arXiv subjects

Joseph Anderson

Publications and source records attributed to Joseph Anderson.

14 recordsLinked to original sources

arXiv2026

SN 2020bij and a Possible Slow-Rise High-Velocity Subclass of Type IIP Supernovae

Mapping how the explosion properties of Type II supernovae (SNe II) relate to the properties of their progenitors can provide strong constraints for understanding the final evolutionary stages of massive stars. Type IIP SNe, linked to the explosions of single red super-giant (RSG) stars, have recently been found to require some form of interaction with circumstellar material (CSM) to reproduce the rapid rise to the plateau often seen in their light curves. In this work, we present observations and analysis of the Type IIP SN 2020bij, characterized by a slow rise to its plateau as well as high expansion velocities. We identify four other SNe IIP from the literature (ASASSN-14kg, SN 2018fif, SN 2021yja and SN 2023axu) with similarly slowly rising light curves and find that they also show high expansion velocities. Using both analytical and numerical models, all five events can be explained with weak to no CSM interaction. We therefore propose that these events constitute a new subclass of Type IIP SNe which could be associated with relatively confined CSM. Early and dense photometric coverage of future SNe IIP together with early spectroscopic observations will further map this subclass and its physical properties. Understanding such rare events could be key to constraining the diversity of late-stage mass-loss in RSGs.

astro-ph.HE↗

arXiv2024

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records

Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines rely primarily on histopathological features, overlooking other important CRC risk factors. Variability in polyp characterization among pathologists also hinders consistent surveillance decisions. Advances in digital pathology and deep learning enable the integration of pathology slides and medical records for more accurate CRC risk prediction. Using data from the New Hampshire Colonoscopy Registry, including longitudinal follow-up, we adapted a transformer-based model for histopathology image analysis to predict 5-year CRC risk. We further explored multi-modal fusion strategies to combine clinical records with deep learning-derived image features. Training the model to predict intermediate clinical variables improved 5-year CRC risk prediction (AUC = 0.630) compared to direct prediction (AUC = 0.615, p = 0.013). Incorporating both imaging and non-imaging data, without requiring manual slide review, further improved performance (AUC = 0.674) compared to traditional features from colonoscopy and microscopy reports (AUC = 0.655, p = 0.001). These results highlight the value of integrating diverse data modalities with computational methods to enhance CRC risk stratification.

cs.CV↗

arXiv2024

The metamorphosis of the Type Ib SN 2019yvr: late-time interaction

We present observational evidence of late-time interaction between the ejecta of the hydrogen-poor Type Ib supernova (SN) 2019yvr and hydrogen-rich circumstellar material (CSM), similar to the Type Ib SN 2014C. A narrow H{\alpha} emission line appears simultaneously with a break in the light-curve decline rate at around 80-100 d after explosion. From the interaction delay and the ejecta velocity, under the assumption that the CSM is detached from the progenitor, we estimate the CSM inner radius to be located at ~6.5-9.1 {\times} 10^{15} cm. The H{\alpha} emission line persists throughout the nebular phase at least up to +420 d post-explosion, with a full width at half maximum of ~2000 km/s. Assuming a steady mass-loss, the estimated mass-loss rate from the luminosity of the H{\alpha} line is ~3-7 {\times} 10^{-5} M_\odot yr^{-1}. From hydrodynamical modelling and analysis of the nebular spectra, we find a progenitor He-core mass of 3-4 M{_\odot}, which would imply an initial mass of 13-15 M{_\odot}. Our result supports the case of a relatively low-mass progenitor possibly in a binary system as opposed to a higher mass single star undergoing a luminous blue variable phase.

