arXiv subjects

Adam Foster

Publications and source records attributed to Adam Foster.

At least 37 records · Page 2Linked to original sources

arXiv2025

Measuring the asymmetric expansion of the Fe ejecta of Cassiopeia A with XRISM/Resolve

The expansion structure of supernova remnants (SNRs) is important for understanding not only how heavy elements are distributed into space, but also how supernovae explode. The ejecta expansion structure of the young core-collapse SNR Cas A is investigated, with Doppler parameter mapping of the Fe-K complex by the Resolve microcalorimeter onboard the X-ray Imaging and Spectroscopy Mission, XRISM. It is found that the Fe ejecta are blueshifted in the southeast (SE) and redshifted in the northwest (NW), indicating an incomplete shell structure, similar to the intermediate mass elements (IMEs), such as Si and S. The Fe has a velocity shift of $\sim1400$ km~s$^{-1}$ in the NW and $\sim2160$ km~s$^{-1}$ in the SE region, with the error range of a few 100s km~s$^{-1}$. These values are consistent with those for the IMEs in the NW region, whereas larger than those for the IMEs in the SE region, although the large error region prevented us from concluding which component has significantly higher velocity. The line broadening is larger in the center with values of $\sim$2000--3000~km~s$^{-1}$, and smaller near the edges of the remnant. The radial profiles of the Doppler shift and broadening of the IMEs and Fe indicate that the Fe ejecta may expand asymmetrically as IME ejacta, although the large error regions do not allow us to conclude it. Moreover, we see little bulk Doppler broadening of the Fe lines in the northeastern jet region whereas the IME lines exhibit significant broadening. No such narrow lines are detected in the NW region. These findings suggest an asymmetric expansion of the ejecta potentially driven by large-scale asymmetries originating from the supernova explosion. This interpretation aligns with the large-scale asymmetries predicted by models of neutrino-driven supernova explosions.

astro-ph.HE↗

arXiv2024

Rethinking Aleatoric and Epistemic Uncertainty

The ideas of aleatoric and epistemic uncertainty are widely used to reason about the probabilistic predictions of machine-learning models. We identify incoherence in existing discussions of these ideas and suggest this stems from the aleatoric-epistemic view being insufficiently expressive to capture all the distinct quantities that researchers are interested in. To address this we present a decision-theoretic perspective that relates rigorous notions of uncertainty, predictive performance and statistical dispersion in data. This serves to support clearer thinking as the field moves forward. Additionally we provide insights into popular information-theoretic quantities, showing they can be poor estimators of what they are often purported to measure, while also explaining how they can still be useful in guiding data acquisition.

cs.LG↗

arXiv2024

Unveiling the Cosmic Chemistry II: "Direct" $T_e$-based metallicity of galaxies at 3 $< z <$ 10 with JWST/NIRSpec

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies.

astro-ph.GA↗

arXiv2024

Highly Accurate Real-space Electron Densities with Neural Networks

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ans\"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties.

physics.chem-ph↗

arXiv2024

Detection of new galaxy candidates at z > 11 in the JADES field using JWST NIRCam

We report the detection of six new galaxy candidates with photometric redshifts $z$ $>$ 11 within the JWST Advanced Deep Extragalactic Survey (JADES) GOODS-S and GOODS-N fields. These new candidates are detected through meticulous analysis of NIRCam photometry in eight filters spanning a wavelength range of 0.8-5.0 $\mu$m. Photometric redshifts of these galaxy candidates are independently measured utilizing spectral energy distribution (SED) fitting techniques using \texttt{EAZY} and \texttt{BAGPIPES} codes, followed by visual scrutiny. One of these galaxy candidates is located in GOODS-S field, while the remaining five galaxies are located in GOODS-N field. Our analysis reveals that the stellar masses of these galaxies typically range from log $M_{\ast}$/$M_{\odot}$ = 7.75--8.75. Furthermore, these galaxies are typically young with their mass-weighted ages spanning from 80 to 240 Myr. Their specific star formation rates (sSFR), quantified as $\log (\text{sSFR}/\text{Gyr}$), are measured to vary between $\sim 0.95$ to 1.46. These new galaxy candidates offer a robust sample for probing the physical properties of galaxies within the first few hundred Myr of the history of the Universe. We also analyze the relationship between star formation rate (SFR) and stellar mass ($M_\ast$) within our sample.Continued investigation through spectroscopic analysis using JWST/NIRSpec is needed to spectroscopically confirm these high-redshift galaxy candidates and investigate further into their physical properties. We plan to follow up on these candidates with future NIRSpec observations.

