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We report on the effects of cosmic ray interactions with the Kinetic Inductance Detector (KID) based focal plane array for the Terahertz Intensity Mapper (TIM). TIM is a NASA-funded balloon-borne experiment designed to probe the peak of the star formation in the Universe. It employs two spectroscopic bands, each equipped with a focal plane of four $\sim\,$900-pixel, KID-based array chips. Measurements of an 864-pixel TIM array shows 791 resonators in a 0.5$\,$GHz bandwidth. We discuss challenges with resonator calibration caused by this high multiplexing density. We robustly identify the physical positions of 788 (99.6$\,$%) detectors using a custom LED-based identification scheme. Using this information we show that cosmic ray events occur at a rate of 2.1$\,\mathrm{events/min/cm^2}$ in our array. 66$\,$% of the events affect a single pixel, and another 33$\,$% affect $<\,$5 KIDs per event spread over a 0.66$\,\mathrm{cm^2}$ region (2 pixel pitches in radius). We observe a total cosmic ray dead fraction of 0.0011$\,$%, and predict that the maximum possible in-flight dead fraction is $\sim\,$0.165$\,$%, which demonstrates our design will be robust against these high-energy events., Comment: 14 pages, 5 figures. Accepted for the publication in Journal of Low Temperature Physics (2024)

Bursty star formation -- a key prediction for high-redshift galaxies from cosmological simulations explicitly resolving stellar feedback in the interstellar medium -- has recently been observed to prevail among galaxies at redshift $z \gtrsim 6$. Line intensity mapping (LIM) of the 158 $\mu$m [CII] line as a star formation rate (SFR) indicator offers unique opportunities to tomographically constrain cosmic star formation at high redshift, in a way complementary to observations of individually detected galaxies. To understand effects of bursty star formation on [CII] LIM, which remain unexplored in previous studies, we present an analytic modeling framework for high-$z$ galaxy formation and [CII] LIM signals that accounts for bursty star formation histories induced by delayed supernova feedback. We use it to explore and characterize how bursty star formation can impact and thus complicate the interpretation of the [CII] luminosity function and power spectrum. Our simple analytic model indicates that bursty star formation mainly affects low-mass galaxies by boosting their average SFR and [CII] luminosity, and in the [CII] power spectrum it can create a substantial excess in the large-scale clustering term. This distortion results in a power spectrum shape which cannot be explained by invoking a mass-independent logarithmic scatter. We conclude that burstiness must be accounted for when modeling and analyzing [CII] datasets from the early universe, and that in the extreme, the signature of burstiness may be detectable with first-generation experiments such as TIME, CONCERTO, and CCAT-DSS., Comment: 16 pages, 7 figures, accepted for publication in ApJ

Far-infrared (far-IR) astrophysics missions featuring actively cooled telescopes will offer orders of magnitude observing speed improvement at wavelengths where galaxies and forming planetary systems emit most of their light. The PRobe far-Infrared Mission for Astrophysics (PRIMA), which is currently under study, emphasizes low and moderate resolution spectroscopy throughout the far-IR. Full utilization of PRIMA's cold telescope requires far-IR detector arrays with per-pixel noise equivalent powers (NEPs) at or below 1 x 10-19 W/rtHz. We are developing low-volume Aluminum kinetic inductance detector (KID) arrays to reach these sensitivities. We will present on the development of our long-wavelength (210 um) array approach, with a focus on multitone measurements of our 1,008-pixel arrays. We measure an NEP below 1 x 10-19 W/rtHz for 73 percent of our pixels., Comment: 9 pages, 5 figures, 20th International Workshop on Low Temperature Detectors, submitted to the Journal of Low Temperature Physics

Given the size of modern cities in the urbanising age, it is beyond the perceptual capacity of most people to develop a good knowledge about the beauty and ugliness of the city at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions like 'where are the worst-looking places in the city that regeneration should give first consideration', or 'in the fast urbanising cities, how is the city appearance changing', etc. To address this issue, we here present a computer vision method for the large-scale and automatic evaluation of the urban visual environment, by leveraging state-of-the-art machine learning techniques and the wide-coverage street view images. From the various factors that are at work, we choose two key features, the visual quality of street facade and the continuity of street wall, as the starting point of this line of analysis. In order to test the validity of this method, we further compare the machine ratings with ratings collected on site from 752 passers-by on fifty-six locations. We show that the machine learning model can produce a good estimation of people's real visual experience, and it holds much potential for various tasks in terms of urban design evaluation, culture identification, etc., Comment: 16 pages, 6 figures