Search

The fundamental theorem of classical optimal transport establishes strong duality and characterizes optimizers through a complementary slackness condition. Milestones such as Brenier's theorem and the Kantorovich-Rubinstein formula are direct consequences. In this paper, we generalize this result to non-linear cost functions, thereby establishing a fundamental theorem for the weak optimal transport problem introduced by Gozlan, Roberto, Samson, and Tetali. As applications we provide concise derivations of the Brenier--Strassen theorem, the convex Kantorovich--Rubinstein formula and the structure theorem of entropic optimal transport. We also extend Strassen's theorem in the direction of Gangbo--McCann's transport problem for convex costs. Moreover, we determine the optimizers for a new family of transport problems which contains the Brenier--Strassen, the martingale Benamou--Brenier and the entropic martingale transport problem as extreme cases.

We introduce a categorical formalization of diffusion, motivated by data science and information dynamics. Central to our construction is the Lawvere Laplacian, an endofunctor on a product category indexed by a graph and enriched in a quantale. This framework permits the systematic study of diffusion processes on network sheaves taking values in a class of enriched categories.

Boubel et al. 2024 (B24) recently used the Tully-Fisher (TF) relation to measure calibrated distances in the Hubble flow and found $H_0= 73.3 \pm 2.1 (stat) \pm 3.5 (sys)$ km/s/Mpc. The large systematic uncertainty was the result of propagating the conflict between two sources of empirical distance calibration: a difference in zeropoint when calibrating the TF relation with Type Ia supernovae (SNe Ia) versus Cepheids and Tip-of-the-Red-Giant-Branch (TRGB) and an apparent difference in zeropoint between two distinct TRGB datasets. We trace the SN Ia-based calibration used in the TF analysis to a study where $H_0$ was fixed to 70 km/s/Mpc rather than measured, (with host distances derived from redshifts and the Hubble law), thus introducing a discrepancy with the other empirically calibrated indicators. In addition, we trace the difference in TRGB zeropoints to a miscalibration of $0.14$ mag that should be $\sim0.01-0.04$ mag. Using the consistent Cepheid and TRGB calibration from B24 while removing the problematic data reduces the systematic error by a factor of two and results in $H_0 = 76.3 \pm 2.1 \textrm{(stat)} \pm 1.5 \textrm{(sys)}$ km/s/Mpc. This measurement is consistent with previous determinations of $H_0$ using the TF relation. We also show that most determinations of $H_0$ measurements that replace Type Ia supernovae measurements with another far-field distance indicator yield $H_0>73$ km/s/Mpc, reinforcing previous findings that the Hubble tension is not tied to any one distance indicator., Comment: Comments very welcome. Submitting to Research Notes of the AAS next week

A quantitative spectroscopic study of blue supergiant stars in the Hubble constant anchor galaxy NGC 4258 is presented. The non-LTE analysis of Keck I telescope LRIS spectra yields a central logarithmic metallicity (in units of the solar value) of [Z] = -0.05\pm0.05 and a very shallow gradient of -(0.09\pm0.11)r/r25 with respect to galactocentric distance in units of the isophotal radius. Good agreement with the mass-metallicity relationship of star forming galaxies based on stellar absorption line studies is found. A comparison with HII region oxygen abundances obtained from the analysis of strong emission lines shows reasonable agreement when the Pettini & Pagel (2004) calibration is used, while the Zaritsky et al. (1994) calibration yields values that are 0.2 to 0.3 dex larger. These results allow to put the metallicity calibration of the Cepheid Period--Luminosity relation in this anchor galaxy on a purely stellar basis. Interstellar reddening and extinction are determined using HST and JWST photometry. Based on extinction-corrected magnitudes, combined with the stellar effective temperatures and gravities we determine, we use the Flux-weighted Gravity--Luminosity Relationship (FGLR) to estimate an independent spectroscopic distance. We obtain a distance modulus m-M = 29.38\pm0.12 mag, in agreement with the geometrical distance derived from the analysis of the water maser orbits in the galaxy's central circumnuclear disk., Comment: accepted for publication in the Astrophysical Journal

