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Cristian Rodriguez

Publications and source records attributed to Cristian Rodriguez.

9 recordsLinked to original sources

Pre-shipment optical characterization of the SCALES instrument

The Slicer Combined with an Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument is a 1-5 micron imager and 2-5 micron integral field spectrograph, currently being commissioned on the Keck II Telescope. SCALES is optimized for exoplanet high-contrast imaging and spectroscopic characterization, and will be sensitive to older, colder exoplanets than existing instrumentation. The 12.3" x 12.3" imaging channel is designed to replicate the capabilities of NIRC2, and the low (R~35-200, 2.2" x 2.2" FOV) and medium (R~2500-5000, 0.36" x 0.34" FOV) spectral resolution modes offer new capabilities compared to existing Keck instrumentation. We present preliminary optical performance results from laboratory testing and commissioning of SCALES.

astro-ph.IM

Collider Probes of Dark Energy Microphysics

The physical origin of dark energy remains one of the most profound open questions in modern physics. Although cosmological observations tightly constrain the equation of state parameter $w$, this information alone does not reveal the underlying microphysics, as many distinct theoretical models can reproduce the same expansion history. A key discriminator among these models is the sound speed of dark energy perturbations, yet this quantity remains largely unconstrained by current astrophysical observations. In this work, we propose a fundamentally new approach: using collider measurements of beyond-the-Standard-Model (BSM) mediator resonances as a probe of dark energy microphysics. We construct a unified effective-field-theory framework in which a dynamical dark energy scalar is coupled, through symmetry-motivated derivative interactions, to a pseudoscalar mediator in the 2HDM+$a$ model. These interactions naturally induce invisible decays and modify the propagation of the BSM mediator in a dark energy background, leading to measurable distortions of resonance properties at colliders such as the LHC. We show that the decay widths, branching ratios, and kinematic structure of the mediator resonance become sensitive to the propagation properties of dark energy fluctuations, in particular the sound speed. As a result, collider observables provide a direct and complementary handle on dark energy microphysics, with the potential to distinguish between models that are otherwise indistinguishable through cosmology alone. Our results establish a new paradigm in which high-energy collider experiments can probe the physics of cosmic acceleration, revealing a connection between the smallest and largest scales in nature and opening a novel experimental pathway to uncover the fundamental origin of dark energy.

hep-ph

Manual-PA: Learning 3D Part Assembly from Instruction Diagrams

Assembling furniture amounts to solving the discrete-continuous optimization task of selecting the furniture parts to assemble and estimating their connecting poses in a physically realistic manner. The problem is hampered by its combinatorially large yet sparse solution space thus making learning to assemble a challenging task for current machine learning models. In this paper, we attempt to solve this task by leveraging the assembly instructions provided in diagrammatic manuals that typically accompany the furniture parts. Our key insight is to use the cues in these diagrams to split the problem into discrete and continuous phases. Specifically, we present Manual-PA, a transformer-based instruction Manual-guided 3D Part Assembly framework that learns to semantically align 3D parts with their illustrations in the manuals using a contrastive learning backbone towards predicting the assembly order and infers the 6D pose of each part via relating it to the final furniture depicted in the manual. To validate the efficacy of our method, we conduct experiments on the benchmark PartNet dataset. Our results show that using the diagrams and the order of the parts lead to significant improvements in assembly performance against the state of the art. Further, Manual-PA demonstrates strong generalization to real-world IKEA furniture assembly on the IKEA-Manual dataset.

cs.CV

Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC

A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model contains a light scalar boson $\phi'$ and a heavy vector-like quark $\chi_\mathrm{u}$ that can be probed at CERN's Large Hadron Collider (LHC). We perform a phenomenology study on the production of $\phi'$ and ${\chi}_u$ particles from proton-proton $(\mathrm{pp})$ collisions at the LHC at $\sqrt{s}=13.6$ TeV, primarily through $g{-g}$ and $t{-\chi_\mathrm{u}}$ fusion. We work under an effective field theory approach, in which the $\chi_\mathrm{u}$ and $\phi'$ masses are free parameters. We perform a phenomenological analysis considering $\chi_\mathrm{u}$ final states to b-quarks, muons, and neutrinos, and $\phi'$ decays to $\mu^+\mu^-$. A machine learning algorithm is used to maximize the signal sensitivity, considering an integrated luminosity of $3000$ $\textrm{fb}^{-1}$. The proposed methodology can be a key mode for discovery over a large mass range, including low masses, traditionally considered difficult due to experimental constraints.