astro-ph.HE↗

arXiv2021

SN 2019hcc: A Type II Supernova Displaying Early O II Lines

We present optical spectroscopy together with ultraviolet, optical and near-infrared photometry of SN 2019hcc, which resides in a host galaxy at redshift 0.044, displaying a sub-solar metallicity. The supernova spectrum near peak epoch shows a `w' shape at around 4000 {\AA} which is usually associated with O II lines and is typical of Type I superluminous supernovae. SN 2019hcc post-peak spectra show a well-developed H alpha P-Cygni profile from 19 days past maximum and its light curve, in terms of its absolute peak luminosity and evolution, resembles that of a fast-declining Hydrogen-rich supernova (SN IIL). The object does not show any unambiguous sign of interaction as there is no evidence of narrow lines in the spectra or undulations in the light curve. Our tardis spectral modelling of the first spectrum shows that Carbon, Nitrogen and Oxygen (CNO) at 19000 K reproduce the `w' shape and suggests that a combination of non-thermally excited CNO and metal lines at 8000 K could reproduce the feature seen at 4000 {\AA}. The Bolometric light curve modelling reveals that SN 2019hcc could be fit with a magnetar model, showing a relatively strong magnetic field (B > 3 x 10^14 G), which matches the peak luminosity and rise time without powering up the light curve to superluminous luminosities. The high-energy photons produced by the magnetar would then be responsible for the detected O II lines. As a consequence, SN 2019hcc shows that a `w' shape profile at around 4000 {\AA}, usually attributed to O II, is not only shown in superluminous supernovae and hence it should not be treated as the sole evidence of the belonging to such a supernova type.

astro-ph.HE↗

arXiv2020

On the three-dimensional spatial correlations of curved dislocation systems

Coarse-grained descriptions of dislocation motion in crystalline metals inherently represent a loss of information regarding dislocation-dislocation interactions. In the present work, we consider a coarse-graining framework capable of re-capturing these interactions by means of the dislocation-dislocation correlation functions. The framework depends on a coarse-graining length to define slip-system-specific dislocation densities. Following a statistical definition of this coarse-graining process, we define a spatial correlation function which will allow relative positions of dislocation pairs, and thus the strength of their interactions at short range, to be recaptured into a mean field description of dislocation dynamics. Through a statistical homogeneity argument, we present a method of evaluating this correlation function from discrete dislocation dynamics simulations. Finally, results of this evaluation are shown in the form of self-correlation of dislocation densities on the same slip-system. These correlation functions are seen to depend weakly on plastic strain (and, in turn, the total dislocation density), but are seen to depend strongly on the coarse-graining length. Implications of these correlation functions in regard to continuum dislocation dynamics as well as future directions of investigation are also discussed.

cond-mat.mtrl-sci↗

arXiv2020

The Tidal Disruption Event AT 2018hyz II: Light Curve Modeling of a Partially Disrupted Star

AT 2018hyz (=ASASSN-18zj) is a tidal disruption event (TDE) located in the nucleus of a quiescent E+A galaxy at a redshift of $z = 0.04573$, first detected by the All-Sky Automated Survey for Supernovae (ASAS-SN). We present optical+UV photometry of the transient, as well as an X-ray spectrum and radio upper limits. The bolometric light curve of AT 2018hyz is comparable to other known TDEs and declines at a rate consistent with a $t^{-5/3}$ at early times, emitting a total radiated energy of $E = 9\times10^{50}$ erg. An excess bump appears in the UV light curve about 50 days after bolometric peak, followed by a flattening beyond 250 days. The light curve shows an excess bump in the UV about 50 days after bolometric peak lasting for at least 100 days, which may be related to an outflow. We detect a constant X-ray source present for at least 86 days. The X-ray spectrum shows a total unabsorbed flux of $\sim 4\times10^{-14}$ erg cm$^{-2}$ s$^{-1}$ and is best fit by a blackbody plus power-law model with a photon index of $\Gamma = 0.8$. A thermal X-ray model is unable to account for photons $> 1$ keV, while the radio non-detection favors inverse-Compton scattering rather than a jet for the non-thermal component. We model the optical and UV light curves using the Modular Open-Source Fitter for Transients (MOSFiT) and find a best fit for a black hole of $5.2\times10^6$ M$_\odot$ partially disrupting a $0.1$ M$_\odot$ star (stripping a mass of $\sim 0.01$ M$_\odot$ for the inferred impact parameter, $\beta=0.6$). The low optical depth implied by the small debris mass may explain how we are able to see hydrogen emission with disk-like line profiles in the spectra of AT 2018hyz (see our companion paper, Short et al.~2020).

