arXiv subjects

Gregory Troiani

Publications and source records attributed to Gregory Troiani.

4 recordsLinked to original sources

arXiv2026

MEGA and SMILES Find Fewer Dusty Galaxies than Expected at Cosmic Noon

We present infrared (IR) luminsosity functions (LFs) and resulting star formation rate densities using the JWST Mid-infrared Instrument (MIRI) observations from the MIRI EGS Galaxy and AGN (MEGA) survey and Systematic MIRI Legacy Extragalactic Survey (SMILES). JWST allows us to perform a robust analysis on the faint end of the IR LF beyond the local universe. We directly measure the 7.7$\mu$m polycyclic aromatic hydrocarbon (PAH) feature using either F1000W, F1500W, or F2100W photometry. This results in a sample of 634 galaxies across the two surveys covering an area of 105 arcmin$^2$ ($\sim$70 in the EGS and $\sim35$ in the GOODS-S/HUDF fields) and spanning $0.2<z<2$. We convert the 7.7$\mu$m PAH luminosity to total IR luminosity, resulting in LFs that are two orders of magnitude fainter than previous studies. In contrast to previous extrapolations based on shallower observations, we find a strong flattening in the faint end of the LF with an average slope of $\alpha\sim0.147$. This indicates that less luminous galaxies do not have as much dust obscured star formation as predicted. We measure the star formation rate density (SFRD) by integrating our new IR LFs and find a slightly lower SFRD in all redshift bins than previous studies made with ALMA, Herschel, and Spitzer. We also measure the contribution to the SFRD as a function of luminosity and confirm that LIRGs and ULIRGs remain the dominant contributors to the dust-obscured star formation at $z\sim1-2$.

astro-ph.GA↗

arXiv2025

AGNBoost: A Machine Learning Approach to AGN Identification with JWST/NIRCam+MIRI Colors and Photometry

We present AGNBoost, a machine learning framework utilizing XGBoostLSS to identify AGN and estimate redshifts from JWST NIRCam and MIRI photometry. AGNBoost constructs 66 input features from 7 NIRCam and 4 MIRI bands to predict the fraction of mid-IR $3$--$30\,\mu$m emission attributable to an AGN power law ($\text{frac}_{\text{AGN}}$) and photometric redshift. Each model is trained on $10^6$ simulated galaxies from CIGALE. Models are tested on mock CIGALE galaxies, an independent set of empirically-derived templates, and 748 observations from the JWST MIRI EGS Galaxy and AGN (MEGA) survey. On idealized noise-free mock CIGALE galaxies, AGNBoost achieves $15\%$ outlier fractions of $1.63\%$ ($\text{frac}_{\text{AGN}}$) and $0.15\%$ (redshift), with $\sigma_{\text{RMSE}} = 0.045$ for $\text{frac}_{\text{AGN}}$ and $\sigma_{\text{NMAD}} = 0.004$ for redshift. When realistic photometric uncertainties are introduced, performance remains robust with median predictions on the 1:1 relation, though outlier fractions increase to $4.38\%$ and $3.35\%$, respectively. On the independent template set, AGNBoost identifies $92.6\%$ of AGN candidates with $\text{frac}_{\text{AGN}} > 0.3$ and $100\%$ with $\text{frac}_{\text{AGN}} > 0.5$, demonstrating generalization beyond the training distribution. On MEGA galaxies with spectroscopic redshifts, AGNBoost achieves $\sigma_{\text{NMAD}} = 0.056$ and $19.79\%$ outliers. AGNBoost $\text{frac}_{\text{AGN}}$ estimates broadly agree with CIGALE fitting ($\sigma_{\text{RMSE}} = 0.178$, $11.96\%$ outliers). The flexible framework allows straightforward incorporation of additional photometric bands and re-training for other variables. AGNBoost's computational efficiency makes it well-suited for wide-sky surveys requiring rapid AGN identification and redshift estimation.

astro-ph.GA↗

arXiv2025

MEGA Mass Assembly with JWST: The MIRI EGS Galaxy and AGN Survey

We present the MIRI EGS Galaxy and AGN (MEGA) survey, a four band MIRI survey with 25 pointing in the Extended Groth Strip (EGS) extragalactic field. Three of the pointings utilized only the three reddest bands (F1000W, F1500W, F2100W) while the remainder of the pointings also add a blue filter (F770W). MEGA builds upon the existing observations in the EGS field by providing MIRI imaging for 68.9% of CEERS NIRCam imaging, filling a cruciality gap in order to understand galaxy evolution by observing the obscured Universe. Here, we present the technical design, data reduction, photometric catalog creation, the first data release, and science drivers of the MEGA survey. Our data reduction starts with the standard JWST calibration pipeline, but adds additional warm pixel masking and custom background subtraction steps to improve the quality of the final science image. We estimate the image depth of the reduced mosaics and present new galaxy number counts in four MIRI bands.

astro-ph.GA↗

arXiv2020

The Baryon Mapping Experiment (BMX), a 21cm intensity mapping pathfinder

The Baryon Mapping eXperiment (BMX) is an interferometric array designed as a pathfinder for a future post-reionization 21 cm intensity mapping survey. It consists of four 4-meter parabolic reflectors each having offset pyramidal horn feed, quad-ridge orthomode transducer, temperature-stabilized RF amplification and filtering, and pulsed noise injection diode. An undersampling readout scheme uses 8-bit digitizers running at 1.1 Gsamples/sec to provide access to signals from 1.1 - 1.55 GHz (third Nyquist zone), corresponding to HI emission from sources at redshift $0 < z < 0.3$. An FX correlator is implemented in GPU and generates 28 GB/day of time-ordered visibility data. About 7,000 hours of data were collected from Jan. 2019 - May 2020, and we will present results on system performance including sensitivity, beam mapping studies, observations of bright celestial targets, and system electronics upgrades. BMX is a pathfinder for the proposed PUMA intensity mapping survey in the 2030s.

astro-ph.IM↗