hep-ph

Temporally Grounding Instructional Diagrams in Unconstrained Videos

We study the challenging problem of simultaneously localizing a sequence of queries in the form of instructional diagrams in a video. This requires understanding not only the individual queries but also their interrelationships. However, most existing methods focus on grounding one query at a time, ignoring the inherent structures among queries such as the general mutual exclusiveness and the temporal order. Consequently, the predicted timespans of different step diagrams may overlap considerably or violate the temporal order, thus harming the accuracy. In this paper, we tackle this issue by simultaneously grounding a sequence of step diagrams. Specifically, we propose composite queries, constructed by exhaustively pairing up the visual content features of the step diagrams and a fixed number of learnable positional embeddings. Our insight is that self-attention among composite queries carrying different content features suppress each other to reduce timespan overlaps in predictions, while the cross-attention corrects the temporal misalignment via content and position joint guidance. We demonstrate the effectiveness of our approach on the IAW dataset for grounding step diagrams and the YouCook2 benchmark for grounding natural language queries, significantly outperforming existing methods while simultaneously grounding multiple queries.

cs.CV

Characterization of diamond-turned optics for SCALES

High-contrast imaging has been used to discover and characterize dozens of exoplanets to date. The primary limiting performance factor for these instruments is contrast, the ratio of exoplanet to host star brightness that an instrument can successfully resolve. Contrast is largely determined by wavefront error, consisting of uncorrected atmospheric turbulence and optical aberrations downstream of AO correction. Single-point diamond turning allows for high-precision optics to be manufactured for use in astronomical instrumentation, presenting a cheaper and more versatile alternative to conventional glass polishing. This work presents measurements of wavefront error for diamond-turned aluminum optics in the Slicer Combined with an Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument, a 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. Wavefront error measurements for these optics are used to simulate SCALES' point spread function using physical optics propagation software poppy, showing that SCALES' contrast performance is not limited by wavefront error from internal instrument optics.

astro-ph.IM

MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration

We present a novel optimization-based Visual-Inertial SLAM system designed for multiple partially overlapped camera systems, named MAVIS. Our framework fully exploits the benefits of wide field-of-view from multi-camera systems, and the metric scale measurements provided by an inertial measurement unit (IMU). We introduce an improved IMU pre-integration formulation based on the exponential function of an automorphism of SE_2(3), which can effectively enhance tracking performance under fast rotational motion and extended integration time. Furthermore, we extend conventional front-end tracking and back-end optimization module designed for monocular or stereo setup towards multi-camera systems, and introduce implementation details that contribute to the performance of our system in challenging scenarios. The practical validity of our approach is supported by our experiments on public datasets. Our MAVIS won the first place in all the vision-IMU tracks (single and multi-session SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the second place.

cs.RO

Aligning Step-by-Step Instructional Diagrams to Video Demonstrations

Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world. To learn this alignment, we introduce a novel supervised contrastive learning method that learns to align videos with the subtle details in the assembly diagrams, guided by a set of novel losses. To study this problem and demonstrate the effectiveness of our method, we introduce a novel dataset: IAW for Ikea assembly in the wild consisting of 183 hours of videos from diverse furniture assembly collections and nearly 8,300 illustrations from their associated instruction manuals and annotated for their ground truth alignments. We define two tasks on this dataset: First, nearest neighbor retrieval between video segments and illustrations, and, second, alignment of instruction steps and the segments for each video. Extensive experiments on IAW demonstrate superior performances of our approach against alternatives.

cs.CV

Action Anticipation By Predicting Future Dynamic Images

Human action-anticipation methods predict what is the future action by observing only a few portion of an action in progress. This is critical for applications where computers have to react to human actions as early as possible such as autonomous driving, human-robotic interaction, assistive robotics among others. In this paper, we present a method for human action anticipation by predicting the most plausible future human motion. We represent human motion using Dynamic Images and make use of tailored loss functions to encourage a generative model to produce accurate future motion prediction. Our method outperforms the currently best performing action-anticipation methods by 4% on JHMDB-21, 5.2% on UT-Interaction and 5.1% on UCF 101-24 benchmarks.

cs.CV