astro-ph.GA↗

arXiv2024

Amortized Active Causal Induction with Deep Reinforcement Learning

We present Causal Amortized Active Structure Learning (CAASL), an active intervention design policy that can select interventions that are adaptive, real-time and that does not require access to the likelihood. This policy, an amortized network based on the transformer, is trained with reinforcement learning on a simulator of the design environment, and a reward function that measures how close the true causal graph is to a causal graph posterior inferred from the gathered data. On synthetic data and a single-cell gene expression simulator, we demonstrate empirically that the data acquired through our policy results in a better estimate of the underlying causal graph than alternative strategies. Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments. Further, our policy also demonstrates excellent zero-shot generalization to design environments with dimensionality higher than that during training, and to intervention types that it has not been trained on.

cs.LG↗

arXiv2024

Making Better Use of Unlabelled Data in Bayesian Active Learning

Fully supervised models are predominant in Bayesian active learning. We argue that their neglect of the information present in unlabelled data harms not just predictive performance but also decisions about what data to acquire. Our proposed solution is a simple framework for semi-supervised Bayesian active learning. We find it produces better-performing models than either conventional Bayesian active learning or semi-supervised learning with randomly acquired data. It is also easier to scale up than the conventional approach. As well as supporting a shift towards semi-supervised models, our findings highlight the importance of studying models and acquisition methods in conjunction.

cs.LG↗

arXiv2023

On the Interpretation of XSPEC Abundances and Emission Measures

The purpose of this work is to describe the assumptions built into the X-ray spectrum fitting software XSPEC for the calculation of element abundances and emission measure of a plasma and to describe the effects when those assumptions are not accurate. The ratio of electron density to hydrogen density in XSPEC is fixed at a constant. The correct ratio can be calculated from the ionization states of the elements. We show the constant value used in XSPEC is valid to within 3.5% for a solar abundance plasma. For a plasma that deviates from solar abundance, such as hydrogen-poor or heavy element rich plasmas as found in the ejecta of supernova remnants, this ratio can smaller by factors of 0.1 to 0.001. The hydrogen emission measure, defined by integral of electron density times hydrogen density over plasma volume, is derived from the norm in XSPEC, but one needs to include the hydrogen abundance factor. For other elements, the emission measures are the XSPEC values multiplied by the element abundance factors. Using the correct electron-to-hydrogen ratio and emission measures, we show the correct electron density is smaller by the square root of the correct electron density ratio divided by the XSPEC value. Element densities and total masses (for given distance and volume) are larger by the abundance factors divided by the above square root. Because hydrogen-poor plasmas occur in the ejecta of Type Ia supernova remnants, previously estimated element masses from X-ray spectra are likely significantly underestimated.

astro-ph.HE↗

arXiv2023

Revolutionary Solar System Science Enabled by the Line Emission Mapper X-ray Probe

The Line Emission Mapper's (LEM's) exquisite spectral resolution and effective area will open new research domains in Astrophysics, Planetary Science and Heliophysics. LEM will provide step-change capabilities for the fluorescence, solar wind charge exchange (SWCX) and auroral precipitation processes that dominate X-ray emissions in our Solar System. The observatory will enable novel X-ray measurements of historically inaccessible line species, thermal broadening, characteristic line ratios and Doppler shifts - a universally valuable new astrophysics diagnostic toolkit. These measurements will identify the underlying compositions, conditions and physical processes from km-scale ultra-cold comets to the MK solar wind in the heliopause at 120 AU. Here, we focus on the paradigm-shifts LEM will provide for understanding the nature of the interaction between a star and its planets, especially the fundamental processes that govern the transfer of mass and energy within our Solar System, and the distribution of elements throughout the heliosphere. In this White Paper we show how LEM will enable a treasure trove of new scientific contributions that directly address key questions from the National Academies' 2023-2032 Planetary Science and 2013-2022 Heliophysics Decadal Strategies. The topics we highlight include: 1. The richest global trace element maps of the Lunar Surface ever produced; insights that address Solar System and planetary formation, and provide invaluable context ahead of Artemis and the Lunar Gateway. 2. Global maps of our Heliosphere through Solar Wind Charge Exchange (SWCX) that trace the interstellar neutral distributions in interplanetary space and measure system-wide solar wind ion abundances and velocities; a key new understanding of our local astrosphere and a synergistic complement to NASA IMAP observations of heliospheric interactions...

astro-ph.IM↗

arXiv2023

New resonance scattering model in AtomDB: application to line suppression in galaxy clusters and elliptical galaxies

