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Joshua Tan

Publications and source records attributed to Joshua Tan.

13 recordsLinked to original sources
arXiv2026

An Optically Motivated Gamma-ray Study of Fermi-LAT Novae

High-energy (GeV) gamma-ray emission from nova eruptions was a surprise detection by the Fermi-LAT in 2010. Since then, it has been suggested that the gamma-rays from these systems are generated in collisionless non-relativistic shocks. In this theory, the observed correlation between optical and gamma-ray nova lightcurves naturally arises if a portion of the optical luminosity is reprocessed shock power. In this work, we investigate this scenario by analyzing Fermi-LAT-detected novae in varied time bins and then correlating the bin size that maximizes the nova's significance (tgam) with the nova's optical eruption data. Furthermore, we investigate whether there is a common optical decay across the sources' measured tgam values in the population. We find that across our population, a nova's $t_3$ (the time it takes the nova V band brightness to decay 3 magnitudes) appears to be the favored analysis bin that optimizes the Fermi-LAT detection significance, although there is significant spread. Additionally, we report V679 Car, a source previously noted as a marginally detected gamma-ray nova, as a $>5 \sigma$ detection in this work. We perform cross-correlation analysis of gamma-ray and optical lightcurves.

astro-ph.HE↗
arXiv2025

If open source is to win, it must go public

Open source projects have made incredible progress in producing widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike previous generations of open source software, open source and open weight AI models require substantial resources to activate and maintain -- e.g., data and compute for pre-training, post-training, and deployment -- which only a few actors can currently provide. This position paper argues that open source AI must be complemented by public AI: infrastructure and institutions that ensure models are accessible, sustainable, and governed in the public interest. To achieve the full promise of AI models as prosocial public goods, we need to build public infrastructure to power and deliver open source software and models.

cs.CY↗
arXiv2024

A Gamma-ray Stacking Survey of Fermi-LAT Undetected Globular Clusters

We present evidence for $\gamma$-ray emission from a stacked population of 39 high-latitude globular clusters (GCs) not detected in the Fermi Point Source Catalog, likely attributable to populations of millisecond pulsars within them. In this work, we use 13 years of data collected by the Large Area Telescope aboard the Fermi Gamma-Ray Space Telescope to search for a cumulative signal from undetected GCs and compared them to control fields (CFs), selected to match the celestial distribution of the target clusters so as to distinguish the $\gamma$-ray signal from background emission. The joint likelihood distribution of the GCs has a significant separation ($\sim4\sigma$) from that of the CFs. We also investigate correlations between detected cluster luminosities and other cluster properties such as distance, the number of millisecond pulsars associated with each cluster, and stellar encounter rate but find no significant relationships.

astro-ph.HE↗
arXiv2024

Binary population synthesis of the Galactic canonical pulsar population

Pulsars are rapidly rotating neutron stars that emit radiation across the electromagnetic spectrum, from radio to gamma-rays. We use the rapid binary population synthesis suite COMPAS to model the Galactic population of canonical pulsars. We account for both radio and gamma-ray selection effects, as well as the motion of pulsars in the Galactic potential due to natal kicks. We compare our models to the catalogues of pulsars detected in the radio, and those detected in gamma-rays by Fermi, and find broad agreement with both populations. We reproduce the observed ratio of radio-loud to radio-quiet gamma-ray pulsars. We further examine the possibility of low spin-down luminosity (Edot) pulsars emitting weak, unpulsed gamma-ray emission and attempt to match this with results from a recent gamma-ray stacking survey of these pulsars. We confirm the correlation between the latitude of a pulsar and its Edot arises due to natal kicks imparted to pulsars at birth, assuming that all pulsars are born in the Galactic disk.

astro-ph.HE↗
arXiv2023

Influence Scores at Scale for Efficient Language Data Sampling

Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic. Understanding \textit{which} examples are important to the performance of a learning algorithm is crucial for efficient model training. Recently, a growing body of literature has given rise to various "influence scores," which use training artifacts such as model confidence or checkpointed gradients to identify important subsets of data. However, these methods have primarily been developed in computer vision settings, and it remains unclear how well they generalize to language-based tasks using pretrained models. In this paper, we explore the applicability of influence scores in language classification tasks. We evaluate a diverse subset of these scores on the SNLI dataset by quantifying accuracy changes in response to pruning training data through random and influence-score-based sampling. We then stress-test one of the scores -- "variance of gradients" (VoG) from Agarwal et al. (2022) -- in an NLU model stack that was exposed to dynamic user speech patterns in a voice assistant type of setting. Our experiments demonstrate that in many cases, encoder-based language models can be finetuned on roughly 50% of the original data without degradation in performance metrics. Along the way, we summarize lessons learned from applying out-of-the-box implementations of influence scores, quantify the effects of noisy and class-imbalanced data, and offer recommendations on score-based sampling for better accuracy and training efficiency.

