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Simran K. Nerval

Publications and source records attributed to Simran K. Nerval.

3 recordsLinked to original sources

Constraining primordial oscillations and inflationary particle production with Planck, ACT DR6, and DESI DR2

Non-standard inflationary models often predict oscillatory features in the primordial power spectrum. We present constraints on general oscillatory templates for primordial power spectra, including those that vary linearly and logarithmically with wavenumber, as well as oscillations induced by inflationary particle production. We utilize the Planck 2018 and Atacama Cosmology Telescope Data Release 6 cosmic microwave background data as well as large-scale structure data from the Dark Energy Spectroscopic Instrument Data Release 2. To efficiently explore the multimodal posteriors for these models as well as performing mode comparisons, we integrate the preconditioned sequential Monte Carlo sampler, pocoMC, into the widely used sampling code, Cobaya. We find that the combined dataset tightens the 95% CL upper bounds on the general oscillation amplitudes to $A_{\text{lin}} < 0.021$, $A_{\text{log}} < 0.022$, and $A_{\text{log rf}} < 0.023$, restricting the amplitude to $\sim 2\%\, A_s$. For the inflationary particle production model, our analysis places a maximum a posteriori constraint on the coupling constant of $g = 0.034$. While these models all provide an improved fit to the data compared to the concordance $\Lambda$CDM model, the Bayesian evidence still reveals a moderate preference for $\Lambda$CDM compared to models with general oscillations and is inconclusive regarding the particle production model, suggesting that the added complexity of these models beyond the standard model is not statistically justified by current data.

astro-ph.CO

The Simons Observatory: Development of a Pipeline to Detect Rapid Transients in Time-Ordered Data

We introduce a method for detecting astrophysical transients evolving on timescales of milliseconds to minutes using cosmic microwave background (CMB) survey telescopes. While previous transient searches in CMB data operate in map space, our pipeline directly processes the raw time-ordered data, enabling sensitivity to fast, dynamic signals. We integrate our detection approach into the Simons Observatory time-domain pipeline and assess the performance on simulated observations with injected stellar flare-like light curves. For events flaring with a timescale of 0.5 s, the pipeline detects $\gtrsim90$% of events at flux densities of 800, 1150, 1650, and 4250 mJy when measured in the 93, 145, 225, and 280 GHz bands respectively. For longer $\ge5$ second flares, the 90% detection thresholds are reduced by a factor of four. We are able to determine the position of detected events in each observing band, with a positional uncertainty at the detection threshold comparable to the telescope resolution at that band. These results demonstrate the readiness of this pipeline for incorporation into upcoming Simons Observatory data analyses.

astro-ph.IM

The Atacama Cosmology Telescope: Machine Learning Driven Tools for Detecting Millimeter Sources in Timestream Pre-processing

We present a new pipeline utilizing machine learning for classifying short-duration features in raw time-ordered data (TOD) of cosmic microwave background survey observations. The pipeline, specifically designed for the Atacama Cosmology Telescope, works in conjunction with the previous TOD preprocessing techniques that employ statistical thresholding to indiscriminately remove all large spikes in the data, whether they are due to noise features, cosmic rays, or true astrophysical sources, in a process called ``data cuts". This has the undesirable effect of excising real astrophysical sources, including transients, from the data. The classification pipeline demonstrated in this work uses the output from these data cuts and is able to differentiate between electronic noise, cosmic rays, and point sources, enabling the removal of undesired signals while retaining true astrophysical signals during TOD preprocessing. We achieve an overall accuracy of 90\% in categorizing data spikes of different origin and, importantly, 94\% for identifying those caused by astrophysical sources. Our pipeline also measures the amplitude of any detected source seen more than once and produces a subminute-to-minute light curve, providing information on its short timescale variability. This automated pipeline for source detection and amplitude estimation will be particularly useful for upcoming surveys with large data volumes, such as the Simons Observatory.

astro-ph.IM