In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserstein distance to construct ambiguity sets around each client's empirical distribution that capture possible distribution shifts in the local data, enabling evaluation of worst-case model performance. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions so that possible biases in the local models caused by statistically heterogeneous client datasets are avoided, and analyze our method for linear and logistic regression models. Finally, we discuss a federated learning protocol that ensures the privacy of client distributions, a critical consideration, for instance, when clients are healthcare institutions. We evaluate our algorithm on synthetic and real-world healthcare data., Comment: 8 pages, 3 figures, Accepted to IEEE CDC 2024

The Dark Energy Spectroscopic Instrument (DESI) collaboration measured a tight relation between the Hubble constant ($H_0$) and the distance to the Coma cluster using the fundamental plane (FP) relation of the deepest, most homogeneous sample of early-type galaxies. To determine $H_0$, we measure the distance to Coma by several independent routes each with its own geometric reference. We measure the most precise distance to Coma from 12 Type Ia Supernovae (SNe Ia) in the cluster with mean standardized brightness of $m_B^0=15.712\pm0.041$ mag. Calibrating the absolute magnitude of SNe Ia with the HST distance ladder yields $D_{\textrm Coma}=98.5\pm2.2$ Mpc, consistent with its canonical value of 95--100 Mpc. This distance results in $H_0=76.5 \pm 2.2$ km/s/Mpc from the DESI FP relation. Inverting the DESI relation by calibrating it instead to the Planck+$\Lambda$CDM value of $H_0=67.4$ km/s/Mpc implies a much greater distance to Coma, $D_{\textrm Coma}=111.8\pm1.8$ Mpc, $4.6\sigma$ beyond a joint, direct measure. Independent of SNe Ia, the HST Key Project FP relation as calibrated by Cepheids, Tip of the Red Giant Branch from JWST, or HST NIR surface brightness fluctuations all yield $D_{\textrm Coma}<$ 100 Mpc, in joint tension themselves with the Planck-calibrated route at $>3\sigma$. From a broad array of distance estimates compiled back to 1990, it is hard to see how Coma could be located as far as the Planck+$\Lambda$CDM expectation of $>$110 Mpc. By extending the Hubble diagram to Coma, a well-studied location in our own backyard whose distance was in good accord well before the Hubble Tension, DESI indicates a more pervasive conflict between our knowledge of local distances and cosmological expectations. We expect future programs to refine the distance to Coma and nearer clusters to help illuminate this new, local window on the Hubble Tension., Comment: v2 - team name fixed

At the low-redshift end ($z<0.05$) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities is of similar size to that due to the limitations of the standardization of the SN Ia light curves. A way to improve the redshift measurement of the SN host galaxy is to utilize the average redshift of the galaxy group, effectively averaging over small-scale/intracluster peculiar velocities. One limiting factor is the fraction of SN host galaxies in galaxy groups, previously found to be 30% using (relatively incomplete) magnitude-limited galaxy catalogs. Here, we do the first analysis of N-body simulations to predict this fraction, finding $\sim$73% should have associated groups and group averaging should improve redshift precision by $\sim$135 km s$^{-1}$ ($\sim$0.04 mag at $z=0.025$). Furthermore, using spectroscopic data from the Anglo-Australian Telescope, we present results from the first pilot program to evaluate whether or not 23 previously unassociated SN Ia hosts belong in groups. We find that 91% of these candidates can be associated with groups, consistent with predictions from simulations given the sample size. Combining with previously assigned SN host galaxies in Pantheon+, we demonstrate improvement in Hubble residual scatter equivalent to 145 km s$^{-1}$, also consistent with simulations. For new and upcoming low-$z$ samples from, for example, ZTF and LSST, a separate follow-up program identifying galaxy groups of SN hosts is a highly cost-effective way to enhance their constraining power., Comment: 17 pages, 12 figures. Published in ApJ

