arXiv subjects

Malina Desai

Publications and source records attributed to Malina Desai.

7 recordsLinked to original sources

arXiv2026

The Lightspeed project: high-speed, ultra-low read noise imaging and polarimetry for the Magellan telescopes

Lightspeed will be an ultra-fast (> kHz), ultra-low read noise, multicolor (ugriz + IR) imager for the 6.5 meter Magellan Clay telescope. In a single-channel configuration, Lightspeed will also enable single-shot linear polarimetry, narrowband imaging, or white-light imaging. Lightspeed is designed around groundbreaking single-photon-resolving detector technologies: deep sub-electron read noise CMOS image sensors from Fairchild Imaging and a HgCdTe avalanche photodiode array from Leonardo. Here we present Lightspeed's basic optical design concept, predict its on-sky performance, and highlight science cases that Lightspeed has the potential to revolutionize. Lightspeed builds upon the success of proto-Lightspeed, a single-channel prototype employing commercial-off-the-shelf re-imaging optics. proto-Lightspeed is now available as a PI instrument on the Clay telescope. We also discuss the current status of proto-Lightspeed and lessons learned from its commissioning, applicable to the development of Lightspeed and any other instrument seeking to integrate scientific CMOS image sensors.

astro-ph.IM↗

arXiv2025

Identifying and Characterizing Very Low Mass Spectral Blend Binaries with Machine Learning Methods

We present an approach to identifying and characterizing unresolved, very low mass spectral blend binaries composed of late-M, L, and T dwarfs using machine learning methodologies. We generated and evaluated a series of hierarchical random forest models to distinguish spectral blends from single very low-mass dwarfs, and to classify their primary and secondary components. Models were trained on a sample of single and synthesized binary templates generated from empirical spectra. We explored various aspects of the design of our models, and find that models trained on a full range of single and binary combinations have the best performance for identification and component classification. These models achieve binary identification recall and precision of $\gtrsim$85%, median component classification errors of $\lesssim$0.1 subtypes, and systematic classification uncertainties of $\lesssim$1 subtype, outperforming index-based methods in terms of fidelity, range, and speed. Optimal performance is achieved for binaries composed of L and T dwarf primaries and late-L and T dwarf secondaries. When applied to the spectra of previously confirmed very low-mass binaries, model performance is degraded due to the prevalence of systems with similar component types, but remains high in the optimal performance range. We propose potential improvements to these models, which can be used to explore binary populations among the thousands to millions of very low-mass stars and brown dwarfs anticipated with large-scale spectral surveys such as SPHEREx and Euclid.

astro-ph.SR↗

arXiv2025

Likelihood-free inference for gravitational-wave data analysis and public alerts

Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing the potential of multi-messenger astronomy. Machine learning based detection and parameter estimation algorithms are emerging as production ready alternatives to traditional approaches. Here, we report validation studies of AMPLFI, a likelihood-free inference solution to low-latency parameter estimation of binary black holes. We use simulated signals added into data from the LIGO-Virgo-KAGRA's (LVK's) third observing run (O3) to compare sky localization performance with BAYESTAR, the algorithm currently in production for rapid sky localization of candidates from matched-filter pipelines. We demonstrate sky localization performance, measured by searched area and volume, to be equivalent with BAYESTAR. We show accurate reconstruction of source parameters with uncertainties for use distributing low-latency coarse-grained chirp mass information. In addition, we analyze several candidate events reported by the LVK in the third gravitational-wave transient catalog (GWTC-3) and show consistency with the LVK's analysis. Altogether, we demonstrate AMPLFI's ability to produce data products for low-latency public alerts.

gr-qc↗

arXiv2025

Kilonova Light Curve Parameter Estimation Using Likelihood-Free Inference

Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference with a pre-trained embedding network, optimized to efficiently process kilonova light curves. Our method is capable of retrieving the mass, velocity, and lanthanide fraction of the neutron star ejecta with an accuracy and precision on par with nested sampling methods while taking significantly less computational time. Our inference uniquely utilizes a pre-trained embedding network that marginalizes the time of arrival and the luminosity distance of the signal, allowing inference of signals at distances up to 200 Mpc. We find that including a pre-trained embedding outperforms the use of likelihood-free inference alone, reducing training time, model size, and offering the capability to marginalize over certain nuisance parameters. This framework has been integrated into the publicly available Nuclear Multi-Messenger Astronomy codebase, enabling the broader scientific community to deploy the model for their inference purposes. Our algorithm is broadly applicable to parameterized or simulated light curves of other transient objects, and can be adapted for quick sky localization.

astro-ph.IM↗

arXiv2025

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

cs.LG↗

arXiv2024

Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has $\sim 6$ million trainable parameters with training times $\lesssim 24$ hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of $\sim 6$s.

gr-qc↗

arXiv2023

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings

Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.

cs.LG↗