cs.LG↗
arXiv2023

An Alternative to Regulation: The Case for Public AI

Can governments build AI? In this paper, we describe an ongoing effort to develop ``public AI'' -- publicly accessible AI models funded, provisioned, and governed by governments or other public bodies. Public AI presents both an alternative and a complement to standard regulatory approaches to AI, but it also suggests new technical and policy challenges. We present a roadmap for how the ML research community can help shape this initiative and support its implementation, and how public AI can complement other responsible AI initiatives.

cs.CY↗
arXiv2023

Open Problems in DAOs

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations.

cs.CY↗
arXiv2023

Political, economic, and governance attitudes of blockchain users

We present a survey to evaluate crypto-political, crypto-economic, and crypto-governance sentiment in people who are part of a blockchain ecosystem. Based on 3710 survey responses, we describe their beliefs, attitudes, and modes of participation in crypto and investigate how self-reported political affiliation and blockchain ecosystem affiliation are associated with these. We observed polarization in questions on perceptions of the distribution of economic power, personal attitudes towards crypto, normative beliefs about the distribution of power in governance, and external regulation of blockchain technologies. Differences in political self-identification correlated with opinions on economic fairness, gender equity, decision-making power and how to obtain favorable regulation, while blockchain affiliation correlated with opinions on governance and regulation of crypto and respondents' semantic conception of crypto and personal goals for their involvement. We also find that a theory-driven constructed political axis is supported by the data and investigate the possibility of other groupings of respondents or beliefs arising from the data.

cs.CY↗
arXiv2022

Utilizing a global network of telescopes to update the ephemeris for the highly eccentric planet HD 80606 b and to ensure the efficient scheduling of JWST

The transiting planet HD80606b undergoes a 1000-fold increase in insolation during its 111-day orbit due to it being highly eccentric (e=0.93). The planet's effective temperature increases from 400K to over 1400K in a few hours as it makes a rapid passage to within 0.03AU of its host star during periapsis. Spectroscopic observations during the eclipse (which is conveniently oriented a few hours before periapsis) of HD80606b with the James Webb Space Telescope (JWST) are poised to exploit this highly variable environment to study a wide variety of atmospheric properties, including composition, chemical and dynamical timescales, and large scale atmospheric motions. Critical to planning and interpreting these observations is an accurate knowledge of the planet's orbit. We report on observations of two full-transit events: 7 February 2020 as observed by the TESS spacecraft and 7--8 December 2021 as observed with a worldwide network of small telescopes. We also report new radial velocity observations which when analyzed with a coupled model to the transits greatly improve the planet's orbital ephemeris. Our new orbit solution reduces the uncertainty in the transit and eclipse timing of the JWST era from tens of minutes to a few minutes. When combined with the planned JWST observations, this new precision may be adequate to look for non-Keplerian effects in the orbit of HD80606b.

astro-ph.EP↗
arXiv2021

A Stacking Survey of Gamma-ray Pulsars

We report on a likelihood stacking search for gamma-ray pulsars at 362 high-latitude locations that coincide with known radio pulsar positions. We observe a stacked signal conservatively 2.5$\sigma$ over the background. Stacking their likelihood profiles in spectral parameter space implies a pulsar-like spectral index and a characteristic flux a factor of 2 below the Fermi-LAT point source sensitivity, assuming isotropic/unbeamed emission from all sample pulsars. The same procedures performed on empty control fields indicate that the pulsars as a population can be distinguished from the background with a $\Delta$(TS) = 28 at the peak location (or 4.8$\sigma$), and the stacked spectra of the control fields are distinctly softer than those of the pulsars. This study also probes a unique region of parameter space populated by low \Edot\ pulsars, most of which have no \gray\ ephemeris available, and is sensitive to high duty cycles. We also discusses the possible \gray\ emission mechanism from such pulsars.

astro-ph.HE↗
arXiv2021

Composing games into complex institutions

Game theory is used by all behavioral sciences, but its development has long centered around tools for relatively simple games and toy systems, such as the economic interpretation of equilibrium outcomes. Our contribution, compositional game theory, permits another approach of equally general appeal: the high-level design of large games for expressing complex architectures and representing real-world institutions faithfully. Compositional game theory, grounded in the mathematics underlying programming languages, and introduced here as a general computational framework, increases the parsimony of game representations with abstraction and modularity, accelerates search and design, and helps theorists across disciplines express real-world institutional complexity in well-defined ways. Relative to existing approaches in game theory, compositional game theory is especially promising for solving game systems with long-range dependencies, for comparing large numbers of structurally related games, and for nesting games into the larger logical or strategic flows typical of real world policy or institutional systems.

cs.GT↗