astro-ph.HE↗

arXiv2017

Geometric Methods for Robust Data Analysis in High Dimension

Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details are being gathered about single user interactions or sensor readings. All of these applications encounter problems with a common theme: use observed data to make inferences about the world. Our work obtains the first provably efficient algorithms for Independent Component Analysis (ICA) in the presence of heavy-tailed data. The main tool in this result is the centroid body (a well-known topic in convex geometry), along with optimization and random walks for sampling from a convex body. This is the first algorithmic use of the centroid body and it is of independent theoretical interest, since it effectively replaces the estimation of covariance from samples, and is more generally accessible. This reduction relies on a non-linear transformation of samples from such an intersection of halfspaces (i.e. a simplex) to samples which are approximately from a linearly transformed product distribution. Through this transformation of samples, which can be done efficiently, one can then use an ICA algorithm to recover the vertices of the intersection of halfspaces. Finally, we again use ICA as an algorithmic primitive to construct an efficient solution to the widely-studied problem of learning the parameters of a Gaussian mixture model. Our algorithm again transforms samples from a Gaussian mixture model into samples which fit into the ICA model and, when processed by an ICA algorithm, result in recovery of the mixture parameters. Our algorithm is effective even when the number of Gaussians in the mixture grows polynomially with the ambient dimension

cs.LG↗

arXiv2017

Heavy-Tailed Analogues of the Covariance Matrix for ICA

Independent Component Analysis (ICA) is the problem of learning a square matrix $A$, given samples of $X=AS$, where $S$ is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each $S_i$ has finite and moderately valued fourth moment. However, there are practical applications where this assumption need not be true, such as speech and finance. Algorithms have been proposed for heavy-tailed ICA, but they are not practical, using random walks and the full power of the ellipsoid algorithm multiple times. The main contributions of this paper are: (1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide theoretical guarantees and show that it outperforms other algorithms in some heavy-tailed regimes, both on real and synthetic data. Like the current state-of-the-art, the new algorithm is based on the centroid body (a first moment analogue of the covariance matrix). Unlike the state-of-the-art, our algorithm is practically efficient. To achieve this, we use explicit analytic representations of the centroid body, which bypasses the use of the ellipsoid method and random walks. (2) We study how heavy tails affect different ICA algorithms, including HTICA. Somewhat surprisingly, we show that some algorithms that use the covariance matrix or higher moments can successfully solve a range of ICA instances with infinite second moment. We study this theoretically and experimentally, with both synthetic and real-world heavy-tailed data.

cs.LG↗

arXiv2016

Regaining the FORS: making optical ground-based transmission spectroscopy of exoplanets with VLT+FORS2 possible again

Transmission spectroscopy facilitates the detection of molecules and/or clouds in the atmospheres of exoplanets. Such studies rely heavily on space-based or large ground-based observatories, as one needs to perform time- resolved, high signal-to-noise spectroscopy. The FORS2 instrument at ESO's Very Large Telescope is the obvious choice for performing such studies, and was indeed pioneering the field in 2010. After that, however, it was shown to suffer from systematic errors caused by the Longitudinal Atmospheric Dispersion Corrector (LADC). This was successfully addressed, leading to a renewed interest for this instrument as shown by the number of proposals submitted to perform transmission spectroscopy of exoplanets. We present here the context, the problem and how we solved it, as well as the recent results obtained. We finish by providing tips for an optimum strategy to do transmission spectroscopy with FORS2, in the hope that FORS2 may become the instrument of choice for ground-based transmission spectroscopy of exoplanets.

astro-ph.IM↗

arXiv2015

Heavy-tailed Independent Component Analysis

Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i.i.d. observations of $X=AS$ where $S \in \mathbb{R}^n$ is a random vector with mutually independent coordinates. This problem has been intensively studied, but all existing efficient algorithms with provable guarantees require that the coordinates $S_i$ have finite fourth moments. We consider the heavy-tailed ICA problem where we do not make this assumption, about the second moment. This problem also has received considerable attention in the applied literature. In the present work, we first give a provably efficient algorithm that works under the assumption that for constant $\gamma > 0$, each $S_i$ has finite $(1+\gamma)$-moment, thus substantially weakening the moment requirement condition for the ICA problem to be solvable. We then give an algorithm that works under the assumption that matrix $A$ has orthogonal columns but requires no moment assumptions. Our techniques draw ideas from convex geometry and exploit standard properties of the multivariate spherical Gaussian distribution in a novel way.

