achieves highly accurate results with Root Mean Square Error (RMSE) of [1.84, 1.42] and Mean Absolute Error (MAE) of [1.50, 1.17] for systolic and diastolic blood pressures, respectively.

We present the first on-sky segmented primary mirror closed-loop piston control using a Zernike wavefront sensor (ZWFS) installed on the Keck II telescope. Segment co-phasing errors are a primary contributor to contrast limits on Keck and will be necessary to correct for the next generation of space missions and ground-based extremely large telescopes (ELTs), which will all have segmented primary mirrors. The goal of the ZWFS installed on Keck is to monitor and correct primary mirror co-phasing errors in parallel with science observations. The ZWFS is ideal for measuring phase discontinuities such as segment co-phasing errors and is one of the most sensitive WFS, but has limited dynamic range. The vector-ZWFS at Keck works on the adaptive optics (AO) corrected wavefront and consists of a metasurface focal plane mask which imposes two different phase shifts on the core of the point spread function (PSF) to two orthogonal light polarizations, producing two pupil images. This design extends the dynamic range compared with the scalar ZWFS. The primary mirror segment pistons were controlled in closed-loop using the ZWFS, improving the Strehl ratio on the NIRC2 science camera by up to 10 percentage points. We analyze the performance of the closed-loop tests, the impact on NIRC2 science data, and discuss the ZWFS measurements., Comment: Accepted for publication in the Astrophysical Journal (ApJ). 17 pages, 16 figures

The 1RXS J034231.8+121622 system consists of an M dwarf primary and a directly imaged low-mass stellar companion. We use high resolution spectroscopic data from Keck/KPIC to estimate the objects' atmospheric parameters and radial velocities (RVs). Using PHOENIX stellar models, we find that the primary has a temperature of 3460 $\pm$ 50 K a metallicity of 0.16 $\pm$ 0.04, while the secondary has a temperature of 2510 $\pm$ 50 K and a metallicity of $0.13\substack{+0.12 \\ -0.11}$. Recent work suggests this system is associated with the Hyades, placing it an older age than previous estimates. Both metallicities agree with current $[Fe/H]$ Hyades measurements (0.11 -- 0.21). Using stellar evolutionary models, we obtain significantly higher masses for the objects, of 0.30 $\pm$ 0.15 $M_\odot$ and 0.08 $\pm$ 0.01 $M_\odot$ (84 $\pm$ 11 $M_{Jup}$) respectively. Using the RVs and a new astrometry point from Keck/NIRC2, we find that the system is likely an edge-on, moderately eccentric ($0.41\substack{+0.27 \\ -0.08}$) configuration. We also estimate the C/O ratio of both objects using custom grid models, obtaining 0.42 $\pm$ 0.10 (primary) and 0.55 $\pm$ 0.10 (companion). From these results, we confirm that this system most likely went through a binary star formation process in the Hyades. The significant changes in this system's parameters since its discovery highlight the importance of high resolution spectroscopy for both orbital and atmospheric characterization of directly imaged companions., Comment: 30 pages, 18 figures, accepted for publication in AJ

Online temporal action segmentation shows a strong potential to facilitate many HRI tasks where extended human action sequences must be tracked and understood in real time. Traditional action segmentation approaches, however, operate in an offline two stage approach, relying on computationally expensive video wide features for segmentation, rendering them unsuitable for online HRI applications. In order to facilitate online action segmentation on a stream of incoming video data, we introduce two methods for improved training and inference of backbone action recognition models, allowing them to be deployed directly for online frame level classification. Firstly, we introduce surround dense sampling whilst training to facilitate training vs. inference clip matching and improve segment boundary predictions. Secondly, we introduce an Online Temporally Aware Label Cleaning (O-TALC) strategy to explicitly reduce oversegmentation during online inference. As our methods are backbone invariant, they can be deployed with computationally efficient spatio-temporal action recognition models capable of operating in real time with a small segmentation latency. We show our method outperforms similar online action segmentation work as well as matches the performance of many offline models with access to full temporal resolution when operating on challenging fine-grained datasets., Comment: 5 pages, 3 figures. Accepted as a short (unindexed) paper at the TAHRI conference

