arXiv subjects

Kamran Hussain

Publications and source records attributed to Kamran Hussain.

4 recordsLinked to original sources

arXiv2026

Characterisation of a strontium cold atom source using fluorescence spectroscopy and time-of-flight

We demonstrate a characterisation methodology for a strontium atomic beam, produced by a two-dimensional magneto-optical trap and delivered via a resonant push beam, using fluorescence spectroscopy and time-of-flight (ToF). This provides insight into the beam characteristics of a cold atom source, allowing for direct measurement of the transverse velocity spread, longitudinal velocity distributions, divergence, and the capturable flux for further cooling. From the ToF measurements, we derive a series of flux-per-longitudinal-velocity distributions at varying push saturation parameters ($s_{\mathrm{push}}$) using both a unidirectional and counter-propagating resonant probe beam. A simulation-derived factor is applied to the unidirectional probe longitudinal velocity distribution to account for differences in the scattering rate scaling. The distributions are integrated up to an estimated 3D-MOT capture velocity of \SI{30}{\meter\per\second}. For our system, we find that at $s_{\mathrm{push}} = 0.45$, we obtain a flux of $(1.7 \pm 0.4)\times10^{8}$ atoms/s and $(1.5 \pm 0.4)\times10^{8}$ atoms/s, using a unidirectional probe beam and counter-propagating probe, respectively. These measurements provide a framework for characterising cold atomic sources for applications such as 3D MOT loading and atom interferometers.

physics.atom-ph↗

arXiv2026

SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting

Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single aggregate Pearson correlation $r$ between predicted and actual spike counts, a number that masks critical structure. We argue that how we evaluate spike forecasting matters as much as what we build, and introduce SpikeProphecy, the first large-scale benchmark for causal, autoregressive spike-count forecasting on real electrophysiology recordings. Our core contribution is a population metric decomposition that separates aggregate performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment. The decomposition surfaces aspects of the underlying data that an aggregate scalar collapses together. We apply the protocol to 105 Neuropixels sessions (Steinmetz 2019 + IBL Repeated Site; ~89,800 neurons) with seven architecture baselines spanning four structural families: four SSMs (three diagonal and one non-diagonal), a Transformer, an LSTM, and a spiking network. The decomposition surfaces a brain-region predictability ranking that reproduces across all seven baselines and survives ANCOVA correction for firing-statistics constraints (region $ΔR^2 = 0.018$ above the firing-statistics covariates). It also exposes a sub-Poisson evaluation floor where rigorous metrics combine with genuine biophysical constraints on regular spike trains, and yields a negative result on KL-on-output-rates distillation for ANN-to-SNN transfer in this Poisson count domain.

q-bio.NC↗

arXiv2026

Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale

Closed-loop brain-computer interfaces often require both a forecast of upcoming neural population activity and a readout of the animal's behavioral state. A single Mamba forecaster, trained only on next-step spike counts at Neuropixels scale, can deliver both in one forward pass. A lightweight per-session linear head reading the model's predicted rates decodes behavior better than the same linear classifier reading the raw spike counts, under matched temporal context. We test on the Steinmetz visual-discrimination benchmark, which spans 39 sessions, roughly 27,000 neurons, and 1,994 held-out trials. Across three training seeds, Mamba's predicted rates decode mouse choice at 75.7$\pm$0.2% trial vote, roughly 2.3 times chance level, and stimulus side at 66.1$\pm$0.6%, about twice chance. Compared to a matched 500 ms-context linear decoder on the raw spike counts, Mamba wins at trial vote by 4-6 pp on response and 4-6 pp on stimulus side. A session-start calibration block of about 100-150 trials brings the readout within 1-2 pp of asymptote, and the full pipeline fits inside the 50 ms bin budget on workstation-class GPUs typical of tethered chronic Neuropixels recordings.

q-bio.NC↗

arXiv2026

A High-Flux Source of Cold Strontium with a Loading Rate of $4 \times 10^{10}$ atoms/s for Open Release

We present a high-flux source of cold strontium atoms based on a two-dimensional magneto-optical trap (2D MOT) and a Zeeman slower. We use the source to load a 3D MOT in a separate science chamber, observing a loading rate of $4 \times 10^{10}$ atoms/s -- to our knowledge, the highest reported loading flux for strontium. To characterise the vacuum pressure in the science chamber, we load the atoms into a magnetic trap and measure a lifetime of between 8 and 24 seconds, depending on oven temperature. Finally, we characterise the atom flux and velocity distributions from the oven and from the 2D MOT source, finding reasonable agreement with models in the free molecular flow regime. Our results show it is possible to readily produce a cold strontium flux at comparable levels to alkali species, at oven temperatures compatible with long-term operation, and at vacuum pressures suitable for state-of-the-art quantum experiments. We make our design available at no cost, to benefit researchers in the quantum community.

cond-mat.quant-gas↗