JWST provides new opportunities to cross-check the HST Cepheid/SNeIa distance ladder, which yields the most precise local measure of H0. We analyze early JWST subsamples (~1/4 of the HST sample) from the SH0ES and CCHP groups, calibrated by a single anchor (N4258). We find HST Cepheid distances agree well (~1 sigma) with all 8 combinations of methods, samples, and telescopes: JWST Cepheids, TRGB, and JAGB by either group, plus HST TRGB and Miras. The comparisons explicitly include the measurement uncertainty of each method in N4258, an oft-neglected but dominant term. Mean differences are ~0.03 mag, far smaller than the 0.18 mag "Hubble tension." Combining all measures produces the strongest constraint yet on the linearity of HST Cepheid distances, 0.994+-0.010, ruling out distance-dependent bias or offset as the source of the tension at ~7 sigma. Yet, measurements of H0 from current JWST subsamples produce large sampling differences whose size and direction we can directly estimate from the full HST set. We show that Delta(H0)~2.5 km/s/Mpc between the CCHP JWST program and the full HST sample is entirely consistent with differences in sample selection. Combining all JWST samples produces a new, distance-limited set of 16 SNeIa at D<25 Mpc and more closely resembles the full sample thanks to "reversion to the mean" of larger samples. Using JWST Cepheids, JAGB, and TRGB, we find 73.4+-2.1, 72.2+-2.2, and 72.1+-2.2 km/s/Mpc, respectively. Explicitly accounting for SNe in common, the combined-sample three-method result from JWST is H0=72.6+-2.0, similar to H0=72.8 expected from HST Cepheids in the same galaxies. The small JWST sample trivially lowers the Hubble tension significance due to small-sample statistics and is not yet competitive with the HST set (42 SNeIa and 4 anchors), which yields 73.2+-0.9. Still, the joint JWST sample provides important crosschecks which the HST data passes., Comment: ApJ accepted, version replaced with accepted version

Image-based biometrics can aid law enforcement in various aspects, for example in iris, fingerprint and soft-biometric recognition. A critical precondition for recognition is the availability of sufficient biometric information in images. It is visually apparent that strong JPEG compression removes such details. However, latest AI-based image compression seemingly preserves many image details even for very strong compression factors. Yet, these perceived details are not necessarily grounded in measurements, which raises the question whether these images can still be used for biometric recognition. In this work, we investigate how AI compression impacts iris, fingerprint and soft-biometric (fabrics and tattoo) images. We also investigate the recognition performance for iris and fingerprint images after AI compression. It turns out that iris recognition can be strongly affected, while fingerprint recognition is quite robust. The loss of detail is qualitatively best seen in fabrics and tattoos images. Overall, our results show that AI-compression still permits many biometric tasks, but attention to strong compression factors in sensitive tasks is advisable.

The Hubble Tension, a >5 sigma discrepancy between direct and indirect measurements of the Hubble constant (H0), has persisted for a decade and motivated intense scrutiny of the paths used to infer H0. Comparing independently-derived distances for a set of galaxies with different standard candles, such as the tip of the red giant branch (TRGB) and Cepheid variables, can test for systematics in the middle rung of the distance ladder. The I band is the preferred filter for measuring the TRGB due to constancy with color, a result of low sensitivity to population differences in age and metallicity supported by stellar models. We use James Webb Space Telescope (JWST) observations with the maser host NGC 4258 as our geometric anchor to measure I-band (F090W vs F090W-F150W) TRGB distances to 8 hosts of 10 Type Ia supernovae (SNe Ia) within 28 Mpc: NGC 1448, NGC 1559, NGC 2525, NGC 3370, NGC 3447, NGC 5584, NGC 5643, and NGC 5861. We compare these with Hubble Space Telescope (HST) Cepheid-based relative distance moduli for the same galaxies and anchor. We find no evidence of a difference between their weighted means, 0.01 +/- 0.04 (stat) +/- 0.04 (sys) mag. We produce fourteen variants of the TRGB analysis, altering the smoothing level and color range used to measure the tips to explore their impact. For some hosts, this changes the identification of the strongest peak, but this causes little change to the sample mean difference producing a full range of 0.01 to 0.03 mag, all consistent at 1 sigma with no difference. The result matches past comparisons of I-band TRGB and Cepheids when both use HST. SNe and anchor samples observed with JWST are too small to yield a measure of H0 that is competitive with the HST sample of 42 SNe Ia and 4 anchors; however, they already provide a vital systematic crosscheck to HST measurements of the distance ladder., Comment: 15 pages, 5 figures, 4 tables, Accepted by ApJ

Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging. This study addresses these challenges for 3D cardiac MRI images in the short-axis view. We propose Latent Diffusion Models that generate synthetic images conditioned on medical attributes, while ensuring patient privacy through differentially private model training. To our knowledge, this is the first work to apply and quantify differential privacy in 3D medical image generation. We pre-train our models on public data and finetune them with differential privacy on the UK Biobank dataset. Our experiments reveal that pre-training significantly improves model performance, achieving a Fr\'echet Inception Distance (FID) of 26.77 at $\epsilon=10$, compared to 92.52 for models without pre-training. Additionally, we explore the trade-off between privacy constraints and image quality, investigating how tighter privacy budgets affect output controllability and may lead to degraded performance. Our results demonstrate that proper consideration during training with differential privacy can substantially improve the quality of synthetic cardiac MRI images, but there are still notable challenges in achieving consistent medical realism.

Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and sharing of data commonly associated to patient privacy concerns. Such concerns are further exacerbated, as traditional deep learning models can inadvertently leak sensitive training information. This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training data generated by our proposed malignancy-conditioned generative adversarial network. We assess these methods via downstream malignancy classification of mammography masses using a transformer model. Our experimental results depict that synthetic data augmentation can improve privacy-utility tradeoffs in differentially private model training. Further, model pretraining on synthetic data achieves remarkable performance, which can be further increased with DP-SGD fine-tuning across all privacy guarantees. With this first in-depth exploration of privacy-preserving deep learning in breast imaging, we address current and emerging clinical privacy requirements and pave the way towards the adoption of private high-utility deep diagnostic models. Our reproducible codebase is publicly available at https://github.com/RichardObi/mammo_dp., Comment: Early Accept at MICCAI 2024 Deep-Breath Workshop

Change of numeraire is a classical tool in mathematical finance. Campi-Laachir-Martini established its applicability to martingale optimal transport. We note that the results of Campi-Laachir-Martini extend to the case of weak martingale transport. We apply this to shadow couplings, continuous time martingale transport problems in the framework of Huesmann-Trevisan and in particular to establish the correspondence between stretched Brownian motion with its geometric counterpart. Note: We emphasize that we learned about the geometric stretched Brownian motion gSBM (defined in PDE terms) in a presentation of Loeper \cite{Lo23} before our work on this topic started. We noticed that a change of numeraire transformation in the spirit of \cite{CaLaMa14} allows for an alternative viewpoint in the weak optimal transport framework. We make our work public following the publication of Backhoff-Loeper-Obloj's work \cite{BaLoOb24} on arxiv.org. The article \cite{BaLoOb24} derives gSBM using PDE techniques as well as through an independent probabilistic approach which is close to the one we give in the present article.

The Astro2020 Decadal Survey recognizes time-domain astronomy as a key science area over the next decade and beyond. With over 30 years of HST data and the potential for 20 years of JWST operations, these flagship observatories offer an unparalleled prospect for a half-century of space-based observations in the time domain. To take best advantage of this opportunity, STScI charged a working group to solicit community input and formulate strategies to maximize the science return in time-domain astronomy from these two platforms. Here, the HST/JWST Long-Term Monitoring Working Group reports on the input we received and presents our recommendations to enhance the scientific return for time-domain astronomy from HST and JWST. We suggest changes in policies to enable and prioritize long-term science programs of high scientific value. As charged, we also develop recommendations based on community input for a JWST Director's Discretionary Time program to observe high-redshift transients., Comment: 17 pages, 3 figures, minor typos corrected, working group information at https://outerspace.stsci.edu/display/HPR/Long-term+variability+monitoring+strategies+for+HST+and+JWST

Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of the recorded audio to the recording location. For example, a voice message may be the only investigative cue to narrow down the candidate sites for a crime. Up to now, several works provide tools for closed-set recording environment classification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, closed-set tools are not applicable without retraining on a sufficient amount of training samples for each case and respective candidate set. In addition, a forensic tool has to deal with audio material from uncontrolled sources with variable properties and quality. In this work, we therefore attempt a major step towards practical forensic application scenarios. We propose a representation learning framework called EnvId, short for environment identification. EnvId avoids case-specific retraining. Instead, it is the first tool for robust few-shot classification of unseen environment locations. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, environment characteristics or recording position mismatches. Our code and datasets will be made publicly available upon acceptance., Comment: This work has been submitted to the IEEE for possible publication

We present phase-corrected photometric measurements of 88 Cepheid variables in the core of the Small Magellanic Cloud (SMC), the first sample obtained with the Hubble Space Telescope's (HST) Wide Field Camera 3, in the same homogeneous photometric system as past measurements of all Cepheids on the SH0ES distance ladder. We limit the sample to the inner core and model the geometry to reduce errors in prior studies due to the nontrivial depth of this cloud. Without crowding present in ground-based studies, we obtain an unprecedentedly low dispersion of 0.102 mag for a period-luminosity (P-L) relation in the SMC, approaching the width of the Cepheid instability strip. The new geometric distance to 15 late-type detached eclipsing binaries in the SMC offers a rare opportunity to improve the foundation of the distance ladder, increasing the number of calibrating galaxies from three to four. With the SMC as the only anchor, we find H$_0\!=\!74.1 \pm 2.1$ km/s/Mpc. Combining these four geometric distances with our HST photometry of SMC Cepheids, we obtain H$_0\!=\!73.17 \pm 0.86$ km/s/Mpc. By including the SMC in the distance ladder, we also double the range where the metallicity ([Fe/H]) dependence of the Cepheid P-L relation can be calibrated, and we find $\gamma = -0.234 \pm 0.052$ mag/dex. Our local measurement of H$_0$ based on Cepheids and Type Ia supernovae shows a 5.8$\sigma$ tension with the value inferred from the cosmic microwave background assuming a Lambda cold dark matter ($\Lambda$CDM) cosmology, reinforcing the possibility of physics beyond $\Lambda$CDM., Comment: Accepted to ApJ. Data Tables are available at: https://github.com/lbreuval/SMC_Cepheids_HST

Supernova (SN) H0pe is a gravitationally lensed, triply-imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Well-observed multiply-imaged SNe provide a rare opportunity to constrain the Hubble constant ($H_0$), by measuring the relative time delay between the images and modeling the foreground mass distribution. SN H0pe is located at $z=1.783$, and is the first SN Ia with sufficient light curve sampling and long enough time delays for an $H_0$ inference. Here we present photometric time-delay measurements and SN properties of SN H0pe. Using JWST/NIRCam photometry we measure time delays of $\Delta t_{ab}=-116.6^{+10.8}_{-9.3}$ and $\Delta t_{cb}=-48.6^{+3.6}_{-4.0}$ observer-frame days relative to the last image to arrive (image 2b; all uncertainties are $1\sigma$), which corresponds to a $\sim5.6\%$ uncertainty contribution for $H_0$ assuming $70 \rm{km s^{-1} Mpc^{-1}}$. We also constrain the absolute magnification of each image to $\mu_{a}=4.3^{+1.6}_{-1.8}$, $\mu_{b}=7.6^{+3.6}_{-2.6}$, $\mu_{c}=6.4^{+1.6}_{-1.5}$ by comparing the observed peak near-IR magnitude of SN H0pe to the non-lensed population of SNe Ia., Comment: Accepted in ApJ