cs.LG↗

arXiv2014

Environments of interacting transients: Impostors and type IIn supernovae

This paper presents one of the first environmental analyses of the locations of the class of `interacting transients', namely type IIn supernovae and supernova Impostors. We discuss the association of these transients with star formation, host galaxy type, metallicity, and the locations of each event within the respective host. Given the frequent assumption of very high mass progenitors for these explosions from various studies, most notably a direct progenitor detection, it is interesting to note the weak association of these subtypes with star formation as traced by H{\alpha} emission, particularly in comparison with type Ic supernovae, which trace the H{\alpha} emission and are thought to arise from high mass progenitors. The radial distributions of these transients compared to type Ic supernovae are also very different. This provides evidence for the growing hypothesis that these `interacting transients' are in fact comprised of a variety of progenitor systems. The events contained within this sample are discussed in detail, where information in the literature exists, and compared to the environmental data provided. Impostors are found to split into two main classes, in terms of environment: SN2008S-like Impostors fall on regions of zero H{\alpha} emission, whereas {\eta}-Carina-like Impostors all fall on regions with positive H{\alpha} emission. We also find indications that the Impostor class originate from lower metallicity environments than type IIn, Ic and IIP SNe.

astro-ph.HE↗

arXiv2014

The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures

In this paper we show that very large mixtures of Gaussians are efficiently learnable in high dimension. More precisely, we prove that a mixture with known identical covariance matrices whose number of components is a polynomial of any fixed degree in the dimension n is polynomially learnable as long as a certain non-degeneracy condition on the means is satisfied. It turns out that this condition is generic in the sense of smoothed complexity, as soon as the dimensionality of the space is high enough. Moreover, we prove that no such condition can possibly exist in low dimension and the problem of learning the parameters is generically hard. In contrast, much of the existing work on Gaussian Mixtures relies on low-dimensional projections and thus hits an artificial barrier. Our main result on mixture recovery relies on a new "Poissonization"-based technique, which transforms a mixture of Gaussians to a linear map of a product distribution. The problem of learning this map can be efficiently solved using some recent results on tensor decompositions and Independent Component Analysis (ICA), thus giving an algorithm for recovering the mixture. In addition, we combine our low-dimensional hardness results for Gaussian mixtures with Poissonization to show how to embed difficult instances of low-dimensional Gaussian mixtures into the ICA setting, thus establishing exponential information-theoretic lower bounds for underdetermined ICA in low dimension. To the best of our knowledge, this is the first such result in the literature. In addition to contributing to the problem of Gaussian mixture learning, we believe that this work is among the first steps toward better understanding the rare phenomenon of the "blessing of dimensionality" in the computational aspects of statistical inference.

cs.LG↗

arXiv2013

Efficient learning of simplices

We show an efficient algorithm for the following problem: Given uniformly random points from an arbitrary n-dimensional simplex, estimate the simplex. The size of the sample and the number of arithmetic operations of our algorithm are polynomial in n. This answers a question of Frieze, Jerrum and Kannan [FJK]. Our result can also be interpreted as efficiently learning the intersection of n+1 half-spaces in R^n in the model where the intersection is bounded and we are given polynomially many uniform samples from it. Our proof uses the local search technique from Independent Component Analysis (ICA), also used by [FJK]. Unlike these previous algorithms, which were based on analyzing the fourth moment, ours is based on the third moment. We also show a direct connection between the problem of learning a simplex and ICA: a simple randomized reduction to ICA from the problem of learning a simplex. The connection is based on a known representation of the uniform measure on a simplex. Similar representations lead to a reduction from the problem of learning an affine transformation of an n-dimensional l_p ball to ICA.

cs.LG↗

arXiv2012

Evidence for asymmetric distribution of circumstellar material around Type Ia Supernovae

We study the properties of low-velocity material in the line of sight towards nearby Type Ia Supernovae (SNe Ia) that have measured late phase nebular velocity shifts (v_neb), thought to be an environment-independent observable. We have found that the distribution of equivalent widths of narrow blended Na I D1 & D2 and Ca II H & K absorption lines differs significantly between those SNe Ia with negative and positive v_neb, with generally stronger absorption for SNe Ia with v_neb > 0. A similar result had been found previously for the distribution of colors of SNe Ia, which was interpreted as a dependence of the temperature of the ejecta with viewing angle. Our work suggests that: 1) a significant part of these differences in color should be attributed to extinction, 2) this extinction is caused by an asymmetric distribution of circumstellar material (CSM) and 3) the CSM absorption is generally stronger on the side of the ejecta opposite to where the ignition occurs. Since it is difficult to explain 3) via any known physical processes that occur before explosion, we argue that the asymmetry of the CSM is originated after explosion by a stronger ionizing flux on the side of the ejecta where ignition occurs, probably due to a stronger shock breakout and/or more exposed radioactive material on one side of the ejecta. This result has important implications for both progenitor and explosion models.

astro-ph.SR↗