In this paper, we present a simple, one-step, self-consistent, and fast resonance scattering model rsapec based on the AtomDB database. This model can be used as an alternative to the commonly used APEC model for fitting such X-ray spectra with optically thick lines. The current model is intended, in general, for verifying the presence of the effect and for spectral modeling of galaxy clusters and elliptical galaxies under applicable assumptions. We test rsapec to derive the line suppression in the elliptical galaxy NGC 4636 and the Perseus cluster of galaxies and obtain resonance suppression of ~ 1.24 and ~ 1.30, respectively.

astro-ph.HE↗

arXiv2023

Prediction-Oriented Bayesian Active Learning

Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BALD score. We highlight that this can be suboptimal from the perspective of predictive performance. For example, BALD lacks a notion of an input distribution and so is prone to prioritise data of limited relevance. To address this we propose the expected predictive information gain (EPIG), an acquisition function that measures information gain in the space of predictions rather than parameters. We find that using EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models, and thus provides an appealing drop-in replacement.

cs.LG↗

arXiv2023

Modern Bayesian Experimental Design

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.

stat.ML↗

arXiv2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design

We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variational methods to simultaneously estimate it and optimize the designs in a single stochastic gradient scheme. In addition, to accommodate discrete actions within our framework, we propose leveraging continuous relaxation schemes, which can naturally be integrated into our variational objective. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems. We illustrate its effectiveness in a number of experiments, where CO-BED demonstrates competitive performance even when compared to bespoke, model-specific alternatives.

stat.ML↗

arXiv2023

Differentiable Multi-Target Causal Bayesian Experimental Design

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.

cs.LG↗

arXiv2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation

The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving contextual treatment assignment decisions: these are personalised treatments applied to e.g. customers, each with their own contextual information, with the aim of maximising a reward. In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design. Specifically, our method is used for the data-efficient evaluation of the regret of past treatment assignments. Unlike approaches such as A/B testing, our method avoids assigning treatments that are known to be highly sub-optimal, whilst engaging in some exploration to gather pertinent information. We achieve this by introducing an information-based design objective, which we optimise end-to-end. Our method applies to discrete and continuous treatments. Comparing our information-theoretic approach to baselines in several simulation studies demonstrates the superior performance of our proposed approach.

stat.ML↗

arXiv2022

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including computational biology. Adopting a Conditional VAE framework, we show that marginal independence between the representation and a condition variable plays a key role in both of these challenges. We propose the Contrastive Mixture of Posteriors (CoMP) method that uses a novel misalignment penalty defined in terms of mixtures of the variational posteriors to enforce this independence in latent space. We show that CoMP has attractive theoretical properties compared to previous approaches, and we prove counterfactual identifiability of CoMP under additional assumptions. We demonstrate state-of-the-art performance on a set of challenging tasks including aligning human tumour samples with cancer cell-lines, predicting transcriptome-level perturbation responses, and batch correction on single-cell RNA sequencing data. We also find parallels to fair representation learning and demonstrate that CoMP is competitive on a common task in the field.

stat.ML↗

arXiv2022

Deep End-to-end Causal Inference

Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery has evolved separately from inference methods, preventing straight-forward combination of methods from both fields. In this work, we develop Deep End-to-end Causal Inference (DECI), a single flow-based non-linear additive noise model that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect (CATE) estimation. We provide a theoretical guarantee that DECI can recover the ground truth causal graph under standard causal discovery assumptions. Motivated by application impact, we extend this model to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Our results show the competitive performance of DECI when compared to relevant baselines for both causal discovery and (C)ATE estimation in over a thousand experiments on both synthetic datasets and causal machine learning benchmarks across data-types and levels of missingness.

stat.ML↗

arXiv2022

Learning Instance-Specific Augmentations by Capturing Local Invariances

We introduce InstaAug, a method for automatically learning input-specific augmentations from data. Previous methods for learning augmentations have typically assumed independence between the original input and the transformation applied to that input. This can be highly restrictive, as the invariances we hope our augmentation will capture are themselves often highly input dependent. InstaAug instead introduces a learnable invariance module that maps from inputs to tailored transformation parameters, allowing local invariances to be captured. This can be simultaneously trained alongside the downstream model in a fully end-to-end manner, or separately learned for a pre-trained model. We empirically demonstrate that InstaAug learns meaningful input-dependent augmentations for a wide range of transformation classes, which in turn provides better performance on both supervised and self-supervised tasks.

cs.LG↗