The Atmospheric Dispersion Corrector (ADC) of the Gemini Planet Imager (GPI) corrects the chromatic dispersion caused by differential atmospheric refraction (DAR), making it an important optic for exoplanet observation. Despite requiring less than 5 mas of residual DAR to avoid potentially affecting the coronagraph, the GPI ADC averages $\sim7$ and $\sim11$ mas of residual DAR in $H$ and $J$ band respectively. We analyzed GPI data in those bands to find explanations for the underperformance. We found the model GPI uses to predict DAR underestimates humidity's impact on incident DAR, causing on average a 0.54 mas increase in $H$ band residual DAR. Additionally, the GPI ADC consistently undercorrects in $H$ band by about 7 mas, causing almost all the $H$ band residual DAR. $J$ band does not have such an offset. Perpendicular dispersion induced by the GPI ADC, potentially from a misalignment in the prisms' relative orientation, causes 86% of the residual DAR in $J$ band. Correcting these issues could reduce residual DAR, thereby improving exoplanet detection. We also made a new approximation for the index of refraction of air from 0.7 microns to 1.36 microns that more accurately accounts for the effects of humidity., Comment: 21 pages, 10 figures, published in JATIS

We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range $0.1-1$ and $w_a<0$. This preference is 2.6$\sigma$ for the DESI+CMB combination, and persists or grows when SN~Ia are added in, giving results discrepant with the $\Lambda$CDM model at the $2.5\sigma$, $3.5\sigma$ or $3.9\sigma$ levels for the addition of Pantheon+, Union3, or DES-SN5YR datasets respectively. For the flat $\Lambda$CDM model with the sum of neutrino mass $\sum m_\nu$ free, combining the DESI and CMB data yields an upper limit $\sum m_\nu < 0.072$ $(0.113)$ eV at 95% confidence for a $\sum m_\nu>0$ $(\sum m_\nu>0.059)$ eV prior. These neutrino-mass constraints are substantially relaxed in models beyond $\Lambda$CDM. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). 68 pages, 15 figures. Version accepted for publication in JCAP

We present the measurement of Baryon Acoustic Oscillations (BAO) from the Lyman-$\alpha$ (Ly$\alpha$) forest of high-redshift quasars with the first-year dataset of the Dark Energy Spectroscopic Instrument (DESI). Our analysis uses over $420\,000$ Ly$\alpha$ forest spectra and their correlation with the spatial distribution of more than $700\,000$ quasars. An essential facet of this work is the development of a new analysis methodology on a blinded dataset. We conducted rigorous tests using synthetic data to ensure the reliability of our methodology and findings before unblinding. Additionally, we conducted multiple data splits to assess the consistency of the results and scrutinized various analysis approaches to confirm their robustness. For a given value of the sound horizon ($r_d$), we measure the expansion at $z_{\rm eff}=2.33$ with 2\% precision, $H(z_{\rm eff}) = (239.2 \pm 4.8) (147.09~{\rm Mpc} /r_d)$ km/s/Mpc. Similarly, we present a 2.4\% measurement of the transverse comoving distance to the same redshift, $D_M(z_{\rm eff}) = (5.84 \pm 0.14) (r_d/147.09~{\rm Mpc})$ Gpc. Together with other DESI BAO measurements at lower redshifts, these results are used in a companion paper to constrain cosmological parameters., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers). Minor changes in v4, version accepted for publication in JCAP

We present the DESI 2024 galaxy and quasar baryon acoustic oscillations (BAO) measurements using over 5.7 million unique galaxy and quasar redshifts in the range 0.1

Broadband low-resolution near-infrared spectrographs in a compact form are crucial for ground- and space-based astronomy and other fields of sensing. Astronomical spectroscopy poses stringent requirements including high efficiency, broad band operation ($>$ 300 nm), and in some cases, polarization insensitivity. We present and compare experimental results from the design, fabrication, and characterization of broadband (1200 - 1650 nm) arrayed waveguide grating (AWG) spectrographs built using the two most promising low-loss platforms - Si$_3$N$_4$ (rectangular waveguides) and doped-SiO$_2$ (square waveguides). These AWGs have a resolving power ($\lambda/\Delta\lambda$) of ~200, a free spectral range of ~ 200-350 nm, and a small footprint of ~ 50-100 mm$^2$. The peak overall (fiber-chip-fiber) efficiency of the doped-SiO$_2$ AWG was ~ 79\% (1 dB), and it exhibited a negligible polarization-dependent shift compared to the channel spacing. For Si$_3$N$_4$ AWGs, the peak overall efficiency in TE mode was ~ 50\% (3 dB), and the main loss component was found to be fiber-to-chip coupling losses. These broadband AWGs are key to enabling compact integrations such as multi-object spectrographs or dispersion back-ends for other astrophotonic devices such as photonic lanterns or nulling interferometers., Comment: 16 pages, 8 Figures, accepted for publication in Optics Express