The first James Webb Space Telescope ({\it JWST}) Near InfraRed Camera (NIRCam) imaging in the field of the galaxy cluster PLCK G165.7+67.0 ($z=0.35$) uncovered a Type Ia supernova (SN Ia) at $z=1.78$, called ``SN H0pe." Three different images of this one SN were detected as a result of strong gravitational lensing, each one traversing a different path in spacetime, thereby inducing a relative delay in the arrival of each image. Follow-up {\it JWST} observations of all three SN images enabled photometric and rare spectroscopic measurements of the two relative time delays. Following strict blinding protocols which oversaw a live unblinding and regulated post-unblinding changes, these two measured time delays were compared to the predictions of seven independently constructed cluster lens models to measure a value for the Hubble constant, $H_0=71.8^{+9.2}_{-8.1}$ km s$^{-1}$ Mpc$^{-1}$. The range of admissible $H_0$ values predicted across the lens models limits further precision, reflecting the well-known degeneracies between lens model constraints and time delays. It has long been theorized that a way forward is to leverage a standard candle, but this has not been realized until now. For the first time, the lens models are evaluated by their agreement with the SN absolute magnifications, breaking degeneracies and producing our best estimate, $H_0=75.7^{+8.1}_{-5.5}$ km s$^{-1}$ Mpc$^{-1}$. This is the first precision measurement of $H_0$ from a multiply-imaged SN Ia and only the second from any multiply-imaged SN., Comment: Accepted to ApJ. 27 pages, 7 Figures

Measurements of Type Ia Supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands ($Y$, $J$, and $H$), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a "mass step" or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is $\textit{harder}$ to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007-0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications., Comment: 22 pages, 10 figures. Accepted by A&A

Reconstruction attacks on machine learning (ML) models pose a strong risk of leakage of sensitive data. In specific contexts, an adversary can (almost) perfectly reconstruct training data samples from a trained model using the model's gradients. When training ML models with differential privacy (DP), formal upper bounds on the success of such reconstruction attacks can be provided. So far, these bounds have been formulated under worst-case assumptions that might not hold high realistic practicality. In this work, we provide formal upper bounds on reconstruction success under realistic adversarial settings against ML models trained with DP and support these bounds with empirical results. With this, we show that in realistic scenarios, (a) the expected reconstruction success can be bounded appropriately in different contexts and by different metrics, which (b) allows for a more educated choice of a privacy parameter.

The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other hand, graph neural networks (GNN), neural architectures for processing data on graphs, excel on tasks with irregular domains, such as sensor networks. In this paper, we introduce a novel approach, Path Signature Graph Convolutional Neural Networks (PS-GCNN), integrating path signatures into graph convolutional neural networks (GCNN), and leveraging the strengths of both path signatures, for feature extraction, and GCNNs, for handling spatial interactions. We apply our method to analyze slow earthquake sequences, also called slow slip events (SSE), utilizing data from GPS timeseries, with a case study on a GPS sensor network on the east coast of New Zealand's north island. We also establish benchmarks for our method on simulated stochastic differential equations, which model similar reaction-diffusion phenomenon. Our methodology shows promise for future advancement in earthquake prediction and sensor network analysis.

The tip of the red giant branch (TRGB) allows for the measurement of precise and accurate distances to nearby galaxies, based on the brightest ascent of low-mass red giant branch stars before they undergo the helium flash. With the advent of JWST, there is great promise to utilize the technique to measure galaxy distances out to at least 50 Mpc, significantly further than HST's reach of 20 Mpc. However, with any standard candle, it is first necessary to provide an absolute reference. Here we use Cycle 1 data to provide an absolute calibration in the F090W filter. F090W is most similar to the F814W filter commonly used for TRGB measurements with HST, which had been adopted by the community due to minimal dependence from the underlying metallicities and ages of stars. The imaging we use was taken in the outskirts of NGC 4258, which has a direct geometrical distance measurement from the Keplerian motion of its water megamaser. Utilizing several measurement techniques, we find $M_{TRGB}^{F090W}$ = -4.362 $\pm$ 0.033 (stat) $\pm$ 0.045 (sys) mag (Vega) for the metal-poor TRGB. We also perform measurements of the TRGB in two Type Ia supernova hosts, NGC 1559, and NGC 5584. We find good agreement between our TRGB distances and previous distance determinations to these galaxies from Cepheids ($\Delta$ = 0.01 $\pm$ 0.06 mag), with these differences being too small to explain the Hubble tension ($\sim$0.17 mag). As a final bonus, we showcase the serendipitous discovery of a faint dwarf galaxy near NGC 5584., Comment: 20 pages, 12 figures, accepted to ApJ

We study stars in the J-regions of the asymptotic giant branch (JAGB) of near-infrared color magnitude diagrams in the maser host NGC 4258 and 4 hosts of 6 Type Ia supernovae (SN Ia): NGC 1448, NGC 1559, NGC 5584, and NGC 5643. These clumps of stars are readily apparent near $1.0

We present high-definition observations with the James Webb Space Telescope of >1000 Cepheids in a geometric anchor of the distance ladder, NGC4258, and in 5 hosts of 8 SNe~Ia, a far greater sample than previous studies with JWST. These galaxies individually contain the largest samples of Cepheids, an average of >150 each, producing the strongest statistical comparison to those previously measured with the Hubble Space Telescope in the NIR. They also span the distance range of those used to determine the Hubble constant with HST, allowing us to search for a distance-dependent bias in HST measurements. The superior resolution of JWST negates crowding noise, the largest source of variance in the NIR Cepheid Period-Luminosity relations (Leavitt laws) measured with HST. Together with the use of two-epochs to constrain Cepheid phases and three filters to remove reddening, we reduce the dispersion in the Cepheid PL relations by a factor of 2.5. We find no significant difference in the mean distance measurements determined from HST and JWST, with a formal difference of -0.01+/-0.03 mag. This result is independent of zeropoints and analysis variants including metallicity dependence, local crowding, choice of filters, and relation slope. We can reject the hypothesis of unrecognized crowding of Cepheid photometry from HST that grows with distance as the cause of the ``Hubble Tension'' at 8.2 sigma, i.e., greater confidence than that of the Hubble Tension itself. We conclude that errors in photometric measurements of Cepheids across the distance ladder do not significantly contribute to the Tension., Comment: ApJ Letters, accepted

The giant spiral galaxy M101 is host to the nearest recent Type Ia Supernova (SN 2011fe) and thus has been extensively monitored in the near-infrared to study the late-time lightcurve of the supernova. Leveraging this existing baseline of observations, we derive the first Mira-based distance to M101 by discovering and classifying a sample of 211 Miras with periods ranging from 240 to 400 days in the supernova field. Combined with new HST WFC3/IR channel observations, our dataset totals 11 epochs of F110W (HST $YJ$) and 13 epochs of F160W (HST $H$) data spanning $\sim$2900 days. We adopt absolute calibrations of the Mira Period-Luminosity Relation based on geometric distances to the Large Magellanic Cloud and the water megamaser host galaxy NGC 4258, and find $\mu_{\rm M101} = $ 29.10 $\pm$ 0.06 mag. This distance is in 1$\sigma$ agreement with most other recent Cepheid and Tip of the Red Giant Branch distance measurements to M101. Including the previous Mira-SNIa host, NGC 1559 and SN 2005df, we determine the fiducial SN Ia peak luminosity, $M^0_B = -19.27 \pm 0.09$ mag. With the Hubble diagram of SNe Ia, we derive $H_0 = 72.37 \pm 2.97 $ km s$^{-1}$Mpc$^{-1}$, a $4.1\%$ measurement of $H_0$ using Miras. We find excellent agreement with recent Cepheid distance ladder measurements of $H_0$ and confirm previous indications that the local universe value of $H_0$ is higher than the early-universe value at $\sim$ $95\%$ confidence. Currently, the Mira-based $H_0$ measurement is still dominated by the statistical uncertainty in the SN Ia peak magnitude., Comment: 22 pages, 11 figures, accepted to ApJ